Abstract: This invention provides the ability for an inventor to determine the probability of their invention being granted a patent. The invention disclosure and/or any type of patent document is classified before, during or after being filed by using any of the existing classification systems (e.g., CPC codes) or others such as a new and novel business vernacular classification system (as described as part of this disclosure). This newly created invention persona (classified document) is then compared to the existing patent database to discover granted patents that are similar and may be compared to other public and non-public databases to discover prior art. An analysis is then performed using various algorithms pertaining to the similarity of the classification by examining the branches, proximity, density etc. of the various assigned codes which will uncover the novelty, non-obviousness, and utility of the invention disclosure.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: The constrained watershed boundary (CWB), defined as a polygon containing all the flow direction grid cells with a surface flow distance less than a user prescribed threshold uses an algorithm that builds upon the HSM algorithm proposed and augments the data structure with a flow distance grid calculated directly from the original flow direction grid.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: A system provides the ability to predict the likelihood that applicants would accept admission into and matriculate at a given institution based on all or a portion of the natural-language text in their application. An embodiment evaluates an individual application to an institution and analyzes the natural-language text sections of the application to predict whether the applicant would or would not be likely to accept and matriculate at a specific institution.

Abstract: This invention provides the ability for an inventor to determine the probability of their invention being granted a patent. The invention disclosure and/or any type of patent document is classified before, during or after being filed by using any of the existing classification systems (e.g., CPC codes) or others such as a new and novel business vernacular classification system (as described as part of this disclosure). This newly created invention persona (classified document) is then compared to the existing patent database to discover granted patents that are similar and may be compared to other public and non-public databases to discover prior art. An analysis is then performed using various algorithms pertaining to the similarity of the classification by examining the branches, proximity, density etc. of the various assigned codes which will uncover the novelty, non-obviousness, and utility of the invention disclosure.

Abstract: A system and method for creating an intelligent agent model for providing an expert opinion on source data includes obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the received training input or prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: A method for performing hierarchical classification of data is disclosed. The method is being executed by at least one processing device. The method includes receiving input data for encoding into multiple channels. Further, the method includes extracting one or more features and one or more temporal structures corresponding to the input data. The method further includes identifying one or more feature dependencies in the input data. Further, the method includes combining the extracted one or more features corresponding to the input data, the extracted one or more temporal structures of the input data, and the identified one or more feature dependencies in the input data into a combined feature set. Thereafter, the method includes classifying the combined feature set into one or more output classes, and thereby performing the hierarchical classification of data.

Abstract: A system and method provides transparency in an artificial intelligence based model. A talent data block reduces bias influencers, an algorithm block coupled to the talent data block provides time-stamped data; and a decisions block coupled to the talent data and algorithm blocks allows auditing of decisions using the time-stamped data.

Abstract: The constrained watershed boundary (CWB), defined as a polygon containing all the flow direction grid cells with a surface flow distance less than a user prescribed threshold uses an algorithm that builds upon the HSM algorithm proposed and augments the data structure with a flow distance grid calculated directly from the original flow direction grid.

Abstract: A system and method for creating an intelligent agent model for providing an expert opinion on source data includes obtaining at least one first data source for training an intelligent agent model, receiving training input on the at least one first data source from an individual, extracting a set of relevant attributes of the at least one first data source based on the received training input, weighting the set of relevant attributes based on at least one of the received training input or prior received training input, forming at least one neural network that comprises the intelligent agent model based on the set of relevant attributes and the weighting, ranking or scoring at least one section of a second data source using the intelligent agent model, and outputting the ranking or scoring.

Abstract: A microprocessor executable method transforms unstructured natural language texts by way of a preprocessing pipeline into a structured data representation of the entities described in the original text. The structured data representation is conducive to further processing by machine methods. The transformation process is learned by a machine learned model trained to identify relevant text segments and disregard irrelevant text segments The resulting structured data representation is refined to more accurately represent the respective entities.

Abstract: The Location Design and Routing problem asks to find a subset of “depot” nodes and a spanning forest of a graph such that every connected component in the forest contains at least one depot. This problem arises in a number of both logistical and computer networking problems, for example, in selecting the number and location of distribution centers in vehicle routing networks. This problem is functionally equivalent to that of supernode selection in peer-to-peer networks. A distributed algorithm approximates a solution to this problem that runs in a logarithmic number of communication rounds with respect to the number of nodes (independent of the topology of the network), and, under assumptions on the embedding of the edge weights, whose solutions are within a factor of 2 of optimal.

Abstract: A microprocessor executable method and system for determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.

Abstract: The Location Design and Routing problem asks to find a subset of “depot” nodes and a spanning forest of a graph such that every connected component in the forest contains at least one depot. This problem arises in a number of both logistical and computer networking problems, for example, in selecting the number and location of distribution centers in vehicle routing networks. This problem is functionally equivalent to that of supernode selection in peer-to-peer networks. A distributed algorithm approximates a solution to this problem that runs in a logarithmic number of communication rounds with respect to the number of nodes (independent of the topology of the network), and, under assumptions on the embedding of the edge weights, whose solutions are within a factor of 2 of optimal.

Abstract: A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning may introduce. A new distance function defined on triangles instead of points is introduced. This technique offers a new way to control the feature decomposition process, which results in extraction of features that are more meaningful from an engineering viewpoint. The technique is computationally practical for use in indexing large models.

Abstract: A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning may introduce. A new distance function defined on triangles instead of points is introduced. This technique offers a new way to control the feature decomposition process, which results in extraction of features that are more meaningful from an engineering viewpoint. The technique is computationally practical for use in indexing large models.