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What is Visualize IP?

Visualize IP (VIP) has introduced an unprecedented professional-grade computer vision patent search tool that employs state-of-the-art image similarity AI technology, which is currently awaiting patent approval. This groundbreaking tool not only reduces costs by an impressive 90% but also boosts accuracy by a remarkable factor of 500, all while providing real-time results. Beyond these enhancements, VIP stands out by narrating the journey of each search, a feature that has remained elusive for many in the artificial intelligence patent sector. The most skilled human patent researchers are adept at crafting narratives around their discoveries, offering customized insights and simplifying intricate data into actionable information, which in turn builds trust in their outcomes. By emulating this effective human approach, VIP guarantees a thorough and enriching search experience. The platform has revolutionized the field of image similarity searching by harnessing the knowledge of former USPTO examiners, patent attorneys, and elite data scientists, leading to an AI system with unmatched precision in image searches. With VIP, the constraints typically associated with traditional human searches are completely removed, significantly enhancing both accuracy and user trust in the results. This transformation marks a pivotal advancement in patent search methodologies, establishing a new benchmark for efficiency and dependability, while also paving the way for future innovations in the field. As the technology continues to evolve, VIP is poised to redefine expectations in patent searching and beyond.

What is PCS?

Dolcera's leading patent analytics platform, known as PCS, features an extensive collection of over 110 million patents from around the world, with daily updates that guarantee prompt and thorough responses to all patent-related questions. Created by experts from Stanford University, this cutting-edge tool employs advanced artificial intelligence to enhance search capabilities and analytics. By harnessing a complex machine learning algorithm, PCS explores diverse data sources beyond conventional patent literature, allowing for the generation of detailed and informative categories. Users are given the ability to formulate precise inquiries, as the system incorporates synonyms, semantics, and various word forms, facilitating rapid and effective search creation without the burden of sifting through dense scientific materials. The platform ensures high-quality analytics by linking patents directly to their rightful owners, which enhances the accuracy of the results produced. Additionally, PCS includes features such as a cluster cloud, CPC taxonomy, and assignee normalization, all of which are praised for their remarkable precision. It provides an intuitive search experience, stripping away unnecessary complexity and making it easy for users without prior search knowledge to navigate. This not only streamlines the process of patent searching but also significantly improves the overall experience for users, making it a valuable tool for both seasoned professionals and newcomers alike. As a result, PCS stands out as an essential resource in the realm of patent analytics.

Media

Media

Integrations Supported

Integrations Supported

Payitiv CRM

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Visualize IP

Company Location

United States

Company Website

www.visualizeip.com

Company Facts

Organization Name

Dolcera

Company Location

United States

Company Website

www.dolcera.com/web/pcs/

Categories and Features

Computer Vision

Not specified

Patent Research

Not specified

Categories and Features

Patent Research

Not specified

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