Abstract: Examples of a process optimization system are provided. The system may obtain a query from a user and process data associated with the query from a plurality of data sources. The system may identify a plurality of factors and a target variable from the process data. The system may identify a factor range for the plurality of factors. The system may deconstruct the factor range to identify a plurality of data partitions. The system may identify a data pruning activator based on the plurality of data partitions and a preponderant data partition therefrom. The system may identify a plurality of clusters associated with the preponderant data partition. The system may identify a preponderant cluster from the plurality of clusters. The system may identify a confidence score associated with the preponderant cluster. The system may generate a process optimization result based on the preponderant cluster and the confidence score.

Abstract: Improved techniques for disaster recover within storage area networks are disclosed. Embodiments include replicating a LIF of a primary cluster on a secondary cluster. LIF configuration information is extracted from the primary cluster. A peer node from a secondary cluster is located. One or more ports are located on the located peer node that match a connectivity of the LIF from the primary cluster. One or more ports are identified based upon one or more filtering criteria to generate a candidate port list. A port from the candidate port list is selected based at least upon a load of the port. Other embodiments are described and claimed.

Abstract: A method for extracting information from a dataset, e.g., a document, includes: receiving the dataset at an information handling device, optionally, extracting, via optical character recognition implemented by a processor of the information handling device, textual information associated with the dataset, and classifying the dataset into one of a plurality of classes. Classifying the dataset may include computing a similarity score for each of the plurality of classes for each of a plurality of window regions of the dataset, calculating a subset of highest similarity scores for each of the plurality of classes for each of the plurality of window regions, determining overall similarity scores for each of the plurality of classes, and classifying the dataset as corresponding to a class with a highest overall similarity score.

Abstract: The disclosed technology relates to a system configured to receive a set of operations configured to converge a server state and a file system state, wherein the server state is for content items stored by a content management system and the file system state is for the content items stored on a client device. The system is further to detect a violation of a rule by an operation in the set of operations, identify resolution actions for the violation of the rule, and perform the resolution actions.

Abstract: A method comprises receiving, by a server computer, a request message comprising at least a credential from a client device. The server computer can hash the credential to form an altered value. The server computer can then determine whether or not the altered value matches one of the hashed values stored in the database. If the altered value matches a matched hashed value, the server computer can determine a range of a plurality of ranges. The range can be associated with the matched hashed value. The server computer can then determine a data item associated with the range. The server computer can provide the data item to the client device.

Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: Methods, systems, and computer-readable media for identifying users, records, and/or devices using anonymous source data records associated with consumer activity are described. In general, the consumer activity may relate to consumer interactions with devices and/or content or data through devices. The source data records may be structured, such as associating the source data records with a sequential timeline. Behavioral markers may be generated by the matching system and associated with the source data records. Each behavioral marker may be analyzed across all of the source data records to determine the effectiveness of the behavioral marker to identify unique source data records. One or more of the behavioral markers may be used to generate a behavioral fingerprint. A behavioral fingerprint may be used to identify unique records, devices, and/or users, including identification based on different sets of source data records.

Abstract: Methods, systems, and computer readable media related to generating a combined search query based on search parameters of a current search query of a user and search parameters of one or more previously submitted search quer(ies) of the user that are determined to be of the same line of inquiry as the current search query. Two or more search queries may be determined to share a line of inquiry when it is determined that they are within a threshold level of semantic similarity to one another. Once a shared line of inquiry has been identified and a combined search query generated, users may interact with the search parameters and/or the search results to update the search parameters of the combined search query.

Abstract: Optimizing file access includes a process for identifying a file access event for a first accessed file, and incrementing a first access counter in an access list in a memory, which also includes access counters for other accessed files. The process further includes exporting the first access counter to a performance monitoring dashboard, or exporting to a storage allocator and, based on the value, moving the first accessed file between a first storage and a second storage. The process also includes determining whether the value of the first access counter meets a first threshold, or a sum of values of the access counters for the other accessed files meets a second threshold. Based on meeting the first threshold or meeting the second threshold, the process includes persisting the access counters on a storage media. The access counters also provide security monitoring (e.g., identifying excessive file access).

Abstract: The present invention is a system and method for agricultural management-zone delineation to be done over broad geographic extents without overly-localized field-specific data. The instant innovation guides precision agricultural sampling and management by delineating enhanced management zones based upon remote sensing and artificial intelligence and combining the two with data derived from an existing countrywide soil survey database. In an embodiment, the instant innovation uses artificial intelligence from multiple sources to provide granular zone detail. Output of the present innovation can be aggregated to produce management zone sizes that have a level of uncertainty compatible with the needs of the customer-farmer and implementable given the capabilities of available equipment.

Abstract: Systems and methods for generating metadata for at least one learning resource are provided. The system includes at least one data storage device storing a plurality of predefined metadata templates, each of the metadata templates having a plurality of metadata fields and a processor in data communication with the at least one data storage device. The processor is configured to receive the at least one learning resource, the at least one learning resource including an electronic file having a learning resource type and content data, select a metadata template from the predefined metadata templates based upon the learning resource type of the at least one learning resource, determine a topic associated with the learning resource, and customize the selected metadata template by adding one or more predefined metadata fields associated with the topic of the learning resource.

