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Development & Protection of AI Technologies
Our Intellectual Property (IP) and Privacy teams work closely together to counsel clients in building, protecting, and commercializing proprietary AI technologies and data; the use of third party or open source AI technologies and data; and the implications that these activities may have under privacy laws.
Experience 32 results
Experience
|September 22, 2025
Winston Paris advised the founders and shareholders of Bylaw, on its acquisition by Septeo
Founded in 2019 by Adrien Aboudaram and Tuan Ardouin, Bylaw has developed next-generation AI technology to automate the processing of legal and financial documents. With a team primarily dedicated to R&D, the legaltech company aims to become the leading AI reference for regulated professions.
Experience
|July 30, 2025
Represent Norman W. Fries, Inc. d/b/a Claxton Poultry Farms in a series of 15+ antitrust class actions consolidated in the Northern District of Illinois and brought by plaintiffs who allege that Claxton and the nation’s other largest poultry producers conspired to fix the price of broiler chickens in a scheme from 2008 to 2016 that raised the price for broiler chickens by artificially reducing supply.
Experience
|July 17, 2025
D. Boral Capital Closes US$15M Robot Consulting IPO
Winston & Strawn LLP represented D. Boral Capital LLC and Craft Capital Management LLC in connection with the US$15M initial public offering of American Depositary Shares (ADSs) of Robot Consulting Co., Ltd. The offering consisted of 3,750,000 ADSs priced at $4.00 per ADS, and the company’s ADSs began trading on the Nasdaq Capital Market under the ticker symbol “LAWR” on July 17, 2025.
Insights & News 693 results
Seminar/CLE
|November 13, 2025
AI in Action: Legal Strategies for Healthcare Innovation
We are bringing together stakeholders across the healthcare ecosystem—from front-line providers to financial backers—for a panel discussion on AI in healthcare.
Winston’s AI Top 10
|October 2025
|5 Min Read
Winston’s AI Monthly Recap - October 2025
Winston’s AI Top 10 summarizes the latest AI developments in the legal industry.
Sponsorship
|October 27, 2025
Winston & Strawn Sponsors Tokyo Forum on U.S. Capital, Digital Assets, and AI Innovation
Winston & Strawn is proud to sponsor the upcoming Beyond Borders, Beyond Markets Tokyo Forum on U.S. Capital, Digital Assets, and AI Innovation. This event brings together global investors to explore the future of finance—from IPO strategies to AI’s role in capital markets. The forum will discuss the nuances of going public in America and offer key considerations for capital access and enhanced liquidity.
Other Results 28 results
Site Content
What Is Artificial Intelligence (AI)?
The definition of artificial intelligence, also known as AI, is the capability of computers or robots to execute tasks that humans normally do. The meaning of AI can also include the development of computer systems that perform intellectual processes. In other words, machines perform tasks intelligently, such as reasoning and generalizing. Narrow AI is a type of artificial intelligence where the focus is placed on specific tasks. An example of this would be a virtual assistant who has targeted abilities, such as the ability to respond to questions. Strong AI is machine intelligence featuring human cognitive capabilities, such as the ability to make judgments, find solutions, or communicate. Today, it’s important to understand what artificial intelligence systems are commonly used for, including visual perception, speech recognition, and decision-making.
Site Content
Site Content
Generative AI tools can create new content, such as text, computer code, images, audio, sound, and video, in response to a user’s prompt, often in the form of a short written description of the desired output. Generative AI tools are based on machine learning, trained using enormous amounts of data.[1] Generative AI tools are built on a system of inputs and outputs. First, the tool goes through a machine learning period whereby it is trained to generate predictive models and creative outputs through a large data set, often varied and diverse but tailored to the goal of the tool (i.e., customer service, generating scientific or marketing models, etc.). For in-house tools, this can be done with the company’s own data; for larger tools such as ChatGPT, this is done with the creator’s data set.[2] Once the tool has been trained, the individual user “inputs” a short prompt for the tool to synthesize and produce an “output.” Inputs are often retained on the servers controlled by the company that supports the tool, for monitoring of the tool’s performance and, in some cases, continued learning. The “outputs” are created by combining the machine learning during the training period with the inputs to produce an output.[3]