Abstract: A method and system for insight creation transparency. Explainable artificial intelligence, in recent years, has become synonymous with a framework through which users, relying on machine learning models for various applications, may come to trust the result(s) outputted by said models through better comprehension of the mechanisms leading to said result(s). Leveraging a vast database of metadata for a plethora of unstructured and structured data/information, embodiments disclosed herein derive, or infer, insights therefrom that best address any user-submitted queries. Embodiments disclosed herein, further, provide transparency information detailing, for example, which input(s) and which technique(s) and/or algorithm(s) were employed to arrive at the derived/inferred insights.

Abstract: A method and system metadata-based query rewriting. Query rewriting may generally refer to the automatic transformation of search queries in order to better represent the intent of the searcher (i.e., reason(s) motivating the searcher to conduct any search query). Embodiments disclosed herein, more specifically, leverage captured asset metadata, for assets hosted across various internal and external data sources, as well as graph techniques, to provide search query results that more closely match the said intent of the searcher. The graph techniques lead to the identification of adjacent areas and topics to the original search query, thereby enhancing the recall and/or precision of any returned search query results.

Abstract: A method and system for business intent-assisted search. A business intent may generally refer to information, respective to an organization user, which may pertain to or describe the engagement of the organization user within and/or outside their organization (e.g., a commercial business, an education institution, etc.). Embodiments disclosed herein, accordingly, implement search query processing based on the business intent modeled for the search query submitter (e.g., an organization user). Further, a recall of any returned information, relevant to the search query, may be contingent on and/or directly correlated with a completeness of the modeled business intent for any given organization user.

Abstract: A method and system for metadata-based feature expansion for automated machine learning processes. In machine learning, feature selection—or a method through model inputs are reduced in dimensionality by retaining the relevant features, while also discarding the noise, thereof—often plays a pivotal role in the availability of algorithm(s) to derive model(s) from, the optimizing of said model(s) through training and/or testing, and, ultimately, the reaching of acceptable performance thresholds evaluating said model(s), which leads to their implementation in solving real-world problems. Embodiments disclosed herein, accordingly, leverage captured dataset metadata, as well as graph techniques, to identify and suggest one or more supplemental features distinct from original features for, and yet relevant to, the real-world problem and any machine learning models being evaluated.

Abstract: A method and system for dynamic data product creation. A data product may refer to a collection of one or more datasets to which versioning, a set of policies, and/or a set of constraints may be attached. Existing systems/solutions offering data products today rely on the availability of static assets supported by static metadata descriptive thereof. As an improvement over said existing systems/solutions, embodiments disclosed herein enable newly introduced and ingested information, from across various data sources, to update any relevant data product(s) accordingly. Further, versions of any data products may be tracked and be made readily available to users seeking to reproduce work contingent on certain versions of one or more assets that may have been used to originally produce said work at a given point-in-time. Moreover, accessibility or inaccessibility to any given asset may depend on the access authority granted to the user(s) seeking said given asset.

Abstract: A method and system for business intent based communications enhancement. In pursuing the achievement of their respective responsibilities, employees or users, affiliated with an organization, often tend to employ a variety of software productivity tools—a subset of which may be directed to facilitating communications between said employees amongst themselves internally, as well as other individuals outside the organization. Concerning communications between any given employee and another employee or an outside individual, a perceived value of (and thus, a likelihood of a positive response to) the message, composed by the employee, is usually limited to a level of business acumen associated with, as well as the skill and/or talent of, said employee. Embodiments disclosed herein, accordingly, leverage a number of factors (including user business intent) in order to enhance employee-composed messages through the proposition of value-supplementing edits.

Abstract: A method and system for impact enhancing recommendations for research papers and initiatives. People, amongst research circles, often write and publish research papers, and/or pursue research initiatives, without any foreknowledge regarding the impact potential (e.g., usefulness and/or success) of their respective research work. Said people, at times, may find that their efforts are wasted due to poor peer-review reception and/or to little to zero response from one or more industries. To ensure that future time and effort, which may be put into any unbegun research work, are not fruitless, embodiments disclosed herein leverage collected metadata on past, impactful research work in order to evaluate said any unbegun research work with respect to an impact factor thereof. Embodiments disclosed herein, further, provide guidance, pertaining to a number of research work related aspects, to enhance the impact factor of said any unbegun research work.

