Company patents
Teradata US, Inc.
Teradata US, Inc. exhibits a highly concentrated patent strategy, with 87.5% of its portfolio in Databases & Information Retrieval, which saw a significant 25.0% decline in 2025 and a further 38.1% decline so far in 2026, indicating a potential shift despite its core focus. Surprisingly, while Machine Learning & AI constitutes 13.5% of its portfolio, its patenting in this area has been declining, with a 40.0% drop in 2024 and a 33.3% drop so far in 2026, suggesting a de-emphasis on this rapidly evolving field.
Patent Trend by Technology Area
Yearly patent publications since 2023
Product themes
Product-level themes inferred from filings since 2023, with category chips showing where each theme appears. Select a theme to filter the patents below.
96 US filings (since 2023) · 7 categories · 13 themes
Methods and systems for integrating, transforming, and managing complex or domain-specific data from disparate sources into a unified structure, often for specific applications like social networks, genomics, or business forms.
Methods and systems for efficiently allocating computing resources, balancing workloads, and managing power states to improve performance, reduce energy consumption, or enhance reliability in computing platforms.
Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.
Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.
Hardware and control techniques for optimizing memory access latency, ensuring data integrity, and managing storage resources efficiently. This includes error correction, read/write voltage control, and intelligent data placement or in-memory computation.
Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.
Encompasses strategies and technologies to ensure the availability, integrity, and recoverability of data and systems, including robust backup, replication, error correction, and efficient data restoration.
Systems that process data to provide personalized recommendations, predict events, or automate decision-making processes based on learned patterns, user behavior, or environmental factors.
Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.
Involves systems designed to automatically detect errors or failures and initiate predefined or intelligent corrective actions, recovery procedures, or notifications to minimize downtime and manual intervention.
Specialized hardware, architectural designs, and computational methods to improve the speed, efficiency, and security of artificial intelligence and machine learning model execution, particularly for inference and data processing.
Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.
Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.
Patents
Showing 1-5 of 5
Large Model Text Generation