Invented by Steven A. Shaya, Neal Matheson, John Anthony Singarayar, Nikiforos Kollias, Jeffrey Adam Bloom, Johnson and Johnson Consumer Inc

The market for intelligent performance-based product recommendation systems is rapidly growing as businesses recognize the importance of personalized and targeted recommendations to enhance customer experiences and drive sales. With the advancement of technology and the availability of vast amounts of data, businesses are leveraging artificial intelligence (AI) and machine learning (ML) algorithms to develop sophisticated recommendation systems that can understand customer preferences and make accurate product recommendations.

One of the key drivers of this market is the increasing demand for personalized shopping experiences. Today’s consumers expect tailored recommendations that align with their individual preferences and needs. They want to feel understood and valued by the brands they interact with. Intelligent performance-based product recommendation systems enable businesses to deliver personalized recommendations based on a customer’s browsing history, purchase behavior, and demographic information. By analyzing these data points, these systems can identify patterns and trends, allowing businesses to offer relevant product suggestions that are more likely to resonate with customers.

Furthermore, these recommendation systems can significantly impact a business’s bottom line. Studies have shown that personalized product recommendations can increase conversion rates, average order values, and customer loyalty. By presenting customers with products they are more likely to be interested in, businesses can improve their chances of making a sale and increasing revenue. Additionally, personalized recommendations can lead to higher customer satisfaction and engagement, which can result in repeat purchases and positive word-of-mouth referrals.

The market for intelligent performance-based product recommendation systems is also driven by advancements in AI and ML technologies. These technologies have made it possible to process and analyze vast amounts of data in real-time, enabling businesses to make accurate and timely recommendations. AI algorithms can learn from customer interactions and continuously improve their recommendations over time, ensuring that they stay relevant and up-to-date. As AI and ML technologies continue to evolve, we can expect even more sophisticated and intelligent recommendation systems to emerge, further fueling the growth of this market.

Moreover, the rise of e-commerce and online shopping has created a need for intelligent recommendation systems. With the vast number of products available online, customers often feel overwhelmed and struggle to find what they are looking for. Intelligent performance-based product recommendation systems can help customers navigate through the vast product catalogs by suggesting relevant items based on their preferences and browsing behavior. This not only improves the customer experience but also helps businesses increase their sales by guiding customers towards products they may have otherwise missed.

In conclusion, the market for intelligent performance-based product recommendation systems is experiencing significant growth due to the increasing demand for personalized shopping experiences, the potential for increased sales and customer loyalty, advancements in AI and ML technologies, and the rise of e-commerce. As businesses continue to prioritize customer personalization and seek ways to enhance their online shopping experiences, the demand for intelligent recommendation systems will continue to rise. This presents a significant opportunity for businesses and technology providers to capitalize on this growing market and develop innovative solutions that can revolutionize the way customers discover and purchase products.

The Johnson and Johnson Consumer Inc invention works as follows

Systems and Methods of Utilizing Communications Networks and Multivariate Analysis to Predict or Recommend Optimal Products from a Predefined Population of Commercially Available Products are Disclosed.” The recommendations are based upon intelligence in the processing elements, and subjective or objective product information that is received by consumers and/or input into the systems during their initial setup. The systems output consists of sets of products they predict consumers will like and/or that perform well in relation to the problems or concerns identified by the consumer. Predictions of performance and preferences are dependent on consumer problems and patterns of product responsiveness. Diagnostic instruments are used to obtain objective product information. Internet can be used to transmit data measured by diagnostic instruments. “The outputs from the data-processing portion of the system can be presented to the consumers via the Internet.

Background for Intelligent performance-based Product Recommendation System

A. “A.

The present invention is a system and method for generating, communicating and analyzing product information. The invention is aimed at gathering objective and subjective data from consumers on the effects of product and using this data to generate product recommendation and ancillary data, improve the accuracy of recommendations periodically, and communicate product recommendations and information to consumers.

B. “B.

Many commercially available products only provide useful results after prolonged usage. Some products may have a gradual effect during use. Consumers may not fully appreciate the changes brought about by many products. The inability of consumers to compare pre- and after-product-use conditions is one of the reasons for this lack of appreciation. As conditions change, even if it is incrementally, the tendency is to compare results with a state that existed before product use. The consumer could get a false sense of efficacy, whether it is positive, negative or neutral.

The difficulty in testing all the options on the market, and the fact that many products do not have labels that communicate their objective performance in relation to conditions of consumer interest further compound the problem. However, the advent of the Internet, as well as developments in neural networks, collaborative filtering and recommendation systems, provides opportunities to solve the problems.

The Internet and Other Public and Private Networks

The term “Internet” is an abbreviation for “Internetwork”. “Internetwork” is abbreviated as “Internetwork”. It is a term used to describe a collection of gateways and networks that use the TCP/IP suite protocols. These are widely known in the field of computer networking. TCP/IP stands for Transmission Control Protocol/Internet Protocol. The Internet is a network of remote computer networks that are geographically dispersed and interconnected using computers that execute networking protocols. This allows users to share information, interact with each other, and exchange data over the networks. Remote networks, such as the Internet, have evolved to an “open” standard due to widespread information sharing. Developers can create software applications that perform specialized services or operations, virtually without restrictions.

