VERSES Announces Patent Filing for Predictive Querying
02 August 2023 - 2:00PM
VERSES AI Inc. (CBOE:VERS) (OTCQX:VRSSF) ("VERSES'' or the
"Company”), a cognitive computing company specializing in the next
generation of artificial intelligence, announces the filing of a
provisional patent application representing a new method for
Predictive Querying on vector graph document databases.
Probabilistic querying is an approach to database queries that
seeks to provide a user with additional information “predicted” to
be of interest to the user, given the context implicit in the query
and around its prompter. VERSES’ novel Predictive Query method
addresses limitations in prior arts by providing a system to
perform probabilistic queries on the most advanced class of
databases: vector graph document databases. Predictive Queries
operate on vector graph document databases by implementing
Hyperspatial Modeling Language (HSML) and an inference algorithm to
generate a probabilistic and contextualized result.
The Predictive Querying method is the first querying method that
allows probabilistic querying on vector graph document databases,
that enables an engine to generate rich predictions about the
information being searched for by the user based on comparative,
relationship and similarity information.
“Through evaluating current data solutions like vector search
databases and graph databases, we identified significant gaps in
effectiveness, so we invented new methods of modeling, managing and
querying data that are tailored more towards new modalities in
artificial intelligence. This provisional patent is an important
milestone that further signals our leadership position in the AI
landscape and I couldn’t be more proud of the team and the tools
we’re building.” said Jason Fox, CTO at VERSES.
Knowledge graphs represent entities – any physical or conceptual
“thing” one can have information about in the real world (e.g., a
robot, a sofa, a waypoint in space, a specification of an activity)
– and the relationships between them. HSML is a modeling language
for qualifying the relationships between entities in a knowledge
graph.
An HSML vector graph document database is structured as an HSML
knowledge graph and allows for information retrieval using complex
queries that can simultaneously involve the comparison of entities
(e.g., “find people older than Steve”), the identification of
cause-effect relations (e.g., “who is Steve’s manager”?), as well
as the evaluation of similarity between entities (e.g., “which
employees have an educational background closest to Steve’s”?).
Compared to vector graph document databases, other classes of
databases are limited to either comparative, relationship or
similarity search.
Now, because of this new method for Predictive Querying on
vector graph document databases, VERSES returns the most probable
and relevant match to a user’s rich implicit goal (e.g., inferring
and returning the most probable brand, model, and location to a
search for “cheap sunglasses” along with the best deals on cycling
clothes matching the style of the sunglasses).
About VERSESVERSES is a cognitive computing
company specializing in next-generation Artificial Intelligence.
Modeled after natural systems and the design principles of the
human brain and the human experience, VERSES flagship offering,
GIA™, is an Intelligent Agent for anyone powered by KOSM™, a
network operating system enabling distributed intelligence. Built
on open standards, KOSM transforms disparate data into
knowledge models that foster trustworthy collaboration between
humans, machines, and AI, across digital and physical domains.
Imagine a smarter world that elevates human potential through
innovations inspired by nature. Learn more
at VERSES, LinkedIn and Twitter.
On Behalf of the Company Eric Holder, Director
of Communications, VERSES AI Inc., press@verses.ai
Investor Relations Inquiries Leo Karabela,
President, Focus Communications, info@fcir.ca 416-543-3120
Forward-Looking Statements Cautionary Note
The NEO has not reviewed or approved this press release for the
adequacy or accuracy of its contents.
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