Abstract: A method and system for insight lineage tracking. Any insight may be defined as a finding (or more broadly, as useful knowledge) gained through data analytics or, more precisely, through the discovery of patterns and/or relationships amongst any given assortment of data/information. Accordingly, any insight may be derived, or inferred, from any number of data/information items, including any number of other insights, as well as any number of employed analytic algorithms or models. In aiming to track these insight-pertinent objects, as well as the dependencies there-between, embodiments disclosed herein create insight lineage graphs visually conveying said objects and dependencies and, moreover, maintain metadata descriptive of each step in an insight creation process leading to the production of insights.

Abstract: A method and system for insight creation filtering. Explainable artificial intelligence, in recent years, has become synonymous with a framework through which users, relying on machine learning models for various applications, may come to trust the result(s) outputted by said models through better comprehension of the mechanisms leading to said result(s). Leveraging a vast database of metadata for a plethora of unstructured and structured data/information, embodiments disclosed herein derive, or infer, insights therefrom that best address any user-submitted queries. Embodiments disclosed herein, further, provide transparency information detailing, for example, which input(s) and which technique(s) and/or algorithm(s) were employed to arrive at the derived/inferred insights. Moreover, embodiments disclosed herein enable users to make adjustments to (e.g.

Abstract: A method and system for intent-driven adaptive learning delivery. Intent-driven adaptive learning delivery may entail the conveyance of a learning curriculum surrounding a learning topic and a learning intent reflecting the reason for pursuing the learning topic. A learning curriculum, in turn, may generally refer to an ordered (or sequenced) manifest of learning materials and/or content that may progressively advance user proficiency in at least the learning topic. Existing systems or solutions offering learning curriculums today advance individual skills based on inferred educational goals for, and provide a static presentation of materials/content to, any given user. As an improvement over said existing systems/solutions, embodiments disclosed herein provide learning curriculums accounting for user learning preferences, the sought learning intent, and the user talent information.

Abstract: A method and system for metadata-based feature expansion for automated machine learning processes. In machine learning, feature selection—or a method through model inputs are reduced in dimensionality by retaining the relevant features, while also discarding the noise, thereof—often plays a pivotal role in the availability of algorithm(s) to derive model(s) from, the optimizing of said model(s) through training and/or testing, and, ultimately, the reaching of acceptable performance thresholds evaluating said model(s), which leads to their implementation in solving real-world problems. Embodiments disclosed herein, accordingly, leverage captured dataset metadata, as well as graph techniques, to identify and suggest one or more supplemental features distinct from original features for, and yet relevant to, the real-world problem and any machine learning models being evaluated.

Abstract: A method and system for dynamic questioning intelligence. Every year, conferences are attended by droves of participants, where most of those that attend reflect some interest and/or involvement in the theme(s) on which said conferences may be centered. Further, as these participants return from attending said conferences, some are expected to report on their respective experiences and observations. Existing systems/solutions directed to facilitating the capture of a participant's said experiences and observations often, if not always, provide pre-canned questions to gather pre-determined types of answers, which ultimately limit the potential knowledge and value that the participant could bring back to their respective organization.

Abstract: A method and system for crowd-sourced research relevance tracking and analytics. Through crowd-sourcing, any number of tasks, information, and/or inputs may be obtained by way of enlisting the services or activities of multiple individuals (i.e., a crowd). Further, the viewpoints of a crowd can often be a strong measure or indicator of current and/or future relevance within various realms such as events, social interactions, research, technologies, markets, and so forth. Embodiments disclosed herein, accordingly, implement a form of crowd-sourcing whereby the online content consumption of subject matter experts may be tracked, captured, and subsequently analyzed to predict or identify emergent trends relevant to their respective knowledge domain(s).

