Abstract: A computer-implemented method for using machine learning to handle data in a computing system with improved efficiency can include obtaining a handling request associated with a data item, the handling request instructing the computing system to perform a handling operation with the data item, obtaining a trace log comprising one or more distributed trace items, the one or more distributed trace items including data from each of one or more services interacting with the data item, providing the trace log to a data characteristic prediction model including one or more machine-learned models, receiving, in response to providing the trace log to the data characteristic prediction model, one or more data characteristic predictions associated with the data item, and selecting a handling operation of a plurality of candidate handling operations based at least in part on the one or more data characteristic predictions.

Abstract: Apparatus and associated methods relate to a rewarded viewing video distribution system configured to reward a user for viewed video content based on presenting video content to a user, assigning the user rewards generated based on the value of the video portion viewed, and automatically presenting the user with marketplace credit to spend the rewards. In some embodiments, the viewed video portion value may be determined as a function of a predetermined reward threshold. In an illustrative example, the predetermined reward threshold may be the video percentage viewed. Various implementations may employ a reward threshold based on advertisements viewed, permitting advertisement spending adapted as a function of verified advertisement views. Some examples may reward views using a video player plug-in. Some embodiments may permit a user to spend viewing rewards on affiliated partner services or products.

Abstract: A system and method for processing promotions in connection with digital purchasing. Data associated with a digital purchase request made by a consumer (the data comprising a token, an amount associated with the transaction, a promotional code for each item included in the digital purchase request associated with a promotion, a default transaction amount, and a single default promotional code) is received from a merchant server. The single default promotional code is assigned to all items included in the digital purchase request that are associated with promotions. Using the token, account information associated with the consumer is retrieved from a database. For each promotional code received, promotional information is retrieved. The retrieved promotional information is displayed. The digital purchase request is submitted for processing using the single default promotional code.

Abstract: In some examples, a computing device may receive data from a plurality of groups of data sources. The computing device may access a plurality of data synthetization machine learning models configured for generating synthetic data. Respective ones of the data synthetization machine learning models may correspond to respective ones of the groups of data sources. The computing device generates first synthetic data by inputting, to a first data synthetization machine learning model, first data received from a first data source group, and generates second synthetic data by inputting, to a second data synthetization machine learning model, second data received from a second data source group. The computing device determines an allocation of resources based at least in part on comparing the first data and the first synthetic data with the second data and the second synthetic data.

Abstract: A feature engineering tool automatically generates a group of features for training a machine learning model. The feature engineering tool selects primitives for a dataset and synthesizes a plurality of features based on the selective primitives and the dataset. The feature engineering tool iteratively applies the plurality of features to a different portion of the dataset to evaluate usefulness of the plurality of features. Based on the evaluation, it removes some of the plurality of features to obtain the group of features. The feature engineering tool also determines the importance factor for each feature in the group. The machine learning model is generated based on the features and their importance factors and can be used to make a prediction based on new data.

Abstract: In some embodiments, a multi-tenant loyalty program configuration platform is provided for selective configuration of loyalty programs in a multi-tenant environment.

Abstract: Provided are a wireless signal apparatus and an elevator service request system and method, which belong to the field of intelligent elevator control technologies. In the elevator service request system of the present invention, a corresponding wireless signal is broadcast by a wireless signal apparatus of an elevator system and the wireless signal is received by a personal mobile terminal. The personal mobile terminal is provided with an elevator service request application component running at an application layer of an operating system thereof. The wireless signal apparatus includes a location notification unit used for broadcasting corresponding location marker data. The elevator service request system includes a wake-up module running at a system layer of the operating system of the personal mobile terminal. The wake-up module is used for waking up the elevator service request application component in response to reception of the location marker data.

Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: A computer system, product, and method are provided. The computer system includes an artificial intelligence (AI) platform operatively coupled to a processor. The AI platform includes tools in the form of a machine learning model (MLM) manager, a metric manager, and a training manager. The MLM manager accesses a plurality of pre-trained source MLMs, and inputs a plurality of data objects of a test dataset into each of the source MLMs. The test dataset includes the plurality of data objects associated with respective labels. For each source MLM, associated labels are generated from the inputted data objects and a similarity metric is calculated. The MLM manager selects a base MLM to be used for transfer learning from the plurality of source MLMs based upon the calculated similarity metric. The training manager trains the selected base MLM with a target dataset for the target domain.

Abstract: Personalizing sensory feedback based on user sensitivity analysis includes maintaining user-specific parameters for provision of sensory feedback to a user in extended reality. The user-specific parameters apply to specific contextual situations and dictate levels of sensory feedback to provide via stimulus device(s) in the specific contextual situations. Based on an ascertained contextual situation of the user interacting in a target extended reality environment, a set of sensory feedback level parameters is selected for provision of sensory feedback to the user in the target extended reality environment, and stimulus device(s) in the target extended reality environment is/are automatically controlled in the provision of the sensory feedback to the user based on one or more of the selected parameters. The automatically controlling includes electronically communicating with the stimulus device(s) to control at least one stimuli provided to the user by the stimulus device(s).

