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AI Patent Innovations Span Cybersecurity to Biotech

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It may seem that generative AI tools, such as AI chatbots, image and video generators, and coding agents recently appeared out of nowhere and astonished everyone with their highly advanced capabilities. However, the underlying technologies of these AI applications, such as various data classification and regression algorithms, artificial neural networks, machine learning models, and natural language processing techniques, to name a few, have been in development for over half a century. These technologies have been successfully implemented by companies across various science, technology and industry sectors for analyzing images and data, recognizing patterns, making predictions, optimizing and automating processes, machine translation and speech synthesis. In fact, hundreds of thousands of patents have been filed and granted worldwide by companies for inventions in various technical fields that incorporate innovative uses of AI.

Cybersecurity

US Patent No. 12,333,009 describes a system for detecting anomalies in computer processes using AI, specifically machine learning combined with Markov chains. The system monitors process execution, comparing events against a behavior model to calculate probabilities and identify anomalies. Machine learning models analyze event data to assess anomalous behavior, enhancing detection accuracy and efficiency. This approach allows for real-time identification of vulnerabilities and threats, overcoming limitations of traditional methods by leveraging AI to adapt to evolving cyber threats.

Cloud Computing

US Patent No. 12,033,002 discloses a system for scheduling program operations on cloud-based services using machine learning techniques. It involves breaking down program operations into sub-operations and matching them with suitable cloud service components, considering user constraints like budget and deadlines. The system uses machine learning to identify optimal service component combinations that meet user constraints while considering processing constraints. A scheduler then generates a cost-effective schedule for executing the operations, ensuring efficient use of cloud resources.

Fintech

US Patent No. 11,562,298 describes an AI-based predictive marketing platform that utilizes predictive analytics with first-party data from long-term conversion entities to optimize media content direction. The patented predictive analytics technique enhances conversion frequency and speed by employing machine learning algorithms to analyze online behavior data. The platform integrates online behavior with Customer Relationship Management (CRM) data to forecast conversion likelihood, facilitating more efficient ad targeting and campaign management.

Data Backup

US Patent No. 12,314,219 discloses a machine learning-based system for data archiving. The system collects statistical information and event data to classify data and predict access demands, effectively training itself to archive and extract data as needed. By identifying access patterns, the system adjusts threshold values for file access and assigns access classifications, enabling the migration of files between hot and cold data areas based on these classifications. This approach optimizes data storage and retrieval by leveraging machine learning to manage file access efficiently.

Networking

US Patent No. 11,966,500 describes a system for isolating private information in streamed data using machine learning techniques. Machine learning algorithms are employed to automatically identify and extract private information, such as facial images and license plate numbers, based on usage-specific rules. This extracted data is stored separately from the modified stream, which has the private information removed, ensuring privacy even if unauthorized access occurs. The system’s machine learning capabilities enable efficient and automated data management, adapting to various data types and usage scenarios to safeguard personal information.

Automotive

US Patent No. 10,579,883 describes a method for vehicle detection in intelligent driving systems using AI technology. It employs computer vision algorithms to process images from a monocular camera, identifying lane lines and determining a valid area for vehicle detection based on these lines and the vehicle’s speed. Machine learning, specifically weak classifiers, is used to detect vehicles within this valid area, optimizing processing efficiency by focusing only on relevant image sections. This approach reduces computational load and enhances the speed and accuracy of vehicle detection in driving assistance systems.

Health Care

US Patent No. 11,849,792 discloses a head-mounted device that leverages AI and machine learning to enhance the accuracy of monitoring a wearer’s physical conditions for heat stroke risk assessment. The device integrates sensors for salinity, humidity, body temperature, and heart rate, using AI to process data and predict potential heat stroke scenarios. Machine learning algorithms analyze the collected data to calculate sweating and salt loss, providing real-time insights and alerts. This intelligent system enables proactive management of heat-related risks, ensuring timely interventions to prevent severe health issues.

