Can AI Address Health Care’s Red-Tape Problem?

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Productivity in the United States’ health care industry is declining — and has been ever since World War II. As the cost of treating patients continues to rise, life expectancy in America is beginning to fall. But there is mounting evidence that artificial intelligence (AI) can reverse the downward spiral in productivity by automating the system’s labyrinth of labor-intensive, inefficient administrative tasks, many of which have little to do with treating patients.

Administrative and operational inefficiencies account for nearly one third of the U.S. health care system’s $3 trillion in annual costs. Labor is the industry’s single largest operating expense, with six out of every 10 people who work in health care never interacting with patients. Even those who do can spend as little as 27% of their time working directly with patients. The rest is spent in front of computers, performing administrative tasks.

Using AI-powered tools capable of processing vast amounts of data and making real-time recommendations, some hospitals and insurers are discovering that they can reduce administrative hours, especially in the areas of regulatory documentation and fraudulent claims. This allows health care employees to devote more of their time to patients and focus on meeting their needs more efficiently.

To be sure, as we’ve seen with the adoption of electronic health records (EHR), the health care industry has a track record of dragging its feet when it comes to adopting new technologies — and for failing to maximize efficiency gains from new technologies. It was among the last industries to accept the need to digitize, and by and large has designed digital systems that doctors and medical staff dislike, contributing to warnings about burnout in the industry.

Adopting AI, however, doesn’t require the Herculean effort electronic health records (EHRs) did. Where EHRs required billions of dollars in investment and multi-year commitments from health systems, AI is more about targeted solutions. It involves productivity improvements made in increments by individual organizations without the prerequisite collaboration and standardization across health care players required with EHR adoption.

Indeed, AI solutions dealing with cost-cutting and reducing bureaucracy — where AI could have the biggest impact on productivity — are already producing the kind of internal gains that suggest much more is possible in health care players’ back offices. In most cases, these are experiments launched by individual hospitals or insurers.

Here, we analyze three ways AI is chipping away at mundane, administrative tasks at various health care providers and achieving new efficiencies.

Faster Hospital Bed Assignments

Quickly assigning patients to beds is critical to both the patients’ recovery and the financial health of hospitals. Large hospitals typically employ teams of 50 or more bed managers who spend the bulk of their day making calls and sending faxes to various departments vying for their share of the beds available. This job is made more complex by the unique requirements of each patient and the timing of incoming bed requests, so it’s not always a case of not enough beds but rather not enough of the right type at the right time.

Enter AI with the capability to help hospitals more accurately anticipate demand for beds and assign them more efficiently. For instance, by combining bed availability data and patient clinical data with projected future bed requests, an AI-powered control center at Johns Hopkins Hospital has been able to foresee bottlenecks and suggest corrective actions to avoid them, sometimes days in advance.

As a result, since the hospital introduced its new system two years ago, Johns Hopkins can assign beds 30% faster. This has reduced the need to keep surgery patients in recovery rooms longer than necessary by 80% and cut the wait time for beds for incoming emergency room patients by 20%. The new efficiencies also permitted Hopkins to accept 60% more transfer patients from other hospitals.

All of these improvements mean more hospital revenue. Hopkins’s success has prompted Humber River Hospital in Toronto and Tampa General Hospital in Florida to create their own AI-powered control centers as well.

Easier and Improved Documentation

Rapid collection, analysis and validation of health records is another place where AI has begun to make a difference. Health care providers typically spend nearly $39 billion every year to ensure that their electronic health records comply with about 600 federal guidelines. Hospitals assign about 60 people to this task on average, one quarter of whom are doctors and nurses.

This calculus changes when providers use an AI-powered tool developed in cooperation with electronic health record vendor Cerner Corporation. Embedded in physicians’ workflow, the AI tool created by Nuance Communications offers real-time suggestions to doctors on how to comply with federal guidelines by analyzing both patient clinical data and administrative data.

