XOi Vision™ Uses Machine Learning to Create an Intelligent Field Communications Platform
When field service organizations first encounter XOi Vision™, they sometimes think it is just a way to take photos and videos on job sites, however, it involves much more than that.
Theoretically, anyone with a smartphone could take a video or photo and send it to a customer or coworker. That’s not what makes visual communication meaningful. To create meaning, you need the usability and intelligent design that comes with a visual content platform.
Machine learning capabilities built into the Vision™ platform create meaning by ensuring your visual content is:
- Searchable—employees need to be able to easily find the content they seek.
- Sharable—technicians must be able to share content with their team and customers.
- Relevant—whether a customer wants an analysis of the history of the performance of equipment or a tech wants to see compare how other technicians perform the same type of repair, you need to be able to identify pieces of content that relate to one another.
- Contextual—content must be accompanied by the supporting information the customer needs to make a decision, such as a work order, invoice, or estimate.
Vision™ is Watching & Listening to Your Technicians
Machine learning streamlines the way information comes into the Vision™ platform and how information flows internally across the organization and also out to customers. To understand how Vision™ deploys machine learning, let’s examine its use of Optical Character Recognition (OCR) and Natural Language Processing (NLP).
Optical Character Recognition Saves Time on Paperwork & Improves Accuracy
Have you ever deposited a check into your online banking application by taking a photo of it with your phone? If so, you have benefitted from Optical Character Recognition.
OCR reads the elements inside a photo, transcribing them into text. When used in depositing a check, it reads the lines and feeds information about the account number, amount, and date into the banking system.
Here’s how it works in Vision™: at the jobsite, the technician takes a photo of the data plate on an HVAC unit with their phone. OCR captures the manufacturer, model number and serial number and transcribes those as keywords in Vision™. Your team can easily search for and find all photo and video content about that asset in the future. OCR also ensures that the front office never has to try to decipher messy handwriting in a work order and techs don’t run the risk of transposing numbers as they write down or key in a serial number.
Natural Language Processing Makes it Easier to Search For & Share Content
Have you ever asked Siri for directions or told Alexa to order something? You’ve experienced Natural Language Processing.
NLP employs Artificial Intelligence so that software can understand spoken human language. (For those of us of a certain age, NLP also explains how David Hasselhoff could chat with KITT, his ultra-smart Pontiac Firebird, in the futuristic 1980’s TV series “Knight Rider”!)
Vision™ uses NLP to listen to the words your technicians say as they record videos and pick out keyword tags to turn the video into highly searchable content. It understands field service industry buzzwords, so when you technician says the condenser coil is dirty, Vision™ indexes it for dirty condenser coil. Later, you can search for all material related to dirty condenser coils for comparison, or search for that keyword plus other identifiers to locate that specific video. In this way, Vision™ makes it easy to review a customer’s history on a single asset, conduct internal training on a type of repair, or optimize asset management by analyzing the performance and reliability of certain brands and models.
Together, OCR and NLP automate both the identification and sharing of content within the Vision™ visual communications platform. Machine learning may sound like a futuristic buzzword, but it has practical uses today in the field service industry.
See It for Yourself
Request a demo to see how Vision™ uses machine learning to improve productivity in the field and create a highly searchable and usable resource with video content.