IP - FinchAI

Our capabilities are built on dozens of unique pieces of intellectual property.

Big Data Compression

Dramatically compressing the size of a dataset while preserving the ability to decompress a single record or field.

Automated Discovery Of New Topics

Using co-occurring topics from different topic models to aid in entity disambiguation; extending conventional LDA topic modeling in so that each component is treated as conditionally-independent.

Event Detection Through Text Analysis Via Trained Template Modes

Using topic models to develop relationships between topics, events and entities; and further use of that data to detect and capture events.

Search Suggestions Via Fuzzy-score Matching And Co-occurrence

Employing a co-occurrence knowledge base to allow a user to submit partial or complete queries, which are processed on-the-fly to detect entities, their misspelled variations, and other conceptual features.

Search Suggestions Via Fuzzy-score Matching And Population Frequencies

Extending existing search suggestion mechanisms to employ an entity co-occurrence knowledge base, a trends co-occurrence knowledge base, fuzzy matching modules and entity extraction modules.

In-loop, Human Validation Of Disambiguated Features

Extending traditional feature disambiguation systems by providing a feedback mechanism to take human verification of the disambiguated results and update the knowledge base on-the-fly.

Fault-tolerant Architecture For Distributed Systems

Providing fault-tolerance in a distributed system by automatically detecting failures and recovering from them by moving processing modules and their dependencies to other nodes in the system.

Real-time, Distributed In-memory Search Architecture

A system of multiple network segments with bandwidth and latency tiers, applied to a distributed in-memory data platform.

Search Suggestions Via Co-occurrence

A search suggestion generation mechanism that employs a co-occurrence knowledge base to suggest new search queries and possible search expansions or suggestions.

Disambiguating Features In Unstructured Text

Employing an evolving and efficiently linkable feature knowledge base capable of storing secondary features to disambiguate entities.

Entity-driven Alerts Based On Disambiguated Features

An alert mechanism based on user-specified disambiguated features, providing a framework to support collaborative knowledge sharing among different users.

Alerting System Based On Newly Disambiguated Features

Leveraging user-specified disambiguated features to provide better relevance and precision than traditional systems, as well as the ability to build alert specifications around secondary features.

Dependency Manager For Databases

Technology to automate the deployment, installation and configuration of data, metadata and software of the primary data store of a distributed database.

Entity Enrichment Enabling Search Functionality

Extending the search capabilities of contentment management systems with additional managed properties obtained through natural language processing of the underlying unstructured text content.

Non-exclusionary Search Within In-memory Databases

Granting users the ability to specify a query and a detailed scoring/ranking specification as separate aspects in a search request.

Faceted Search And Search Suggestion Method

Generating facets and corresponding frequency counts as part of the search process by running them as part of the query in an in-memory database.

Extracting Facts From Unstructured Text

Associating extracted facts with other features retrieved from the text and employing a fact-template store containing commonly used fact-sentence structures.

Design And Implementation Of Clustered Imdbmss

Broad system architecture of our in-memory computing platform, FinchDB.

Event Detection Through Text Analysis Via Dynamic, Self Learning Module

Learning different event types by extracting entities and topic vectors and comparing them to existing entities and topic vectors; developing new knowledge base entries as new entities and topic vectors are discovered.

Discovering And Exploring A Feature Knowledge Base

An on-the-fly, analytics-based search mechanism for live and large data streams.

Automated Discovery Of Topic Relatedness

Computing topic relatedness based on topic models and using those relationships as a supplementary feature in various data and text analytics.

Searching Via Co-occurrence

Granting users the ability to find entities of interest via entity co-occurrence, even as the data is updated and new information is discovered.

Pluggable Architecture For Embedding Analytics In Clustered Imdbmss

Ensuring each database query can specify which analytics modules and parameters are to be applied on-the-fly to intermediate query answers without having to first retrieve data out of the database.

Feature Co-occurrence Knowledge Base From A Corpus Of Documents

A feature co-occurrence knowledge base that exploits the full range of state-of-the-art natural language process tools.

Data Ingestion Module For Event Detection And Increased Situational Awareness

A generalizable framework to train customized and source-specific event templates and models for different input data streams.