They Should Be Business Tools

A few weeks ago, Google decided to quietly sunset Google Glass. They never said it publicly and in fact they might be considering another strategy for the device, but by all measures, Google Glass as we know it has failed. There are many theories for the reasons of this failure ranging from privacy concerns to the lack of social acceptance for walking around with geeky glasses. My theory is that the failure may be related more to the $1,500 price tag as consumer gadgets are simply not supposed to be that expensive.

Sergey Brin wearing Google Glass

That’s perhaps really the problem. Google Glass was certainly too expensive as a gadget for consumers but it likely wouldn’t have been too expensive as a business productivity tool. Most companies wouldn’t have a problem with the devices price, particularly with the customary volume discounts, if it demonstrated tangible benefits.

I can think of numerous business applications for Google Glass – from instructions while operating or repairing complex machinery, to patient records during surgery, to production data on the assembly line and supplier data in the warehouse.

But none of that was ever a priority apparently. Instead, Google put all their efforts into marketing Glass to consumers. The consumers may represent a greater opportunity in terms of volume but the enterprise market may represent a greater opportunity to optimize revenue. Just ask Microsoft.

There have been other technologies that I thought would have benefited from this strategy. Microsoft Kinect for Xbox comes to mind. While popular with gamers, the novelty of gaming via full body motion control is now wearing off. Let’s face it, most gamers want to shoot at aliens while sitting on their sofa and the Kinect is not the optimal weapon for that.

I would have hoped that we’d see Kinect being used in business – the repair technicians with oily hands reviewing designs, foremen at construction sites reviewing blue prints, surgeons with sterile hands reviewing patient records, farmers with dirty hands, lab technicians working in gloves – there are many use cases for gesture-based interaction.

When gestures are not practical, voice-based interaction might be appropriate. This is another technology that might have a greater application in the professional world than in the consumer space, at least given the current state of voice recognition. While the consumers relish in finding out the shortcomings of the still relatively new technologies such as Apple Siri or Amazon Echo, the business use cases may be more feasible. The business vocabulary is more precise and predictable, particularly in the given context. Professionals usually have to learn their business vocabulary as part of their job training and that makes it easier and less ambiguous.

Consumers usually resort to calling a company’s 800-number only after they failed to accomplish something online. At that point, we are exposing the already frustrated consumer to a voice recognition system that is far less mature than the web site and expect it to deliver a great experience. People usually don’t call in to do something that can be done with a smartphone app – like to check their account balance. In the business world, on the other hand, users are already trained to use a fairly precise language and their voice commands are usually in the context of a specific data set or business process.

“I need the Q3 revenue data for Europe broken down by product group” is much easier for a machine to understand and act upon than: “I want to buy a companion ticket for my spouse using my miles to match an already issued ticket purchased by my employer”. This relatively common task requires many additional data points - ticket number, flight numbers, account number, name and DOB of the traveler, seating preferences, credit card number, etc. – and that is very difficult for a voice-driven system to piece together.

Apple Watch. Yes, I want one!

A few weeks ago, Apple launched its new Watch. By all measures, it is already a success even though it won’t ship for another few days. The demo by the Apple team was very impressive and the press reviews are glowing. I have no doubt that the Apple Watch will become a success. But I wonder about the practical use cases of the Watch for consumers. So far, most wearable devices have focused on fitness but that market is very saturated already. The serious athletes will be hard to separate from their specialized Garmin, Timex, and Suunto watches. The hobby athletes are well served by the Fitbit, Jawbone, and Nike Fuel fitness trackers or they simply keep using their smartphones.

I can’t help but to wonder about the business use cases for the Apple Watch. There are many possibilities – approving process tasks, participating in simple collaboration activities, delivering business context-relevant information, etc. Smart watches are looking for a killer app and sharing your heartbeat is probably not it.  I suspect that we could find it sooner in business rather than the consumer space. 

Google Glass page on March 31, 2015

Parallels between Document Capture and Voice Recognition

I was doing some research about the history of document capture last week. As I was reading about the early imaging machines capable of scanning 30 checks or lottery tickets per second, I came to realize an interesting parallel between the world of document capture and voice recognition.

At first, the purpose of document capture was just creating a readable image of a paper document which could be electronically stored and shared. That alone was a big improvement in efficiency. The analogy in the audio world would be the creation of the MP3 standard which allowed us to make inexpensive recordings of music and share them easily via services such as Napster. Too easily, complained the entertainment industry over and over, until Apple came and took over their business.

The next milestone in image capture was optical character recognition (OCR) which allowed us to extract the text from the image and make it searchable. Intelligent character recognition (ICR) augmented these capabilities by extracting hand-written text. That was particularly important to those high-volume imaging systems processing millions of checks or lottery tickets. In the audio world, the OCR and ICR capabilities are akin to the speech recognition software such as Naturally Speaking by Nuance or IBM’s ViaVoice. The purpose of this software is to convert speech into searchable text - just like OCR.

