PATENT DROP: Google goes to medical school

Plus: Twilio susses out your data; Goldman Sachs’ cloudy day

Happy Thursday. Welcome back to Patent Drop! 

Today, we’re checking out Google’s step into the hospital room; Twilio’s tech to pick and choose which information it wants to protect; and Goldman Sachs’ goal to help Wall Street break down its enormous pile of data. 

Let's dive right in.

#1. Google listens to your heart

Dr. Google will see you now. 

Google is looking to patent a device that can determine “chest conditions from radiograph data” using machine learning. This AI-powered tech processes x-rays of a patient to offer up a preliminary diagnosis by determining the likelihood that a patient has certain chest ailments. 

The system relies on a machine-learned “pathology model” which generates “risk data,” meaning it predicts the likelihood that a patent may have issues requiring medical attention. Google noted that the model can be trained to have at least 90% sensitivity (it’s ability to predict true positives) and at least 70% specificity (it’s ability to predict true negatives). The machine learning model is built with a “deep learning system architecture” trained on tons of patients chest x-rays “from a plurality of countries,” Google noted. 

Once the model determines whether or not a patient has a chest condition, the system creates an overlay on the patient’s x-ray pointing out “visual cues of suspicious areas,” and recommend follow-up actions to the clinician.  

Google’s patent discusses specifically how this kind of tech is beneficial for tuberculosis diagnoses in countries with high numbers of HIV cases and “limited accessibility to proper testing due to the lack of medical professionals and the high cost of certain tests.” The company also noted that this model could be trained to cover other chest conditions at the different stages, as well as “general diagnosis for any body part.” 

Photo via the U.S. Patent and Trademark office.

It goes without saying that Google is a key player in the rapidly growing AI industry. And it isn’t new to the health space, either: The company already offers Google Health, which tackles a wide range of areas ranging from personal fitness to health research to its clinician workflow support tool, Care Studio. Since starting its health tech division in 2018, Google has added more than 500 employees and hired the FDA's former digital health chief. 

That said, Google Health is an afterthought in terms of revenue. But medical tech is a priority of rivals like Apple and Amazon, who both operate robust health tech businesses, so Google may be scrambling to keep up with the competition, forging its own niche in healthcare with its AI capabilities. 

However, this tech also fits the bill for a project under Google’s 80/20 rule, which encourages employees to spend 20% of their time on personal passion projects, said Dr. Sharief Taraman, CEO of Cognoa, which develops AI-based diagnostic and therapeutic products. Given this patent’s focus on helping low-income countries, Taraman said, “I don't think Google's looking necessarily to make money (from this).” 

Whether or not the tech in Google’s patent makes its way into hospitals, the filing further proves that AI is integrating itself into clinical settings. While AI likely won’t ever replace the job of a doctor, Taraman said, the pandemic “decimated our healthcare workforce,” and AI has the capability to fill the widening support gaps with quicker tools for diagnosis. 

“The future of healthcare with AI, in my mind, one where we don't have delayed diagnoses,” he said. “I think that that's going to be a big win in helping address shortages of healthcare professionals, because whether we want to admit it or not, we're in a big crisis.” 

#2. Twilio’s data cherry-picker

Twilio wants to suss out which data needs the strongest protection.

The enterprise communications company is seeking to patent a “standard compliant data collection system” for sensitive data. Essentially, this patent offers a way for Twilio clients to be selective about the data they’re going to collect by activating what the company calls a  “standard compliant data collection mode” when sensitive data needs to be sent. 

When activated, this system takes out the middleman: Rather than giving personal or sensitive data to a business’s agent, a user is routed to a standard compliant data collection system, where they can directly and securely give out their information. Meanwhile, the agent is placed on hold, and “cannot receive communications transmitted by the client device of the user providing the sensitive data.” 

For example, if you’re going back and forth with a customer service agent and you need to give their system your credit card information and address to process a payment, Twilio’s system will switch on only for the time it takes to give that information to its system. Twilio noted that this tool may cover a wide variety of communications, including messaging, SMS and video calls. 

Then, if a company wants a record of the user-agent communication for training purposes, a “data anonymization system” kicks in, which fills in the sensitive data that was given as part of the chat with “default replacements.” 

Twilio noted that keeping up with compliant data collection can be an arduous task, “particularly for smaller businesses that have insufficient resources and technical knowledge and/or when multiple communication channels are used to collect the sensitive data.” 

Photo via the U.S. Patent and Trademark Office.

With more than 150,000 enterprise clients that operate in dozens of states and countries, including the likes of Twitter, Lyft, and Airbnb, Twilio adding in a feature like this only makes sense. A messaging API with a system that automatically takes into account different regions’ data collection policies could save its clients a great deal of time and effort, said Ari Weil, VP of marketing of cloud data security firm Cyera.

