Tag Archives: IoT

People Turn Toward “Data Banks” to Commoditize on their Purchase and User Behavior Profiles

Anyone who is anti “Big Brother”, this may not be the article for you, in fact, skip it. ūüôā

 

The Pendulum Swings Away from GDPR

In the not so distant future, “Data Bank” companies consisting of¬†Subject Matter Experts¬†(SME) across all verticals, ¬†may process¬†your data¬†feeds collected from your purchase and user behavior profiles.¬† Consumers will be encouraged to submit their data profiles into a Data Bank who will offer incentives such as a reduction of¬†insurance premiums to cash back rewards.

 

Everything from activity trackers, home¬†automation, to¬†vehicular automation¬†data may be captured and aggregated. ¬† ¬†The data collected can then be sliced and diced to provide macro and¬†micro views of the information. ¬† ¬†On the abstract, macro level the¬†information¬†may allow for demographic, statistical correlations, which may¬†contribute to corporate strategy. On a¬†granular¬†view, the data¬†will provide “data banks” the opportunity to sift through data to perform analysis and correlations that lead to actionable information.

 

Is it secure?  Do you care if a hacker steals your weight loss information? May not be an issue if collected Purchase and Use Behavior Profiles aggregate into a Blockchain general ledger.  Data Curators and Aggregators work with SMEs to correlate the data into:

  • Canned, ‘intelligent’ reports targeted for a specific subject matter, or across silos of¬†data types
  • ‘Universes’ (i.e. ¬†Business Objects) of data that may be ‘mined’ by consumer approved, ‘trusted’ third party companies, e.g. your insurance companies.
  • Actionable information based on AI subject matter rules engines and consumer rule transparency may be provided.

 

¬†“Data Banks” may be required to report to their customers who agreed to sell their data examples of specific rows of the data, which was sold on a “Data Market”.

Consumers may have¬†the option of sharing their personal¬†data with specific companies by proxy, through a ‘data bank’¬†granular to the data point¬†collected.¬† Sharing of Purchase and User Behavior Profiles:

  1. may lower [or raise] your insurance premiums
  2. provide discounts on preventive health care products and services, e.g. vitamins to yoga classes
  3. Targeted, affordable,  medicine that may redirect the choice of the doctor to an alternate.  The MD would be contacted to validate the alternate.

 

The curriated data collected may be harnessed by thousands of affinity groups to offer very discrete products and services.  Purchase and User Behavior Profiles,  correlated information stretches beyond any consumer relationship experienced today.

 

At some point, health insurance companies may require you to wear a tracker to increase or slash premiums.  Auto Insurance companies may offer discounts for access to car smart data to make sure suggested maintenance guidelines for service are met.

 

You may approve your “data bank”¬†to give access¬†to specific soliciting government agencies or private firms looking to analyze data for their studies. You may qualify based on the demographic, abstracted data points collected for incentives provided may be tax credits, or paying studies.

Purchase and User Behavior Profiles:  Adoption and Affordability

If ‘Data Banks’ are allowed to collect Internet of Things (IoT)¬†device profile and the devices themselves are cost prohibitive. ¬†here are a few¬†ways to increase their adoption:

  1.  [US] tax coupons to enable the buyer, at the time of purchase, to save money.  For example, a 100 USD discount applied at the time of purchase of an Activity Tracker, with the stipulation that you may agree,  at some point, to participate in a study.
  2. Government subsidies: the cost of aggregating and archiving Purchase and Behavioral profiles through annual tax deductions.  Today, tax incentives may allow you to purchase an IoT device if the cost is an itemized medical tax deduction, such as an Activity Tracker that monitors your heart rate, if your medical condition requires it.
  3. Auto, Life, Homeowners, and Health policyholders may qualify for additional insurance deductions
  4. Affinity branded IoT devices, such as American Lung Association may sell a logo branded Activity Tracker.  People may sponsor the owner of the tracking pedometer to raise funds for the cause.

The World Bank has a repository of data, World DataBank, which seems to store a large depth of information:

World Bank Open Data: free and open access to data about development in countries around the globe.”

Here is the article that inspired me to write this article:

http://www.marketwatch.com/story/you-might-be-wearing-a-health-tracker-at-work-one-day-2015-03-11

 

Privacy and Data Protection Creates Data Markets

Initiatives such as¬†General Data Protection Regulation (GDPR) and other privacy initiatives which seek to constrict access to your data to you as the “owner”, as a byproduct, create opportunities for you to¬†sell your data.¬†¬†

 

Blockchain: Purchase, and User Behavior Profiles

As your “vault”, “Data Banks” will collect and maintain your two primary datasets:

  1. As a consumer of goods and services, a Purchase Profile is established and evolves over time.¬† Online purchases are automatically collected, curated, appended with metadata, and stored in a data vault [Blockchain].¬† “Offline” purchases at some point, may become a hybrid [on/off] line purchase, with advances in traditional monetary exchanges, and would follow the online transaction model.
  2. User Behavior (UB)¬† profiles, both on and offline will be collected and stored for analytical purposes.¬† A user behavior “session” is a use case of activity where YOU are the prime actor.¬† Each session would create a single UB transaction and are also stored in a “Data Vault”.¬† ¬†UB use cases may not lead to any purchases.

