How I (nearly) reinvented the wheel

Patents are an overlooked tool in inventing and problem solving.

A colleague and I were discussing the challenge of inflating tires to an optimal pressure. He is an avid Off road driver and was thinking of ways to ensure his tire pressure matches the road ( or dirt or quick sand ) conditions.

In trucks, a central compressor pumps air through a tube system, slip ring and into the wheel. This system is cumbersome and expensive for the average off road vehicle or even your suburban SUV. He was thinking of a compressor fitted on the wheel and we were discussing power requirements, battery considerations and prototyping routes.

I always start my inventing journey with looking at what’s been done. A quick search identified

https://patents.google.com/patent/US5975174A/en 

and then searching the patents citing it found some additional concepts.

https://patents.google.com/patent/US20110129360A1/en

More importantly, the identified patents help refine the terminology used by people in the field. Now a google search found a company with a product

http://www.aperiatech.com

and related patents

https://patents.google.com/patent/US9222473B2

https://patents.google.com/patent/US20140314602A1

Reading through these, you realize the clever way they use the wheel rotation and weighted item to create a peristaltic pump. Using TRIZ principles raises the question can we do with less ? The simple answer : use the car weight.
The tube should be fitted in the tire so the car weight compresses the tube along its length, generating the pressure to pump the tire. Now that there is a concise description of the invention its useful to go back to a patent and general search. Very few things are really new and indeed the concept has already been conceived by http://www.selfinflatingtire.com

While the dream of striking it rich with a novel way of maintaining tire pressure is replaced by practical business thoughts. The process leading to the solution highlights the interplay between inventive tools and search. Using search to identify methods of solving problems, makes methods like TRIZ extremely effective for individuals or teams.

The evolution of the corporate labs

Edison is credited with the creation of the first corporate research lab. In contrast to corporate R&D activity the research labs have pushed the boundaries of science and engineering and brought to the world the transistor, unix and C, fiber optics communication, Ethernet, cellphones, and even evidence for the Big Bang. These labs were famous for hiring the best and brightest scientists and then basically letting them do their own thing. Some were guided by the needs of the corporation, and others were more free spirited. In the 70s and 80s the perceived value of the labs was eroded and as the corporates found themselves out innovated by the new kids on the block such as Microsoft, Intel, and later Google, Apple and others. The new comers did not have the free wheeling research approach and have typically focused their research activity deep in their technical roadmap. 

In the past couple of years this has changed and today many of the new kids have their own corporate research labs. However the new labs  resemble Edison Menlow Park more than AT&T Bells lab. The new labs tend to focus on grand projects which aim to disrupt existing markets such as autonomous cars, VR and AR replacement for smart phone, global internet reach or even space travel. The similarity to Edison is that the vision of these labs is laid down by the founders or chief executives of the companies and not by free wheeling scientists. Time will tell if the new breed of labs fare better in scientific research or business outcome than the previous ones. 

When things don’t work

R&D is a delicate balance between optimism and realism. During development things don’t work. it requires tremendous effort to debug problems and make the things work. 

The realistic voice lingering in the background seems to speak words of wisdom. Maybe the theory is wrong, maybe there are no bugs just a problem in the assumptions. While this voice is important in quarterly strategic meetings, this voice kills the energy and enthusiasm required to push forward and weed out the bugs. 

In projects, especially high risk ones,  we need to keep in mind that when things don’t behave as expected it’s not always the problem with the expectations but more often with the things. 

Never wait.

Often, when I find myself advising or mentoring entrepreneurs they say ” we are just waiting a few days till we have our important meeting/investor feedback/POC ready” or a wide range of other reasons. 

Waiting is always a bad option. It instills passivity and overemphasizes random occurances in the life of a project.  Customer leads fizzle out, investors get cold feet, development gets stuck, and in general bright skies become overcast in the blink of an eye. 

The proactive option to waiting is to execute a strategic plan which well communicates the project plan to all potential stakeholders. That crucial meeting is one of many. Some occur in parallel, some serially. The outcome of a single meeting, or conversely a project milestone should never be posed as a critical aspect of any plan. Things always fail or come short of expectations. That’s why a long range plan helps all stakeholders maintain perspective even in rough waters. 

AI and Job (in)Security

The increasing capabilities of AI has created concern and even apprehension in the Natural Intelligence community.  The concern begins with our limited understanding of evolution; where we assume that all AI will behave like humans and go about destroying mentally inferior species such as the homo sapiens. But then, even if it doesn’t go about annihilating us, it will still take our jobs.  So people are writing learned articles about which jobs will still be around when AI is as commonplace as a smart phone.

The doom of driving (e.g. taxi drivers) is predicated to be just around the corner as AI starts driving autonomous cars. Beyond that we imagine smart helpers relieving our every day chores, maybe even doing our shopping and paper work for us.

The common prevailing conception is that low skill jobs would be the most endangered. But in terms of return on investment it seems the greatest impact would actually be in highly skilled jobs. Google already is toying with the idea of creating art with AI but how about engineering.  Can we replace engineers with AI ? Or at least augment their capabilities ? and how about inventors ?

Engineering consists of a toolbox of methods to solve a problem and a weight function to determine the worth of one solution compared to another. The challenge is often there is no ideal solution and the optimization consists of trade offs.  Moreover, the models are partial, the data collection limited and the decision process fraught with estimations.  However, engineers can solve problems because they apply prior knowledge and recognize similarities with past problems.  This is exactly where AI excels. If it can play GO, it can probably solve engineering problems.

But can AI invent things ?  It probably can.  Not only that, since there are already systematic approaches to inventing its surprising that we don’t already have AI that replaces the innovation leads and pathfinders in the R&D.  Google should probably be developing AI for inventing and not for doodling.  Or maybe its done that already.

