Machine learning

SYSTEM AND METHOD OF PREDICTION OF PRESENCE/ABSENCE FOR THE THREE GENERA OF LARVAE (CULEX, AEDES, ANOPHELES) IN BREEDING SITES

Resumen de: WO2024100428A1

System and method for determining the presence/absence of larvae for the three genera (Culex, Aedes, Anopheles) per breeding site, using a cloud server for processing the collected inspection and geospatial data and applying Machine Learning (RF/ XGBoost) algorithms.

SYSTEM AND METHOD FOR BUILDING MACHINE LEARNING OR DEEP LEARNING DATA SETS FOR RECOGNIZING LABELS ON ITEMS

Resumen de: US2024158123A1

This application relates to a method and a system for building machine learning or deep learning data sets for automatically recognizing labels on items. The system may include an optical scanner configured to capture an item including one or more labels provided thereon, the item captured a plurality of times at different positions with respect to the optical scanner. The system may further include a robotic arm on which the item is disposed, the robotic arm configured to rotate the item horizontally and/or vertically such that the one or more labels of the item are captured by the optical scanner at different positions with respect to the optical scanner. The system may include a database configured to store the captured images.

SYSTEMS AND METHODS FOR ENHANCED MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE MAP GENERATION AND USER INTERFACE PRESENTATION

Resumen de: WO2024102449A1

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learningbased models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

SYSTEM CONFIGURED TO DETECT AND BLOCK THE DISTRIBUTION OF MALICIOUS CONTENT THAT IS ATTRIBUTABLE TO AN ENTITY

Resumen de: WO2024102310A1

An online portal receives digital content from a user device. The online portal is communicably coupled to a computer server hosting an online media service in the public or non-public domain. The user device is associated with an online account on the online media service. Based on the digital content, at least one requirement associated with the online account is identified. One or more respondent services are determined satisfy the requirement. By each respondent service, the digital content is processed using a respective machine learning model trained, based on user-attributable content, to generate a respondent evaluation. A quorum of respondent evaluations is generated. The quorum of respondent evaluations is determined to achieve a respondent consensus. Responsive to determining that the respondent consensus satisfies an approval condition, the digital content is sent from the online portal to the computer server for posting the digital content on the online media service.

MACHINE LEARNING MODEL REGISTRY

Resumen de: US2024161018A1

Systems and methods to utilize a machine learning model registry are described. The system deploys a first version of a machine learning model and a first version of an access module to server machines. Each of the server machines utilizes the model and the access module to provide a prediction service. The system retrains the machine learning model to generate a second version. The system performs an acceptance test of the second version of the machine learning model to identify it as deployable. The system promotes the second version of the machine learning model by identifying the first version of the access module as being interoperable with the second version of the machine learning model and by automatically deploying the first version of the access module and the second version of the machine learning model to the plurality of server machines to provide the prediction service.

ENTERPRISE DOCUMENT CLASSIFICATION

Resumen de: US2024160768A1

A collection of documents or other files and the like within an enterprise network are labelled according to an enterprise document classification scheme, and then a recognition model such as a neural network or other machine learning model can be used to automatically label other files throughout the enterprise network. In this manner, documents and the like throughout an enterprise can be automatically identified and managed according to features such as confidentiality, sensitivity, security risk, business value, and so forth.

ADD-ON TO A MACHINE LEARNING MODEL FOR INTERPRETATION THEREOF

Resumen de: US2024161005A1

There is provided an add-on component configured for: receiving features and an outcome of an ML model, wherein at least two of the features are correlated by a covariance value above a threshold, computing, for each of the features, a respective contribution coefficient denoting an initial value, identifying a certain feature with highest contribution coefficient indicative of a relative contribution to the outcome, computing, for a subset of features that are non-independent with respect to the certain feature, a respective subsequent value for the contribution coefficient by adjusting the respective initial value according to a covariance with the contribution coefficient of the certain feature, iterating the identifying and the computing to compute a subsequent certain feature with highest contribution coefficient for the remaining features, and re-adjusting the respective contributing coefficient according to a covariance with the contribution coefficient of the subsequent certain feature, and providing the respective contribution coefficient(s).

