Observation platform using structured communications for gathering and reporting employee performance information
A method for collecting data and deriving performance metrics for one or more users within an observation platform environment is described. The method includes recognizing a user identity based on signals from the communications device; Capturing a user's voice message and the secondary signal indicative of the location of the communications device or the relative proximity of the communications device to other devices; Deriving statistical data comprising: primary statistics, secondary statistics, and higher-order statistics from the secondary signal and voice message; and Storing the user identity, the voice message, the location of the communications device, and the statistical data in a database forming a user record, wherein the user record is processed by an inference engine to derive performance information about the user.
1. A method for collecting data and deriving performance metrics for one or more users within an observation platform environment comprising:
providing one or more of said users with a communications device capable of sending a user's voice and a secondary signal containing information regarding a location of said communications device or a relative proximity of said communications device to other devices; and
providing an observation platform having a communications system for receiving and sending data from and to at least one of said communications device, an Internet of things (IoT) device, and an external system, said observation platform comprising:
a computer system with a memory containing instructions for:
recognizing a user identity based on signals from the communications device;
capturing a user's voice message and the secondary signal indicative of the location of said communications device or the relative proximity of said communications device to other devices;
deriving statistical data about employee performance of the user comprising: primary statistics, secondary statistics, and higher-order statistics from said secondary signal and voice message;
storing said user identity, the voice message, the location of the communications device, and the statistical data in a database forming a user record, wherein the user record is processed by an inference engine to derive performance information about said user; and
providing, by said computer system, an employer of said user with a ranking for said employee performance of said user versus other employees of said employer, said ranking based on said performance information.
2. The method of claim 1 wherein the primary statistics are used to derive the secondary statistics and the higher-order statistics.
3. The method of claim 1 further comprising:
utilizing the primary statistics, secondary statistics, and higher-order statistics to determine a user performance score, a relative ranking of a plurality of user performance scores, a performance standard, and a benchmark for user performance.
4. The method of claim 3 further comprising:
basing the user performance score on a role of the user.
5. The method of claim 3 further comprising:
basing the user performance score on a group membership and a group activity of the user.
6. The method of claim 3 further comprising:
basing the user performance score on the location of said communications device, a time spent within an assigned location, and communication activities within and external to the assigned location.
7. The method of claim 3 further comprising:
basing the user performance score on an employment time of the user.
8. The method of claim 3 further comprising:
basing the user performance score on a request and response behavior and action.
9. The method of claim 1 further comprising:
utilizing the primary statistics, secondary statistics, and higher-order statistics to determine a group performance score, a team performance score, a relative ranking of performance scores, a performance standard, a benchmark for group performance, and a benchmark for team performance.
10. The method of claim 1 further comprising:
adjusting the primary statistics, secondary statistics, and higher-order statistics by additional subjective information provided by a manager or a peer.
11. A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
recognize an identity of a user of a communications device based on a secondary signal from the communications device, the secondary signal indicative of a location of said communications device or a relative proximity of said communications device to other devices;
capture a user's voice message and the secondary signal;
derive statistical information about employee performance of the user from said secondary signal and the user's voice message, the statistical information comprising:
primary statistics,
secondary statistics, and
higher-order statistics;
store said user identity, the user's voice message, the location of said communications device, and the statistical information in a database forming a user record;
process the user record by an inference engine to derive performance information about said user, wherein the derived performance information is published to a shared library of current and historical performance of the user for use by an employee, an employing enterprise or another outside enterprises; and
providing an employer of said user with a ranking for said employee performance of said user versus other employees of said employer, said ranking based on said performance information.
12. The non-transitory computer-readable medium of claim 11 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
utilize the primary statistics, secondary statistics, and higher-order statistics to determine a user performance score, a relative ranking of a plurality of user performance scores, a performance standard, and a benchmark for user performance.
13. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
base the user performance score on a role of the user.
14. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
base the user performance score on a group membership and a group activity of the user.
15. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
base the user performance score on the location of said communications device, a time spent within an assigned location, and communication activities within and external to the assigned location.
16. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
base the user performance score on an employment time of the user.
17. The non-transitory computer-readable medium of claim 12 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
base the user performance score on a request and response behavior and action.
18. The non-transitory computer-readable medium of claim 11 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
utilize the primary statistics, secondary statistics, and higher-order statistics to determine a group performance score, a team performance score, a relative ranking of performance scores, a performance standard, a benchmark for group performance, and a benchmark for team performance.
19. The non-transitory computer-readable medium of claim 11 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
adjust the primary statistics, secondary statistics, and higher-order statistics by additional subjective information provided by a manager or a peer.
20. The non-transitory computer-readable medium of claim 11 , where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
use the primary statistics to derive the secondary statistics and the higher-order statistics.