 

 

Last slide standing

How do you end your slide deck ?

With a big thank you slide ?  

Have you really thought about that thank you slide ?  

The most valuable real estate in presentation is devoted to being polite. That’s certainly a worthwhile trait but realistically, by the time you are proudly being polite people have already forgotten your presentation. 

So if you feel compelled to say thank you, than put it in the summary slide. 

The ABC of presentation is tell what you are going to tell them (outline) tell them (body) and tell them what you told them (summary). 

The last slide standing should summarize your presentation and if you feel compelled add  a thank you as the last line to the last slide. 

Marketing mosquito traps

While mosquitos are small in size, their impact on our well being is disporptionaly large. From malaria to zika, or just being a nuisance late at night mosquitoes are a problem waiting to be solved.  

The mosquito fight has shifted from wide range chemical warfare, with a broad and severe environmental impact, to local skirmishes. As usual, the individual consumer is left alone in the night, combatting the buzzing sound of a hungry mosquito. Hence it is no wonder that many companies have jumped into the void and offer mosquito traps or repellents. 

The amusing fact is that reading the existing studies reveals that these traps or repellents don’t really work. But the traps are an amazing study in marketing products. 

Most traps work with UV light. Studies have shown that many insects are attracted to UV light but not mosquitoes. Mosquitoes are actually attracted to heat and CO2. Some traps utilize TiO2 which is a white material commonly used in paint and sun protection lotion and claim when UV light iradiating TiO2 generates CO2. 

So there is some science in place. Now placing a trap in open space results in bucket loads of insects. It’s actually impossible to discern mosquitoes from other pests but the sheer amount provides proof the trap is doing its job. 

The lesson to be learnt is that while its best to solve the job at hand, selling is sometimes about demonstrating results. 

Automated shade system: a crowd funding story for August

Inventions come from necessity and many times as I find myself spending some time outside I wish for some shade. This got me thinking about a drone assisted solar powered shade system. 

Four drones carry a canopy which has a solar panel. The drones position the canopy in the sky to create a shade above the user. 

Crazy as it sounds its actually feasible. ( the less physic’s inclined can skip this part). A ten meter square canopy made or organic solar cells should be able to generate around 50 W and yet be light. Furthermore the canopy can be shaped as a wing so the drones don’t actually lift it but only define the angle and speed providing an elevation force for the panel. The drone battery should suffice for the initial elevation but once in place in the sky the sun power and canopy lift will take over. 

Positioning is done using a camera on a drone. Once in the sky, the user marks the location to be shaded on his cellphone and the drones do the rest.

So who will take up the challenge and go for a Kickstarter campaign. 

Why Waze shows you the past

As usual, when driving back home during rush hour, I turned on Waze to get insights on the road conditions.  After a few minutes Waze diverted me to a side road to avoid a traffic congestion.  However as I looked at the highway, traffic was flowing freely while I was stuck in a traffic light on the diversion.  It then dawned on me that the success of Waze in directing people to the diversion, means that Waze is blind to the changing conditions on the highway.

Various solutions to this problem are possible.  One example is A/B testing, i.e. having some users diverted while others are not diverted.  In this manner Waze can keep up with traffic conditions, but at the expense of its users and it risks loosing its users trust if they are used as experimental pawns.  Another example is reducing the persistence time of events, i.e. in the absence of active reports or driving speeds Waze reverts back to normal conditions.

While the consequences of this in Waze are mainly loss of time, a future with autonomous cars may be more complicated.  Autonomous vehicles will pass through three phases

• a minority of autonomous cars and limited functionality
• a majority of autonomous cars and car to car and car to device communications
• only autonomous cars allowed on roads

Its reasonable to assume that there will be at most a handful of car operating systems and probably all will meet some basic criteria.  This means that the diversity of driving patterns we see on the roads today will disappear. So we have a situation of similar objects with many degrees of freedom and interacting in a non linear way.  In physics such system are invariably chaotic.  A simple way to understand the complexity of the system consider a single road.  When the distance between cars is large and there is no interaction the traffic flows.  However as more cars populate the road they start to interact.   When one car brakes so do all the others.  We see this even in human driving, as highways become congested the traffic is erratic even without visible obstructions.  For autonomous cars this will be accentuated since all cars have the same underlying logic, so certain situations are prone to trouble.  Since this is a road specific and multi car scenario, it requires extensive and accurate simulations to create safeguards in the car operating system which identify and change the interaction rules to delay the onset of chaos. These situations will be exasperated in mixed driving conditions when humans try to take advantage of the predefined rules of the autonomous vehicles.  For example, if an autonomous car always keeps it distance, there will always be a human driver cutting in, which will push the autonomous car further back in line.  The mixed driving period will be challenging in every aspect as even innocent behavior can have far reaching consequences raising questions of liability and fault.

 

 

 

Who will drive the driverless car

When RCA invented the car radio a century ago the household names of today, Google, Facebook, Apple Samsung and many others existed at best as an abstraction in a science fiction story. However a coalescence of trends is brewing a disruption to From the VW debacle to Apple I Car activity the car industry seems ripe for disruption.     

There is only one small thing missing. The business case. Autonomous cars fall under one of two categories 

– enhancing safety and convenience 

– replacing taxi drivers by car sharing 

The business case of replacing taxi drivers is interesting since it hinges on the gig economy and the success of Uber and its fellow companies. The unanswered question is will this increase or decrease overall car sales.   Initial logic implies decreasing. As “public transportation” prevails, car ownership goes down. However hype crowding logic says “as every car becomes a profit center, more cars will be bought”. Time will tell, what prevails. 

The first aspect of increasing safety and convinience is the practical route and it seems that at least for image based technologies the price point is right and ripe for inclusion in the basic package of cars.