Use this API to search Patent Examination Status Codes Dataset 2015. The Status Codes dataset includes descriptions of the status codes used to populate the appl_status_code variable in the application dataset. The descriptions can be linked to application data using this variable. There are 225 unique application status codes represented in this file. See Table A-3 in documentation for a list and brief description of the two variables included in this dataset.

Use this API to search Patent Litigation Cases. The Patent Litigation Dataset contains detailed patent litigation data on 74,623 unique district court cases filed during the period 1963-2015. OCE collected all of the data from the Public Access to Court Electronics Records (PACER) and RECAP, an independent project designed to serve as a repository for litigation data sourced from PACER.

The Patent Trial and Appeal Board (PTAB) API is a RESTful API with an easy to use search interface. You can easily browse USPTO PTAB public documents, search for specific content, and request a bulk download of PTAB content. The PTAB API synchronizes close to real time with the PTAB E2E (End-to-End) system making the latest public America Invents Action (AIA) Trial information and documents available. PTAB API v2 has text search capabilities for decision documents.

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.

The Trademark Image Search API (beta) allows searching Trademark Images using various attributes like feature, accuracy, metricspaces and candidates. This API also allows searching Trademark documents using various metadata attributes like Specimen Description, Filing Description, Filing Basis, Owner's Name, Street Address, etc.

The Trademark Status and Document Retrieval (TSDR) API allows you to make programmatic requests for trademark case status, documents, and images. Beginning on October 2, 2020, you will need an API key to download bulk data from TSDR using the TSDR API. Using API keys will help optimize usage of our TSDR system, which will help enable users like yourself to better access the information you need from TSDR. The below resources will provide you with more information on using the TSDR API.

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.