Not all Purchase and User Behavior profiles are created equal.¬† Eg. One person’s profile may show a monthly spend higher than another.¬† The consumer who purchases more may be entitled to more benefits.

These datasets wholly owned by the consumer, are safely stored, propagated, and immutable with a solution such as with a Blockchain general ledger.

Hey Siri, Ready for an Antitrust Lawsuit Against Apple? Guess Who’s Suing.

The AI personal assistant with the “most usage” spanning ¬†connectivity across¬†all¬†smart devices, will be¬†the anchor upon which users will gravitate to control their ‘automated’ lives. ¬†An Amazon commercial just aired which depicted ¬†a dad with his daughter, and the daughter was crying about her boyfriend who happened to be in the front yard yelling for her. ¬†The dad says to Amazon’s¬†Alexa, sprinklers on, and yes, the boyfriend got soaked.

What is so special about top spot for the AI Personal Assistant? Controlling the ‘funnel’ upon which all information is accessed, and actions are taken means the intelligent ability to:

  • Serve up content / information, which could then be mixed in with advertisements, or ‘intelligent suggestions’ based on historical data, i.e. machine learning.
  • Proactive, suggestive¬†actions ¬†may lead to¬†sales of goods and services. e.g. AI Personal Assistant flags potential ‘buys’ from eBay based on user profiles.

Three main sources of AI Personal Assistant value add:

  • A portal to the “outside” world; E.g.¬†If I need information, I wouldn’t “surf the web” I would ask Cortana to go “Research” XYZ; ¬† in the Business Intelligence / data warehousing space, a business analyst may need to run a few queries in order to get the information they wanted. ¬†In the same token, Microsoft Cortana may come back to you several times to ask “for your guidance”
  • An abstraction layer between the user and their apps; ¬†The user need not ‘lift a finger’ to any app outside the Personal Assistant with noted exceptions like playing a game for you.
  • User Profiles derived from the first two points; I.e. data collection on everything from¬†spending habits, or other day to day ¬†rituals.

Proactive and chatty assistants may win the “Assistant of Choice” on all platforms. ¬†Being proactive means collecting data more often then¬†when it’s just you asking questions ADHOC. ¬†Proactive AI Personal Assistants that are Geo Aware may may make¬†“timely appropriate interruptions”(notifications) that may be based on time and location. ¬†E.g. “Don’t forget milk” says Siri, ¬†as your passing the grocery store. ¬†Around the time I leave work Google maps tells me if I have traffic and my ETA.

It’s possible for the [non-native] AI Personal Assistant to become the ‘abstract’ layer on top of ANY¬†mobile OS (iOS, Android), and is the funnel by which all actions / requests are triggered.

Microsoft Corona has an iOS app and widget, which is wrapped around the OS. ¬†Tighter integration may be possible but not allowed by the iOS, the iPhone, and the Apple Co. Note:¬†Google’s Allo does not provide an iOS widget at the time of this writing.

Antitrust violation by¬†mobile smartphone maker Apple: ¬†iOS must allow for the ‘substitution’ of a competitive¬†AI Personal Assistant to be triggered in¬†the same manner as the native Siri, ¬†“press and hold home button” capability that launches the default packaged iOS assistant Siri.
Reminiscent of the Microsoft IE Browser / OS antitrust violations in the past.

Holding the iPhone Home button brings up Siri. There should be an OS setting to swap out which Assistant is to be used with the mobile OS as the default. ¬†Today, the iPhone / iPad iOS only supports “Siri” under the Settings menu.

ANY AI Personal assistant should be allowed to replace the default OS Personal assistant from Amazon’s Alexa, Microsoft’s Cortana to any startup company with expertise and resources¬†needed to¬†build, and deploy a Personal Assistant solution. ¬†Has¬†Apple has taken steps to¬†tightly couple Siri with it’s iOS?

