Resumen de: US2023080439A1

According to an aspect of an embodiment, operations may include receiving an ML project stored in an ML corpus database. The operations may further include mutating a first ML pipeline, of a set of first ML pipelines associated with the received ML project, to determine a set of second ML pipelines. The mutation of the first ML pipeline may correspond to a substitution of a first ML model associated with the first ML pipeline with a second ML model associated with one of the set of predefined ML pipelines. The operations may further include selecting one or more ML pipelines from the set of second ML pipelines based on a performance score associated with each of the determined set of ML pipelines. The operations may further include augmenting the ML corpus database to include the selected one or more ML pipelines and the set of first ML pipeline.

Resumen de: US2023082729A1

A system and method for generating a document control item is described. The system accesses a text document and extracts a portion of the text document. The portion comprises some but not all of the text document. The system sends the portion of the text document to a trained machine learning model and, in response, receives a classification of the portion as demarking a section break. The system modifies the text document by inserting a document control item into the text document at a location of each portion classified as demarking a section break. The system stores the modified document.

Resumen de: US2023078246A1

Aspects of the disclosure are directed to a central management plane (CMP) of one or more processors for regulating streams of data from each of a number of network nodes of a distributed network. The one or more processors can train and deploy machine learning models across the network nodes, and the CMP can generate policies for each network node. The generated policies specify how a network node is to transmit data to the platform for further training or retraining of the deployed machine learning models. The CMP generates the policies using metric data characterizing each network node and respective streams of input data, and are generated based on a number of objectives, including model output quality of the deployed models, and operational cost to transmit and process streams of data across the distributed network.

Resumen de: US2023080773A1

Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Resumen de: US2023083846A1

Data Shapley is an approach to understand the role of data in a decision-making process. The present invention involves a process to connect Data Shapley to a data analytics and machine learning based decision-making environment through the use of utility functions. In the present invention a problem is structurally analyzed using machine learning and data analytics to determine structural trends. Data is then analyzed using Data Shapley to determine what additional information is needed to make a decision. This allows for the relevant data to be collected to estimate utility functions for participants. Data Shapley is then used again to decompose the decision-making process and look for trends in the process, and machine learning is applied to see if there are commonalities across the criteria in the decision-making process. After this, the decision-making process selects a strategy as the decision. If new information becomes available or an event occurs that makes a change of strategy necessary, then Data Shapley is used to guide the data acquisition and decision-making process. If no new information is available or an event does not occur, event occurrence is dynamically predicted using data analytics and Data Shapley proactively recommends what data streams to monitor and collect.

Resumen de: US2023084869A1

A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Resumen de: US2023082292A1

A method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.

Resumen de: US2023081540A1

Examples of methods for media classification are described herein. In some examples, a method includes analyzing text associated with media using a first machine learning model to produce a first result. In some examples, the method includes analyzing numerical metadata associated with the media using a second machine learning model to produce a second result. In some examples, the method includes inputting the first result and the second result to a third machine learning model to determine a classification of the media.

Resumen de: US2023075295A1

According to an aspect of an embodiment, operations may include receiving an ML project including a data-frame and an ML pipeline including a plurality of code statements associated with a plurality of features corresponding to the ML project. The operations may further include determining one or more atomic steps corresponding to the ML pipeline to determine an atomized ML pipeline. The operations may further include instrumenting the atomized ML pipeline to determine an instrumented ML pipeline including one or more operations corresponding to the ML project. The operations may further include executing the instrumented ML pipeline to capture one or more data-frame snapshots based on each of the one or more operations. The operations may further include constructing a feature provenance graph (FPG). The operations may further include identifying one or more discarded features, from the plurality of features corresponding to the ML project, based on the constructed FPG.

Resumen de: US2023075196A1

A disclosed example includes aggregating first performance results of computer-generated machine learning models to generate aggregated performance results, the first performance results based on a comparison of audience member demographic data to training results, the training results generated by the computer-generated machine learning models based on at least one of: (a) a composition of a household, (b) a type of first media, (c) a daypart during which the first media was accessed, or (d) a time at which the first media was accessed; selecting at least one of the computer-generated machine learning models based on a comparison of ones of the first performance results to the aggregated performance results; and applying the at least one of the computer-generated machine learning models to correct a computer-generated error in computer-collected audience measurement data, the computer-collected audience measurement data corresponding to accesses to second media.

Resumen de: US2023070833A1

A fraud detection model is used by a computer system to evaluate whether to grant a request to access a secure electronic resource. Before granting the request, the computer system evaluates the request using a multi-partite graph model generated using a plurality of previous requests. The multi-partite graph model includes at least a first set of nodes for sender accounts, a second set of nodes for recipient accounts, and a third set of nodes.

Resumen de: US2023072878A1

Methods, apparatus, and processor-readable storage media for automated topology-aware deep learning inference tuning are provided herein. An example computer-implemented method includes obtaining input information from one or more systems associated with a datacenter; detecting topological information associated with at least a portion of the systems by processing at least a portion of the input information, wherein the topological information is related to hardware topology; automatically selecting one or more of multiple hyperparameters of at least one deep learning model based on the detected topological information; determining a status of at least a portion of the detected topological information by processing, during an inference phase of the at least one deep learning model, the detected topological information and data from at least one systems-related database; and performing, in connection with at least a portion of the selected hyperparameters, one or more automated actions based on the determining.