Electronic data transferred between data processing networks is typically presented in hypertext. Hypertext is a metaphor that presents information in a way in which text images sounds and actions are linked together into a complex, non-sequential network of associations, which allows the user to browse? “Electronic information transferred between data-processing networks is usually presented in hypertext, a metaphor for presenting information in a manner where text, images and sounds are linked together into a complex non-sequential web of associations that allows the user to ‘browse’ The order in which the topics are presented does not matter. The author and the user of a hypertext file can both create these links, depending on their intent. Traveling among links to the term ‘iron’ is an example. In an article that is displayed in a graphical interface of a data processing system, the user may be led to the periodic tables of chemical elements by clicking on links to the word “iron”. The word “iron” may lead to an article about the use of weapons made from iron in Europe during the Dark Ages. The term “hypertext” was coined in the 1960s to describe documents, as presented by a computer, that express nonlinear structure of ideas. The term?hypertext’ was first used in the 1960s for documents that are presented on a computer and express nonlinear ideas as opposed to linear formats of books, films, or speech.

A typical hypertext-based networked system follows a client/server architectural design. The client is the?client’. The?client? is a person or class that is not related to another group or class. In computing, the client is a program (roughly a set or instructions) that requests services from another program. The client process uses the service requested without needing to “know” anything about it. The client process does not need to?know? In a Client/Server architecture, especially a networked computer, a “client” is usually a machine that connects to shared network resources offered by another computer.

Clients and servers communicate using the Hypertext-Transfer Protocol” (HTTP). The World Wide Web or simply the “Web” is also known as the World Wide Web. The servers that adhere to this standard (i.e. HTTP) are accessible by clients via an computer or data processing system network address, such as a Uniform Resource Location (URL). A network address is also known as an Universal Resource Locator. Communication can, for example, be done over a communication medium. Client and server can be connected via TCP/IP for high-capacity communications. A first process is active within the client, referred to as a “browser”. The browser establishes a connection with the server, and displays information to the client. HTTP responses are presented to the client by the server’s server software. HTTP responses are ‘web pages’. The HTML or other server generated data is used to construct the web pages. Web pages can be called “pages” or simply “web pages”.

The client displays information from the server via the network using a browser, a software program. Modern browsers feature graphical user interfaces capable of displaying, manipulating and navigating various types of information. A graphical interface is a display format which allows a user select commands, launch programs and view lists of files or other options on screen by pointing at pictorial representations. In general, choices can be selected using a keyboard or mouse. Internet services can be accessed using a unique network (i.e. a URL). The URL address consists of two components: the protocol and object pathname. For example, the URL address, ? The URL address?’ (i.e. the home page of the U.S. Patent and Trademark Office) specifies a HTTP and a server pathname (? The server name corresponds to one or more TCP/IP equivalent addresses.

Neural Network Analysis

Neural Network Analysis is a way to model non-linear relationships among independent and dependent variables. This analysis is done by creating a neural network that accurately represents the relationship between independent and dependent variables. Once a valid network has been created, it can be used for predicting values of unknown dependent variables based on known independent variables. In neural network analysis, the independent variables are known as inputs, and the dependent variables are known as outputs.

The power of a network is in the nonlinear equations that it uses to represent the relationship between inputs and outputs. The equation is defined by connection weights, a complex function. A training algorithm examines training data to determine the specific values of connection weights. The training data is made up of inputs and outputs that represent the nonlinear relationship to be modeled. The training algorithm analyses the inputs of the training data and determines a set connection weights to minimize the error between predicted outputs of the neural networks and the outputs of the training data.

A neural network is composed of three layers: an input layer, one (or more) hidden layer and an output layer. The hidden and output layers are made up of interconnected processing components, which are the main building block of the neural networks. Input layer’s primary function is to send input values to the processing elements in the first hidden layers. Each processing element multiplies inputs by different connection weight values to produce a product, and then adds all the products. The results are then passed through a nonlinear function to produce the processing element output. The outputs from one layer of processing elements are sent to the inputs for processing elements of the next layer, where similar processing is repeated. The output layer is the final layer of a neural net and may contain non-linear or linear processing elements. Non-linear elements process inputs the same way as described above. Linear processing elements pass inputs from the processing element directly to its output. “The outputs of processing elements in output layer produces the final output of neural network.

Another neural network design consideration is whether the network is fully connected or a feedforward. A neural network is considered fully connected when all the outputs of one layer are inputs for the next layer. “A neural network is considered feedforward when there are no feedback loops.

The first thing to do when creating a network of neural networks is to decide what the output will be. These outputs are the final outputs from the neural network. Next, identify all variables which will have a material impact on the value of outputs. These variables are the inputs for the neural network. After identifying the inputs and outputs of the network, the structure of the neural networks, including its number of layers, processing elements, and number of layers in each layer can be determined.

Once the structure of the network has been determined, it is possible to create the neural network. The neural network is then trained with training data. The training data is a collection of input variables and output variables that represent the statistical relationships to be modelled by the neural networks. “The more data you collect and use, the better. This is especially true if your relationship is statistical.

The neural network implements a training algorithm. The training algorithm selects the appropriate connection weights to model the relationship between inputs of the training data, and outputs of the training data.

Once the network is trained, its performance can be assessed using test data. The following steps are taken to test a neural net. The neural network is fed with test data individually. Each test input is then run through the neural network and outputs predicted are generated. To determine whether the neural network performs properly, predicted outputs are compared with actual test data. “A neural network that does poorly on test data shouldn’t be used.

After a network has been trained, it can be used for predicting outputs using various inputs. The predictions can then be used to achieve the goal for which the neural networks was designed. In U.S. Pat., examples of neural networks are described and shown. No. No. 5,724,258 entitled?Neural Network Analyses For Multifocal Contact Lens Designs? U.S. Pat. No. No. Both of these patents are fully incorporated by reference. In ‘Artificial Neuronal Networks’, you can find more details and principles about neural networks. Robert J. Schalkoff, McGraw-Hill 1997, is also fully incorporated by reference.

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