Abstract: Systems and methods are provided for leveraging data lineage information of datasets to estimate the merit (e.g., worth, value, or importance) of these datasets in performing a future task. For example, the dataset may have been historically applied to train an artificial intelligence (AI) model to perform a task (e.g., an artificial intelligence (AI) task like image recognition or object prediction/detection). The learned merit of the dataset in performing the task may be used as input to train a regressor model, and the trained regressor model can be used to predict future merit of the dataset characteristics in performing another task. The predicted future merit of the dataset characteristics can be mapped to the merit of the dataset in performing another task. The future merit may be related to the same dataset or a different dataset, based on the shared characteristics of the datasets.

Abstract: Systems and methods are provided for completing a knowledge graph associated with an artificial intelligence pipeline. For example, the system may determine a missing data element in a knowledge graph, initiate an extraction process from a manuscript file associated with identifying the missing data element in the knowledge graph, initiate an intent identification process of the manuscript file that generates a label for a cluster of terms of the manuscript file, provide the cluster of terms from the manuscript file and the label as input to a large language model (LLM), and iteratively update the knowledge graph with output from the LLM.

Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.

Abstract: A system and method of managing a network with assets are described. The method includes generating a directed graph with each of the assets represented as a node, determining individual failure probability of each node, computing downstream failure probability of each node according to an arrangement of the nodes in the directed graph, computing upstream failure probability of each node according to the arrangement of the nodes in directed graph, and computing network failure probability for each node based on the corresponding individual failure probability, the downstream failure probability, and the upstream failure probability. Managing the network is based on the network failure probability of the assets.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: The present disclosure is directed to resolving customer service issues associated with online shopping. When a customer requiring assistance regarding price matching the price of an item in the customer's active shopping cart or a previously ordered item to a competitor price, the customer contacts the customer service agent. The customer service agent, using the disclosed customer service assistance system, may use one or more administrative options otherwise unavailable to the customer to execute a modification in the price of the item or issue a concession for the item. The customer service assistance system may automatically collect and display competitor pricing, stock availability, selling entity information to assist the customer service agent in resolving the customer's price matching request quickly and efficiently.

Abstract: A system and method for generating question-answer pairs is disclosed. The system and method can receive a document. A sentence and/or a further sentence in the document may be identified. A syntactic map for the sentence and/or the further sentence may be generated. Noun phrases and prepositional phrases may be identified based on the syntactic map. Sentence level questions may be generated based on phrases identified using natural language processing (NLP) techniques. Document level questions can also be generated based on syntactic maps generated and NLP techniques.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.

Abstract: A system and method to schedule inspections for a plurality of assets at a corresponding plurality of outdoor locations are described. The method includes retrieving location attributes of each of the plurality of assets, retrieving terrestrial geotagged images that include one or more of the plurality of assets, obtaining parameters associated with each of the plurality of assets based on information from the images, and determining risk factors associated with each of the plurality of assets based on the associated parameters and a failure mapping. The method also includes determining a risk score for each of the plurality of assets based on the corresponding risk factors, and scheduling inspections of the plurality of assets based on the risk score associated with each of the plurality of assets.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: A system and method of managing a network that includes assets are described. The method includes modeling the network as a directed graph with each of the assets represented as a node and determining alternative paths to each node from each available corresponding source of the node. The method also includes computing upstream robustness of each node, computing upstream robustness of the network, and computing downstream criticality of each node. Managing the network and each asset of the network is based on the upstream robustness and the downstream criticality of each node.

Abstract: Devices and methods are provided for remote scheduling of recorded content for digital video recorders. The device may determine a first channel, a first date, and a first time at which a first program will be presented. The device may determine a first recording schedule for a remote digital video recording (DVR) device, wherein the first recording schedule includes the first program at the first channel, the first date, and the first time. The device may receive a synchronization notification that the first recording schedule has been modified at the remote DVR device to include a second program to be recorded. The device may determine a second recording schedule, wherein the second recording schedule includes the second program at the second time. The device may send the second recording schedule to the remote DVR device.

Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: A method for creating a spatio-temporal schedule includes receiving a set of available work crews and a set of workorders to be scheduled, sequencing the set of workorders, wherein each workorder of the set of workorders is associated with a geographic location, a start time and an end time, and iterating through the set of available work crews, assigning a plurality of workorders of the set of workorders fully allocating a current crew before selecting a next crew, until all work crews are fully allocated and/or all workorders are assigned, wherein the assignment of workorders for a given work crew generates a route comprising the plurality of workorders.