Telecommunications

US Patent No. 11,616,879 describes a system that uses machine learning to handle unwanted telephone calls, such as those involving fraud or spam. The system intercepts and records calls, converting the audio into digital information using an automatic speech recognizer. Machine learning algorithms analyze this data to classify calls as unwanted or genuine based on their content. The classification model is continuously trained and improved using user feedback, enhancing its ability to accurately identify and manage unwanted calls, thereby improving information security.

Computer Vision

US Patent No. 12,080,054 discloses a method for improving small object detection in images using machine learning, specifically neural networks. Conventional neural networks struggle with detecting small objects, but the proposed method restructures the network by shifting detection layers to earlier stages, where resolution is higher, enhancing the network’s ability to detect small objects without increasing input image size. This approach allows for real-time detection in applications like sports broadcasts, where small objects are identified quickly.

Biotech

US Patent No. 11,259,721 describes a method for noninvasively detecting total hemoglobin concentration in blood using AI technology. It involves determining a differential path factor based on physiological parameters and photoplethysmography (PPG) signals at two different wavelengths. Machine learning, specifically neural networks, is used to establish a relationship between physiological parameters and differential path factors, enhancing the accuracy of hemoglobin concentration measurements. This approach allows for precise, real-time monitoring of hemoglobin levels without the need for invasive blood sampling.

Industry

US Patent No. 12,182,700 discloses a method for improving fault diagnosis in blast furnaces using a deep neural network combined with decision trees. The approach leverages the high precision of deep neural networks to model historical fault data, converting this knowledge into understandable rules for operators. This method addresses the challenges of traditional expert systems and data-driven models by providing a more reliable and interpretable solution. The system enhances the automation and intelligence of iron-making processes, enabling effective human-machine collaboration and improving fault diagnosis accuracy in industrial applications.

Drones

US Patent No. 11,618,562 utilizes AI in the context of unmanned aerial vehicles (UAVs) to subdue targeted individuals. The system employs AI algorithms to analyze real-time data from the UAV’s sensors, enabling it to identify, track, and engage with target individuals autonomously. For example, machine learning is used to detect aggressive individuals based on gesture analysis. By leveraging machine learning and computer vision techniques, the UAV can make informed decisions about the most effective methods to subdue a target, ensuring precision and minimizing the risk of collateral damage.

Robotics

US Patent No. 11,297,755 describes a method for controlling soil-working machines, such as lawn mowers and harvesters, using AI technology, specifically convolutional neural networks (CNNs). These networks process images to create a synthetic descriptor of the soil, enabling machines to operate autonomously by recognizing soil characteristics and obstacles. The AI-driven system eliminates the need for external infrastructure like GPS or beacons, offering robust performance in dynamic environments with varying conditions. This approach enhances the machine’s ability to navigate and perform tasks efficiently, adapting to unexpected changes in the environment.

Resource Management

US Patent No. 12,242,996 discloses a system for managing schedule data by interpreting, detecting, and correcting schedule anomalies based on historical data. It includes components that interpret schedule data, identify violations of schedule norms, and generate corrective actions to adjust the schedule. Machine learning, particularly neural networks, is used to identify trends in historical data, which help establish schedule norms. The system can issue alerts or adjust parameters to ensure compliance with these norms, addressing issues such as excessive overtime or unfavorable shift patterns.



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Polimorphic Raises $18.6M as It Beefs Up Public-Sector AI

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The latest best on public-sector AI involves Polimorphic, which has raised $18.6 million in a Series A funding round led by General Catalyst.

The round also included M13 and Shine.

The company raised $5.6 million in a seed round in late 2023.


New York-based Polimorphic sells such products as artificial intelligence-backed chatbots and search tools, voice AI for calls, constituent relationship management (CRM) and workflow software, and permitting and licensing tech.

The new capital will go toward tripling the company’s sales and engineering staff and building more AI product features.

