公开(公告)号：US20240354731A1

公开(公告)日：2024-10-24

申请号：US18028552

申请日：2022-09-28

Applicant: JIO PLATFORMS LIMITED

Inventor： Naveen Kumar PANDEY ,  Balakrishna PAILLA ,  Shailesh KUMAR ,  Abhinav ANAND ,  Suman CHOUDHARY ,  Divya BHAGWAT ,  Hemant KASHNIYAL

IPC: G06Q20/20 ,  G06Q20/18 ,  G06T7/62 ,  G06V10/764 ,  G06V20/52 ,  G06V20/64 ,  G06V20/68

CPC classification number: G06Q20/208 ,  G06Q20/18 ,  G06T7/62 ,  G06V20/52 ,  G06V20/68 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30232 ,  G06T2207/30242 ,  G06V10/764 ,  G06V20/64 ,  G06V2201/09

Abstract: This present disclosure proposes a system and method for providing a 3D computer vision-assisted frictionless self-checkout experience in a traditional retail store by using two RGB-D camera sensors and a conveyer belt. The disclosure provides a self-checkout counter (106) that enables a customer (102) to go through a self-service checkout process by simply placing the collected products one by one on a conveyer belt. One vertical and another horizontal RGB-D sensor (108) mounted in a housing frame attached towards the end of the conveyer belt capture RGB and depth image of each product passing through the housing and pass it to a product recognition engine (216). The engine (216) identifies the unique product along with its volumetric attributes processing the RGB-D data that is further compared with a master product database and processed for invoicing. The customer wallet and payment may be integrated with the customer phone number at the self-checkout counter (106) for providing a completely automated checkout experience.

公开(公告)号：US20230354045A1

公开(公告)日：2023-11-02

申请号：US18246277

申请日：2022-07-29

Applicant: JIO PLATFORMS LIMITED

Inventor： Shailesh KUMAR ,  Anil MITTAL ,  Prateek Kumar JAIN ,  Avnish KUMAR

IPC: H04W16/10 ,  H04L41/14

CPC classification number: H04W16/10 ,  H04L41/14

Abstract: Present disclosure generally relate to wireless networks, more particularly relates to systems and methods for determining spatial clusters in a network to enable connected community of telecommunication cellular towers. The system may prepare cell data using one or more circle data, city data, cell Identity (ID) data, latitude data, longitude data, azimuth data, and height data. System may compute geohash based on creating geohash neighbours and geohash bounding box data and compute sectors of the telecommunication towers. Further, the system may compute sector affinity of the telecommunication towers and perform clustering of the telecommunication towers.

公开(公告)号：US20250046450A1

公开(公告)日：2025-02-06

申请号：US18713962

申请日：2022-11-25

Applicant: JIO PLATFORMS LIMITED

Inventor： Chetan B. BHATT ,  Amrita Virendra VERMA ,  Ritesh SHAH ,  Kalyan TADEPALLI ,  Neeraj YOGENDRA ,  Vamshi CHITTALA ,  Hareesh KUMAR ,  Shailesh KUMAR

IPC: G16H50/20 ,  G16H10/60

Abstract: The present invention provides solution to the above-mentioned problem in the art by providing a system and a method for efficiently identifying from a medical domain a plurality of entities that may be designed and engineered for a diagnostic reasoning system or symptom checker. The identified plurality of entities forms nodes of a knowledge graph and may be interconnected to each other with edges representing finely curated weights. The curated weights may form a dynamic repository that can constantly evolves to best reflect the latest medical knowledge. Apart from diseases and symptoms, the system is also capable of extending and scaling to other dimensions of medical knowledge. The knowledge graph thus developed is fundamental to the entire diagnostic system and is used by various components of the diagnostic engine to power the AI-based reasoning.