Abstract: A method and system for the prediction of and support readiness for future business intents. A current business intent may generally refer to information, respective to an organization user, which may pertain to or describe any historical and/or up-to-date engagement(s) of the organization user within and/or outside their organization. Said information may be reflected through a set of business intent parameters. A future business intent may specify predictions outlining a possible or potential evolution in one or more of said business intent parameters.

Abstract: A method and system metadata-based query rewriting. Query rewriting may generally refer to the automatic transformation of search queries in order to better represent the intent of the searcher (i.e., reason(s) motivating the searcher to conduct any search query). Embodiments disclosed herein, more specifically, leverage captured asset metadata, for assets hosted across various internal and external data sources, as well as graph techniques, to provide search query results that more closely match the said intent of the searcher. The graph techniques lead to the identification of adjacent areas and topics to the original search query, thereby enhancing the recall and/or precision of any returned search query results.

Abstract: A method and system for insight value assessments using post-insight feedback. Any insight may be defined as a finding (or more broadly, as useful knowledge) gained through data analytics or, more precisely, through the discovery of patterns and/or relationships amongst any given assortment of data/information. Further, the re-use (or plagiarism) of any insight, within and/or outside an organization, is commonplace. For insight inference models dependent on diverse information to achieve high inference accuracy, the re-use of any same insight, across numerous data/information assets, tends to create bias(es) affecting the inferred insight(s). In addition, the insight value for any insight is often lost once said insight leaves a controlled ecosystem. Embodiments disclosed herein, accordingly, implement a framework through which a lifecycle of any insight may be traced in order to address at least the aforementioned issues.

Abstract: A method and system for learning acceleration using insight-assisted introductions. More often than not, and still overwhelmingly unresolved due to difficulty in its identification, the duplication of work and effort, across large organizations, tend to grossly contribute to the many inefficiencies afflicting said organizations. One approach to minimizing, if not eliminating, this dilemma may be through the encouragement of collaborations. Finding the appropriate talent(s) within a large organization, however, to pursue said collaborations poses yet another hindrance. Embodiments disclosed herein, accordingly, leverage captured user metadata, as well as graph techniques, to identify organization personnel best suited for collaborations involving at least partially overlapping subject matter.

Abstract: A method and system for metadata-based learning curriculum generation. A learning curriculum may generally refer to an ordered (or sequenced) manifest of learning materials and/or content that may progressively advance user proficiency in at least a learning topic of interest. Existing systems or solutions offering learning curriculums today rely on the availability of static assets supported by static metadata descriptive thereof. As an improvement over said existing systems/solutions, embodiments disclosed herein enable newly introduced and ingested information, from across various data sources, to dynamically update the metadata and, therefore, dynamically update any learning curriculum(s) contingent on said metadata. Moreover, accessibility or inaccessibility to any given asset (e.g., learning material and/or content) may depend on the access authority granted to the user(s) seeking said given asset.

Abstract: A method and system for insight gap recommendations. Research can be defined as the studious and systematic investigation of a topic or topics, and may often entail extensive, non-trivial (and sometimes, costly) efforts and activities. By enduring research, organizations and individuals alike can gain insights, accolades, and/or profits. The issue with research, as it stands today, however, is its oversaturation—that is, a lot has been accomplished—and, accordingly, novel avenues through which to identify research niches sparsely, or even yet to be, pursued are fervently sought. Embodiments disclosed herein, therefore, leverage captured asset metadata, as well as graph techniques, to isolate and recommend gap research subareas to consider. Furthermore, captured user metadata may be leveraged in order to select or suggest an individual, or individuals, best-suited to pursuing any recommended gap research subareas.

Abstract: A method and system for insight creation transparency. Explainable artificial intelligence, in recent years, has become synonymous with a framework through which users, relying on machine learning models for various applications, may come to trust the result(s) outputted by said models through better comprehension of the mechanisms leading to said result(s). Leveraging a vast database of metadata for a plethora of unstructured and structured data/information, embodiments disclosed herein derive, or infer, insights therefrom that best address any user-submitted queries. Embodiments disclosed herein, further, provide transparency information detailing, for example, which input(s) and which technique(s) and/or algorithm(s) were employed to arrive at the derived/inferred insights.