Hubspot, Inc.

Intentsify, LLC

Marco Lagi

Jared Williams

Stuart P Layton

Bryan Ash

Hector Urdiales

Sophie Higgs

Robert McEneaney

Dylan Sellberg

Kevin M Walsh

Stephen J Purcell

Anna Perko

Scott Judson

Caleb Castleberry

Ihab Khoury

Charles Ronald Allieri

Vedant Misra

Enrique Pons

Vicent Alabau

Anna Coffey

A method includes receiving target input data that includes keyword input data and accessing processed network data that includes a plurality of groups. Each of the groups includes a plurality of URL data objects and is associated with an entity. The method includes generating a dynamic intent score for each group by, for each of the URL data objects, extracting keywords from a webpage associated with the URL data object that are similar to keywords of the target input data, comparing the extracted keywords with the target input data, generating a keyword comparison value for the URL data object, and generating the dynamic intent score based on the keyword comparison values. The method includes ranking the groups according to their respective dynamic intent scores, selecting a subset of the groups according to the ranking, and generating a target account list including the entities associated with the subset of the groups.

Machine-learned classification of network traffic

A method for classifying network traffic includes accessing processed network data that includes multiple groups. Each group includes multiple URL data objects and is associated with an entity. The method includes generating a dynamic intent score for each group by generating a comparison value for each URL data object within a group, selecting highest comparison values for the URL data objects within the group, generating the dynamic intent score by averaging the selected highest comparison values, and ranking the groups according to their respective dynamic intent scores. The comparison value for each URL data object is generated by scraping a webpage associated with a URL data object to generate a first scraped text data object, creating web embeddings by providing the scraped text data object to a machine learning module, and generating a comparison value by comparing the web embeddings with reference embeddings.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the 'platform' or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Multi-service business platform system having entity resolution systems and methods

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

Methods and systems for automated generation of personalized messages

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the 'platform' or the 'system') that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Growing community of inventors

Medford, MA, United States of America

Marco Lagi