Tata Consultancy Services Limited Patent Portfolio Statistics

Tata Consultancy Services Limited

Profile Summary

This article summarizes the perfomance of the assignee in the recent years. The overall statistics for this portfolio help to analyze the areas where the assignee is performing well. The filing trend, perfomance across the tech centers and the perfomance of the recent applications has been mentioned below. All the stats are calculated based on the perfomance in USPTO.

How does the overall patent portfolio of Tata Consultancy Services Limited look like?

Total Applications: 1,367
Granted Patents: 855
Grant Index 83.58 %
Abandoned/Rejected Applications: 168 (16.42%)
In-Process Applications: 328
Average Grant Time: 2.87 Years
Average Office Actions: 1.77

Which Technology Area Tata Consultancy Services Limited is filing most patents in? (Last 10 years)

Art Unit Definition Total Applications
Opap Parked GAU 155
3683 Business Methods 42
3623 Business Methods – Incentive Programs, Coupons; Operations Research; Electronic Shopping; Health Care; Point of Sale, Inventory, Accounting; Cost/Price, Reservations, Shipping and Transportation; Business Processing 40
3624 Electronic Commerce 32
2191 Interprocess Communication and Software Development 29

How many patents are Tata Consultancy Services Limited filing every year?

Year Total Applications
2022 0*
2021 116*
2020 168
2019 136
2018 149

*The drop in the number of applications filed in last two years compared to previous years is because applications can take up to 18 months to get published

Recently filed patent applications of Tata Consultancy Services Limited in USPTO?

Publication number:
Application number: 17/431,134


Publication date:
Applicant: Tata Consultancy Services Limited
Inventors: Kimbahune Sanjay

Publication number: US20220027711A1
Application number: 17/357,614

This disclosure relates generally to a system and a method for mitigating generalization loss in deep neural network for time series classification. In an embodiment, the disclosed method includes compute an entropy of a timeseries training dataset, and a mean and a variance of the entropy and a regularization factor is computed. A plurality of iterations are performed to dynamically adjust the learning rate of the deep Neural Network (DNN) using a Mod-Adam optimization, and obtain a network parameter, and based on the network parameter, the regularization factor is updated to obtain an updated regularized factor. The learning rate is adjusted in the plurality of iterations by repeatedly updating the network parameter based on a variation of a generalization loss during the plurality of iterations. The updated regularized factor of the current iteration is used for adjusting the learning rate in a subsequent iteration of the plurality of iterations.

Publication date: 2022-01-27
Applicant: Tata Consultancy Services Limited
Inventors: Bandyopadhyay Soma

Publication number: US20210401328A1
Application number: 17/353,920

Elderly people suffer from health issues, and timely detection can save lives. State of the art techniques either make certain assumptions or require clinical data in order to perform the frailty detection, which affects the quality as well as cause inconvenience to the users. The disclosure herein generally relates to patient monitoring and, more particularly, to frailty detection using pedometer sensor data, PIR sensor data, and door sensor data. The system determines activity levels of the user being monitored, based on data from the pedometer sensors, PIR sensors, and door sensors, and based on the determined activity levels, further determines whether the user has frailty or not.

Publication date: 2021-12-30
Applicant: Tata Consultancy Services Limited
Inventors: Anirudh Purushothaman Thenguvila

How are Tata Consultancy Services Limited’s applications performing in USPTO?

Application Number Title Status Art Unit Examiner
17/431,134 System And Method For Computing Burning Index Score Pertaining To Crops
17/357,614 System And Method For Mitigating Generalization Loss In Deep Neural Network For Time Series Classification OPAP Central, Docket
17/353,920 Method And System For Frailty Detection Docketed New Case – Ready for Examination OPAP Central, Docket
17/352,461 Autonomous Surface Crawling Robot Docketed New Case – Ready for Examination OPAP Central, Docket
17/349,440 Method And System For Visio-Linguistic Understanding Using Contextual Language Model Reasoners OPAP Central, Docket