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Method and Apparatus for Trust, Vicinity, Natural Language Processing Based Collaboration Leveraging Insights-Based Cognitive Models

IP.com Disclosure Number: IPCOM000241376D
Publication Date: 2015-Apr-21
Document File: 4 page(s) / 49K

Publishing Venue

The IP.com Prior Art Database


Disclosed is an apparatus that overcomes the problem of current gaps or issues faced in collaboration tools. This collaboration interface dynamically and proactively provides insights and information to the end user, and provides real-time, relevant, useful, and timely insights and suggestions by using varying degrees of trust models with the user about other associated interacting individuals/groups.

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Method and Apparatus for Trust , Vicinity, Natural Language Processing Based Collaboration Leveraging Insights-Based Cognitive Models

Organizations are trying to leverage the value of data spanning the various domains of big data from various internal and external sources. Data defining opinions and sentiments can provide value to organizations when carrying out personalized actions. While the data gathered from internal sources has a greater confidence level, data gathered from external sources possesses less trust; however, organizations have to leverage it for various actions. In addition, the context of physical proximity as well as the available channels at the time of actions play a key role in the success of the actions performed (e.g., supporting the collaborators in real-time in either a social or an enterprise chat).

Existing models do not provide any insights-based augmentation based on big data gathered from different sources. The collaboration interfaces are not intelligent and dynamic in nature. Traditional implementations use relatively static models and do not provide a mechanism to intelligently augment the interaction based on changing behaviors and situational patterns. None of the current solutions proactively provides dynamically generated insight to the individual based on the interpretation of real time or near real time information captured during the collaboration. Current solutions do not provide relevant, useful, or timely insights to an end user about other interacting users
in the user's groups or circles.

The novel contribution is an apparatus to blend trust, vicinity, and cognitive models to support dual and multi-channel collaboration using Natural Language Processing (NLP). The novel solution overcomes the gaps in the current solution by including a Graphical User Interface (GUI) based apparatus as well as a cognitive and collaboration platform in blue stack that can be efficiently used for third party modelling tools.

The GUI-based capability enables the creation of vicinity-based groups formation based on interest categories and tags/key words for one or more groups/individuals. It can derive a metadata library using an NLP interpreter, which can be used by a run time engine to pull personalized insights about other user(s) participating in the collaboration. The user can set privacy/visibility settings, based on either depth of relationship (e.g., first, second, third circle, etc.) with other users or for individual groups/users for establishing higher trust and proximity.

The system can use privacy/visibility settings to provide run-time suggestions and information to the user via the graphical collaboration interface, based on insights and confidence level built on other individuals participating in the collaboration. The system has the ability to use social insights to provide prompts to initiate a new online or offline collaboration with one or more other users based on matching interests, p...