For instance, that includes the continued development of the voice AI offering, which can now work with live data — a bonus when it comes to utility billing — and even informs callers to animal services which pets might be up for adoption, CEO and co-founder Parth Shah told Government Technology in describing his vision for such tech.

The company also wants to bring more AI to CRM and workflow software to help catch errors on applications and other paperwork earlier than before, Shah said.

“We are more than just a chatbot,” he said.

Challenges of public-sector AI include making sure that public agencies truly understand the technology and are “not just slapping AI on what you already do,” Shah said.

As he sees it, working in governments in such a way has helped Polimorphic to nearly double its customer count every six months. The company has more than 200 public-sector departments at the city, county and state levels using the company’s products, he said — and such growth is among the reasons the company attracted this new round of investment.

The company’s general sales pitch is increasingly familiar to public-sector tech buyers: Software and AI can help agencies deal with “repetitive, manual tasks, including answering the same questions by phone and email,” according to a statement, and help people find civic and bureaucratic information more quickly.

For instance, the company says it has helped customers reduce voicemails by up to 90 percent, with walk-in requests cut by 75 percent. Polimorphic clients include the city of Pacifica, Calif.; Tooele County, Utah; Polk County, N.C.; and the town of Palm Beach, Fla.

The fresh funding also will help the company expand in the company’s top markets, which include Wisconsin, New Jersey, North Carolina, Texas, Florida and California.

The company’s investors are familiar to the gov tech industry. Earlier this year, for example, General Catalyst led an $80 million Series C funding round for Prepared, a public safety tech supplier focused on bringing more assistive AI capabilities to emergency dispatch.

“Polimorphic has the potential to become the next modern system of record for local and state government. Historically, it’s been difficult to drive adoption of these foundational platforms beyond traditional ERP and accounting in the public sector,” said Sreyas Misra, partner at General Catalyst, in the statement. “AI is the jet fuel that accelerates this adoption.”

Thad Rueter writes about the business of government technology. He covered local and state governments for newspapers in the Chicago area and Florida, as well as e-commerce, digital payments and related topics for various publications. He lives in Wisconsin.





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AI enters the classroom as law schools prep students for a tech-driven practice

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When it comes to using artificial intelligence in legal education and beyond, the key is thoughtful integration.

“Think of it like a sandwich,” said Dyane O’Leary, professor at Suffolk University Law School. “The student must be the bread on both sides. What the student puts in, and how the output is assessed, matters more than the tool in the middle.”

Suffolk Law is taking a forward-thinking approach to integrating generative AI into legal education starting with requiring an AI course for all first-year students to equip them to use AI, understand it and critique it as future lawyers.

O’Leary, a long-time advocate for legal technology, said there is a need to balance foundational skills with exposure to cutting-edge tools.

“Some schools are ignoring both ends of the AI sandwich,” she said. “Others don’t have the resources to do much at the upper level.”

Professor Dyane O’Leary, director of Suffolk University Law School’s Legal Innovation & Technology Center, teaches a generative AI course in which students assess the ethics of AI in the legal context and, after experimentation, assess the strengths and weaknesses of various AI tools for a range of legal tasks.

One major initiative at Suffolk Law is the partnership with Hotshot, a video-based learning platform used by top law firms, corporate lawyers and litigators.

“The Hotshot content is a series of asynchronous modules tailored for 1Ls,” O’Leary said, “The goal is not for our students to become tech experts but to understand the usage and implication of AI in the legal profession.”

The Hotshot material provides a practical introduction to large language models, explains why generative AI differs from tools students are used to, and uses real-world examples from industry professionals to build credibility and interest.

This structured introduction lays the groundwork for more interactive classroom work when students begin editing and analyzing AI-generated legal content. Students will explore where the tool succeeded, where it failed and why.

“We teach students to think critically,” O’Leary said. “There needs to be an understanding of why AI missed a counterargument or produced a junk rule paragraph.”

