Although we are a long way (perhaps) from the Artificial Intelligence that Will Smith fights against in I, Robot, wielding some of its power is as close as your laptop. Just as people talk to “Sonny” in the film, you can use natural language to find answers to complex, multi-layered questions.
The Humanization of Searching
In the areas of patent searching and intellectual property discovery and management, it can take years to develop investigation expertise. Professional searchers know how to craft search strings using strategically selected keywords and Boolean operators. And then there is the metadata to consider, especially with patent-related content: tags, codes, dates, assignees, etc. Analyzing the desired outcome for the search and then synthesizing a query to produce the desired results is an advanced skill that allows people to “speak” to a search engine. Owners of these skills are often professional patent searchers, intellectual property lawyers, or members of specialized business units.
If you are an entrepreneur with a small company, an inventor with a handful of ideas, or an engineer within a large company hoping to push your innovative ideas through the patenting process, you need to do some searching to find out whether your idea is patentable and marketable. Sitting down to launch the perfect keyword or Boolean search is daunting and, frankly, unnatural as you dissect your own thoughts. What are the Boolean operators doing? How do you now if you are asking the right things? How do you know if you have missed anything important? Are you even looking in the right places?
But, thanks to advancements in database tools and search capabilities, IP analytics and management solutions are not just for patent attorneys and IP lawyers anymore. Natural Language Processing (NLP) technologies have enabled the development of semantic searches that “seek information based on the intent of the searcher and the context of the
search terms, rather than relying on simply matching the exact words used in the query”*. The semantic query suits a range of users, and with the right database and tools, can take ideation to precise answers in a matter of minutes.
Semantic search queries take the uncomfortable, robot-like query structuring process to a method of human input. You can really write the to the machine and tell it what you are looking for in your own words. You can even copy text from a document and enter that as a search query. The semantic engine derives the context, associates meanings, and identifies the documents that have the concepts most relevant to your needs. The output matches the idea that you input; much better for the discovery of innovations. It’s new stuff – there are different ways of expressing it, so you need a semantic search to recognize when a different expression represents a similar idea.
If you are already an expert searcher or have the benefit of a team of searchers at your disposal – think of how your power can be expanded with semantic searching. You can have keywords, plus Boolean, plus semantic searching. The opportunities for discovery are great.
And what about AI?
Artificial Intelligence comes from machine learning algorithms. In tools such as InnovationQ and InnovationQ Plus, machine learning algorithms combined with semantic-based algorithms help the searcher find the most relevant documents through natural language. Specific learning algorithms identify slight changes in meanings and conceptual relationships over time. The system knows how to navigate the intricacies of language depending on the context. It recognizes many meanings for one term or multiple words that can have the same meaning, and then accordingly matches the document in which they appear with the identified concept in the query. The algorithms here have a technical focus, adapted for patents and technical documents. In addition, the neural network learning algorithms provide the capacity to consume large blocks of text, so you don’t have to limit your query to a list of keywords or small number of characters – copy and paste pages into the query field if that is what you need to do.
Beginning with a semantic search supports specialized analytics capabilities. These search tools are not strictly automated. They identify key concepts, which come from your natural language queries and subsequent semantic analysis. From your results set, you can select a document to feed into another search. Filter the set by assignee, date, inventor, and more. You can perform visualizations to look at the scope of the results from multiple perspectives. Then, drill down or expand your search. The analytics features step you further into the discovery process that already had a head start with AI.
AI is generated when the system applies machine learning based on semantic input. The intelligence you gain, however, is far from artificial. It is applicable. Leverage it to make critical business decisions. Use it to prove your idea is not only new and useful, but also marketable. Show that it has its place and can move to monetization. Find other organizations that are doing similar work and partner with them or license your technology.
Why is semantic searching different? Because it is smarter. For real.
How has the influx of big data affected your company? Even for the most established organizations, mismanagement of it can lead to disaster. However, for a typical Fortune 1000 company, just a 10% increase in data accessibility could result in more than $65 million additional net income.
The primary issues facing innovation professionals today are: 1) the explosion of data; 2) keeping up with data security; 3) the growing complexity of protecting intellectual property (IP) rights; 4) finding tools that deliver actionable intelligence and predictive analytics. Disruptive technologies and business models are increasingly threatening even the most well-established organizations. Senior executives at those organizations face the challenges of identifying early threats based on globally-available evidence, communicating them within the organization and then determining effective responses. For early-stage companies, understanding the competitive landscape is crucial for informing commercialization strategies for those same types of disruptive technologies and business models.