OCR and voice recognition are both about searchable text

Finally, document capture evolved to the point where it became possible to automatically detect the document type through document recognition (i.e invoice, application, job application, or travel expenses) and subsequently extract the actual data value from the document. Not just text, but rather metadata fields such as billing address, date, total, or payment terms. As a result, document capture can be connected directly with process automation software such as workflow or business process management (BPM) to gain even greater efficiencies from automated document processing.  

In the audio world, the analogous technology is voice control or the recently introduced personal voice assistant Siri by Apple. The idea of this software is to issue voice commands together with the dictation (voice-to-text capture). The commands can make the computer perform a task or a process step. Many phones understood basic voice operations such as “Call home” but those are just shortcut commands comparable to bar-codes and QR codes in the document capture world.

Understanding the meaning from natural language without learning predefined commands takes voice recognition to a different level. Such voice control has been featured in many sci-fi movies from Space Odyssey to Avatar but remains so far mostly in the experimental stage. Microsoft promised to ship a new version of Xbox with voice control for task such as movies or music search which could be extremely useful. Siri appears to be the first intelligent voice control-based software entering the mass market with capabilities such as scheduling appointments, searching for music, sending messages, or checking the weather.

The voice recognition technology has been following a similar innovation trajectory as document capture. Today, software such as Siri raises voice technology onto a level that is on par with the state of the art in document capture. It will be interesting to see what innovations will emerge in both of these worlds. In the mean time, we should practice the interaction with a computer in natural language because Voice Recognition is about to Re-Wire our Brains.

Voice Recognition Is About to Re-Wire Our Brains

Voice-based data input to a computer is not a new idea. While the keyboard, mouse, and more recently, gestures, have been the primary way of interacting with computers, the idea of voice-based interaction is as old as the HAL 9000, the talking computer from Space Odyssey. Software such as Dragon Naturally Speaking (since 2005 owned by Nuance) or IBM’s ViaVoice have been around for almost two decades and let’s not forget the often infuriating Interactive Voice Recognition (IVR) used by most telephone support departments today.

Voice recognition software stole the spotlight last week when Apple released its new iPhone 4S with built-in Siri software. Embedding voice recognition directly into the operating system is a major milestone and having it included in a mobile device makes perfect sense as we can see from the video commercial by Apple. Only the TV set is a device that needs voice control even more – I am still waiting for the kind of interaction Marty McFly (Michael J. Fox) was using in the Back to the Future II movie. In fact, Siri for Apple TV is rumored to be on the way and Microsoft recently demonstrated voice-based movie search on Xbox 360.



So, how come we have not been talking to our computers for the last decade since the technology was there? Well, part of it was the accuracy of the recognition. When I used Naturally Speaking Back in the 90s, I had to train the software to understand me which was a lot of work for meager results. We all know the frustration with any IVR based system: “Sorry, I didn’t quite catch that. Could you please try it again?”. And while Siri represents the next generation of voice recognition, plenty of stories about the funny results that its use can result in circulated on the Web immediately after the new iPhone was released.

Source: STST

With increased computing power and better software algorithms, the quality is becoming less of an issue. One day, the software might even understand dialects or foreign accents like mine. But I suspect that’s only part of the adoption challenge. The other part lies in our ability to express our thoughts verbally to a computer. Most of our verbal communication is not very straightforward and we even enjoy taking our time before coming to the point. In places where communication has to be clear and precise such as military orders, radio protocol, or business negotiations, it is only possible after many hours of training. Naturally, people don’t speak that way.

However, just some 30 years ago typical managers didn’t have computers on their desks. They would spend several hours each day responding to correspondence by dictating letters onto a tape which their secretaries would later transcribe on a typewriter and later on a word processor and eventually on a PC using Word Perfect. Another 10 years before that, the dictation was done in real-time and the secretary had to know short-hand to keep up. It took years before the PC made it to the manager’s desk. What amazes me today is that the managers were able to dictate complete letters in full, well articulated sentences.

For most of us, that’s not so easy anymore. Today, we have a generation of PC users spoiled by the editing power at our fingertips. Most of us, knowledge workers, formulate our sentences as we write them and since it is so easy to rephrase any sentence or start from the beginning, we do it all the time. I’ve been observing many people doing this and I know that I am not alone. Most humans, even professional writers, would have a difficult time dictating in complete sentences. Giving commands to the computer such as search requests is one thing but authoring text via voice recognition requires a new skill set that is underdeveloped in most of us today. We know from the past that we humans are capable of such skills but the last 30 years of PC revolution have re-wired our brains differently.

Now Siri and other voice recognition software may be starting a new era. An era where we can – and perhaps must - express ourselves verbally in a new way. Let’s see how it goes. [computer, strike last sentence] Ehm…

By the way, when is Siri going to be available on iPhone 4?