By following the AI tool’s recommendations, some health care providers have cut the time spent on documentation by up to 45% while simultaneously making their records 36% more compliant.

Automated Fraud Detection

Fraud, waste, and abuse also continues to be a consistent drain. Despite an army of claims investigators, it annually costs the industry as much as $200 billion.

While AI won’t eliminate those problems, it does help insurers better identify the claims that investigators should review — in many cases, even before they are paid — to more efficiently reduce the number of suspect claims making it through the system. For example, startup Fraudscope has already saved insurers more than $1 billion by using machine learning algorithms to identify potentially fraudulent claims and alert investigators prior to payment. Its AI system also prioritizes the claims that will yield the most savings, ensuring that time and resources are used where they will have the greatest impact.

Getting Ready for AI

When it comes to cutting health care’s administrative burden through AI, we are only beginning to scratch the surface. But the industry’s ability to amplify that impact will be constrained unless it moves to remove certain impediments.

First, healthcare organizations must simplify and standardize data and processes before AI algorithms can work with them. For example, efficiently finding available hospital beds can’t happen unless all departments define bed space in the same terms.

Second, health care providers will have to break down the barriers that usually exist between customized and conflicting information technology systems in different departments. AI can only automate the transfer of patients from operating rooms to intensive care units (ICU) if both departments’ IT systems are able to communicate with each other.

Finally, the industry’s productivity will not improve as long as too many health care personnel continue in jobs that don’t add value to the business by improving outcomes. Health care players need to begin reducing their workforces by taking advantage of the industry’s 20% attrition rate and automating tasks, rather than filling positions on autopilot.

The task of improving productivity in health care by automating administrative tasks with AI will not be completed quickly or easily. But the progress already achieved by AI solutions is encouraging enough for some to wonder whether re-investing savings from it might also ultimately cut the overall cost of health care as well as improve its quality. For an industry known for its glacial approach to change, AI offers more than a little light at the end of a long tunnel.

Source: Harvard Business Review

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Come and say hello

Tech Summit

NYU is hosting the inaugural Technology Summit to celebrate and showcase innovative and emerging technologies used in teaching, learning, research, administration, and entrepreneurial efforts in tech, and beyond.

I’ll be giving a keynote on AI in Healthcare. Come and say hi on 11/14 at Kimmel Center for University Life, 60 Washington Square S., New York, NY 10012

Driverless car makers could face jail if AI causes harm

AI technologies which harm workers could lead to their creators being prosecuted, according to the British government.

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Makers of driverless vehicles and other artificial intelligence systems could face jail and multi-million pound fines if their creations harm workers, according to the Department of Work and Pensions.

Responding to a written parliamentary question, government spokesperson Baroness Buscombe confirmed that existing health and safety law “applies to artificial intelligence and machine learning software”.This clarifies one aspect of the law around AI, a subject of considerable debate in academic, legal and governmental circles.

Under the Health and Safety Act of 1974, directors found guilty of “consent or connivance” or neglect can face up to two years in prison.

This provision of the Health and Safety Act is “hard to prosecute,” said Michael Appleby, a health and safety lawyer at Fisher Scoggins Waters, “because directors have to have their hands on the system.”

However, when AI systems are built by startups, it might be easier to establish a clear link between the director and the software product.

Companies can also be prosecuted under the Act, with fines relative to the firm’s turnover. If the company has a revenue greater than £50 million, the fines can be unlimited.

The Health and Safety Act has never been applied to a case of artificial intelligence and machine learning software, so these provisions will need to be tested in court.

Source: Sky.com

3 ways to make better decisions by thinking like a computer

If you ever struggle to make decisions, here’s a talk for you. Cognitive scientist Tom Griffiths shows how we can apply the logic of computers to untangle tricky human problems, sharing three practical strategies for making better decisions — on everything from finding a home to choosing which restaurant to go to tonight.

