Abstract: In an illustrative embodiment, an automated system engineers customized feature vectors from geospatial information system (GIS) metadata. The system may include computing systems and devices for extracting metadata for GIS features located within a predetermined distance of a property from a GIS map file and storing the extracted GIS features within a feature vector. The system can augment each of the extracted GIS features with amplifying data features extracted from external data sources. The system can calculate a distance between the property and each extracted GIS feature, which establishes a relationship between the property and each GIS feature and associated amplifying data features. Amounts of correlation between each of the extracted GIS features and associated amplifying data features within the feature vector and a market assessment of the property location can be identified using a data model trained with a data set customized to characteristics of the property.

Abstract: This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate data associated with a plurality of entities, the aggregated data reflecting one or more relationships between two or more of the plurality of entities. Attribute data identifying loan amounts, property values, and appraisal sources may be extracted from the aggregated data. A knowledge graph data structure may be generated having a plurality of subject notes corresponding to the extracted attribute information. Statistical distributions of attributes associated with one or more appraisal sources may be generated and an anomaly in a first statistical distribution may be detected based on a comparison of the first statistical distribution with a second statistical distribution.

Abstract: This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate, from the at least one database, entity data for a plurality of homes. Attribute information identifying geographic locations of the plurality of homes and relationships between pairs of the plurality of homes is extracted from the aggregated data. Knowledge graph data structures are populated with the extracted attribute information. A home knowledge graph is built, having nodes corresponding to the plurality of homes and edges corresponding to the identified relationships. A hierarchical cluster tree structure of the plurality of homes is outputted, wherein levels of the hierarchical cluster tree correspond to clusters of homes determined based in part on the knowledge graph edges.

Abstract: In an illustrative embodiment, an automated system engineers customized feature vectors from geospatial information system (GIS) metadata. The system may include computing systems and devices for extracting metadata for GIS features located within a predetermined distance of a property from a GIS map file and storing the extracted GIS features within a feature vector. The system can augment each of the extracted GIS features with amplifying data features extracted from external data sources. The system can calculate a distance between the property and each extracted GIS feature, which establishes a relationship between the property and each GIS feature and associated amplifying data features. Amounts of correlation between each of the extracted GIS features and associated amplifying data features within the feature vector and a market assessment of the property location can be identified using a data model trained with a data set customized to characteristics of the property.

Abstract: This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate, from the at least one database, entity data for a plurality of homes. Attribute information identifying geographic locations of the plurality of homes and relationships between pairs of the plurality of homes is extracted from the aggregated data. Knowledge graph data structures are populated with the extracted attribute information. A home knowledge graph is built, having nodes corresponding to the plurality of homes and edges corresponding to the identified relationships. A hierarchical cluster tree structure of the plurality of homes is outputted, wherein levels of the hierarchical cluster tree correspond to clusters of homes determined based in part on the knowledge graph edges.

Abstract: This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate, from the at least one database, entity data for a plurality of homes. Attribute information identifying geographic locations of the plurality of homes and relationships between pairs of the plurality of homes is extracted from the aggregated data. Knowledge graph data structures are populated with the extracted attribute information. A home knowledge graph is built, having nodes corresponding to the plurality of homes and edges corresponding to the identified relationships. A hierarchical cluster tree structure of the plurality of homes is outputted, wherein levels of the hierarchical cluster tree correspond to clusters of homes determined based in part on the knowledge graph edges.

Abstract: Apparatus and methods associated with controlling seizure activity with electrical stimulation that either suppress axonal conduction between brain structures and/or that generate a desired response in a targeted neuronal pool are described. One example apparatus includes an implantable stimulating electrode that provides an electrical stimulus to fiber tracts of the hippocampal commissure of the brain of a subject. The stimulus may be a high frequency structure that prevents communication of signals associated with an epileptic episode and/or prevents seizure activity in a target nucleus. The example apparatus may also include a detection logic that detects specific electrical activity in the central nervous system that identifies that an epileptic episode is imminent. The example apparatus includes a pacing system to selectively configure and apply the electrical stimulus to fiber tracts of the hippocampal commissure of the brain.

Abstract: Apparatus and methods associated with controlling seizure activity with electrical stimulation that either suppress axonal conduction between brain structures and/or that generate a desired response in a targeted neuronal pool are described. One example apparatus includes an implantable stimulating electrode that provides an electrical stimulus to fiber tracts of the hippocampal commissure of the brain of a subject. The stimulus may be a high frequency structure that prevents communication of signals associated with an epileptic episode and/or prevents seizure activity in a target nucleus. The example apparatus may also include a detection logic that detects specific electrical activity in the central nervous system that identifies that an epileptic episode is imminent. The example apparatus includes a pacing system to selectively configure and apply the electrical stimulus to fiber tracts of the hippocampal commissure of the brain.