ORGANIZING UNSTRUCTURED AND STRUCTURED DATA BY NODE IN A HIERARCHICAL DATABASE
This document presents methods, systems, and apparatuses for self-building hierarchically indexed multimedia databases and product and service-hierarchy databases that include multiple branches and multiple trees of nodes. The databases hierarchically organize video, audio, and documents per node. The documents can be architectural plans, investor presentations, technical specifications, product or service guides, market research reports), news, messages, industry information, regulatory status, licensing, blogs, etc. in some embodiments, the databases disclosed organize and track company market performance and stock investment information for issuers and inventors based on the products and services produced and offered by each competitor. The databases also organize and track podcasts-by-node, messages-by-node, text, voice messages-by-node, and voice calls-by-node.
1 . A computer-implemented method for performing a per-node operation, the method comprising:
receiving, by a computer system, a request at a first node of a hierarchically indexed multimedia database categorizing products and services,
wherein the database hierarchically organizes video, audio, and documents per node,
wherein the request is for performing a node-to-node action involving the first node and a second node of the database, and
wherein the request excludes a location of the second node in the database;
extracting, from the request, features indicative of the location of the second node;
locating, using a machine learning module based on the features, a branch supporting a node tree of the database,
wherein the machine learning module is trained using other received requests involving the second node, and
wherein the node tree includes the second node;
traversing the node tree using a structure of the database to determine the location of the second node;
performing the node-to-node action involving the first node and the second node to satisfy the request; and
transmitting a response to the at least one issuer entity or investor entity,
wherein the response indicates that the node-to-node action has been performed.
2 . The method of claim 1 , wherein the database hierarchically organizes at least one of podcasts-per-node or messaging-per-node.
3 . The method of claim 1 , wherein the request indicates a search for digital content posted by the second node, and
wherein performing the node-to-node action comprises:
searching, using the machine learning module, the digital content based on social media engagement performed at the first node.
4 . The method of claim 1 , wherein performing the node-to-node action comprises:
weighting a relevance metric associated with the second node based on a number of times digital content for a particular industry was accessed at the second node; and
retrieving, at the first node, multimedia content based on the relevance metric associated with the second node.
5 . The method of claim 1 , wherein performing the node-to-node action comprises:
sending, at the first node, a video of a website to the second node; and
responsive to receiving, from the second node, an indication of receipt of the video, opening a communications channel between the first node and the second node.
6 . The method of claim 1 , wherein performing the node-to-node action comprises:
grouping employment categories referenced by the first and second nodes into groups of jobs based on shared characteristics;
assigning a taxonomic rank to each group;
aggregating groups of a particular rank to generate a taxonomic hierarchy; and
generating an employment taxonomy based on the taxonomic hierarchy.
7 . The method of claim 1 , wherein performing the node-to-node action comprises:
transferring ownership of a non-fungible token (NFT) from the first node to the second node,
wherein the NFT is stored on a blockchain, and
wherein at least one key referencing the NFT is sent from the first node to the second node to transfer the ownership.
8 . The method of claim 1 , wherein performing the node-to-node action comprises:
generating a comparison between first technical indicators for a first industry associated with the first node and second technical indicators for a second industry associated with the second node; and
sending the comparison to the at least one issuer entity or investor entity.
9 . The method of claim 1 , wherein the machine learning model is trained to perform search ranking using the other received requests.
10 . The method of claim 1 , wherein the machine learning model performs at least one of query classification or query expansion on the request.
11 . The method of claim 1 , wherein the machine learning model performs intent disambiguation on the request.
12 . A computer system for performing a per-node operation, the computer system comprising:
at least one computer processor; and
a non-transitory computer-readable storage medium storing computer instructions, which when executed by the at least one computer processor cause the computer system to:
receive a request at a first node of a hierarchically indexed multimedia database categorizing products and services,
wherein the database hierarchically organizes video, audio, and documents per node,
wherein the request is for performing a node-to-node action involving the first node and a second node of the database, and
wherein the request excludes a location of the second node in the database;
locate, using a machine learning module, a branch supporting a node tree of the database,
wherein the node tree includes the second node;
traverse the node tree using a structure of the database to determine the location of the second node;
perform the node-to-node action involving the first node and the second node to satisfy the request; and
transmit a response to the at least one issuer entity or investor entity,
wherein the response indicates that the node-to-node action has been performed.
13 . The computer system of claim 12 , wherein the request indicates a search for digital content posted by the second node, and
wherein the instructions to perform the node-to-node action cause the computer system to:
search, using the machine learning module, the digital content based on social media engagement performed at the first node.
14 . The computer system of claim 12 , wherein the instructions to perform the node-to-node action cause the computer system to:
weight a relevance metric associated with the second node based on a number of times digital content for a particular industry was accessed at the second node; and
retrieve, at the first node, multimedia content based on the relevance metric associated with the second node.
15 . The computer system of claim 12 , wherein the instructions to perform the node-to-node action cause the computer system to:
send, at the first node, a video of a website to the second node; and
responsive to receiving, from the second node, an indication of receipt of the video, open a voice over IP (VoIP) channel between the first node and the second node.
16 . The computer system of claim 12 , wherein the instructions to perform the node-to-node action cause the computer system to:
group employment categories referenced by the first and second nodes into groups of jobs based on shared characteristics;
assign a taxonomic rank to each group;
aggregate groups of a particular rank to generate a taxonomic hierarchy; and
generate an employment taxonomy based on the taxonomic hierarchy.
17 . The computer system of claim 12 , wherein the instructions to perform the node-to-node action cause the computer system to:
transfer ownership of a non-fungible token (NFT) from the first node to the second node,
wherein the NFT is stored on a blockchain, and
wherein at least one key referencing the NFT is sent from the first node to the second node to transfer the ownership.
18 . The computer system of claim 12 , wherein the instructions to perform the node-to-node action cause the computer system to:
generate a comparison between first technical indicators for a first industry associated with the first node and second technical indicators for a second industry associated with the second node; and
send the comparison to the at least one issuer entity or investor entity.
19 . The computer system of claim 12 , wherein the machine learning model is trained to perform search ranking using the other received requests.
20 . A non-transitory computer-readable storage medium storing computer instructions, which when executed by at least one computer processor cause the at least one computer processor to:
receive a request at a first node of a hierarchically indexed multimedia database categorizing products and services,
wherein the database hierarchically organizes video, audio, and documents per node,
wherein the request is for performing a node-to-node action involving the first node and a second node of the database, and
wherein the request excludes a location of the second node in the database;
locate, using a machine learning module, a branch supporting a node tree of the database,
wherein the node tree includes the second node;
traverse the node tree using a structure of the database to determine the location of the second node;
perform the node-to-node action involving the first node and the second node to satisfy the request; and
transmit a response to the at least one issuer entity or investor entity,
wherein the response indicates that the node-to-node action has been performed.