Abstract: In some implementations, a verification device may receive a request for an electronic exchange of information. The verification device may receive geographic location information associated with the request for the electronic exchange of information. The verification device may determine, using a machine learning model and based on the geographic location information, a validity of the request for the electronic exchange of information. The verification device may determine whether to execute the electronic exchange of information based on the determination of the validity of the request for the electronic exchange of information. The verification device may perform one of executing the electronic exchange of information or rejecting the request for the electronic exchange of information based on the determination of whether to execute the electronic exchange of information.

Abstract: A method and related system for training a machine learning model using a federated learning structure by selectively sharing condensed data between devices includes operations to obtain datasets from client devices comprising a first client device and a second client device and updating a datasets subset to comprise a first dataset. The method further includes updating the datasets subset to comprise a second dataset based on a result indicating that a feature space distance between the first dataset and the second dataset satisfies a set of criteria and sending, to a third client device, the datasets subset comprising the first dataset and the second dataset. The method further includes obtaining, from the third client device, a set of model parameters that is derived from training based on the datasets subset and updating a server version of a machine learning model based on the set of model parameters.

Abstract: In some implementations, a system may receive a data stream input. The system may identify one or more sets of content parameters associated with the data stream input, each set of content parameters being associated with a different entity. The system may determine a ruling set of content parameters based on the one or more sets of content parameters. The system may selectively modify the data stream input based on a determination of whether a condition indicated by the ruling set of content parameters is satisfied in the data stream input. The system may provide a data stream output resulting from the selective modification of the data stream input.

Abstract: Disclosed embodiments include systems and methods for providing a secure physical storage system. The system may receive a request to register a first physical storage box from an entity including location information and security information. The request can be validated by comparing the security information to a predetermined security threshold. The location information associated with the first physical storage box can be recorded to a blockchain. The first registration information can be received from a first user device. The first physical storage box can be assigned to the first user device and a first identity token associated with the first physical storage box. The assignment information associated with the first physical storage box can be recorded to the blockchain. A first identity token can be received from the first user device. Instructions can be transmitted to the first physical storage box to transition from a locked to an unlocked state.

Abstract: A method for object protection identification is provided. The method may: monitor, using a processor, at least one of audio data or history data associated with a user; detect an object replacement triggering event, based on a result of the monitoring; receive user data for a user associated with the at least one of audio data or history data; identify an object corresponding to the object replacement triggering event based on the user data; identify object-related information, the object-related information including at least one of an object protection information or an object replacement information; and transmit the at least one of the object protection information or the object replacement information based on identifying the object-related information.

Abstract: Systems as described herein may include detecting live browser information that a user navigates to a first website displayed in a first open browser tab and a second website displayed in a second open browser tab. A data sharing server may provide the live browser information to a machine learning model as input. Based on feedback from the machine learning model, one or more similar products displayed in the first website and the second website may be determined. The data sharing server may detect an update on the one or more similar products, and cause a user device to display an alert indicating the update on the similar products.

Abstract: Systems and methods for generating contextual data for downstream models using explainability vectors. The system receives training data for an upstream machine learning model. The training data comprises values for a first set of features. The system trains the upstream machine learning model using the training data. The system processes the upstream machine learning model to extract an explainability vector. Based on the explainability vector, the system processes the first set of features to generate a second set of features. The system processes the second set of features and the output of the upstream machine learning model to generate an explanative factor and trains a downstream model using the explanative factor and a third set of features.

Abstract: Systems and methods for ranking user interface elements using explainability vectors. The system receives training data for a ranking machine learning model. The training data comprises values for a first set of features. The system trains the ranking machine learning model using the training data. The system processes the ranking model to extract an explainability vector. Based on the explainability vector, the system processes the first set of features to generate a second set of features. The system processes the second set of features and the output of the predictive machine learning model to generate an explanative factor and trains a ranking model using the explanative factor and a third set of features. The system receives as output from the ranking model a vector indicating display positions and rankings of user interface elements.

Abstract: In some implementations, an expert system may receive, from a user device, a data structure representing a project associated with a technical request. The expert system may map the data structure to a graph representation of a user space to determine a set of users. The expert system may determine a set of statuses, corresponding to the set of users, associated with availability of the set of users. The expert system may transmit, to the user device, instructions for a user interface including visual indicators for the set of users and the set of statuses. The expert system may receive, from the user device, an indication of a selected user from the set of users and may transmit a message addressed to the selected user.

Abstract: A method and related system perform operations to obtain data points in a feature space for a set of input records, determine explainability parameters for the data points and a prediction model using an explainability model, and determine a score associated with a candidate data point of the data points based on a first set of values and a second set of values determined with the explainability parameters. Some embodiments may select the candidate data point based on a result indicating that the score satisfies a threshold and store, in a memory, an indication of a candidate record associated with the candidate data point.

Abstract: A method and related system operations for summarizing information includes determining a plurality of local explainability parameters for a set of inputs using a local explainability model for a prediction model. The method further includes generating a plurality of feature effect indicators for a target prediction and generating a plurality of phrases based on the plurality of feature effect indicators by using a text generation model. The method further includes generating a summarization using a large language model based on the plurality of phrases.