IP.com believes rapid, easy-to-use access to highly relevant information enables better decisions. We know that companies need competitive intelligence to influence their own strategic decision-making. Our solutions enable rapid and precise natural language search of massive databases covering patents, technical literature, US litigation data, licensable technologies and more. It is an invaluable tool for C-suite executives and senior-level professionals. Better searches, analytics and industry-leading content allow organizations to make smarter, more cost-effective decisions about their entire innovation lifecycle that result in higher shareholder value.
Analytic tools and workflow processes, applied to patent and non-patent technical literature, are enabling business tasks to be carried out faster and on a larger scale than ever before. This includes identifying competitors and business partners, discovering emerging technologies and identifying potential markets for products and services. Deploying AI that predictably and accurately searches massive amounts of data – possibly in ways that call for changes in workflow processes — will become increasingly more critical to the success of every organization’s lifecycle of innovation.
Organizations that don’t manage and monetize their innovation pipeline effectively, and accurately predict competitive product developments, could find themselves: 1) losing markets to the competition; 2) embroiled in litigation for infringement on another’s idea/IP; 3) investing research and development dollars on unmarketable concepts; 4) ignoring opportunities to increase value from existing IP assets. In the worst-case scenario, organizations that do not actively manage the process of recognizing and anticipating the competitive and potentially disruptive changes around them run the risk of total obsolescence and bankruptcy.
At the rate at which data and our ability to analyze it is growing, businesses of all sizes will be using some form of predictive data analytics in the next five years – IP.com will be at the leading edge of that movement. Can you afford NOT to have an AI predictive analytics partner that can keep your focus on your future?
For more information, please reach out on our contact page.
Oh, it’s a wonderful time of the year! Time to hit the road and visit the family. With a final scan of the house, you set the security system, holler at everyone to get in the car, and follow the people, packages, and pies to take your place in the driver’s seat. Set the GPS for the fastest route. Check the time: on schedule. Your adrenaline is pumping, you feel good. The 10-minute drive to the Interstate is uneventful. The first five miles are smoo… stop!
Red tail lights glare at you for as far as you can see and a full three lanes wide. You feel the blood drain into your stomach and know you are in for a long trip. Your right foot twitches in anticipation of braking off and on. You figured that there would be some traffic, but how can it be this bad? Is there a better way?
Communication & Collaboration
Some are working hard at a solution that requires communication and collaboration, not among humans, but between vehicles.
With the advent, rapid growth, and consumer acceptance of Internet of Things (IoT) technologies, machines working together to work for us is becoming commonplace. Biometric sensors on our wrists tell associated systems to prompt us to exercise, alert us to take medication, and automatically adjust the thermostat to make us more comfortable.
The autonomous vehicle (AV) is a scaled-up version of interactive technology. The purpose is to efficiently and safely move from point A to point B. Theoretically, removing human variables such as emotion and delayed reaction time from the driving equation as much as possible increases predictability, which increases reliability and safety.
So, when you load the minivan to go to you sister’s house in the future, you will set the GPS as you normally would. Then, instead of taking the wheel, you sit back, sip your coffee, and let the AV take over. And, because many other people have AVs, and they communicate with each other, the ride is well-paced and congestion-free.
Autonomous cars are complex, inter- and intra-dependent systems that include multiple levels of hardware and software. The hardware shapes the methods of input and output. Sensors gather information from GPS/Inertial Measurement Units (IMUs), cameras, LiDar, and radar. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) components enable vehicles to capture data from and communicate with each other as well as signal lights, signage, roads, etc. The final pieces of hardware are the actuators, which enable control and actually move the machine. 
Software analyzes the input from other vehicles (speed, lane changes, braking, etc.), the environment (to identify weather conditions, road conditions, objects, etc.), and infrastructure (traffic signals, railroad crossings, construction warnings, etc.). Some of this information might come to a vehicle from one that is well ahead of it on the road – an advantage that human drivers do not have, not in real time. The AV system compares the real time data to configured rules, both practical and ethical, for how to navigate the current surroundings under the identified conditions. Then, based on the analysis, the navigation settings, and the safety and efficiency objectives, the system determines and executes the appropriate actions.
