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Location Based Project Identification for Labor and Expense

IP.com Disclosure Number: IPCOM000240808D
Publication Date: 2015-Mar-04
Document File: 2 page(s) / 45K

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The IP.com Prior Art Database


Disclosed is a system for automatically populating a labor claim. The system identifies a practitioner’s social neighborhood in order to predict the areas in which the user is working and the associated time spent working on the project(s).

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Location Based Project Identification for Labor and Expense

Reporting labor claims is important for client billing and cost recovery, but it can be time consuming and prone to human error. This can contribute to inaccuracies that ultimately negatively impact the bottom line.

A system is needed to easily and accurately produce labor claims for a business .

The novel contribution is a system that automatically identifies a practitioner's social neighborhood in order to predict the areas in which the user is working and the associated time spent working on the project(s). With this information, the system can prefill a timesheet and allow the tracker to easily review/submit the labor claim. The system collects data using a Global Positioning System (GPS), wireless (Wi-Fi) communication, Big Data, artificial intelligence processing, and social media.

The system gathers information based on environmental changes (e.g., GPS, WI-FI Service Set Identifier (SSID), etc.), social behavior (e.g., emails, texts, etc.), and application/tool usage at specified intervals. When the user begins a labor claim , the system combines this data with information extracted from the claiming database (e.g.,

work items the user is entitled to use, social network information on others who have claim to those) to generate an initial set of possible suggested work items and time allocations.

An intelligent processing engine can further scope and apply a confidence factor to the set of options. The system then presents the user with a list of weighted options , allowing the user to select appropriate claim items based upon knowledge of the activities performed. The system gathers information from three main sources :

• Usage behavior is captured from the user's activities. This is a combination of data captured in slices based on:

- Mobile/social behavior (e.g., emails, texts, calendar, and other social activities performed on the mobile device)

- Environmental changes, changes in the physical location of the mobile device (e.g.,, data from GPS location, available WI-FI SSIDs, and connected WIFI SSID)

- Workstation activities, behavior of the category of applications used, time spent at the top of the Z order, and other pertinent information from the machine

• Usage behavior information from other users enrolled in the system . This is historical based information that tracks the data captured from the above and the actual work items claimed by those users.

• Creation of social neighborhoods to which the user belongs , based on this information and personal history information from the user . A social neighborhood is defined as people who work in the sample physical location , performing similar types...