Use this API to search the Office of the Chief Economist (OCE) Patent Examination Event Codes Dataset 2015. The Event Codes dataset includes short descriptions of the event codes used in the transactions dataset. The descriptions can be linked to transactions using the event_code variable. There are 1,873 unique event codes represented in this data file.

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.

We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description:
In response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.