How AI is Revolutionizing Marketing

businessman hand working with new modern computer and business sWith computing power now virtually limitless, the possible applications of artificial intelligence on all aspects of consumer culture likewise seem to be without end. This phenomenon has the potential to launch marketing into a new golden age, and companies that do not take advantage of the new technologies at their disposal are severely limiting their reach and potential. AI and machine learning can be a boon to marketers like no other, but not until these marketers understand how it can work for them and for customers.

AI’s burgeoning presence in marketing not only provides a more satisfying customer experience, but it also boosts marketing campaign effectiveness and opens opportunities for businesses of all sizes and in all industries. Here are four of the myriad ways AI is transforming the marketing landscape for the better.

Personalized Content

Perhaps AI’s most valuable impact in marketing is its ability to understand user preferences and interpret data from them to present consumers with the content most relevant to their interests. Just like Netflix observes what users watch and recommends similar titles accordingly, websites use AI to evaluate extensive amounts of data from users’ browsing habits and present them with information that suits their preferences exactly.

According to Infosys, 86% of consumers noted that personalization impacted their purchases. And 56% of consumers actually expect their interactions with brands to be personalized. Any company that misses out on this opportunity to connect with consumers is running the risk of losing business and customer loyalty.

Predictive Analytics

Predictive analytics can provide marketers with insights into consumer behaviors by creating detailed mathematical models based on data received from each individual customer. A report by Aberdeen Group summarized:

“Predictive technologies can enable more precise segmentation of potential buyers and facilitate a deeper understanding of those buyers, their needs, and their motivations. By optimizing the marketing offer and message directed at these buyers, predictive analytics provides an effective path to delivering better marketing ROI – as evidenced by the superior click-through rates and incremental sales lift.”

Their study revealed a 76% increase in click-through rates through the use of predictive analytics. By programming AI to efficiently carry out these processes, marketing teams can observe the patterns and develop effective strategies accordingly.

Customer Engagement

AI can also be employed to identify certain customer segments that are not as engaged as others, and curate content based on the information it gathers from them. Companies like Dynamic Yield have developed AI engines to acquire insights on each customer through millions of data points that, when analyzed, can determine which customers are loyal to your brand and which ones need more incentivizing. This allows for deeper consumer relationships, where each individual customer feels seen and has their preferences acknowledged. Targeted communications like these have the potential to increase revenue growth by 10-30%, according to McKinsey & Company.

Efficiency and Revenue Growth

It is no secret that AI can outperform humans at many menial tasks. But developing AI systems to take over simple processes can free up the time and capabilities of a business’s human employees, so they can focus more on their specialized skills. Their time can then be redirected to focus on other more complex areas, like content creation, and leave the busy work to the machines. How much does it actually help, you may ask? According to Harvard Business Review and Infosys, the addition of AI has been shown to reduce customer acquisition costs by 50% and increase revenue by 43%.

The possibilities of this new frontier in marketing are vast, as are the rewards. From opening new job opportunities in AI programming, to helping to prevent fraud, the inclusion of computer learning in marketing efforts means benefits for consumers and creators alike.

The numbers don’t lie: There is profit and efficiency in computer learning. Companies that employ it are developing lasting customer relationships and rapidly outpacing their competitors. If you haven’t incorporated AI into your company’s marketing strategy, you’re falling behind. The landscape is being transformed before our eyes—it’s time to take advantage of the new opportunities while they last.

Source: LinkedIn

AI is coming for industrial design

An algorithm could never replace human designers… oh, who are we kidding? We’re screwed.

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MIT researchers have debuted a tool that automatically generates products–and analyzes them in detail–on your behalf.

Take these two task lamps. They each have three heads, bent and placed in very different ways. So which has the better stability? It’s a trick question. They’re equally stable–and that was discovered by an algorithm, which designed them both.

MIT researchers, in conjunction with Columbia University, have unveiled a new tool for designers who work with computer-aided drafting software. Building on previous work over the past year, their technique can optimize a design for any object, like a lamp or boat or wrench, for all sorts of metrics like mass, drag, and stress tolerance. And then it can create dozens of designs of that object, each tuned to different optimal efficiencies.

