US 12,475,437 B2
Intelligent time and attendance management system and method thereof
Devendra Brid, Navi Mumbai (IN); Asim Prakash Jena, Bengaluru (IN); and Arvind Maurya, Noida (IN)
Assigned to HCL Technologies Limited, New Delhi (IN)
Filed by HCL Technologies Limited, New Delhi (IN)
Filed on Feb. 16, 2023, as Appl. No. 18/110,393.
Claims priority of application No. 202211017642 (IN), filed on Mar. 27, 2022.
Prior Publication US 2023/0306374 A1, Sep. 28, 2023
Int. Cl. G06Q 10/1091 (2023.01); G06F 1/16 (2006.01)
CPC G06Q 10/1091 (2013.01) [G06F 1/163 (2013.01)] 13 Claims
OG exemplary drawing
 
1. An intelligent time and attendance management system, the system comprising:
a plurality of custom wearable devices configured to be worn by a plurality of users within a set of predefined regions, wherein each of the plurality of custom wearable devices record a set of evaluation parameters associated with a corresponding user, from among the plurality of users, using a set of sensors;
wherein the custom wearable device comprises a wrist strap with sensors, a gyroscope, an accelerometer, a wrist size calculator,
wherein custom wearable device is at least one of wrist bands, smart watches, body mounted sensors, and rings; and
wherein the set of evaluation parameters comprises at least one of gyroscope data, accelerometer data, and a wrist size of the corresponding user;
an Intelligent Access Point Network (IAPN) installed within each of the set of predefined regions and configured to receive the set of evaluation parameters from each of a set of custom wearable devices, from among the plurality of custom wearable devices, within a corresponding predefined region using a wireless signal, wherein the IAPN comprises:
a distance calculator configured to calculate a distance of each of the set of custom wearable devices from the IAPN based on a wavelength and a frequency of the wireless signal received from the corresponding custom wearable device; and
an intelligent monitoring subsystem configured to:
determine, for each of the plurality of custom wearable devices, a valid usage of the corresponding custom wearable device based on the set of evaluation parameters using a first trained machine learning model, wherein determining the valid usage comprises determining whether the user wearing the corresponding custom wearable device is an authorized user to wear the custom wearable device;
wherein the first trained machine learning model is trained based on historical evaluation parameters data, wrist size of user, straight distance data of the custom wearable device from the IAPN, authorized predefined region historic data, custom wearable device actual region historic data, and a unique user identity associated with each of the plurality of custom wearable devices;
determine, for each of the plurality of custom wearable devices, one of a valid presence or a valid movement of the corresponding custom wearable device based on the set of evaluation parameters and the distance using a second trained machine learning model, wherein determining the valid presence or the valid movement comprises determining whether the corresponding custom wearable device is within or outside an authorized predefined region;
wherein the second trained machine learning model is trained based on the historical evaluation parameters data, historical distance information of the custom wearable device from the IAPN, authorized predefined region boundary parameters, distance data of the custom wearable device from the IAPN, and custom wearable device information; and
generate, for one or more of the plurality of custom wearable devices, an alert in response to determination of at least one the valid usage or the valid presence.