These exercises help students learn that AI can support brainstorming and outlining but isn’t yet reliable for final drafting or legal analysis.

Suffolk Law is one of several law schools finding creative ways to bring AI into the classroom — without losing sight of the basics. Whether it’s through required 1L courses, hands-on tools or new certificate programs, the goal is to help students think critically and stay ready for what’s next.

Proactive online learning

Case Western Reserve University School of Law has also taken a proactive step to ensure that all its students are equipped to meet the challenge. In partnership with Wickard.ai, the school recently launched a comprehensive AI training program, making it a mandatory component for the entire first-year class.

“We knew AI was going to change things in legal education and in lawyering,” said Jennifer Cupar, professor of lawyering skills and director of the school’s Legal Writing, Leadership, Experiential Learning, Advocacy, and Professionalism program. “By working with Wickard.ai, we were able to offer training to the entire 1L class and extend the opportunity to the rest of the law school community.”

The program included pre-class assignments, live instruction, guest speakers and hands-on exercises. Students practiced crafting prompts and experimenting with various AI platforms. The goal was to familiarize students with tools such as ChatGPT and encourage a thoughtful, critical approach to their use in legal settings.

Oliver Roberts, CEO and co-founder of Wickard.ai, led the sessions and emphasized the importance of responsible use.

While CWRU Law, like many law schools, has general prohibitions against AI use in drafting assignments, faculty are encouraged to allow exceptions and to guide students in exploring AI’s capabilities responsibly.

“This is a practice-readiness issue,” Cupar said. “Just like Westlaw and Lexis changed legal research, AI is going to be part of legal work going forward. Our students need to understand it now.”

Balanced approach

Starting with the Class of 2025, Washington University School of Law is embedding generative AI instruction into its first-year Legal Research curriculum. The goal is to ensure that every 1L student gains fluency in both traditional legal research methods and emerging AI tools.

Delivered as a yearlong, one-credit course, the revamped curriculum maintains a strong emphasis on core legal research fundamentals, including court hierarchy, the distinction between binding and persuasive authority, primary and secondary sources and effective strategies for researching legislative and regulatory history.

WashU Law is integrating AI as a tool to be used critically and effectively, not as a replacement for human legal reasoning.

Students receive hands-on training in legal-specific generative AI platforms and develop the skills needed to evaluate AI-generated results, detect hallucinated or inaccurate content, and compare outcomes with traditional research methods.

“WashU Law incorporates AI while maintaining the basics of legal research,” said Peter Hook,associate dean. “By teaching the basics, we teach the skills necessary to evaluate whether AI-produced legal research results are any good.”

Stefanie Lindquist, dean of WashU Law, said this balanced approach preserves the rigor and depth that legal employers value.

“The addition of AI instruction further sharpens that edge by equipping students with the ability to responsibly and strategically apply new technologies in a professional context,” Lindquist said.

Forward-thinking vision

Drake University Law School has launched a new AI Law Certificate Program for J.D. students.

The program is a response to the growing need for legal professionals who understand both the promise and complexity of AI.

Designed for completion during a student’s second and third years, the certificate program emphasizes interdisciplinary collaboration, drawing on expertise from across Drake Law School’s campus, including computer science, art and the Institute for Justice Reform & Innovation.

Students will engage with advanced topics such as machine vision and trademark law, quantum computing and cybersecurity, and the broader ethical and regulatory challenges posed by AI.

Roscoe Jones, Jr., dean of Drake Law School, said the AI Law Certificate empowers students to lead at the intersection of law and technology, whether in private practice, government, nonprofit, policymaking or academia.

“Artificial Intelligence is not just changing industries; it’s reshaping governance, ethics and the very framework of legal systems,” he said. 

Simulated, but realistic

Suffolk Law has also launched an online platform that allows students to practice negotiation skills with AI bots programmed to simulate the behavior of seasoned attorneys.