In other words, it removes iteration from the design process–and it could be applied to the design and engineering of consumer goods and industrial parts, replacing some of the human guesswork of product design and augmenting the intuition of designers themselves.

“A fundamental limitation of typical design optimization techniques is that they require a single objective function for evaluating performance. In most applications, however, multiple criteria are used to evaluate the quality of a design,” the paper explains. “Structures must be stable and lightweight. Vehicles must be aerodynamic, durable, and inexpensive to produce. In most cases, the performance objectives are not only multiple but also conflicting: improving a design on one axis often decreases its quality on another axis. In reality, designers and engineers navigate a complex landscape of compromises, generating objects that perhaps do not optimize any single quality measure but rather are considered optimal under a given performance trade-off.”

Take the humble wrench. Why is it shaped the way it is, with that bulbous head, and a skinny neck? This is exactly what MIT’s system can answer as it generates alternatives to the wrench we all know by reshaping the design to optimize for a myriad of factors at once. Through its generated CAD models, we see that a wrench with the most torque for force would actually have a very elongated and skinny design. But there’s a catch: It couldn’t take much stress. A short, fat wrench could take a lot of stress, but it couldn’t generate much torque. And in fact, right in the balance of all the most important factors–weight, torque, and stress–we see the wrench design we all know.

In other words, this machine logic can do in seconds what people perfected over centuries, arriving at the exact same conclusion–which is validating and humbling at the same time.

But of course, we know how to make a wrench, and we’ve known for a long time. What’s so promising about MIT’s research is that it can work on virtually any CAD model you throw at it, and for the most part, do so within existing workflows. That means it could help designers optimize their existing processes–and, crucially, deconstruct what works and what doesn’t, sooner.

Right now, all these design alternatives are spit out through complex graphs which are hardly navigable to the average person, and the software also isn’t available in any sort of downloadable tool for you to run. (The video you see above is from a different project from last year.) The researchers recognize this–that while they’ve created an AI that might replace a team of designers, getting it to work well for those designers is another challenge altogether: “One important consideration comes from the human-computer interaction…what is the best way to display it to an engineer who must digest the space of candidate designs?” they ask in the paper’s conclusion.

Could we become overly reliant on a single system with a single approach to design optimization, and perhaps lose some of our own creativity and ingenuity in the process? Indeed, how exactly AIs work with designers of tomorrow is the billion dollar question behind the future of design–let alone the world’s next great wrench.

Source: FastCompany

The future is ear: Why “hearables” are finally tech’s next big thing

The explosive growth of their AI voice assistants has Google, Apple, and Amazon racing to put your entire smartphone in an earpiece.

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In October 2016, an impressive group of tech industry royalty took the time to get a demonstration of a product from a startup called Doppler Labs. Microsoft cofounder Bill Gates and CEO Satya Nadella each got one, as did Apple Internet chief Eddy Cue and Jimmy Iovine, head of Apple’s Beats headphone group. So did C-suite executives from Amazon, Facebook, Google, and Tencent.

Donning pre-production versions of Doppler’s Here One wireless earbuds, they experienced the device’s ability to cancel out unwanted background noise, amplify the voice of a particular person in the room, and even converse with people speaking in another language. About a half-second after a Doppler staffer asked a question in Spanish, the wearer heard a computerized translation back into English.

At least two of the companies made informal acquisition bids, but none offered a high-enough price to convince Doppler to give up its dreams of launching a momentous new product. Sales failed to take off, and a year later the company was shuttered. But that’s not the end of the story. Within weeks of its closing, more than half of Doppler’s top technologists were working for the tech giants.