It Could Work
Several studies show that human beings are their own greatest threats on the road. A 2017 Rutgers University study, backed by the National Science Foundation (NSF), confirmed that common driving acts such as changing lanes or tapping the brakes cause stop-and-go traffic. It is human nature to waver when the environment around them changes; further, when one person reacts, then every person in the line of following vehicles reacts, perhaps with incrementally greater force. When the AV was introduced into the traffic flow during the field experiment, it helped interrupt the stop-and-go pattern and create a steadier flow. “The researchers determined that even a small percentage of autonomous vehicles (5 percent) could have a significant impact in eliminating waves and reducing the total fuel consumption by up to 40 percent and the braking events by up to 99 percent.” 
In another NSF-funded study, held in 2016 and published in 2018, researchers performed an experiment on a closed track. Amidst 21 manually driven vehicles was one self-driving vehicle, with a driver behind the wheel. “When all the vehicles were driven by humans, we would see these stop-and-go waves… But when we activated the autonomous vehicle, the stop-and-go waves stabilized.”  Even though this was a closed environment, the idea of inserting AVs into the traffic pattern to maintain a steady pattern shows some efficacy.
A traffic flow expert at Virginia Tech, Professor of Engineering Hesham Rakha, ran a simulation similar to the 2016 study. The results were also similar: “In Rakha’s simulation, as the number of self-driving cars increases, we can see how the red dots indicating congestion on the screen gradually clears and become green, moving dots.”  The self-driven cars overcame the traffic congestion.
As the AVs communicate and collaborate they automatically adjust to accommodate changes in the environment and the traffic patterns. And the assigned intentions never change: be safe, be efficient, reach the target destination.
Evidence of Progress
Because of the number and variety of components needed to make AV transportation optimal, inventors in many disciplines are involved in research and development. Following are a few recent inventions meant to move the mission forward.*
US9916703 Calibration for autonomous vehicle operation. Zoox, Inc. (Menlo Park, CA, US). March 2018.
US10049505 Systems and methods for maintaining a self-driving vehicle. STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY (Bloomington, IL, US). August 2018.
US9833901 General purpose robotics operating system with unmanned and autonomous vehicle extensions. Perrone Robotics, Inc. (Charlottesville, VA, US). December 2017.
US9989967 All weather autonomously driven vehicles. Cybernet Systems Corporation (Ann Arbor, MI, US). June 2018.
US10013893 Driver training. Lifelong Driver LLC (Incline Village, NV, US). July 2018.
We still have a long way to go. Operating autonomous vehicles is not a perfect solution, mainly because the human factor can never be omitted. Humans are inherently unpredictable and have their own intentions and missions that do not necessarily align with one another – let alone with a robot-like car. There are, of course, studies opposing the idea altogether. But, current work might be putting us on the right track to better traffic control and reduced fuel consumption. If it reduces the holiday travel stress, we’ll take it!
When the Keurig launched and started to take hold, it revolutionized how people drank coffee. If you wanted just a single cup, you no longer had to make a trip to your closest barista. If you just wanted to pour your own first cup and did not want the rest of your coffee to get stale, brewing an entire pot was taken out of the equation. Solving those consumer needs paid off well for Keurig – in 2018, they merged with the Dr Pepper Snapple Group to create the 7th largest food and beverage company in the United States with a combined net sales of $11 billion in 2017 . Not a bad success story.
In the wake of Keurig has come a company called Güdpod. They have identified a need as well – people not wanting to clean blades every time they want to create blended beverages. Similar in concept to a single-serve coffee system that uses brewer-pods, Güdpod pods have blades that are in the pod itself. When inserted into the Güdpod machine, the pod descends and opens, releasing the ingredients directly into a liquid, like water or juice. Blender blades then mix the ingredients in about 40 seconds. Güdpod takes the mess out of making nutritional beverages because the blending system is self-contained within the pod; there are no blades, blending container or rubber gasket to clean. Güdpod can be used with any liquid – such as water or any kind of milk or juice. Additionally, the pods are 100% recyclable, unlike pods used in other systems .
Continued growth from original concept
Additionally, Güdpod is not resting on their laurels – they continue to build upon their initial patent. Recently they announced they will be granted their fifth and sixth U.S. utility patents and its first U.S. design patent on technology housed in the first-of-its-kind “blender in a pod.” The company also holds patents and patents pending on the technology in Australia, Asia and the European Union. 