PROCESS MAPPING AND MONITORING USING ARTIFICIAL INTELLIGENCE

Resumen de: US2024160550A1

The disclosure describes a system for the advanced delivery of information. In some embodiments, the system creates a display in response to an alarm. In some embodiments, the information on the display is a function of attribute mapping and/or analysis performed by the system. The system uses one or more of manual links, statistical analysis, correlations, maintence data, and/or historical data as tools during determination of what to display according to some embodiments. In some embodiments, the system uses one or more of these tools in conjunction with one or more of process simulators, artificial intelligence, machine learning, and/or real process feedback in the analysis to determine what to display to a user during an emergency and/or an anomalous event.

USING MACHINE LEARNING FOR ICONOGRAPHY RECOMMENDATIONS

Resumen de: US2024161367A1

In some implementations, a recommendation system may input text into a machine learning model that was trained using input specific to an organization associated with the text and was refined using input specific to a portion of the organization. The recommendation system may receive, from the machine learning model, a recommendation indicating one or more visual components, stored in a database associated with the organization, to use with the text. The machine learning model may use natural language processing and sentiment detection to parse the text. Accordingly, the recommendation system may receive the one or more visual components from the database and generate an initial draft including the text and the one or more visual components.

NEXT-GENERATION MOLECULAR PROFILING

Resumen de: EP4369356A2

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).

GENERATING ENCODED TEXT BASED ON SPOKEN UTTERANCES USING MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2024152684A1

Systems and methods for generating encoded text representations of spoken utterances are disclosed. Audio data is received for a spoken utterance and analyzed to identify a nonverbal characteristic, such as a sentiment, a speaking rate, or a volume. An encoded text representation of the spoken utterance is generated, comprising a text transcription and a visual representation of the nonverbal characteristic. The visual representation comprises a geometric element, such as a graph or shape, or a variation in a text attribute, such as font, font size, or color. Analysis of the audio data and/or generation of the encoded text representation can be performed using machine learning.

ARTIFICIAL-INTELLIGENCE-ASSISTED CONSTRUCTION OF INTEGRATION PROCESSES

Resumen de: US2024152811A1

A substantial learning curve is required to construct integration processes in an integration platform. This can make it difficult for novice users to construct effective integration processes, and for expert users to construct integration processes quickly and efficiently. Accordingly, embodiments for building and operating a model to predict next steps, during construction of an integration process via a graphical user interface, are disclosed. The model may comprise a Markov chain, prediction tree, or an artificial neural network (e.g., graph neural network, recurrent neural network, etc.) or other machine-learning model that predicts a next step based on a current sequence of steps. In addition, the graphical user interface may display the suggested next steps according to a priority (e.g., defined by confidence values associated with each step).

MACHINE-LEARNING SYSTEM FOR INCOMING CALL DRIVER PREDICTION

Resumen de: US2024155054A1

A method includes selecting a customer of a company; constructing a digital footprint of the selected customer. The method includes inputting the digital footprint to an artificial intelligence (AI) engine. The method includes obtaining one or more probability values from the AI engine based on the input digital footprint. The method includes selecting a call driver, from among a plurality of call drivers, as a predicted call driver. The method includes providing the predicted call driver to a call center associated with the company.

METHODS FOR MITIGATION OF ALGORITHMIC BIAS DISCRIMINATION, PROXY DISCRIMINATION AND DISPARATE IMPACT

Resumen de: US2024152818A1

A method is provided for debiasing machine learning models. The method includes obtaining (i) an initial model that is a trained and tree-based machine learning model and (ii) a minimum acceptable threshold accuracy, for (iii) one or more protected classes. The initial model demonstrates adverse impact on one or more protected classes. The method includes identifying branches of the initial model to prune, based on the branches' impact on one or more protected classes. The method includes applying a pruning algorithm to prune the branches of the initial model to generate one or more forest models, such that (i) predictive accuracy of the one or more forest models is above the minimum threshold accuracy, and (ii) the one or more forest models are less discriminatory than the initial mode.

MACHINE LEARNING MONITORING SYSTEMS AND METHODS

Resumen de: US2024152810A1

A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.

PROACTIVELY DETECTING AND PREDICTING POTENTIAL BREAKAGE OR SUPPORT ISSUES FOR IMPENDING CODE CHANGES

Resumen de: US2024152784A1

In some implementations, a regression prediction platform may obtain one or more feature sets related to an impending code change, wherein the one or more feature sets may include one or more features related to historical code quality for a developer associated with the impending code change or a quality of a development session associated with the impending code change. The regression prediction platform may provide the one or more feature sets to a machine learning model trained to predict a risk associated with deploying the impending code change based on a probability that deploying the impending code change will cause breakage after deployment and/or a probability that the impending code change will cause support issues after deployment. The regression prediction platform may generate one or more recommended actions related to the impending code change based on the risk associated with deploying the impending code change.

