G06F16/909

Systems and methods for location representation using a discrete global grid system
11536853 · 2022-12-27 · ·

Embeddings of spherical triangles onto a planar surface permit locations on a sphere to be represented as cells on the planar surface. Embeddings can define paths based on one or more sets of great circles on the sphere which can in turn be based on rotations of an icosahedron about various axes. Distances between locations as well as locations themselves can be determined as integer values unlike conventional latitude/longitude based systems that require floating point arithmetic. Some locations correspond to cells on different paths defined by one or more sets of great circles. Distance between two locations can be estimated as a minimum of distances associated with the cell locations on the different paths. Methods for processing data defined with respect to an origin point in three-dimensional space include establishing a set of concentric spherical shells with the origin point as their origin and establishing a discrete global grid on each of the concentric spherical shells. Target locations are assigned in the three-dimensional space using a corresponding index on a spherical shell.

USING ON-LINE AND OFF-LINE PROJECTIONS TO CONTROL INFORMATION DELIVERY TO MOBILE DEVICES

A system for processing information requests associated with mobile devices comprises an evaluation module configured to determine at least one performance measure for each of a plurality of information documents using at least data in one or both of a requests database and events database. The at least one performance measure includes at least one of an impression-based performance measure, a click/call-based performance measure, and an off-line site-visit-based performance measure. The system further comprises an information server configured to select a first information document for transmitting to a first mobile device to fulfill a first request. The information server includes a volume control unit configured to derive an off-line site visit projection in response to the first document being selected based at least in part on an off-line site-visit-based performance measure and having been impressed on the first mobile device, and to adjust a budget associated with the first document using the off-line site visit projection.

USING ON-LINE AND OFF-LINE PROJECTIONS TO CONTROL INFORMATION DELIVERY TO MOBILE DEVICES

A system for processing information requests associated with mobile devices comprises an evaluation module configured to determine at least one performance measure for each of a plurality of information documents using at least data in one or both of a requests database and events database. The at least one performance measure includes at least one of an impression-based performance measure, a click/call-based performance measure, and an off-line site-visit-based performance measure. The system further comprises an information server configured to select a first information document for transmitting to a first mobile device to fulfill a first request. The information server includes a volume control unit configured to derive an off-line site visit projection in response to the first document being selected based at least in part on an off-line site-visit-based performance measure and having been impressed on the first mobile device, and to adjust a budget associated with the first document using the off-line site visit projection.

Personalized search based on account attributes

A system stores resources such as text articles, videos, and so forth for an organization. During operation, the system receives a query and provides a response. During initial use of the system, there is little or no historical data available to help determine which resource is most relevant to a particular query. In this “cold-start” situation, the system determines attributes associated with a user account of the user making the query. The query is used to search a data store and retrieve a set of resources based on a term match with the query and to find the resources which correspond to the attributes of the user account. This allows the system to provide simplified output that is more likely to be relevant to that particular user in the “cold-start” situation.

Personalized search based on account attributes

A system stores resources such as text articles, videos, and so forth for an organization. During operation, the system receives a query and provides a response. During initial use of the system, there is little or no historical data available to help determine which resource is most relevant to a particular query. In this “cold-start” situation, the system determines attributes associated with a user account of the user making the query. The query is used to search a data store and retrieve a set of resources based on a term match with the query and to find the resources which correspond to the attributes of the user account. This allows the system to provide simplified output that is more likely to be relevant to that particular user in the “cold-start” situation.

Forensic criminal investigation digital data intersection
11531711 · 2022-12-20 · ·

Systems and methods are disclosed for finding intersections between digital interactions of two users (or a user and specific data) based on two different digital interaction data sets. For example, a digital interaction database may include a first user's mobile phone records from a first mobile company and a second user's mobile phone records from a second mobile phone company. The first user's mobile phone records and the second user's mobile phone records may be organized differently, may have different data elements, may have different format styles, etc. Yet an intersection between the first user and the second user may be found by searching the first user's mobile phone records and/or the second user's mobile phone records and retrieving related digital interactions such as, for example, phone records showing a phone call or message between the first user and the second user.

Forensic criminal investigation digital data intersection
11531711 · 2022-12-20 · ·

Systems and methods are disclosed for finding intersections between digital interactions of two users (or a user and specific data) based on two different digital interaction data sets. For example, a digital interaction database may include a first user's mobile phone records from a first mobile company and a second user's mobile phone records from a second mobile phone company. The first user's mobile phone records and the second user's mobile phone records may be organized differently, may have different data elements, may have different format styles, etc. Yet an intersection between the first user and the second user may be found by searching the first user's mobile phone records and/or the second user's mobile phone records and retrieving related digital interactions such as, for example, phone records showing a phone call or message between the first user and the second user.

METHODS AND SYSTEMS FOR MOBILITY SOLUTION RECOMMENDATIONS USING GEOSPATIAL CLUSTERING

In an embodiment, recommending mobility solutions includes receiving a set of geospatial data corresponding to a geographic location, and generating a set of geospatial clusters based on the geographic location and the set of geospatial data, wherein each geospatial cluster of the set of geospatial clusters has a set of mobility solutions and a set of geographic regions. The method also includes receiving a set of profiling data corresponding to a set of users in each geospatial cluster, and generating a set of profile sub-clusters corresponding to each geographic region based on the set of profiling data. The method further includes identifying a set of met needs and a set of unmet needs of the set of users in each profile sub-cluster, and generating a mobility solution recommendation associated with a set of unmet needs of a set of users in a profile sub-cluster of a geospatial cluster.

Encoding knowledge graph entries with searchable geotemporal values for evaluating transitive geotemporal proximity of entity mentions

A controller, responsive to detecting geospatial and temporal information associated with a mention of an entity in a document, for converting the geospatial information into a specified geospatial format and the temporal information into a specified temporal format. The controller for computing prefix-based geospatial values for the converted geospatial information and prefix-based temporal values for the converted temporal information. The controller for encoding an entry in a knowledge graph for the mention of the entity from the document with the prefix-based geospatial values and the prefix-based temporal values, wherein each digit of the prefix-based geospatial values and the prefix-based temporal values in the knowledge graph that matches another one or more prefix-based geospatial values and another one or more prefix-based temporal values in another entry for another entity mention in the knowledge graph reflects a degree of granularity of geotemporal proximity of the entity and the another entity.

Encoding knowledge graph entries with searchable geotemporal values for evaluating transitive geotemporal proximity of entity mentions

A controller, responsive to detecting geospatial and temporal information associated with a mention of an entity in a document, for converting the geospatial information into a specified geospatial format and the temporal information into a specified temporal format. The controller for computing prefix-based geospatial values for the converted geospatial information and prefix-based temporal values for the converted temporal information. The controller for encoding an entry in a knowledge graph for the mention of the entity from the document with the prefix-based geospatial values and the prefix-based temporal values, wherein each digit of the prefix-based geospatial values and the prefix-based temporal values in the knowledge graph that matches another one or more prefix-based geospatial values and another one or more prefix-based temporal values in another entry for another entity mention in the knowledge graph reflects a degree of granularity of geotemporal proximity of the entity and the another entity.