AI Personal Assistant ‘Wish” list:

  • Interactive, Voice Menu Driven Dialog; The AI Personal Assistant should know what installed [mobile] apps exist, as well as their actionable, hierarchical taxonomy of feature / functions. ¬† The Assistant should, for example, ask which application the user¬†wants to use, and if not known by the user, the assistant should verbally / visually list the apps. ¬†After the user selects the app, the Assistant should then provide a list of function choices for that application; e.g. “Press 1 for “Play Song”
    • The interactive voice menu should also provide a level of abstraction when available, e.g. User need not select the app, and just say “Create Reminder”. ¬†There may be several applications on the Smartphone that do the same thing, such as Note Taking and Reminders. ¬†In the OS Settings, under the soon to be NEW menu ‘ AI Personal Assistant’, a list of installed system applications compatible with this “AI Personal Assistant” service layer should be listed, and should be grouped by sets of categories defined by the Mobile OS.
  • Capability to interact with IoT using user defined¬†workflows. ¬†Hardware and software may exist in the Cloud.
  • Ever tighter integration with native as well as 3rd party apps, e.g. Google Allo and Google Keep.

Apple could already be making the changes as a natural course of their product evolution. ¬†Even if the ‘big boys’ don’t want to stir up a hornet’s nest, all you need is VC and a few good programmers to pick a fight with Apple.

G.E. Plans Big Entry into IoT, Providing Analytics and Predictive Rules

G.E. Plans App Store for Gears of Industry

The investment of $500 million annually signals the importance of the so-called Internet of Things to the future of manufacturing.

G.E. expects revenue of $6 billion from software in 2015, a 50 percent increase in one year. Much of this is from a pattern-finding system called Predix. ¬†G.E. calls its new service the Predix Cloud, and hopes it will be used by both customers and competitors, along with independent software developers. ‚ÄúWe can take sensor data from anybody, though it‚Äôs optimized for our own products,‚ÄĚ Mr. Ruh said.

[Competitive solutions from IBM, Microsoft, and Google] raises the stakes for G.E. ‚ÄúIt‚Äôs a whole new competition for them,‚ÄĚ said Yefim Natis, a senior analyst with Gartner. ‚ÄúTo run businesses in a modern way you have to be analytic and predictive.‚ÄĚ

G.E. is running the Predix Cloud on a combination of G.E. computers, the vast computing resources of Amazon Web Services, and a few [local] providers, like China Telecom.

China, along with countries like Germany, [are] sensitive about moving its data offshore, or even holding information on computers in the United States.  
The practice of “Ring fencing” ¬†data exists in dozens of jurisdictions globally. ¬†Ring fencing of data may be a legal and/or regulatory issue, that may¬†inhibit the global growth of cloud services moving forward.

Source: G.E. Plans App Store for Gears of Industry

Alzheimer’s Inflicted: Technology to Help Remember Habitual Activities ¬†

Anyone ever walk into a room and forget why on Earth you were there?  Were you about to get a cup of coffee, or get your car keys?  Wonderful!  It’s frustrating on my level of distraction, now magnify that to the Nth degree, Alzheimer’s.  Apply a rules and Induction engine, and poof!  A step further away from a managed care facility.

Teaching the AI Induction and rules engine may require the help of your 10 year old grandson.  Relatively easy,  you might need your grandson to sleep over for a day or two.

It’s all about variations of the same theme, tag a location, a room in an apartment, also action tag, such as getting a cup of coffee from the kitchen.  The repetitive nature of the activities with a location tag draws conclusions based on historical behavior.  The more variations of action and coinciding location tags, will begin to become ‘smarter’ about your habitual activities.  In addition, the calculations create a bell curve, a way to prioritize the most probable Location/Action tags used for the suggested behavior.    The ‘outliers’ on the bell curve will have the lowest probability of occurrence.

In addition, RFID tags installed in your apartment will increase the effectiveness of the ‘advice’ engine by adding more granular location tags.

Microchip_rfid_rice
Microchip RFID compared to the size of a grain of rice.
Beyond this ‘black box’ small, lightweight computer (smartphone) integrate a Bluetooth, NFC, WiFi antenna, a mobile application and you’re set.  A small, high quality Bluetooth microphone to interact with the app.  There’s also potential for exploring beyond the home.

Kidding, you don’t need that Grandson to help.  Speak into the mic, “Train” go into the room and say your activity, coffee.  This app will correlate your location, and action.  Everyone loves to be included in the Internet of Things, so app features like alerts for deviation from the location ‘map’ are possible.

In earnest, I am mostly certain that this type of solution exists.  Barriers to adoption could be computer/ smartphone generational gap.  Otherwise, someone is already producing the solution, and I just wasted a bus ride home.

Additionally, this software may be integrated with Apple’s Siri, Google Now,  Yahoo Index, Microsoft Cortana,  an extension of the Personal Assistant.

Human Evolution: Technology Continues to Transform Socieities for Generations

In the last 20 years, I’ve observed technology trends, and Tech achievements have¬†risen and fallen from the mainstream. ¬†Tech has augmented our lives, and enhanced our human capabilities. ¬†Our evolution will continue to be molded¬†by technology and shape humanity for years to come.