公开(公告)号：US20250029723A1

公开(公告)日：2025-01-23

申请号：US18713072

申请日：2022-11-24

Applicant: JIO PLATFORMS LIMITED

Inventor： Chetan B. BHATT ,  Shailesh KUMAR ,  Amrita Virendra VERMA ,  Kalyan TADEPALLI ,  Ritesh SHAH ,  Vamshi CHITTALA ,  Hareesh KUMAR ,  Neeraj YOGENDRA

IPC: G16H50/20 ,  G06F16/242 ,  G16H10/60 ,  G16H50/30

Abstract: Present disclosure generally relates to computer-assisted medical diagnosis systems, particularly, to system and method for generating adaptive medical queries to determine disease symptoms of a user. System receives inputs from user, in response to queries corresponding to symptoms of disease. Further, system extracts contextual attributes corresponding to medical context, from inputs and identifies batch-wise candidate attributes from extracted contextual attributes, and sorts batch-wise candidate attributes corresponding to each of symptoms. Furthermore, system maps symptoms in sorted batch-wise candidate attributes to disease, by searching medical knowledge database, and calculates disease-symptom weighted score. Additionally, system filters symptoms based on age and gender of user, and sorts symptoms based on calculated disease-symptom weighed score and inter-dependency on other attributes. Furthermore, system generates subsequently, adaptive medical queries in human-understandable form, by predicting adaptive medical queries based on converting symptoms, attribute canonical names, and unique Identities (IDs), to determine disease of user.

公开(公告)号：US20230394512A1

公开(公告)日：2023-12-07

申请号：US18246357

申请日：2022-06-25

Applicant: JIO PLATFORMS LIMITED

Inventor： Akansha KUMAR ,  Shailesh KUMAR ,  Akhil PATEL PATLOLLA ,  Bangari Sai SWARAJ ,  Athira SURENDRAN

IPC: G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204

CPC classification number: G06Q30/0206 ,  G06Q30/0202 ,  G06Q30/0204

Abstract: The present disclosure generally relates to profit optimization, and more particularly to methods and systems for profit optimization for an online/offline retail/wholesale category. Profit optimization includes demand forecasting, inventory planning, assortment planning, discount recommendation, price optimization, revenue optimization, and profit optimization in the online/offline retail/wholesale category. The method of profit optimization includes a hierarchical demand forecasting of products by using a stacked Long- and Short-Term Memory (LSTM) neural network architecture. Further, the method includes a quantification of price-demand causal effects and inter-product cross-causal effects using an eXtreme Gradient Boosting (XGBoost) technique. The method further includes a simulation of an effect of discount on a demand of products, and recommendation of an optimal discount using a non-linear optimization technique. The method includes performing a product segmentation into clusters by using a K-Nearest neighbour technique. The method further includes determining an optimal price of the products using a real-valued Genetic Algorithm (GA).

公开(公告)号：US20230377199A1

公开(公告)日：2023-11-23

申请号：US18000364

申请日：2021-05-28

Applicant: JIO PLATFORMS LIMITED

Inventor： Raghuram LANKA ,  P. BALAKRISHNA REDDY ,  Arun BANERJEE ,  Pradip GUPTA ,  Shubham BHARDWAJ ,  Shailesh KUMAR ,  Santanu DASGUPTA ,  Rahul BADHWAR ,  Kenny PAUL

IPC: G06T7/90 ,  G06T7/20 ,  G06T7/70 ,  G06T7/50 ,  G06T7/11

CPC classification number: G06T7/90 ,  G06T7/20 ,  G06T7/70 ,  G06T7/50 ,  G06T7/11 ,  G06T2207/30241 ,  G06T2207/10024 ,  G06T2207/10036 ,  G06T2207/20081 ,  G06T2207/20212 ,  G06T2207/30188 ,  G06T2207/30004

Abstract: A system and method for extracting a full range hyperspectral data from one or more RGB images. The method encompasses pre-processing, the one or more RGB images. Further the method encompasses estimating, an illumination component associated with each pre-processed RGB image. The method thereafter comprises removing, the illumination component from the each pre-processed RGB image. Further the method encompasses tracking, a trajectory of pixel(s) over frame(s) associated with the each pre-processed RGB image. The method then leads to identifying, a position of the pixel(s) in one or more adjacent frames of the frame(s) based on a patch defined around said one or more pixels. Thereafter the method encompasses extracting, the full range hyperspectral data from the each pre-processed RGB image based on at least one of the removal of the illumination component, the trajectory of the pixel(s) and the position of the pixel(s).

