Invented by Charles Howard Cella, Gerald William Duffy, JR., Jeffrey P. McGuckin, Mehul Desai, Strong Force IoT Portfolio 2016 LLC
The market for systems and methods for learning data patterns predictive of an outcome is rapidly growing as businesses and organizations seek to gain a competitive edge through data analysis. These systems and methods allow companies to analyze large amounts of data and identify patterns that can be used to predict future outcomes.
One of the key drivers of this market is the increasing availability of data. With the proliferation of digital devices and the internet of things, there is a wealth of data available for analysis. This data can be used to gain insights into customer behavior, market trends, and other factors that can impact business performance.
Another driver of this market is the increasing sophistication of machine learning algorithms. These algorithms can analyze large amounts of data and identify patterns that humans may not be able to detect. This allows businesses to make more informed decisions based on data-driven insights.
There are a variety of systems and methods available for learning data patterns predictive of an outcome. Some of the most popular include machine learning algorithms, neural networks, and decision trees. These systems can be used to analyze a wide range of data, including customer data, financial data, and operational data.
One of the key benefits of these systems and methods is their ability to improve business performance. By analyzing data and identifying patterns, businesses can make more informed decisions about marketing, product development, and other key areas. This can lead to increased revenue, improved customer satisfaction, and other benefits.
However, there are also challenges associated with this market. One of the biggest challenges is the need for skilled data analysts and data scientists. These professionals are in high demand and can be difficult to find. Additionally, there are concerns around data privacy and security, as businesses need to ensure that they are protecting sensitive customer data.
Despite these challenges, the market for systems and methods for learning data patterns predictive of an outcome is expected to continue to grow in the coming years. As businesses seek to gain a competitive edge through data analysis, these systems and methods will become increasingly important. With the right tools and expertise, businesses can leverage data to improve performance and drive growth.
The Strong Force IoT Portfolio 2016 LLC invention works as follows
The system and method for learning data patterns that predict an outcome is described. A system example may include a plurality input sensors communicatively connected to a controller, a data-collection circuit structured to collect data output from the plurality input sensors, and a machine-learning data analysis circuit configured to receive the data output, learn the received output data patterns indicative an outcome, as well as learn a preferred data input collection band out of a plurality available data input collection bands. The machine learning data analyzer circuit can be configured to learn output data patterns based on feedback specific to an industry. The result may be: a response rate, a volume of production, or maintenance required.
Background for Systems and Methods for Learning Data Patterns Predictive of an Outcome
1. Field
2. “2.
Architectures and Applications
Transport Layer Architectures
Kernel Implementation
Alternative Software Implementations
First Alternative Proxy Node.
Integrated Proxy
Second Alternative Proxy Node.
Intermediate Proxy
Recoding Node
Multipath Transmission
Single Endpoint Couple
Distributed Source
Distributed content delivery
Multicast
Further Illustrative Example
Inter-Packet Coding
Transmission rate control
Pacing control by sender
Pacing Receiver