Abstract: A computer-implemented method includes determining a set of identigens for each word of patent claim elements to produce sets of identigens and interpreting pairs of adjacent identigens of adjacent sets of identigens to determine a most likely meaning of the claim elements and produce a claim entigen group. The method further includes determining a level of match between the claim entigen group and a comparison matter entigen group. When the level of match between the claim entigen group and the comparison matter entigen group is greater than a threshold level, the method further includes indicating potential utilization of the claim by the comparison matter or potential prior art status of the comparison matter.

Abstract: A computer-implemented method of performing queries using Artificial Intelligence (AI) database embeddings includes the operations of generating a plurality of vector embeddings describing a training data from a database for training a machine learning model. A test vector embedding is generated from the plurality of vector embeddings based on training data for unseen data from one or more rows of the database. One or more vectors from the plurality of vector embeddings describing the training data that are a closest match to the test vector embedding are identified. A task is determined based upon the unseen data. The determined task is performed using the trained machine learning model.

Abstract: A method of clustering data generated by an unknown model, the method including accessing the data, wherein the data includes a prediction target and a confidence, extracting a data group with high prediction confidence from the data, wherein the data group comprises a plurality of data cases, and where each of the data cases is described by a plurality of predictors, identifying high rank predictors of each the data cases in the data group, transforming the data group into a transformed data group including only the high rank predictors for each of the data cases, wherein the high rank predictors are ranked within each of the data cases included in the transformed data group, clustering the transformed data group to generate clusters, and profiling the clusters to extract an insight about the unknown model.

Abstract: A cloud-based industrial data services (IDS) architecture leverage smart tags, asset models, and data service applications to facilitate secure transaction and exchange of contextualized factory data between different parties as part of a combined technology and commerce platform, or to perform provide asset owners with insights into operation of their industrial assets. The IDS platform supports a set of services that connect providers of smart industrial devices to plant floor and systems owned by the end users of these devices. The cloud-based platform allows asset providers to publish data service applications for purchase and use by end users of their assets, and allows equipment owners to control remote access to selected sets of their industrial data via the cloud platform.

Abstract: A computer device, a non-transitory computer storage medium, and a computer-implemented method of pattern recognition utilizing an elastic clustering algorithm. A sequence of input datapoints are assigned to a particular cluster of K clusters based on a distance from a centroid k representing a center of the particular cluster. The centroid k in each of the K clusters is shifted from a first position to a second position closer than the first position from the sequence of input datapoints. A location of the centroid k in each of the K clusters is relaxed from the second position toward an equilibrium point in the particular cluster of the K clusters. The relaxing of the location of the centroid k occurs according to an elasticity pull factor based on a distance between the centroid k of the particular cluster at a time t.

Abstract: A computer-implemented method includes determining a set of identigens for each word of a query to produce sets of identigens and interpreting pairs of adjacent identigens of adjacent sets of identigens to determine a most likely meaning of the query and produce a query entigen group. The method further includes identifying entigens of a knowledge database that compare favorably to the query entigen group to produce an initial entigen group and determining whether the initial entigen group requires one or more future available entigens to provide a complete response to the query. When required, the method further includes creating the one or more future available entigens based on obtained targeted data, adding the one or more future available entigens to the initial entigen group to produce an updated entigen group, and outputting the complete response based on the updated entigen group.

Abstract: An approach is provided for combining location data sources. The approach, for instance, involves generating a first context-aware vector representation of a first location entity in a first data source and a second context-aware vector representation of a second location entity in a second data source. The approach also comprises processing the first context-aware vector representation and the second context-aware vector representation using a machine learning model to perform a classification of the first location entity as the same as the second location entity. The approach further comprises combining the first data source and the data source into a new database based on the classification and providing the new database as an output.

Abstract: The present disclosure provides a method, system, and device for security object synchronization at multiple nodes of an active-active environment. To illustrate, a source node may generate a corresponding security object sync request for each of multiple target nodes. The source node may send the security object sync request to the target nodes via a source queue and, for each target node, a corresponding distribution queue. A distribution queue may be closed based on an acknowledgement received from a corresponding target node, after a time period, or after a number of transmission attempts. A synchronization log may be maintained to indicate which security object sync requests have been delivered to which target nodes. In some implementations, the source node and the target nodes are part of an active-active environment that may be synchronized in time so the nodes resolve conflicts between received security object updates initiated from two different nodes.

Abstract: Embodiments of the present disclosure provide for improved mapping from sequential semi-structured data of a first custom data format to a second custom data format. The improved mechanism for mapping between custom data formats significantly reducing the amount of manual effort otherwise used for creating mapping rules. Example embodiments utilize a state extractor model that extracts an untrained modified finite state machine embodied by a skeleton set of extracted states from particular sequential semi-structured input data, and generate a trained modified finite state machine that maps the first custom data format to the second custom data format based at least in part on the untrained modified finite state machine, sequential semi-structured input data of the first custom data format, and database structured output data of the second custom data format. The trained modified finite state machine may be used for subsequent processing data of the first custom data format.