Methods and architecture for using sensor data to offer content, such as purchase or viewing suggestions, to a viewer, are disclosed. Wearable or other external sensor devices may monitor a viewer while the viewer consumes a first media element. The sensor data may then be used to determine an emotional response of the viewer to the media element or to entities found within that element, such as actors, scenes, brands, or objects. Emotional response data for a user may be stored, and may be used either immediately or at a later time to deliver content or make content or purchase suggestions to the viewer.

Disclosed is an electronic device for processing a touch input. The electronic device may comprise: a touch screen; a biometric sensor disposed overlappingly at a position of at least a part of the touch screen; and a processor for acquiring biometric information of a user from an input relating to an object displayed through the touch screen, by using the biometric sensor, receiving a payment command associated with a payment function for the object, and performing the payment function for a product corresponding to the object by using the biometric information according to the payment command. Various other embodiments may be provided.

Methods and systems are presented for providing dynamic and interactive content in an online chat session. When it is determined that an online chat session is established between two devices, an interactive chat engine may begin monitoring chat messages exchanged between the devices to derive a context for the online chat session. A particular workflow from multiple workflows may be selected and initiated for the online chat session based on the derived context. The interactive chat engine may generate a chat object for presenting dynamic and interactive content to the user related to the workflow within the online chat session. The chat object may be inserted into the chat flow of the online chat session. Once inserted the chat object may generate and subsequently modify presentations for the devices participating in the online chat session.

In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

Embodiments described herein are generally directed to an improved product support system. In an example, one or more computer systems of a product support system, capture data relating to product support cases including one or more levels of support data from multiple data sources in accordance with a predefined or configurable schedule. Access to historical versions of a set of metrics for the data by or on behalf of one or more product support personnel is enabled by creating and persisting time-series data in near real-time based on periodic snapshots of the product support cases including counts of the product support cases associated with one or more categories.

The present invention relates to a method and system for tracking abnormal transactions in e-commerce, and an object of the present invention is to track abnormal transactions by analyzing complex characteristic data of product information uploaded to an e-commerce platform. In order to achieve this object, a method for detecting an abnormal transaction in an electronic device according to the present invention includes: step a of generating an identity map based on first transaction information previously stored in an e-commerce server; step b of collecting second transaction information newly uploaded to the e-commerce server; step c of extracting a first identifier and second identifiers included in the second transaction information and generating a third identifier by combining the plurality of second identifiers; and step d of determining whether the second transaction information is an abnormal transaction by searching the identity map for the first identifier and the third identifier.

Techniques are described for receiving an online request to verify an asset, e.g., a line-item from an online profile. The technique determines a verifying entity, such as an educational institution, that has authority to verify the line-item, e.g., an earned certificate, and sends a request to such verifying entity to do so. The asset is added to and verified on the distributed ledger or is verified before adding to the distributed ledger. A verification indicator is coupled to the asset, signaling that the asset has been verified once and need not be verified again. A notification that the asset is verified is transmitted to interested parties. The verification may be at a level, such as a one day verification of a negative drug test. The verifications are searchable on the distributed ledger, e.g., an employer may query for verified assets that match job-related requirements.

Systems and methods which provide for exposing alternate shipping service provider services in shipping management systems are described. Embodiments provide a generic alternate carrier component used to configure configured alternate carrier components of carrier support logic for exposing corresponding alternate shipping service providers for selection and use by users of a shipping management system. A generic alternate carrier component may be configured for a respective alternate shipping service provider for interfacing selected carrier shipping services offerings supported by the alternate shipping service provider with a shipping management platform of a shipping management system. Carrier support modules may be provided for each shipping service provider of a plurality of shipping service providers, wherein the carrier support modules may be configured to interface services logic for a corresponding shipping service provider with a shipping management platform using one or more configured alternate carrier components.

Systems and methods provide customers with a need-based warranty using a deep learning neural network. After categorizing, a customer need is mapped to a warranty type based on the SLA needs. Warranties may then be suggested based on customer need. In another embodiment, systems and methods detect an optimal warranty based on part failure and performance of a server. A mean time to resolve or replace can be minimized in future failure instances by comparing the derived replacement time with available warranty offerings to identify potential deviations and thereby recommend an optimal warranty from the available offerings. In a further embodiment, systems and methods identify and offer additional service contracts for vender services. A warranty proposer looks for warranty types that are emitted by a warranty-types analyzer and by a technical-support analyzer. The overlapping warranty offers are provided to customers.

Systems and methods provide a logical-physical warranty that is applied to nodes at a logical grouping level, such as a cluster. A logical warranty is associated with the nodes in the logical group or cluster in addition to each node's original individual warranty. The logical warranty stretches the expiration dates for individual warranties to a worst-case date inside the logical group. Customers build, teardown and extend the clusters, and the logical warranty is assigned to nodes in the cluster. The logical warranty is associated with a cluster of a defined size, such as a number of nodes expected, which can be expanded in the future as needed. The logical warranty ensures that there is uniform Service Level Agreement (SLA) for the nodes in the cluster during the warranty lifetime thereby simplifying the support for the cluster.