Methods and systems for a content development and management platform

Abstract

The present system and method relate to enhancements to online search engines and search result rankings, which can benefit from large scale analysis of online content (e.g., Web pages) and vast amounts of information kept and processed from prior searches to develop intelligent associations between various content with one another. Some aspects employ machine learning systems and methods to further enhance the present goals. Other aspects employ novel arrangements of data in data stores to extract best associations and deliver greater search engine rankings to users in an increasingly context-based or personalized type of searching environment.

What is claimed is: 
     
         1 . A system for generating a cluster of correlated content from a primary online content object, comprising:
 an automated crawler for crawling the primary online content object and storing a set of results from the crawling in a data storage facility;   a parser for parsing the stored set of results from the crawling to generate a plurality of key phrases and to generate a content corpus from the primary online content object;   a plurality of models for processing at least one of the plurality of key phrases and the content corpus, the plurality of models comprising at least two of a word2vec model, a doc2vec model, a latent semantic analysis (LSA) extraction model, and a key phrase logistic regression model, wherein the processing results in a plurality of content clusters representing topics within the primary online content object;   a content cluster data store for storing the plurality of content clusters; and   a suggestion generator for generating, using output from at least one of the plurality of models, a suggested topic that is similar to at least one topic among the plurality of content clusters and for storing the suggested topic and information regarding a similarity of the suggested topic to at least one content cluster in the content cluster data store.   
     
     
         2 . The system of  claim 1 , wherein the plurality of models comprises at least one of the word2vec model, the doc2vec model, the latent semantic analysis extraction model, the latent semantic indexing model, the principle component analysis model, and the key phrase logistic regression model. 
     
     
         3 . The system of  claim 1 , wherein the parser uses a machine learning system to parse the crawled content. 
     
     
         4 . The system of  claim 3 , wherein the machine learning system iteratively applies a set of weights to input data, wherein the set of weights are adjusted based on a parameter of success, wherein the parameter of success is based on a success of suggested topics in an online presence of an enterprise. 
     
     
         5 . The system of  claim 4 , wherein the machine learning system is provided with a training data set that is created based on human analysis of the crawled content. 
     
     
         6 . The system of  claim 1 , wherein at least one of the plurality of models uses a machine learning system to cluster content. 
     
     
         7 . The system of  claim 6 , wherein the machine learning system iteratively applies a set of weights to input data, wherein the set of weights are adjusted based on a parameter of success, wherein the parameter of success is based on a success of suggested topics in an online presence of an enterprise. 
     
     
         8 . The system of  claim 7 , wherein the machine learning system is provided with a training data set that is created based on human clustering of a set of content topics. 
     
     
         9 . The system of  claim 1 , wherein the suggestion generator uses a machine learning system to suggest topics. 
     
     
         10 . The system of  claim 9 , wherein the machine learning system iteratively applies a set of weights to input data, wherein the set of weights are adjusted based on a parameter of success, wherein the parameter of success is based on a success of suggested topics in an online presence of an enterprise. 
     
     
         11 . The system of  claim 10 , wherein the machine learning system is provided with a training data set that is created based on human creation of a set of suggested topics. 
     
     
         12 . The system of  claim 1 , further comprising, an application for developing a strategy for development of online presence content, the application accessing the content cluster data store and having a set of tools for exploring and selecting suggested topics for online presence content generation. 
     
     
         13 . The system of  claim 12 , wherein the application provides a list of topics that are of highest semantic relevance for an enterprise based on the parsing of the primary online content object. 
     
     
         14 . The system of  claim 13 , further comprising, a user interface of the application for presenting the suggested topic, wherein the suggested topic is presented with an indicator of the similarity of the suggested topic to a content cluster topic as calculated by at least one of the plurality of models. 
     
     
         15 . The system of  claim 13 , further comprising a user interface of the application for presenting the suggested topic, wherein the user interface facilitates generation of content related to the suggested topic. 
     
     
         16 . The system of  claim 15 , wherein the user interface includes at least one of key words and key phrases that represent the suggested topic. 
     
     
         17 . The system of  claim 16 , wherein the at least one of key words and key phrases are used to prompt a user with content for generation of online presence content. 
     
     
         18 . The system of  claim 17 , wherein the online presence content is at least one of website content, mobile application content, a social media post, a customer chat, a frequently asked question item, a product description, a service description and a marketing message. 
     
     
         19 . The system of  claim 18 , wherein the user interface for generation of content includes a plurality of suggested topics, each associated with an indicator of the similarity of a given suggested topic to a content cluster topic as calculated by at least one of the plurality of models. 
     
     
         20 . The system of  claim 1 , wherein the data storage facility is a cloud-based storage facility. 
     
     
         21 . The system of  claim 1 , wherein the data storage facility is a distributed data storage facility. 
     
     
         22 . The system of  claim 1 , wherein the primary online content object is a web page of an enterprise. 
     
     
         23 . The system of  claim 1 , wherein the primary online content object is a social media page of an enterprise. 
     
     
         24 . The system of  claim 1 , further comprising, an application for developing a strategy for development of online presence content, the application accessing the content cluster data store and having a set of tools for exploring and selecting suggested topics for online presence content generation, wherein the application further accesses the content of a customer relationship management system. 
     
     
         25 . The system of  claim 24 , wherein the application comprises a user interface for developing content regarding the suggested topic for presentation in a communication to a customer, wherein selection of the suggested topic for presentation to the customer is based at least in part on a semantic relationship between the suggested topic as determined by at least one of the plurality of models and at least one data record relating to the customer stored in the customer relationship management system.