Abstract: Embodiments are described for a system and method for providing a list of current offers from retailers to a user operating a mobile client device. Aspects of the method comprise analyzing a transaction history of the user to identify a vendor who has transacted with the user; determining a present geographic location of the user based on a location of the mobile client device; determining a location of the vendor proximate the user based on the transaction history and the present geographic location of the user; searching for a current promotion offered by the vendor; and facilitating the serving of the current promotion to the user through the mobile client device.

Abstract: Systems and methods for inferring medication adherence levels using markers and predictive models are described. In some implementations, a system evaluates a degree to which a set physiological parameters is predictive of whether a subject has taken a medication. The system stores a predictive marker for inferring medication adherence. The system selects a subject that is designated to take the medication, causes one or more devices associated with the selected subject to change data collection parameters to measure values for the set of physiological parameters, and determines whether the selected subject took the medication based on the predictive marker and the data collected from the one or devices.

Abstract: A processing system for fuel transactions analyzes transactions for symbols indicating a purchase amount and directs information about the purchase to a loyalty platform. The loyalty platform may identify and communicate in real time or near real time with a personal device associated with the purchaser of the fuel. A user interface of the personal device may allow the purchaser to review and select options for processing the fuel purchase using value from a loyalty program account. The selected option may be used to modify fuel purchase parameters prior to settlement of the transaction or simply add points to the user loyalty program account.

Abstract: Disclosed herein is a medical image reading assistant apparatus. The medical image reading assistant apparatus includes a computing system. The computing system includes: a receiver interface configured to receive a first medical image and a second medical image following the first medical image; and at least one processor configured to: generate follow-up information between first findings on the first medical image and second findings on the second medical image by comparing the locations of the first findings and the locations of the second findings; select at least some of the first findings as first valid findings based on a first threshold of diagnostic assistant information for each of the first findings; set a second threshold of diagnostic assistant information for each of the second findings based on the follow-up information; and select at least some of the second findings as second valid findings based on the second threshold.

Abstract: The systems and methods that generate a one-touch frame are provided. A line of code including a tag is inserted into a webpage. The tag includes a link to a server from which parameters for the frame are obtained. The browser uses the parameters to generate the frame. In addition, the server or a local memory of a computing device on which the frame is generated also store credentials associated with a user account. These credentials are also obtained and incorporated into the frame. The frame may then be activated to perform one-touch functions using the credentials.

Abstract: Exemplary system, method, and computer-accessible medium can be provided for effectuating a video overlay during a medical procedure(s) performed on a patient(s) can include, for example, receiving a live video(s) of the medical procedure(s) performed on the patient(s), identifying, in real time, a target area(s), an area of danger(s) and/or at least partially obscured anatomical structures of significance on a patient's anatomy by applying a machine learning procedure to the live video(s), and providing the video overlay based on the identification of the target area(s) and the area of danger(s). The video overlay can be continuously modified, in real-time, using the machine learning procedure. The medical procedure(s) can be a gallbladder surgery. The machine learning procedure can include a neural network, which can be a convolutional neural network.

Abstract: Devices, systems, and methods are provided for encapsulating machine learning in a clean room to generate a goal-based output. A method may include identifying, by a device operating within a clean room, an agreement between multiple parties to share data for use in machine learning to generate a goal-based output; retrieving the data; selecting the machine learning model based on a goal indicated by the agreement; generating, using the data as inputs to the selected machine learning model, a first set of probabilities indicative that a respective user may perform an action; generating, using the selected machine learning model and the first set of probabilities, a second set of probabilities indicative that a respective user may perform the action; generating the goal-based output based on the second set of probabilities; and sending the goal-based output from the clean room to a destination location.

Abstract: A method includes identifying records in a database for labeling; presenting one of the records in the database to a first labeling entity; and receiving a first observation on an information source in the one of the records from the first labeling entity. The first observation has one of a plurality of observation types associated therewith. The plurality of observation types including a validation observation type in which the first observation comprises a confirmation of whether a second observation on the information source in the one of the records from another labeling entity is accurate and an edit for the second observation when the second observation is confirmed as inaccurate. The one of the records is updated in the database with the first observation on the information source in the one of the records from the first labeling entity.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, in a collaboration space, a point conversion interface; passing, via the point conversion interface, user credentials to a third-party point issuer; receiving, via the point conversion interface and from the third-party point issuer, a number of points, wherein the number of points is associated with the user credentials; multiplying the number of points by a conversion factor, wherein the multiplying produces a number of unified points; crediting a points pool associated with a collaboration space, with the number of unified points; and receiving, at the collaboration space and from a user of the collaboration space, an instruction to apply the number of unified points to a travel objective, wherein the travel objective is included on a digital itinerary provided by the collaboration space.