Amazon, Apple, and Google each have high-priority projects to pick up where Doppler left off. All three are working on products that combine the utility of the hearing aid with the entertainment value of a pair of high-end headphones, and potentially much more, say sources. Since all three have announced plans to get into healthcare, they could easily add fitness and health monitoring sensors for everything from counting steps to measuring oxygen saturation. And while it may take years to happen, none want to be left behind should it become possible to create a general purpose, in-ear computer that allows consumers to leave their phone in the desk drawer.

“Ultimately, the idea is to steal time from the smartphone,” says Gints Klimanis, Doppler’s former head of audio engineering. “The smartphone will probably never go away completely, but the combination of voice commands and hearing could become the primary interface for anything spontaneous.”

Spokespeople for Amazon, Apple, Google, and Microsoft all declined to comment for this article.

Why Hearables, Why Now?

For half-a-decade, Doppler and other startups have been trying—and failing—to come up with a “hearable” with the combination of sound quality, battery life and cool factor to become a mass market hit. So. why the sudden interest from the big guys? Because personal voice assistants such as Amazon’s Alexa, Apple’s Siri, Google Assistant and Microsoft’s Cortana, have suddenly emerged as the biggest interface revolution since the iPhone popularized the touchscreen.

Our desire to use technology without the hassle of touching it has made smart speakers the fastest growing new hardware market in years, says Strategy Analytics’ analyst Cliff Raskind. By 2023, 63 percent of U.S. homes will have a device like the Amazon Echo or Google Home, up from .03 percent in 2014 and 16 percent in 2017.

By then, Americans will speak rather than type more than half of their Google search queries, predicts Comscore. The market for ads delivered in response to voice queries will be $12 billion, according to Juniper Research. And these predictions don’t even contemplate a future when consumers have computers in their ears for more of their waking hours, providing tech giants with even more data on their movements and desires—not to mention a channel into their brains that makes shopping as frictionless as saying “Alexa, buy (fill in blank).”

The Future is Ear

“There’s much more that tech companies can do with ears than amplify music and make phone calls,” says Satjiv Chahil, a former Apple marketing executive who has advised hearing aid maker Starkey Hearing Technologies in recent years. “It’s about allowing your virtual assistant to whisper in customers’ ears throughout the day, while also enhancing their health and well-being.”

Your ears have some enormously valuable properties. They are located just inches from your mouth, so they can understand your utterances far better than smart speakers across the room. Unlike your eyes, your ears are at work even when you are asleep, and they are our ultimate multi-taskers. Thousands die every year trying to text while they drive, but most people have no problem driving safely while talking or dictating messages–even if music is playing and children are chatting in the background. Ears are not in the front of your face, so it may be easier for the Jony Ive’s of the future to come up with fashionable or even invisible designs for the ear than for the eye. Remember Google Glasses?

Those are just the obvious advantages. With the right sensors and processing on board, a hearable can tell if your head is pointed toward a store shelf in front of your face or at a billboard down the road. Add in a heart-rate monitor to measure stress and an electroencephalogram sensor to analyze spatial brain activity, and it could know what you are thinking about to some degree—say, how much of your attention is being paid to the sound of footsteps coming up behind you, says Poppy Crum, chief scientist at Dolby Laboratories.

Yes, AI-enhanced hearables in the future will be able to understand more than the words we speak. A Cambridge, UK-based startup called Audio Analytic is already licensing the ability for a device to recognize the sound of a window breaking or a baby crying. At this rate, it won’t be long before Amazon can send ads for Robitussin when it hears you cough.

The Hearable Challenge

The ear also presents nasty challenges for any company hoping to sell a mass-market computing device. Such a device must be tiny, nearly weightless and fit perfectly in each person’s anatomically unique ear canal to be comfortable for long stretches of time. At the same time, it must have enough battery power to last at least as long as a smartphone, not to mention a strong antenna and on-board processor. There’s also the problem of earwax, and the unsolved mystery of how to use an ears-only device without too much head-shaking, hand-waving, ear-tapping, or self-talking. According to one recent study, only six percent of Americans said they were comfortable talking to their voice assistant in public.