Digital Asset Management (DAM)

Everything you might find on your computer from emails to video are digital assets.  Content from providers, team collaboration,  push and/or pull asset distribution, and archiving content are the workflows of DAM.

DAM solutions are rapidly going main stream as small to medium sized content providers look to take control of their content from ingestion to distribution.  Shared digital assets will continue to grow rapidly.  Pressure by stockholders to maximize use of digital assets to grow revenue will fuel initiatives to  globally share and maintain digital asset taxonomies.  For example, object recognition applied to image, sound and video assets will dynamically add tags to assets in an effort to index ever growing content.  If standard taxonomies are not globally adopted, and continually applied to assets, digital content stored will become, in essence, unusable.

The Internet of Things (IoT)

All devices across all business verticals will become ‘Smart’ devices with bidirectional data flow. ¬†Outbound ‘Smart’ device data flow is funneled into repositories for analysis to produce dashboards, reporting, and rules suggestions.

Inbound ‘Smart’ device data can trigger actions on the device. Several devices may work in concert defined by ‘grouping’ e.g. Home: Environmental. Remote programming updates may be triggered by the analysis of data.

  • AI Rules Engine runs on ‘backend’. ¬†Rules defined by Induction, ¬†through data analysis, and human set parameters, ¬†executed in sequence
  • Device optimization updates, presets on devices may be tuned based on ‘transaction’ history, feedback from user, and other ‘Smart’ devices.
  • Grouped ‘Smart’ devices, e.g. health monitors’ data uploaded, analyzed, and correlating across group. ¬†Updated rules, and notifications triggered.
  • Manual user commands, ad hoc or scheduled

… as a Service

Cloud ‘Services’ enables scalability on demand, relatively lower cost [CapEx] overhead, offsite redundancy, etc. ¬†Provides software solutions companies to rapidly deploy to Dev., Test, and Prod. environments. ¬†Gaming, storage, and virtual machines are just a few of the ‘…as a service’ offerings. ¬†IoT analysis may reveal a new need for another service.

Human Interface

  • Augmented Reality A.R.

Integrates user to surrounding environment with overlay images to your eyes to REpresent anything, e.g. Identifies surrounding people with Twitter handle/user name above their heads. ¬†Interacts with smartphone for Inbound and outbound data flow. ¬†May allow App and OS programmers to enable users to interact with their ‘traditional’ software in new ways, e,g. Microsoft Windows 8+, current interaction with ’tiles’, may shift from a two to three dimensional manipulation and view of the tiles. ¬†Tiles (apps) pop up when, through object recognition, predefined characteristics match, e.g. ¬†Looking at a bank check sent to you from the mail? ¬†Your Bank of America tile / app may ask if you want to deposit the check right now?

  • Virtual Reality, V.R.

As more drones, for example, collect video footage, may be used for people to experience the landscapes, beaches, cities, mountains, and other features of a potential destination, which may lead to tourism. ¬†In fact, travel agencies may purchase the V.R. Headsets, and subscribe to a library of V.R. content. ¬†Repository platform would need to be created. ¬†Specs for the ‘How To’ on collecting V.R. Video footage should be accessible. ¬†Hathaway real estate offers a V.R. tour of the house, from their office.

Autonomous  Vehicles (Average Consumer or hobbyist)

  • Cars¬†
  • Drones
  • Satellites¬†

Social Media Evolution

Driving forces to integrate with society puts pressure on individuals to integrate with the collective social conscious.  As digital assets are published, people will lunge at the opportunity to self tag every digital asset both self and community shared assets.  Tagging on social media platforms is already going ahead.   Taxonomies are built, maintained and shared across social media platforms.  Systematically tagged [inanimate] objects occur using object recognition. Shared, and maintained global taxonomies not only store data on people and their associated meta data, (e,g,  shoe size, education level completed, HS photo,etc.) but also store meta data about groups of people, relationships and their tagged object data.

The taxonomies are analyzed and correlated, providing better, more concise demographic profiles.  These profiles can be used for 

  • Clinical trials data collection
  • Fast identification of potential outbreaks, used by the CDC
  • The creation and management of AI produced Hedge Funds
  • Solicitation of goods and services

Out of Compliance

These three dreaded words you are guaranteed to see more and more often. ¬†As all aspects of our lives become meta data on a taxonomy tree, the analysis of information will make correlations which drive consumers and members of society ‘out of compliance’. ¬†For example, pointers to your shared videos of you skydiving will get added to your personal taxonomy tree. ¬†Your taxonomy tree will be available and mandatory to get life insurance from a tier 1 company. ¬†Upon daily inspection of your tree by an insurance AI engine, a hazardous event was flagged. Notifications from your life insurance company reminding you ‘dangerous’ activities are not covered on your policy. ¬†Two infractions may drive up your premiums.