“They’re not scripted. They’re human-like,” she said. “Sometimes polite, sometimes bananas. It mimics real negotiation.”

These interactive experiences in either text or voice mode allow students to practice handling the messiness of legal dialogue, which is an experience hard to replicate with static casebooks or classroom hypotheticals.

Unlike overly accommodating AI assistants, these bots shift tactics and strategies, mirroring the adaptive nature of real-world legal negotiators.

Another tool on the platform supports oral argument prep. Created by Suffolk Law’s legal writing team in partnership with the school’s litigation lab, the AI mock judge engages students in real-time argument rehearsals, asking follow-up questions and testing their case theories.

“It’s especially helpful for students who don’t get much out of reading their outline alone,” O’Leary said. “It makes the lights go on.”

O’Leary also emphasizes the importance of academic integrity. Suffolk Law has a default policy that prohibits use of generative AI on assignments unless a professor explicitly allows it. Still, she said the policy is evolving.

“You can’t ignore the equity issues,” she said, pointing to how students often get help from lawyers in the family or paid tutors. “To prohibit [AI] entirely is starting to feel unrealistic.”





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Microsoft pushes billions at AI education for the masses • The Register

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After committing more than $13 billion in strategic investments to OpenAI, Microsoft is splashing out billions more to get people using the technology.

On Wednesday, Redmond announced a $4 billion donation of cash and technology to schools and non-profits over the next five years. It’s branding this philanthropic mission as Microsoft Elevate, which is billed as “providing people and organizations with AI skills and tools to thrive in an AI-powered economy.” It will also start the AI Economy Institute (AIEI), a so-called corporate think tank stocked with academics that will be publishing research on how the workforce needs to adapt to AI tech.

The bulk of the money will go toward AI and cloud credits for K-12 schools and community colleges, and Redmond claims 20 million people will “earn an in-demand AI skilling credential” under the scheme, although Microsoft’s record on such vendor-backed certifications is hardly spotless.

“Working in close coordination with other groups across Microsoft, including LinkedIn and GitHub, Microsoft Elevate will deliver AI education and skilling at scale,” said Brad Smith, president and vice chair of Microsoft Corporation, in a blog post. “And it will work as an advocate for public policies around the world to advance AI education and training for others.”

It’s not an entirely new scheme – Redmond already had its Microsoft Philanthropies and Tech for Social Impact charitable organizations, but they are now merging into Elevate. Smith noted Microsoft has already teamed up with North Rhine-Westphalia in Germany to train students on AI, and says similar partnerships across the US education system will follow.

Microsoft is also looking to recruit teachers to the cause.

On Tuesday, Microsoft, along with Anthropic and OpenAI, said it was starting the National Academy for AI Instruction with the American Federation of Teachers to train teachers in AI skills and to pass them on to the next generation. The scheme has received $23 million in funding from the tech giants spread over five years, and aims to train 400,000 teachers at training centers across the US and online.

“AI holds tremendous promise but huge challenges—and it’s our job as educators to make sure AI serves our students and society, not the other way around,” said AFT President Randi Weingarten in a canned statement.

“The direct connection between a teacher and their kids can never be replaced by new technologies, but if we learn how to harness it, set commonsense guardrails and put teachers in the driver’s seat, teaching and learning can be enhanced.”

Meanwhile, the AIEI will sponsor and convene researchers to produce publications, including policy briefs and research reports, on applying AI skills in the workforce, leveraging a global network of academic partners.

Hopefully they can do a better job of it than Redmond’s own staff. After 9,000 layoffs from Microsoft earlier this month, largely in the Xbox division, Matt Turnbull, an executive producer at Xbox Game Studios Publishing, went viral with a spectacularly tone-deaf LinkedIn post (now removed) to former staff members offering AI prompts “to help reduce the emotional and cognitive load that comes with job loss.” ®



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