USING EMAIL HISTORY TO ESTIMATE CREDITWORTHINESS FOR APPLICANTS HAVING INSUFFICIENT CREDIT HISTORY

Resumen de: US2024152997A1

In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

AUTOMATIC ADJUSTMENT OF LIMITS BASED ON MACHINE LEARNING FORECASTING

Resumen de: AU2022304697A1

There are provided systems and methods for automatic adjustment of limits based on machine learning forecasting. An entity, such as company or other entity, may purchase items utilizing a payment instrument or card provided to the company by a credit provider system or entity. In order to provide proper underwriting for credit extensions, such as balances and limits of extendable credit, the credit provider system may utilize a forecasting machine learning (ML) model trained to predict a future global balance of funds or a likelihood of repayment of the extended credit limit. This may be based on information retrievable balances from a banking system and a staleness of this data. When the data is stale and has not been updated, the forecasted balance may have a wider range, and thus risk factors may designate less risky and lower limits.

TECHNIQUES FOR VALIDATING FEATURES FOR MACHINE LEARNING MODELS

Resumen de: US2023004856A1

A system and method for machine learning features validation. A method includes: performing statistical testing on a plurality of pairs of features, each pair of features including a test feature of a plurality of test features extracted from a first data set and a corresponding training feature extracted from a second data set during a training phase for a machine learning model, wherein the statistical testing is performed under a null hypothesis that the first data set and the second data set are drawn from a same continuous distribution, wherein performing the statistical testing further comprises determining a degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature; and determining, based on the degree to which each test feature of the plurality of pairs of features deviates from the corresponding training feature, whether the plurality of test features is validated.

Root cause analysis for deterministic machine learning model

Resumen de: GB2624143A

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.

DATA COMPRESSION TECHNIQUES FOR MACHINE LEARNING MODELS

Resumen de: AU2022360356A1

In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.

Wireless Device Power Optimization Utilizing Artificial Intelligence and/or Machine Learning

Resumen de: AU2024202383A1

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 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.

FIREWALL INSIGHTS PROCESSING AND MACHINE LEARNING

Resumen de: US2024146695A1

A computer-implemented method causes data processing hardware to perform operations for training a firewall utilization model. The operations include receiving firewall utilization data for firewall connection requests during a utilization period. The firewall utilization data includes hit counts for each sub-rule associated with at least one firewall rule. The operations also include generating training data based on the firewall utilization data. The training data includes unused sub-rules corresponding to sub-rules having no hits during the utilization period and hit sub-rules corresponding to sub-rules having more than zero hits during the utilization period. The operations also include training a firewall utilization model on the training data. The operations further include, for each sub-rule associated with the at least one firewall rule, determining a corresponding sub-rule utilization probability indicating a likelihood the sub-rule will be used for a future connection request.

AI-DRIVEN INTEGRATION PLATFORM AND USER-ADAPTIVE INTERFACE FOR BUSINESS RELATIONSHIP ORCHESTRATION

Resumen de: US2024146733A1

A system, method and tangible non-transitory storage medium are disclosed. The system includes an integration platform configured to generate in interactive graphical user interface (GUI) that simultaneously displays and provides access to a combination of services, internal resources and external resources. Responsive to receiving input from a user device, the interactive GUI provide access to one or more selected services, internal resources and/or external resources. The integration platform may also monitor and capture interaction data associated with activity between the user device and the integration platform, execute machine learning model(s) to predict user-specific interaction tendencies, and revise one or more aspects of interactive GUI based on the predicted user-specific interaction tendencies.

PREDICTING USER STATE USING MACHINE LEARNING

Nº publicación: US2024144034A1 02/05/2024

Solicitante:

UBER TECH INC [US]
Uber Technologies, Inc

Resumen de: US2024144034A1

A system coordinates services between users and providers. The system trains a computer model to predict a user state of a user using data about past services. The prediction is based on data associated with a request submitted by a user. Request data can include current data about the user's behavior and information about the service that is independent of the particular user behavior or characteristics. The user behavior may be compared against the user's prior behavior to determine differences in the user behavior for this request and normal behavior of prior requests. The system can alter the parameters of a service based on the prediction about the state of the user requesting the service.