“From the beginning, we invested in developing and protecting our intellectual property because we knew what we had was special and that it would invite replication,” said Gian-Carlo Ochoa, Ph.D., founder and executive chairman, Güdpod Corp. “As a result, we’re several steps ahead of any potential competitors and we will maintain that advantage by actively defending our existing IP while simultaneously investing in ongoing development.” 
How IP.com can help your innovation process
As discussed, Güdpod is an excellent example of a company that continued to innovate upon a similar invention. IP.com’s InnovationQ product line can be a useful tool in that regard as well. It makes it easy for everyone on your team to search, find and review crucial patent documentation with minimal training. Simply type in a patent number or invention idea and instantly get a comprehensive ranking of relevant results. Through our proprietary combination of concept-based, common-language semantic search and enhanced Boolean search filters, you also have the potential to come up with your own innovations.
When you go to the polls this Tuesday, November 6, 2018, you might stand behind a thick curtain and flip a lever to cast your ballot. You might sit in a cubicle or stand at a table with dividers between you and another voter. It depends on your state; across the US, in 2018, different states use one of four different voting mechanisms. The level of complexity varies.
The Direct Recording Electronic (DRE) Systems allow voters to directly enter their selections into an associated computer’s memory via a touch screen, dial, or button. The computer’s hard drive or cartridge immediately stores the input. In some locations, the system also produces a hard copy of the voter’s choices for the person to review before casting the final votes. These paper records can be used later to resolve any discrepancies. To accommodate disabled or otherwise compromised DRE users, Ballot Marking Devices and Systems can supply methods for manual input and tabulation. A little farther down on the technological scale are the Optical Scan Paper Ballot Systems. The voter receives a paper ballot and uses a marking pen to fill in an open shape (e.g., circle, rectangle) next to the desired candidate or other item. Then, under the supervision of a voting station volunteer, the voter feeds the ballot sheet into a scanner (alternatively, the ballots are collected and scanned at another site), which records and stores the information. Finally, some states use the Punch Card Voting Systems, which are more traditional. Given a paper card and a clipboard-like device, the voter indicates their selection by punching small holes in the card. The votes are then manually tabulated or fed into a scanner for review. 
Regardless of which system you use, second only to the need for accuracy is the need for privacy and secrecy during the voting process. When did voting systems and methods begin to ensure that our choices were safeguarded from misuse or misinterpretation?
The first solution to the secrecy problem was presented 1858 in Australia. Concerned about election fraud as well as privacy, the state of Victoria wanted to simplify the ballots. The government printed the ballots and each voter received on at the designated polling place. The ballot had columns for each political party and rows for the individual candidates. If a voter wanted to select along party lines, they could simply mark the circle at the top of the respective column and vote for all candidates in that party; otherwise, a person could select individuals by row. The Australian secret ballot laid the foundation for modern polling. 
Later into the 1800s in the United States, protecting voter privacy and preventing fraud remained a concern. The main problem with the Australian secret ballot was not it the submission, but in the counting. There was some subjectivity and room for interpretation when reviewers looked at the voters’ written entries. If an X did not fall exactly within the bounds of the entry box, it might not have been counted. If a checkmark was too rounded or didn’t look exactly right, then the vote could be rejected. Thousands of ballots were discarded. Even with oversight measures, at that time, the manual entry and tally process left room for corruption and tainted elections. 
In 1889, Jacob H. Meyers, of Rochester, NY, presented a high-tech solution with the first voting machine. In the introduction to his patent, Meyers states the objective for the machine:
… to provide one by the employment of which an honest vote can be had and counted without liability of voters being intimidated, the balloting being secret, or of their voting more than once for the same candidate or different candidates for the same office, and as the votes are counted as fast as the voter indicates his preference the total number cast for each candidate can be ascertained rapidly and accurately at the close of the polls. 
Meyer’s machine comprised a booth with a series of doors that automatically closed the voter inside, simultaneously ensuring privacy and preventing any unnecessary coming and going. The ballot mimicked the Australian paper ballot in that it followed the column-and-row design, with the inclusion of color coding. Red represented Republican candidates and blue represented Democrats. The voter pushed keys, which locked in a single vote at a time. Further, Meyer wanted to make sure that every man that came to the polling place would have access to his machine. Many eligible voters were either illiterate or literate in a foreign language but not in English. Using the color-coded system, Meyers had a diagram of the ballot hung on the outside of the voting machines. The voter could, with some assistance, identify his selections before going into the booth, and then simply punch the appropriate keys to cast his vote once inside.
