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10 Charts That Will Change Your Perspective Of Amazon’s Patent Growth

10 Charts That Will Change Your Perspective Of Amazon's Patent Growth

  • Since 2010 Amazon has grown its patent portfolio from less than 1,000 active patents in 2010 to nearly 10,000 in 2019, a ten-fold increase in less than a decade.
  • Amazon heavily cites Microsoft, IBM, and Alphabet, with 39%, 32% and 28% of Amazon’s total Patent Asset Index
  • Amazon’s patent portfolio is dominated by Cloud Computing, with the majority of the patents contributing to AWS’ current and future services roadmap. AWS achieved 41% year-over-year revenue growth in the latest fiscal quarter, reaching $7.6B in revenue.

Patents are fascinating because they provide a glimpse into potential plans, and roadmaps tech companies are considering. Amazon has one of the most interesting patent portfolios today that encompass a wide spectrum of technologies, from aircraft technology, drones, cloud computing, to machine learning. Interested in learning more about Amazon’s unique patent portfolio, I contacted PatentSight, a LexisNexis company, one of the leading providers of patent analytics and provider of the PatentSight analytics platform used for creating the ten charts shown below.

  • Amazon patents grew at a Compound Annual Growth Rate (CAGR) of above 35% between 2010 and 2019. PatentSight’s analysis shows that Amazon’s patent portfolio has increased tenfold in the last decade, and is comprised entirely of organic patents with only a small percentage gained from acquisitions. PatentSight also finds that Amazon’s patents have a falling average quality as measured by their Competitive Impact score shown on the vertical axis of the chart below. As Amazon’s patent portfolio has grown, there has been a downward trend of quality. William Mansfield, Head of Consulting and Customer Success at LexisNexis PatentSight explains why. “To maintain a high quality when growing the portfolio is difficult, as each patent would need to be equally as good as or better than the previous,” he said. Mr. Mansfield’s analysis found that Amazon’s portfolio has an average Competitive Impact of 2 today, double the PatentSight database average of 1.

  • Amazon’s patent portfolio is unique in that 100% of it is protected in the U.S. “The protection strategy of Amazon is also uncommon. While it can be the case that US firms tend to be US-centric, Amazon is an extreme case,” said William Mansfield. It’s surprising how many Amazon patents are active only in the USA (86%) and invented in the USA and active only in the USA (81%). William explained that “one factor for this US-centricity could be the great acceptance of software patents in the USA, we do also see high US-only filing for other tech giants, but are a level of around 60% vs. Amazon’s 86%.”

  • PatentSight found that the majority of the Amazon portfolio falls in the 2nd decile of Competitive Impact (top 20% – 10%). Comparable technology-based organizations have a higher density of patents in the top 10% of Competitive Impact, which is another unusual aspect regarding Amazon’s patent growth. “This is unusual compared to other big tech companies which have more in the top 10%, it could be Amazon is holding onto more lower value assets than required,” William Mansfield remarked.

  • Amazon’s patent citations most often cite Microsoft, IBM, and Alphabet, with 39%, 32% and 28% of Amazon’s total Patent Asset Index. Interesting that PatentSight’s analysis finds the reciprocal is not the case. A much smaller percentage of companies cite Amazon in return. This can be attributed to a few other firms having the breadth and depth of patent development that Amazon does today.  PatentSight found that less than 10% of their respective portfolios even mention Amazon.  William Mansfield explains that “one factor here is the larger size of these companies, vs. Amazon. However, even in absolute terms, Microsoft and IBM cite Amazon much less than the other way round. However, citation value is close to equal in absolute terms between Amazon and Alphabet.”

  • Relying on patents to keep AWS’ rapid growth going appears to be Amazon’s high priority patent strategy today. As can be seen from the portfolio below, Cloud Computing patents dominate Amazon’s patent portfolio today. In the latest fiscal quarter ending March 31, 2019, AWS delivered $7.9B in revenue and$2.2B in operating income, growing 41% year-over-year. “Amazon’s ongoing developments in alternative delivery methods in Urban Logistics and Drones are noteworthy with Drones being one area of particular strength in the portfolio as seen from the high Competitive Impact, despite the smaller portfolio size,” notes William Mansfield.

  • Amazon’s prioritization of cloud computing, AI, and machine learning patents is evident when 18 years of patent history is compared. The proliferation of AI and machine learning-based services on the AWS platform is apparent in the trend line starting in 2014. The success of Amazon’s SageMaker machine learning platform is a case in point. Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at scale.

  • Amazon is already one of the top 10 patent holders in Drone technology, just behind Alphabet and Toyota Motors. PatentSight defines Drone technology as encompassing aviation, autonomous robots, and autonomous driving. Amazon’s rapid ascent in this area is attributable to the logistics and supply chain efficiencies possible when Drones and their related technologies are applied to their supply chain’s more complex challenges.

  • PatentSight finds that FinTech is an area of long-standing strength in the Amazon patent portfolio, attribute to their payment systems being the backbone of their e-commerce business. Reflecting how diverse their business model has become, Amazon is now one of the top 15 patent holders in this area due to cloud computing, AI, and machine learning taking precedence. “FinTech is a highly competitive field with many established players, and while Amazon is not in the top 10, but top 15 players, it’s still an impressive achievement,” said William Mansfield.

  • Amazon’s patent portfolio in speech recognition encompasses Alexa, its related patents, and Amazon Lex, an AWS service used for creating conversational interfaces for applications. Alphabet, Apple, Microsoft, and Samsung are patent leaders, according to PatentSight’s analysis. The fact that Amazon is in the top 10 speaks to the level of activity and patent production going on in the Alexa research and development and product teams.

  • Amazon’s patent strategy is eclectic yet always anchored to cloud computing to make AWS the platform of choice. The following selected patens reflect how broad the Amazon patent portfolio is. What each share in common is a reliance on AWS as the platform to ensure service consistency, reliability, and scale. An example of this is their patents Video Game Streaming.

10 Ways Machine Learning Is Revolutionizing Sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritization are the top three AI and machine learning use cases in sales.

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The Race for Sales-Focused AI & Machine Learning Patents Is On

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue aggressively invest in Research & Development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3B or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 Ways Machine Learning Is Revolutionizing Sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionizing selling today:

 

  1. AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers. Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritizing new prospects using this approach saves sales teams thousands of hours a year.
  2. Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), strengthening sales pipelines in the process. One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.
  3. Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts. Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.
  4. Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value. Analyzing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritized roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.
  5. Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights. All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.
  6. Guided Selling is progressing rapidly from a personalization-driven selling strategy to one that capitalized on data-driven insights, further revolutionizing sales. AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  7. Improving the sales team’s productivity by using AI and machine learning to analyze the most effective actions and behaviors that lead to more closed sales. AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.
  8. Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyze pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimized price more achievable.
  9. Personalizing sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning. Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalized nature of the content itself. Combining analytics, personalization and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.
  10. Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning. CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.
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