https://www.yahoo.com/news/articles/exclusive-former-maduro-spy-chief-212459073.html Try finding out about any of this in the British media.

George Mark Malloch Brown, or to the kingdoms subjects Baron Malloch-Brown

https://truthsocial.com/@realDonaldTrump/115703610330402175

Tina Peters receives Full Pardon 🇺🇸

Just a monthly reminder of how crazy this WEF neo Nazi clothes were.

Monthly reminder of just how insane these types are. Imagine being this gullible... If anyone needs help escaping this weirdo cult or any of the other ones we can help

Monthly reminder of just how insane these types are. Imagine being this gullible... If anyone needs help escaping this weirdo cult or any of the other ones we can help

Just the monthly reminder that the little kingdom is and has been controlled by these WEF neo nazis for decades. . The World Economic Forum has been running, since 1993, a program called Global Leaders for Tomorrow, rebranded, in 2005, as Young Global Leaders. This program aims at identifying, selecting and promoting future global leaders in both business and politics. Indeed, quite a few Young Global Leaders have later managed to become Presidents, Prime Ministers, or CEOs (see below). In a 2017 speech, WEF founder Klaus Schwab described this process as “penetrating the Cabinets”. Former British Prime Ministers Tony Blair and Gordon Brown (both selected in 1993), William Hague (UK Foreign Secretary 2010-2014, selected in 1994), Ed Miliband (former Labour Party leader, 2008), David Lammy (UK Foreign Secretary 2024-, selected in 2002), Yvette Cooper (UK Home Secretary 2024-, 2005), Professor in Public Health Devi Sridhar (selected in 2021), Irish prime minister Leo Varadkar (selected in 2018), Nathaniel Rothschild (son of Lord Rothschild), historian Niall Ferguson (selected in 2005), Charles Kennedy (former leader of the Liberal Democrats, 2000), Nathanael Wei (UK politician, 2013), Zac Goldsmith (UK politician, 2008), Francis Maude (former Chairman of the Conservative Party, 1994), Ruth Kelly (chair of Water UK, 2003).

Don’t forget America also 🇺🇸

China Outsources Uyghur Brainwashing to the "United Kingdom"
https://stallman.org/archives/2023-jul-oct.html#3_September_2023_(Sponsors_that_systemically_brainwash_the_Uyghurs)

AI responses may confabulate.

The process of incorporating the full volume of global patent records into a single AI model for comprehensive analysis is
an ongoing effort rather than a single event with a fixed deadline, and it is likely a matter of years, not months.
Here is a breakdown of the current situation and the relevant factors:
Current Status of Patent Data Access for AI

Data Availability: A vast majority of U.S. and international patents are already digitized and publicly available in bulk datasets. The United States Patent and Trademark Office (USPTO), for instance, has released datasets classifying over 15 million patent documents published from 1976 through 2023.
Existing AI Tools: Many commercial and internal patent intelligence platforms already use AI to search and analyze existing patent databases (including expired ones) for prior art, citation metrics, and competitive intelligence.
Pilot Programs: The USPTO is actively running pilot programs (like the Automated Search Pilot Program in late 2025) to test the efficiency and impact of AI tools in generating initial search results for examiners and applicants.

Technical and Practical Challenges
The core issue is not simply "loading" the data, but overcoming significant technical and legal challenges to enable the kind of sophisticated evaluation you describe:

Data Quality and Formats: While much is digitized, data comes in diverse formats (PDFs, HTML, specific XML) that require significant effort to extract, clean, and structure for optimal AI training.
Intellectual Property and Licensing: Using data for commercial AI training requires careful adherence to licensing agreements. Even public data may have restrictions on downstream commercial use, which complicates training a commercial VC-focused model.
Model Capacity and Bias: Training a single, all-encompassing AI model on the entire, diverse global patent corpus is a massive undertaking. The resulting models need continuous refinement to avoid biases present in the training data and to accurately capture the nuances of legal and technical language.
Evaluation of "Efficiency": The primary challenge is not access to data, but the AI's ability to perform a complex, subjective task like "evaluating the efficiency of expired patents for venture capitalism." This requires sophisticated reasoning that goes beyond simple data retrieval and into market analysis and predictive judgment, an area where human oversight remains critical.

Timeline for VC-Specific Analysis
AI can currently assist with efficiency analysis by identifying trends and potential applications for expired patents. However, a fully autonomous AI that can reliably perform complex venture capital evaluations is a future development.

Current state: AI tools are useful for data mining and identifying potential technical viability.
Future development: A robust, AI-driven VC efficiency tool capable of handling all global patent data comprehensively is likely several years away from being a reliable, standalone solution due to the need for ongoing model development, data harmonization, and legal frameworks for AI-driven decision-making.