公开(公告)号：US20230066478A1

公开(公告)日：2023-03-02

申请号：US17822013

申请日：2022-08-24

Applicant: Jio Platforms Limited

Inventor： Shailesh KUMAR ,  Palash SETHI

IPC: G06N3/04 ,  G06N3/08

Abstract: The present disclosure generally relates to handling data of non-linear, multi-variable complex systems. More particularly, the present disclosure relates to methods and systems for training machine learning-based computing devices to ensure adaptive sampling of highly complex data packets. The present invention provides a robust and effective solution to implement a complexity-based sampling methodology that trains the neural network in complex mapping regions, by iteratively sampling the DBMS function and training the neural network in complex regions. The system (110) for training the complex and non-linear neural network may be equipped with a Machine Learning (ML) Engine (214) to solve the problem efficiently.

公开(公告)号：US20250056495A1

公开(公告)日：2025-02-13

申请号：US18720118

申请日：2022-12-16

Applicant: JIO PLATFORMS LIMITED

Inventor： Shailesh KUMAR ,  Krusheel MUNNANGI ,  Vinit LUDHANI

IPC: H04W64/00 ,  G01S19/46

Abstract: Present disclosure generally relates to location tracking and communication systems. More particularly, the present disclosure relates to systems and methods for predicting user location from radio data of telecommunication network. The system may utilize Global Positioning System (GPS) data along with Radio Frequency (RF) data obtained from interactions between the first computing device and network elements such as cells. The RF data along with the GPS data may be used for learned distance prediction models in AI engine. When the first computing device may be latched to a cell, the system via the first computing device can observe one or more neighbour cells. To estimate location of the user, predicted distances from cells and grids that can be served from each of cells may be used by system to estimate location of the user. System may use predicted distance from different cells to estimate the location of the user.

公开(公告)号：US20240419136A1

公开(公告)日：2024-12-19

申请号：US18699979

申请日：2022-10-29

Applicant: JIO PLATFORMS LIMITED

Inventor： Shailesh KUMAR ,  Palash SETHI

IPC: G05B13/04 ,  G05B13/02

Abstract: The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating creation of a digital twin of a process unit which can perform constrained optimization of control parameters to minimize or maximize an objective function. The system can capture non-linearities of the industrial process while the current Industrial Process models try to approximate non-linear process using linear approximation, which are not as accurate as Neural Networks. The proposed system can further create an end-to-end differentiable digital twin model of a process unit, and uses gradient flows for optimization as compared to other digital twin models that are gradient-free.

公开(公告)号：US20240089751A1

公开(公告)日：2024-03-14

申请号：US18246310

申请日：2022-07-28

Applicant: JIO PLATFORMS LIMITED

Inventor： Shailesh KUMAR ,  Krusheel MUNNANGI ,  Meghna PUSALA ,  Vinit LUDHANI

IPC: H04W24/02 ,  H04W16/28

CPC classification number: H04W24/02 ,  H04W16/28

Abstract: The present invention provides a method and system for predicting tropospheric interference using a digital twin model for detecting tropospheric interference and suggesting actions for mitigating the effects of the tropospheric interference. The system (110) may include a machine learning engine (216) having a digital twin model to predict/score the likelihood of cell pairs having the tropospheric interference at a given point of time using cell configuration data, weather data, Hepburn data at that point of time by identifying cell pairs whose likelihood of interference is high. The system (110) is further configured to explore the effect of changing cell configuration parameters like remote electrical tilt and identify actions that can mitigate tropospheric interference with minimal impact on coverage. The digital twin model is learned for interference using historical data of interference pairs combined with cell configuration parameters, weather parameters, and tropospheric interference data like Hepburn data.