Then there’s the stubborn stigma against hearing aids. While hundreds of millions of people think nothing of wearing head or earphones, only 16 percent of the 48 million Americans who could benefit from hearing aids have purchased a pair, says the Hearing Loss Association of America. Those that do buy tend to put it off for an average of seven years.

Tight regulation of the industry hasn’t helped. Because hearing aids have been defined as medical devices, manufacturers must get products approved by the Food & Drug Administration, and consumers need to get a doctor’s prescription and pay to see an audiologist, usually with no help from insurance. Content to go for profit margins over sales growth, a stodgy oligopoly of five companies has been able to dominate the $6 billion-a-year hearing aid industry , selling products that cost an average of $2,700 per pair, according to Consumer Reports. A top-of-the-line pair will set you back $10,000 or more.

Now, that regulatory anchor is about to come loose. Last August, Congress passed the “OTC Hearing Aid Law of 2017”. When it goes into effect in August 2020, if not sooner, companies will be able to sell hearing aids over the counter to people with mild to medium impairment online or at any drugstore, just like glasses makers sell $10 readers to people who don’t want to bother with an optometrist.

This opens a large and growing market. The World Health Organization says that 1.1 billion children and young adults around the world are at risk of hearing loss, having grown up with earphones blasting away at point-blank range.

The law could have dramatic impact. Suddenly, anyone who finds themselves saying “what?” more often than they would like will be able to walk into a Walgreens and buy a consumer-y looking device for a few hundred dollars. Very likely, they will pick it up in the electronics aisle next to colorful iPhone covers and FitBits, not in the aisle for Depends and other products for the elderly. The device may not be marketed as a hearing aid at all, but as Bluetooth earphones with “hearing enhancement” or “personalization.”

“I’ve been waiting for this moment for 20 years,” says KR Liu, Doppler’s former vice president of accessibility, who has worn hearing aids to battle severe hearing loss since she was three. “You have these amazing companies that can do amazing things and have the branding power to de-stigmatize hearing aids.”

Doppler’s Influence

Doppler didn’t invent the hearable, but it had outsized influence during its brief existence. Music industry exec Noah Kraft and former Microsoft executive Fritz Lanman created the company in 2013 to come up with a product the Coachella crowd could use to customize the sound of live music, such as the ability to add a “fuzz” effect or put an upper limit on the volume. By early 2016, it had assembled an impressive team of audio experts and was working on the Here One, which added more “hearing augmentation” features as well as the ability to make phone calls and stream music.

As word of the product’s capabilities began to spread, Doppler began getting inquiries from tech giants interested in catching the hearable wave. Although the Here One was supposed to come to market within months, Kraft, the company’s CEO, declared that October to be “Demo Month.” A small team set up shop in a swank conference room overlooking San Francisco Bay in the offices of Universal Music Group, one of Doppler’s first investors.

Visitors included venture capitalists such as Mary Meeker and Yuri Milner, and teams from Amazon and its Silicon Valley-based R&D unit, Lab 126, as well Google, Apple, and Facebook. While a few companies put some informal bids on the table, none offered anything close to the valuation Kraft and Doppler’s board felt the company was worth.

Any unicorn dreams faded away after the Here One went on sale in early 2017. The tech press praised the device for its innovative design, but abysmal battery life and difficulty explaining why this wasn’t just another wireless earphone led to poor sales. There was one bright spot, however. Nearly a quarter of the buyers had purchased it as cheaper, better sounding alternative to hearing aids—without any marketing effort by Doppler to reach this audience. With Apple’s AirPods taking the consumer earphone market by storm, Doppler decided to pivot. While the engineering team focused on hearing functionality, Liu took a lead role in lobbying for the OTC bill in Washington D.C.