Abstract: Systems and methods for executing resource availability notifications to user systems are described. In some aspects, the system receives, for a first plurality of user systems, a first plurality of user profiles and a plurality of resource availability values. Each user profile includes values for a set of features. The system processes a first machine learning model which generates resource availability values from the set of features and extracts an explainability vector. The system uses the explainability vector to generate an embedding map that translates feature values into a corresponding embedding in an embedding space. The system encodes a second plurality of user profiles and processes the resulting user profile vectors using a second machine learning model to generate clusters of user profile vectors. The system selects a cluster from the clusters of user profile vectors and determines user systems corresponding to the cluster for executing resource availability notifications.

Abstract: Systems and methods for forecasting data drift for model monitoring. In some aspects, the system receives a current explainability vector for a machine learning model and a data drift vector for historical data profiles. The machine learning model is trained on historical data including values for a first set of features. The system generates a projected synthetic dataset using the data drift vector and updates the machine learning model based on the projected synthetic dataset. Using the current explainability vector and a future explainability vector for the updated model, the system generates a second set of features and determines a drift threshold vector for the second set of features based on values in the explainability vectors. The system determines a discrepancy score for each feature of the second set of features. The system generates an alert including features in the second set of features and their associated discrepancy scores.

Abstract: In some implementations, a server may receive, from a user device, one or more credentials associated with a user. Accordingly, the server may transmit, to the user device, instructions for generating a user interface including a plurality of visual components based on authenticating the user with the one or more credentials. The server may detect, during a first interval, a lack of interaction with the user interface. Accordingly, the server may remove one of the plurality of visual components from the user interface based on the lack of interaction. Additionally, the server may detect, during a second interval, one or more interactions with the user interface and may update a similarity score associated with the user based on properties associated with the one or more interactions. Accordingly, the server may restore the visual component to the user interface based on the updated similarity score satisfying a similarity threshold.

Abstract: Methods and systems are described herein for determining data quality using data reconstruction models. The system receives a dataset including entries and features and generates a machine learning model for each feature of the dataset. Each model may be trained to generate predictions for a corresponding feature based on other features of the dataset. The system may input, into each model, values of the other features to obtain prediction values for the corresponding feature. For a subset of entries for which a difference between the predicted and actual values of the corresponding feature satisfies a threshold, the system may determine relative impacts of the other features on the corresponding feature. The system may then transmit, to a user, a subset of the other features having relative impacts that meet a feature impact threshold.

Abstract: The invention relates to executing an action for a cryptographic storage application by comparing a movement dataset, which may contain positional and inertial datasets, with a user profile dataset. The system may receive a first cryptographic storage application address, retrieve a first movement dataset corresponding to a first time period, retrieve a user profile dataset, compare the first movement dataset with the user profile dataset, and execute an action for the first cryptographic storage application.

Abstract: A method and related system operations include determining a predicted category by providing a prediction model with a set of input feature values and generating a plurality of conditionals based on the set of input feature values for a set of features and the predicted category. The method also includes filtering the plurality of conditionals based on a knowledge base to obtain a selected conditional by generating a set of sub-conditional paths by providing, as an input for a prompt generator model, a candidate conditional of the plurality of conditionals to the prompt generator model and selecting the candidate conditional as the selected conditional based on a determination that the set of sub-conditional paths satisfies a set of criteria associated with a set of sequences of the knowledge base. The method further includes storing the selected conditional in a data structure in association with the set of input feature values.

Abstract: Methods and systems are described herein for updating machine learning models using weights. The system inputs, into a machine learning model, a dataset including entries and features to obtain a relative impact of each feature. The system generates, using the relative impacts, a sparsity metric for each entry, each sparsity metric indicating a measure of a number of features used to generate a corresponding prediction. The system retrieves a sparsity threshold for assigning weights to the plurality of entries. The system generates an updated dataset based on assigning, to each entry within the dataset, a corresponding weight. Each corresponding weight is determined based on a relation of the sparsity metric to the sparsity threshold. The system inputs, into the machine learning model, the updated dataset to update the machine learning model based on the corresponding weights, where the machine learning model relies more heavily on entries with higher corresponding weights.

Abstract: Methods and systems are described herein for updating machine learning models based on the impact of features on predictions. The system inputs, into a machine learning model, a dataset including entries and features to obtain predictions. The machine learning model is trained to generate predictions for entries based on features. The system generates, for each entry, feature impact parameters indicating a relative impact of each feature on each prediction. The system determines a feature impact threshold for assessing which features have contributed to each prediction and generates, using the feature impact parameters and the feature impact threshold, a sparsity metric for each prediction. The sparsity metric indicates which features have relative impacts that meet the feature impact threshold for the prediction. The system generates a global sparsity metric for the machine learning model and updates the machine learning model based on the global sparsity metric.

Abstract: Disclosed embodiments may include a system for fraud detection. The system may identify, using a web browser extension, that a user has navigated to a webpage on a user device. The system may receive, via the webpage, data associated with a transaction. Responsive to receiving the data, the system may retrieve search history data corresponding to a searching session associated with the data, and may identify a searching session path corresponding to the transaction. The system may determine, using a machine learning model (MLM) and based on the search history data and the searching session path, a likelihood of fraud associated with the data. The system may determine whether the likelihood exceeds a predetermined threshold. Responsive to determining the likelihood exceeds the predetermined threshold, the system may conduct one or more fraud prevention actions.