Meyer’s Automatic Booth lever voting machine was first used in a town election in Lockport, NY in April 1892. At this scale, the machine was a success. Local governments in the Western NY area saw the benefits of the automated voting process and wanted to use it.
The inner workings of the Meyers voting machine were complex for the time and held thousands of springs. This was the design flaw, as the springs could not tolerate the repeated use and friction against one another, and corroded during an 1896 election in Rochester, NY. It was a disaster that resulted in miscounted votes and political lawsuits. Meyers did not recover from this and lost his business.
The spring mistake aside, the 1889 Voting-Machine, Patent #415,549, set a standard for the evolution of voting machines. According to early inventors, the machines that collect our voices must be accessible to all, regardless of social or economic standing. Disabilities should not prevent a person from reaching the poll. Voters are entitled to secrecy and privacy. Tallying the votes must be simple, accurate, and fast. Meyer’s design flaw was quickly corrected by Slyvanus Davis with a spring-free direct action method for casting votes. Another inventor, Gillespie, replaced the booth with the curtain.
On November 6th, whether you stand behind a curtain and push a lever, sit at a desk and fill in circles, or step to a booth and enter your votes on a touch screen, consider the innovations that are behind you that allow you to exercise this important right.
It Happened on Halloween
Churning, churning, churning… continuously updating innocently standing rows. The UPDATE operation, though built by its makers to do good, was set loose to wreak havoc on a multitude of payroll records. It was only supposed to allocate 10% pay increases to employees that had a yearly salary of less than $25,000. But no. That wasn’t enough for the beast. It plowed through the records, disseminating raises. It ran over and over the database until every last employee was assigned a yearly salary of $25,000.
The updated rows tried to stay ahead of it, tried to stay out of the way – but to no avail. The hungry query still hunted every row within its parameters, pulled it in, changed it, altered it forever, and then left it in the same path where it could again fall prey. The brute would not be satisfied until the company that created it was gushing dollars from the wound of its hacking bite.
It would have been a quiet Friday afternoon. But was Halloween. A monster was loosed, and nothing could be done to right the wrong until Monday morning.
That was 1976. And to this day, engineers and developers are still looking for the best strategies to defeat the Halloween Problem.
What is It, Really?
The most concise definition of the problem comes from a Microsoft® blog, The “Halloween Problem” for XML APIs: “Halloween protection is needed to prevent a situation where the physical location of a row within a table changes due to an UPDATE operation. As a result, the same row may be revisited multiple times within the context of a single logical operation, which should not occur.” 
The incident on that fateful Halloween day occurred when the update operation kept searching for payroll data rows of a minimum value, found them, and then updated them multiple times until they all maxed-out at $25,000.
This is really a problem for developers to address, so database users might not have heard about it, and probably don’t need to be looking under the bed for it. However, because such database errors can be far-reaching and costly, companies must be vigilant in their watch.
Who are the Heroes?
The Halloween Problem does raise its head as a not-completely-contained animal. Many companies are still working on solutions. An InnovationQ® concept query, taken from the above quotation (i.e., protection is needed to prevent a situation where the physical location of a row within a table changes due to an UPDATE operation) and given a publication date filter of one year, produced a results list of 1,101 US patents and applications of good relevance. That is a good body of work on a similar problem in one year.
So, who is doing all this work? Who are the monster-hunters?
Given the top 1,500 relevant results, it looks like IBM is generating the most patents and applications in this area.
Figure 1: For the query: protection is needed to prevent a situation where the physical location of a row within a table changes due to an UPDATE operation, IBM is the top producer of relevant work in the past year.
The quantity of patents and applications does not necessarily mean the most relevant is among them. When the same 1,500 results are sorted by relevance, the German-based SAP comes out on top.
Figure 2: Companies at the top of the list have the patents or applications with the highest relevance to the query
Also considering the most recent work along with the most relevant, SAP has an application in the #1 spot from September of 2018: US20180253468 In-memory row storage durability. The novel architecture addresses extreme Online Transaction Processing (xOLTP) performance, as its use is growing, and aims to build a controlled environment in which to provide accelerated database access and processing. It recognizes the need to manage transactions and sequences of operations between rows.
