This tab-delimited file, stc2015_5yr.txt, prepared from the U.S. Patent and Trademark Office (USPTO), Technology Assessment and Forecast (TAF) database, displays a listing of geographic regions from which utility patents (i.e., patents for invention) originated during the indicated 5 year time period and a corresponding count of patents for each of the years of the period. Each displayed annual count corresponds to the count of patents received in a calendar year (January 1 to December 31). It will import into a Microsoft Excel spreadsheet.

This tab-delimited file, stc2015_5yr.tsv, prepared from the U.S. Patent and Trademark Office (USPTO), Technology Assessment and Forecast (TAF) database, displays a listing of geographic regions from which utility patents (i.e., patents for invention) originated during the indicated 5 year time period and a corresponding count of patents for each of the years of the period. Each displayed annual count corresponds to the count of patents received in a calendar year (January 1 to December 31). It will import into a Microsoft Excel spreadsheet.

The PatentsView API is intended to inspire the exploration and enhanced understanding of US intellectual property (IP) and innovation systems. The database driving the API is regularly updated and integrates the best available tools for inventor disambiguation and data quality control. We hope researchers and developers alike will explore the API to discover people and companies and to visualize trends and patterns across the US innovation landscape.

Offers exclusive access to patent application status information for unpublished patent applications only to the applicant/inventor or his/her representative(s).

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.).

This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.

This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.

Every Federal agency that operates or directs one or more Federal laboratories or that conducts research and development is required to prepare and submit an annual report of its technology transfer activities as described in 15 U.S.C. § 3710(f).

This is a searchable database of NIST patents