By the time the bill became law last August, Doppler was in dire straits. Kraft re-approached potential acquirers, who immediately agreed to meet. A team from Microsoft, including Nadella, explored whether hearables could be used to boost worker productivity. The companies collaborated on several interesting ideas. Since the Here One had an inward facing microphone that amplified the sound of the wearers own voice, why not create software commands Word or Excel users could whisper so quietly that workmates wouldn’t even notice. In the end, Microsoft decided to pass.

Several teams from Google took another look and passed, including one from the X “moonshot factory” and one from the company’s hardware division, which was looking for help finishing up its soon-to-be-panned Pixel Buds (aka “Pixel Duds”).

In September 2017, Apple sent a large team for another round of talks. It clearly didn’t need Doppler. Apple had been learning about hearing technology since 2011, when it began forging partnerships with hearing aid makers, so customers could pipe sound picked up by the mic in an iPhone directly into their hearing aids (a student, for example, could put their iPhone near a teacher at the front of the room to hear the lecture more clearly). The company had poured big money into creating technology such as the W1 communications chip, which has helped make the AirPods a stand-out in terms of sound quality, battery life and ease-of-use. AirPods captured 24 percent of all wireless earphone sales in the first half of this year, far ahead of runner-up Beats with just 3 percent, according to NPD Group. Still, Apple remained interested in an aqui-hire of key Doppler technologists, particularly those working on hearing algorithms, but wasn’t willing to pay enough to interest Doppler.

Talks with Amazon lasted the longest and were the most serious. With a powerful hearable, it’s customers would be able to shop via Alexa even when not near a smart speaker—and without having to depend on an iPhone or Android device. The Lab 126 team had been looking for years for a way to “get Alexa out of the house.” But aware of Doppler’s fast-declining finances—and possibly because it had learned so much about Doppler’s technology and business during negotiations–Amazon’s deal-makers only offered a low-ball bid

Rather than accept any of the bids, Kraft chose to shut down the company a few weeks later. He later sold Doppler’s intellectual property to Dolby, which specializes in audio software to enhance the sound of movies and other media. Dolby has not confirmed any new products based on Doppler’s patents, but “we are spending time identifying how our technology, ecosystems, and knowledge are relevant to the hearable marketplace,” says Crum, the company’s chief scientist.

“It’s good to hear that Doppler’s vision lives on even though the company doesn’t,” says Kraft via email. “We’re proud of what we built and proud that the Doppler team is helping others bring the in-ear computer to fruition.” Kraft declined to comment on any discussions with Amazon, Apple or any other suitors.
Next Up

Doppler is gone, but the vital signs of the hearables market are getting stronger. Salaries for audio technologists are soaring, with big tech companies often paying $200,000 salaries to top talent from startups and the traditional hearing aid companies. Mobile chip giant Qualcomm introduced its first family of chips specifically for hearables in March, and other chip companies are expected to follow suit by the end of the year.

Amazon, Google, and Apple are keeping their cards to the vest. Three former Doppler employees say Amazon already had a team of 70 people working on hearables when the companies were in talks last year. While Google’s hardware team continues works on Pixel Buds and other products, Google’s X unit is looking at developing fully independent in-ear computers, while the Google Voice unit focuses on ways to make that personal assistant more accessible via ear-based devices, says a person that’s had dealings with all three.

Apple is also marching ahead in its deliberate way. Rather than build a revolutionary new product to usher in the hearable era, it will continue to add new capabilities in familiar form factors, sources say. According to Bloomberg, the company will announce high-end headphones for music lovers by the end of the year, and will introduce a water-resistant upgrade of the AirPods, that includes the ability to activate the device by saying “Hey, Siri.”

Other pioneers of the hearables market are already preparing for the big guys’ arrival. Bragi, a Belgian company founded shortly before Doppler, recently decided to stop selling its hearable devices in favor of licensing its software.

“When you’ve got Apple and others coming directly after you, you need to change where you invest,” says CEO Nikolaj Hvvid. “On the other hand, it’s nice to suddenly be getting all this company.”

Source: FastCompany