TCS Patent: AI-Powered Spatio-Temporal Intercrop Layout System Using 3D-CNN-LSTM
Summary
Tata Consultancy Services Limited filed patent application US20260111617A1 on April 23, 2026, for a method and system using a pretrained 3D-CNN-LSTM large vision model to generate optimal spatio-temporal intercrop layouts in agriculture. The system receives user requests, removes noise and performs normalization, then generates intercrop combinations with dynamic visual insights and reinforcement learning for iterative improvements. Application No. 19336347 was filed on September 22, 2025.
“The method is a pre-trained spatio-temporal crop interaction model based on a 3D-CNN-LSTM to recommend optimal intercrop combinations.”
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USPTO classification G06N covers computer systems based on specific computational models: neural networks, knowledge representation, fuzzy logic, expert systems, evolutionary algorithms. With the AI patent boom, this is one of the most-filed application classes in the office. Every newly published application in G06N lands in this feed, around 230 a month. Patent applications publish 18 months after filing, so this feed reveals what AI labs and companies were working on in the prior year and a half. Watch this if you compete in machine learning, file freedom-to-operate analyses, scout acquisition targets in AI infrastructure, or track which research groups are converting publications to patents. GovPing pulls each application with the filing number, title, applicant, and abstract.
What changed
Tata Consultancy Services Limited filed patent application US20260111617A1 with the USPTO on April 23, 2026, covering a method and system for generating spatio-temporal intercrop layouts using a pretrained 3D-CNN-LSTM large vision model. The technology processes user requests by removing noise and normalizing inputs, then generates optimal intercrop combinations with dynamic visual insights showing crop growth and interaction patterns. The system incorporates user feedback and applies reinforcement learning for iterative model improvements.
Agricultural technology developers, precision farming companies, and AI/ML researchers in the agritech sector should monitor this filing for competitive intelligence purposes. The patent establishes intellectual property claims around applying 3D convolutional and LSTM neural network architectures specifically to agricultural intercropping optimization, which may affect freedom-to-operate considerations for similar agricultural AI systems.
Archived snapshot
Apr 24, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
METHOD AND SYSTEM TO GENERATE SPATIO-TEMPORAL INTERCROP LAYOUTS USING 3D-CNN-LSTM BASED LARGE VISION MODEL
Application US20260111617A1 Kind: A1 Apr 23, 2026
Assignee
Tata Consultancy Services Limited
Inventors
JAYANTRAO MOHITE, DINESHKUMAR JANG BAHADUR SINGH, SURYAKANT ASHOK SAWANT, SRINIVASU PAPPULA
Abstract
This disclosure relates generally to method and system to generate spatio-temporal intercrop layouts using 3D-CNN-LSTM based large vision model. The method is a pre-trained spatio-temporal crop interaction model based on a 3D-CNN-LSTM to recommend optimal intercrop combinations. The method initially receives a user request as input which is preprocessed by removing noise and performing normalization. Further, a pretrained 3D-CNN-LSTM model is utilized to generate an optimal spatio-temporal intercrop layout for each intercropping scenario. The method also provides providing dynamic visual insight for the spatio-temporal intercrop layout comprising crop growth and corresponding intercrop interaction. Finally, an user feedback is obtained from at least one of the user or non-invasively in response to the user request for iterative improvements and fine-tuning the 3D-CNN-LSTM model using reinforcement learning.
CPC Classifications
G06F 30/13 G06N 3/0442 G06N 3/0464 G06N 3/096
Filing Date
2025-09-22
Application No.
19336347
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