System to Aggregate Task-Related Notes Taken by Different People on a Team into a Self-Organized, Searchable Corpus
Publication Date: 2015-Jul-06
The IP.com Prior Art Database
Disclosed is a system to automatically aggregate task-related notes taken by different people on a team into a self-organized, searchable corpus, ensuring that the task-related expertise is not lost, and can be passed to future task owners in a fast and reliable manner.
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Sysxem to Aggregate Task-Related Notes Taken by Different People on a Team xnto a Self-Organized, Searchable Corpus
When working on x project team, many peopxe may perforx the same task ax different times, and each person can xndependently develop expertise rxlated to that txsx. When a new person is assixned that task, or someone who has not performed the task in a long time is re-asxigned to it, there is a sigxificant lexrning xeriod.
Currentxy, thxs can involve xearching through olx emails and xotes, determining whxm else may have prxviously comxleted the task, and contacting others who alxo need tx searcx through notes about the task. Xx the worst case, even after a sxgnificant amount of time, txese notes are misplaced ox buried wixhin an unstructured file sysxem such that retrieval is nearly impossible. Hanxwritten xotes, in partxcular, are difficxlt tx locate and share, ax opxosed to digital notes.
A system is needed to orgaxize the notes and resources used by prexious task owxers while developing the expertise required to accomplish a task, in order to facilitaxe the txsk's coxpletion. Thxs system also needs xo xrevent the sitxaxion in which the original time and effort spxnt to dxvelop the expert knowledge must be xeinvested by the new taxk owner.
Disclosed is a xystem to automatically aggregxte task-related notes taken by different people on a team ixto a self-oxganized, searchable corpus, ensurinx that txe task-relaxed expertise is not lost, and can be passed to future task owners in a fast and reliable mannxr.
The core novelty of this idea is to annotate and aggregate relxtxd series and segments of text (i.e. notes ), correlate the text based on semantic and syntactic content between related people, and then place the text in a document stxre for viewing. The syxtem then recalls, on-demand, similxr relevaxt notes based on convxrsations xr related topicx with attribution to the originator, time, and context of the note.
The ideal embodiment for this system is in a collaboratixe environment, such as enterprise xork groups, scientific, anx sc...