Oracle has a granted patent in the #2 spot from May of 2018: US9965535 Client-side handling of transient duplicates for row-level replication. Here, the inventors directly face the challenge of databases going wrong while updating rows. The method seeks to ensure that row changes only execute when it is “safe” to do so, effectively avoiding constraint violations and promoting successful updates.
For 42 years, engineers and developers have turned inventors in a quest to finally put the full Halloween Problem down. As recently as last month, they are still introducing data processing approaches to do so. As we become more dependent on the nature of electronics, as we increasingly and trustingly invite the Internet of Things into our homes, offices, and automobiles, and share our data with solicitors across the globe, we hope that the Halloween Problem is solved and the heroes at SAP, IBM, Oracle, and others, continue to watch for monsters.
As an independent inventor, taking steps to bring your idea to market can be terrifying. But you can do it with a little help.
An invention is a creation that solves a problem. Whether the problem is understood by a handful of experts or is a challenge that millions of people face every day – the solution must be unique. It could be an algorithm that is only known deep within the working of software. The solution could be a simple item presented to the masses on a Saturday morning infomercial. To move into production and the market, the uniqueness requires protection. Without protection, the newly-hatched intellectual property, along with the dollars in which it nests, are at risk of being swept away. What are the frightening parts of getting an invention protected, so you can have a marketable product?
Mark Newburger and Jeffrey Simon are the inventors of the Drop Stop®, a seat-gap filler for your car, truck, or van. You know the gap: that black abyss like the crevasse in a glacier that swallows everything that slips over the edge of a car seat. And when something falls, it is human nature to bend down to pick it up. The problem that the inventive friends identified was deeper than losing change and French fries, though; their first concern was about a cell phone and our addiction to it. In the age of distracted-driving awareness (or lack thereof), not only is the cell phone a danger when it is in a driver’s hand, but also when it falls away from their hand. When a driver realizes that their phone has slipped into the gap-of-no-return, they repeatedly take their eyes off the road, reach and grab for the phone, and in doing so often turn the steering wheel with them. The car can swerve, go off the road, and possibly damage other vehicles or injure someone. Now, the seat gap has gone from an annoyance to a dangerous agent of driving interference.
Newburger had such a harrowing experience. He did go off the road and nearly into a pedestrian and a pole while reaching for a wayward cell phone. The close-call not only scared him, but also motivated the invention of the Drop Stop as a way to keep drivers focused on the road and not on what is dropping between the seats. The Drop Stop is a soft, flexible neoprene wedge that secures to the seat belt and fills the gap between the car seat and the center console. It is a seemingly simple solution, and a good idea for reducing driver distraction (and maybe keeping the car a little cleaner).
But developing good ideas, even those that seem straightforward, is an expensive venture. Unlike scientists in the labs at global corporations, the two California-based short filmmakers/producers did not have the capital nor a budget for product development. They turned to friends, relatives, and personal caches to fund their endeavor. An interview with CNBC revealed: “the two ‘inventors’ started asking friends and family for money, without telling them what the product was. Simon was terrified someone would steal their idea.” They were building an unprotected invention.
People gave them faith and funding. As their backers joined, the duo promised multiple people $1 million each when the Drop Stop was a success. With these promises came heavier responsibility, greater risk, and a new kind of fear for Newburger and Simon.
The best way to assuage the terror and protect their intellectual property was to secure a patent. Then, the idea could not only be revealed to their immediate supporters, but also be presented for more serious investment deals. With due diligence, the inventors first determined that their idea was, indeed, unique. Very little prior art was available that addressed the car-seat-gap problem, and none used the materials and notch for the seatbelt anchor that their design presented. The Stop Drop has a hole that secures over the seatbelt, so that it moves with the seat. This is a core innovative component.
On the day that Simon was about to make another frightening dip of $350,000 into some inheritance money to put toward the cause, Newburger received an email from their attorney that the badly needed patent grant had come through. On September 18, 2012, US8267291: Apparatus for Closing Gaps, was published by the USPTO . This was a huge relief, a tremendous way to secure the value of the intellectual property, and a much-needed key to open more doors.