Last summer, Twilio suffered two major security breaches from the “0ktapus” hacker group. In August, the cybercriminals pilfered the data of more than 200 customers in an SMS phishing attack. It was later discovered that the group gained access to an undisclosed number of user accounts through a voice phishing attack in June. 

With this patent, Weil said, Twilio may be looking to prevent attacks by collecting and storing data in a more secure fashion, while minimizing the amount of data that’s collected overall.

“If you'd be stopped from taking the information in the first place, or are taking it and storing it in a compliant fashion, then you're ahead of the curve as far as how you would try to protect that data,” Weil noted. 

But Twilio’s solution is only one piece of a much larger puzzle. While it certainly doesn’t hurt to collect sensitive data in the right way, determining what is and isn’t considered sensitive remains a big challenge, Weil said. For example, while a single piece of information about a user – such as their first name or IP address – may not be considered highly sensitive, the whole is often greater (and much more dangerous) than the sum of its parts in the hands of a bad actor. 

“It's not just about intaking (the data) in a compliant fashion,” Weil said. “What Twilio is doing is starting a long journey, involving most likely a lot of education internally about, once you collect data, how you manage it and utilize it downstream.”  

#3. Goldman Sachs has its head in the clouds

Goldman Sachs wants to be the Marie Kondo of Wall Street data. 

The financial institution is seeking to patent a system for “managed data services” on cloud platforms, helping them declutter in a way Kondo would appreciate. This filing details a number of ways that Goldman’s system aims to sift through Wall Street’s data stockpile, but the goal of it is to gather, clean and organize data in real-time, and provide actionable analysis with little to no work on the client’s part. 

The focus of this patent is specifically “timeseries data,” or data that is recorded consistently over time, “often at high rates of ingestion,” for example, price fluctuations of a material or stock price data.

Basically, data is replicated across the “nodes” of this system, each of which handles a “microservice.” These microservices can be anything from data intake to answering client questions. All of these small services come together to handle requests with low latency, even during usage peaks, because each node has consistent access to all of the data it needs to do its individual job. Goldman noted that this system can be set up on a company’s proprietary cloud platform, or one that they’re renting from (a.k.a. AWS, Azure, etc.).

While this all may sound abstract, the outcome is clean, easy-to-access data that can be used for a number of business processes, including answering queries, generating reports and analysis “presented in real-time, down to nanosecond granularity,” that can inform decisions. 

Photo via the U.S. Patent and Trademark Office.

Investment banks and financial firms have a constant and growing hunger for market data – and that hunger comes at a steep price. 

Demand for financial data has bubbled over the past few years, with the London Stock Exchange Group’s $27 billion acquisition of Refinitiv in 2019 being an early signal of a craze to come. Since then, it’s only gotten more pronounced: According to an April report from Burton-Taylor International Consulting, global spending on financial data reached a record $37.3 billion in 2022.

But actually putting this pricey cache of data to use is a time suck, Goldman said in its filing. Firms often spend “an inordinate amount of time is spent finding, cleaning and organizing or maintaining data feeds, such as up to 80%, leaving little time to spend performing data analysis.” By securing a patent on a system that can do all the tedious work for you, Goldman could lock down a lucrative market. 

Goldman isn’t the only company interested in making data easier to understand. Rival JPMorgan Chase is seeking to patent no-code machine learning that basically eats client data and pops-out a ready-to-use AI model for business intelligence.

While the companies’ methods are vastly different, the goal remains the same: To straighten out data so that their clients – and in-turn, themselves – can more quickly turn their money into more money. 

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Extra Drops

A few more tech tidbits before you head out. 

• Meta wants to set you up. The company is seeking to patent a dating interface based on “interest-based communities.” Though the company shut down “Sparked,” its former video speed-dating app, its matchmaking ambitions may not be over yet. 

• Uber wants to predict where you want to go. The company is seeking to patent tech that makes “trajectory-aware” ride suggestions based on where you are and where you’ve traveled in the past with the app. 

• Sony wants to know where you’re looking. The company wants to patent a system for the “pre-loading of rendering data,” which tracks gaze and gesture data to reduce processing power needed for game rendering and limit delays. (If this sounds familiar, it’s because it is – Apple is working on a similar concept.) 

What else is new? 

• Google told employees that it’s cracking down on in-office attendance by including office attendance in performance reviews and tracking badge data.

• AI startup Cohere raised $270 million in its latest funding round, with funds coming from both VC firms and tech companies like Oracle and Nvidia.

• AWS is partnering with the Allen Institute to build a map of the human brain in a project dubbed the Brain Knowledge Platform to better understand what links genetics and cognitive functions.

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