AI innovation development system and method
An artificial intelligence (AI) innovation development system and method ingests global datasets of information including cross-disciplinary data, data mines the information within a knowledge graph including using white space analysis, performs recursive cycles of evolutionary inference with integrated feedback loops and ultimately identifies one or more innovation candidates that may be potentially patentable.
1 . A computer-implemented method for generating innovation candidates, comprising:
ingesting, by one or more processors, heterogeneous technical data from a plurality of repositories including at least one of patent documents, scientific literature, and internal disclosures;
parsing and normalizing the heterogeneous technical data and extracting technical entities and relationships from the heterogeneous technical data;
generating, for at least some of the extracted technical entities and relationships, respective multi-dimensional feature vectors that encode at least (i) a semantic representation and (ii) a temporal indicator;
populating a time-aware, multi-modal knowledge graph with nodes and edges representing the at least some of the extracted technical entities and relationships, wherein the nodes are associated with the multi-dimensional feature vectors;
deploying a plurality of analytical agents configured to query the time-aware, multi-modal knowledge graph to produce mining outputs including at least one of (i) whitespace mapping identifying underexplored regions of the knowledge graph, (ii) temporal trend extraction and projection, and (iii) cross-domain transferability analysis;
generating, based on the mining outputs, an initial set of innovation candidates, each innovation candidate comprising a machine-readable representation of a proposed technical concept;
evaluating the innovation candidates using a multi-agent evaluator ensemble that produces, for each innovation candidate, a composite score based on a plurality of heuristics including novelty and feasibility; and
iteratively refining the innovation candidates using an evolutionary refinement process that selects higher-scoring innovation candidates and computationally recombines inventive concepts to generate new innovation candidates,
wherein the deploying, evaluating, and iteratively refining are repeated in a recursive feedback loop until a termination condition is satisfied, and wherein one or more final innovation candidates are output responsive to satisfaction of the termination condition.
2 . The method of claim 1 , wherein ingesting heterogeneous technical data comprises continuously retrieving data from a plurality of external databases and providing it to a retrieval-augmented generation (RAG) preprocessing subsystem.
3 . The method of claim 1 , wherein parsing and normalizing the heterogeneous technical data comprises extracting technical entities using a predefined schema that identifies at least functional elements, operating parameters, and performance metrics.
4 . The method of claim 1 , wherein generating the multi-dimensional feature vectors comprises encoding at least one of:
a novelty score relative to prior art embeddings,
a non-obviousness indicator based on rarity of co-occurring features, and
a utility indicator derived from claimed performance improvements.
5 . The method of claim 1 , wherein populating the time-aware, multi-modal knowledge graph comprises linking nodes using at least one of similarity metrics, causal dependencies, and temporal progressions.
6 . The method of claim 1 , wherein the plurality of analytical agents comprises at least one whitespace agent configured to identify sparsely connected regions of the knowledge graph using density-based clustering.
7 . The method of claim 1 , wherein the whitespace mapping comprises identifying clusters having a higher density of academic literature nodes than patent document nodes.
8 . The method of claim 1 , wherein the temporal trend extraction comprises tracking topic trajectories across successive time windows and classifying topics as emerging, plateauing, or declining.
9 . The method of claim 1 , wherein the cross-domain transferability analysis comprises identifying structurally similar subgraphs across different technical domains having low application overlap.
10 . The method of claim 1 , wherein evaluating the innovation candidates comprises computing a composite score that combines evaluator-agent scores with scores derived from the multi-dimensional feature vectors.
11 . The method of claim 1 , wherein the recursive feedback loop further comprises re-querying the knowledge graph using refined innovation candidates as inputs.
12 . The method of claim 1 , wherein the termination condition comprises satisfaction of a predefined innovation-quality score threshold or detecting convergence of innovation candidates across successive refinement iterations.
13 . A system for generating innovation candidates, comprising:
one or more processors; and
one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to:
ingest heterogeneous technical data from a plurality of repositories including at least one of patent documents, scientific literature, and internal disclosures;
parse and normalize the heterogeneous technical data, and extract technical entities and relationships;
generate multi-dimensional feature vectors for the extracted technical entities and relationships, the multi-dimensional feature vectors encoding at least a semantic representation and a temporal indicator;
construct and maintain a time-aware, multi-modal knowledge graph comprising linked datapoints represented as nodes connected by edges representing detected relationships, wherein the nodes are associated with the multi-dimensional feature vectors;
execute a plurality of domain-specialized agents that query the knowledge graph to generate mining outputs including whitespace mapping and at least one of temporal trend projection or cross-domain transfer analysis;
generate innovation candidates based on the mining outputs;
score the innovation candidates using a multi-agent evaluator ensemble configured to apply distinct heuristics to produce composite scores; and
perform evolutionary refinement by selecting a subset of the innovation candidates based on the composite scores and recombining elements of the selected subset to generate additional innovation candidates, while requesting updated mining outputs from the plurality of domain-specialized agents as part of a recursive refinement loop,
wherein the system outputs one or more innovation candidates when a termination criterion corresponding to at least one of a quality threshold, a stability criterion, or a resource budget is met.
14 . The system of claim 13 , wherein the multi-agent evaluator ensemble comprises evaluator agents applying distinct heuristics including novelty scoring, technical feasibility, and cross-domain applicability.
15 . The system of claim 13 , wherein the evolutionary refinement is guided by updated mining outputs generated during each refinement iteration.
16 . The system of claim 13 , wherein the system is configured to dynamically ingest newly published literature while maintaining the knowledge graph in near real time.
17 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause performance of operations comprising:
constructing a time-aware, multi-modal knowledge graph from heterogeneous technical data by extracting technical entities and relationships and associating nodes in the knowledge graph with multi-dimensional feature vectors including temporal indicators;
performing knowledge-graph mining using a plurality of analytical agents to generate at least whitespace mapping output identifying sparsely connected regions of the knowledge graph; and
generating and evolving innovation candidates via an evolutionary inference engine that (i) generates candidate concepts based on the mining, (ii) evaluates the candidate concepts using a plurality of evaluator agents applying distinct heuristics, and (iii) iteratively recombines higher-scoring candidate concepts in a recursive loop that incorporates updated mining output until a termination condition is satisfied.
18 . The non-transitory computer-readable medium of claim 17 , wherein the operations further comprise computing citation network features indicative of non-obviousness.
19 . The non-transitory computer-readable medium of claim 17 , wherein the operations further comprise identifying innovation candidates representing cross-domain transfer of mechanisms between unrelated technical fields.
20 . The non-transitory computer-readable medium of claim 17 , wherein the recursive loop comprises periodically injecting interdisciplinary data to increase diversity of innovation candidates.