Figure: Apparatus for Closing Gaps 
On season 4 of Shark Tank, Newburger and Simon made their Drop Stop pitch to the Sharks. Although they had already begun selling on QVC and on their website, the inventors-now-entrepreneurs needed help expanding their market. Following the team’s 2013 presentation on the show, Shark Lori Greiner joined as an owner. She would receive 20% of the company and provide additional funds as well as expert guidance into more retailers. Now, you can get a Drop Stop at Walmart or on Amazon.
Of course, you can see Newburger and Simon in their loud and awkwardly-excited infomercial. But this story is not about whether an advertisement for a new product is smooth and sexy. It is about how an invention was born out of fear, how the inventors overcame terror, and how a simple idea became protected intellectual property that is now working to protect people – with a healthy return on investment! As of December 2017, the Drop Stop had evolved from a pile of possibilities to a $24 million business .
The Drop Stop came to life in a learn-by-doing inventive process. It was grass-roots: identify a problem, create a solution, figure out how to make it, get some money behind it, and then do your homework to make sure the idea is unique. Then, apply for a patent with the knowledge that your innovation is new, useful, and not already obvious to people familiar with the art.
But you don’t always have to trip your way through the idea-to-monetization progression. There are tools for searching and analytics to help make sure you are headed in the right direction. Check out the Prior Art Database, InnovationQ and InnovationQ Plus, and Insight Reports at IP.com. They will save you time and money, and take some of the fear out of navigating the IP landscape.
On September 30, IP.com pushed live several updates to our InnovationQ and InnovationQ Plus platforms including integrating the Society of Petroleum Engineers (SPE) OnePetro library, adding more patent authorities and many other user-requested upgrades. In conjunction with the 4.3 release, IP.com is introducing Insight Reports that analyze actionable data on technologies and patents to drive business decisions. Learn more about all these exciting changes below:
OnePetro is an online library of technical literature for the oil and gas exploration and production (E&P) industry. With contributions from 20 publishing partners, and access to 200,000 documents, OnePetro is the definitive resource on upstream oil and gas. The SPE uses its resources and technology to operate OnePetro on behalf of its publishing partners.
With the addition of indexing the full-text OnePetro content, IP.com expands its database of more than 100 million patent and non-patent literature documents within one platform. Besides the OnePetro library, IP.com has the only semantic tool that indexes the full text of both the Institute of Electrical and Electronics Engineers (IEEE) and the Institution of Engineering and Technology (IET) publications. Additionally, InnovationQ and InnovationQ Plus search the most robust Prior Art Database, global patent authorities and the IBM Technical Disclosure Bulletin, among other sources.
Insight Reports act as a snapshot of the past, present and emerging trends in the marketplace. They enable the evaluation of queries through a variety of statistical, modeling, data mining and machine-learning techniques. The result is a streamlined process that helps you make critical decisions faster.
Our September release of InnovationQ/InnovationQ Plus also addresses some of the most highly requested features from our users. We have added a significant new non-patent literature (NPL) collection, new patent authorities, extended patent family, additional options to export claims to Excel, and more. The aim is to continue to help users find and understand relevant art efficiently and gain actionable insight.
Family-enhanced fields feature
Global patents and applications are more important than ever, and our new “family-enhanced” feature helps fill the gap of global patents and applications in English. When assignee or inventor names, titles, or abstracts are unavailable in English, we provide supplemental information from a simple family member equivalent to make it easier to find patent documents when searching by names and understand important documents by displaying the supplemental information.
Extended Patent Families
In addition to the DOCDB-based simple patent family, we have added an extended patent family. Extended patent family is integrated throughout InnovationQ/InnovationQ Plus everywhere simple family is, including filtering, document preview, de-dup options and exports. Extended patent family information can enable greater understanding to a patent assignee’s effort or a patent’s breadth and geographic spread.
Improved Visuals Workflow
The improvements help integrate accessing charts for visual searching, review and gaining insight into the workflow. Access charts without navigating away from the result set, and more easily find and navigate between charts of interest. These visuals help make sense of potentially thousands of documents to provide insight into trends, opportunities and risks that you just can’t see by looking at a list of documents.
Updated/New Patent Authorities
Our Australian patent coverage has improved, and our current single Australian collection will be split into two: patents and patent applications. Additionally, we have added several more patent authorities to our already robust list: Portugal, Greece, Bulgaria, Slovakia, Czechoslovakia, and Yugoslavia/Serbia and Montenegro.