Resumen de: AU2021332209A1

A model is trained through a hybrid machine learning process. In the hybrid machine landing process, an automatic machine learning process is performed on a dataset to generate a model for making a prediction. The automatic machine learning process uses a pipeline to train the model and makes decisions in the steps of the pipeline. After the model is trained through the automatic machine learning process, a representation of the pipeline is generated and presented to a user in a user interface. The user interface allows the user to modify at least some decision made in the automatic machine learning process. One or more modifications are received from the user through the user interface and are used to refine the trained model. The refined model is deployed to make the prediction based on new data.

Resumen de: US2023075812A1

A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.

Resumen de: US2023072862A1

A machine learning (ML) model publisher can, responsive to an indication that a ML model is ready for publication, generate a publication request form or page on a user device. The ML model publisher can be invoked from within a ML modeling application. Responsive to an instruction received through the publication request form or page, the ML model publisher can access a data structure in memory used in training the ML model and populate the publication request form or page with attributes required by the ML model to run. Responsive to activation of a single-click publication actuator, the ML model publisher can publish the ML model directly from the ML modeling application to a target computing system by providing, to the target computing system, a path to a repository location where the ML model is stored and information on the attributes required by the ML model to run.

Resumen de: US2023073197A1

A method of reducing a power consumption of wireless communication circuitry of an edge device according to one embodiment includes determining a delivery traffic indication map (DTIM) interval of a wireless access point communicatively coupled to the edge device via the wireless communication circuitry of the edge device and adjusting a wake-up interval of the wireless communication circuitry of the edge device based on the DTIM interval to reduce the power consumption of the wireless communication circuitry of the edge device.

Resumen de: US2023072966A1

Systems and methods for processing and using sensor data. The methods comprise: obtaining semantic labels assigned to data points; performing a supervised machine learning algorithm and an unsupervised machine learning algorithm to respectively generate a first confidence score and a second confidence score for each semantic label of said semantic labels, the first and second confidence scores each representing a degree of confidence that the semantic label is correctly assigned to a respective one of the data points; generating a final confidence score for each said semantic label based on the first and second confidence scores; selecting subsets of the data points based on the final confidence scores; and aggregating the data points of the subsets to produce an aggregate set of data points.

Resumen de: US2023075424A1

Systems and methods are disclosed for a decision tree processing system. A machine learning decision tree architecture, such as a Random Forest, can be very intense in computation and can require a large amount of memory. To account for such, the systems and methods herein can implement a hardware approach where the training for the decision trees can be performed in advance via firmware (or an algorithm implemented via any other software and processing system) and the hardware can implement a circuit to process the decision trees. In some examples, multiple decision trees may be processed in parallel. Also, a circuit can compute the best outcome for a decision tree based on a random feature and a pre-determined threshold for the random feature assigned to each node of the decision tree.

Resumen de: US2023074788A1

Machine learning to predict a layout type that each of a plurality of portions of a document appears in. This is done even though the computer-readable representation of the document does not contain information at the granularity of the prediction to be made that identifies which layout type that each of the plurality of document portions belongs in. For each of a plurality of the portions, the machine-learning system predicts the layout type that the respective portion appears in, and indexes the document using the predictions so as to result in a computer-readable index. The index represents a predicted layout type associated with each of the plurality of portions of the document. Thus, the index can be used to search based on position of a searched term within the document.

Resumen de: US2023074025A1

A plurality of initial machine learning models are determined based on a plurality of original features. The plurality of initial machine learning models are filtered by selecting a subset of the initial machine learning models as one or more surviving machine learning models. One or more evolved machine learning models are generated. At least one of the evolved machine learning models is based at least in part on one or more new features, which are based at least in part on a transformation of at least one of features of the one or more surviving machine learning models. Corresponding validation scores associated with the one or more evolved machine learning models and corresponding validation scores associated with the one or more surviving machine learning models are compared. At least one of the one or more evolved machine learning models or the one or more surviving machine learning models are selected as one or more new selected surviving machine learning models.

Resumen de: US2023074056A1

The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.

Resumen de: US2023066201A1

There is a need for more effective and efficient constrained-optimization-based operational load balancing. In one example, a method comprises determining constraint-satisfying operator-unit mapping arrangements that satisfy an operator unity constraint and an operator capacity constraint; for each constraint-satisfying operator-unit mapping arrangement, determining an arrangement utility measure; processing each arrangement utility measure using an optimization-based ensemble machine learning model that is configured to determine an optimal operator-unit mapping arrangement of the plurality of constraint-satisfying operator-unit mapping arrangements; and initiating the performance of one or more operational load balancing operations based on the optimal operator-unit mapping arrangement.

Resumen de: US2023064674A1

The present disclosure relates to a computer receiving a current training dataset. A first fraction of the training dataset comprises synthetic training data and a remaining second fraction of the training dataset comprising real-life training data. The real-life training data is user defined data and the synthetic training data is system defined data. A machine learning based engine is trained and may repeatedly be performed by using the current training dataset. In each iteration or a subset of the iterations, the training dataset is updated by adding real-life training data, thereby increasing the second fraction in the updated training dataset and reducing the first fraction of the synthetic training data.

Resumen de: US2023061280A1

Techniques for identifying a root cause of an operational result of a deterministic machine learning model are disclosed. A system applies a deterministic machine learning model to a set of data to generate an operational result, such as a prediction of a “fault” or “no-fault” in the system. The set of data includes signals from multiple different data sources, such as sensors. The system applies an abductive model, generated based on the deterministic machine learning model, to the operational result. The abductive model identifies a particular set of data sources that is associated with the root cause of the operational result. The system generates a human-understandable explanation for the operational result based on the identified root cause.