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Method for Identifying Accomplishments and Failures over a Designated Period

IP.com Disclosure Number: IPCOM000241833D
Publication Date: 2015-Jun-02
Document File: 2 page(s) / 90K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to use natural language processing (NLP) to analyze a user’s work-related communication and documentation and identify the associated professional accomplishments and failures over a designated period.

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This is the abbreviated version, containing approximately 51% of the total text.

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Method for Identifying Accomplishments and Failures over a Designated Period

For a yearly performance review, many employers ask employees to document the year's accomplishments. Employees commonly find this to be a tedious, time-intensive task. To find the necessary information and documentation, employees may sift through a variety of sources including e-mail chains, calendar entries, meeting notes, authored code, work items, presentations, reports, etc. Such manual research does not guarantee that all the necessary documentation is found; the employee can easily overlook or disregard an important piece. This might negatively impact the individual's performance review.

Disclosed is a method to use natural language processing (NLP) to identify an individual's accomplishments and failures over a designated period.

To implement the method, the user accesses the system and provides it with various sources of data such as e-mail and e-mail attachments, online storage services, instant messages, meeting transcripts, calendar records, work items, documents, presentations, social media accounts, and organization charts. Optionally, the system can store credentials for those data sources or prompt the user for credentials as appropriate.

The user indicates:


• The period for which to analyze the data (e.g., a particular week, month, year, all)


• (Optionally) The sentiment range for which to analyze (e.g., positive, neutral, negative, or all)


• (Optionally) The number of results the system is to return for viewing (e.g., top five, top 20, or all)


• (Optionally) Associated job role or what types of accomplishments are high

priority (e.g., code implemented, meetings with external customers, etc.)

The system uses NLP techniques to determine themes that represent the user's work, matches the indicated sentiment, and prioritizes the results based on which item had the greatest impact. When organizational structure data is available to the system, the system takes into account scope (i.e. a...