An integrated neural network and semantic system applies a neural network to interpret an image, determines a syntactical element corresponding to the image in accordance with the interpretation, and determines a first probability that represents a confidence level that the correspondence is accurate. A semantic chain and associated second probability are then generated based on the syntactical element and the first probability, whereby the second probability represents the system's confidence level that the semantic chain accurately reflects objective reality. A natural language communication is generated for delivery to a user that comprises syntactical elements that are in accordance with the semantic chain and the second probability. The communication may further be expected to result in receiving information that will influence the confidence level that the semantic chain accurately reflects objective reality.

An integrated neural network and semantic system applies a neural network to interpret an image, determines a syntactical element corresponding to the image in accordance with the interpretation, and determines a first probability that represents a confidence level that the correspondence is accurate. A semantic chain and associated second probability are then generated based on the syntactical element and the first probability, whereby the second probability represents the system's confidence level that the semantic chain accurately reflects objective reality. A natural language communication is generated for delivery to a user that comprises syntactical elements that are in accordance with the semantic chain and the second probability. The communication may further be expected to result in receiving information that will influence the confidence level that the semantic chain accurately reflects objective reality.

It is described at least a method for performing vehicle operation analysis of a driver, comprising retrieving data related to the operation of the vehicle via a mobile device, wherein the mobile device is placed inside the vehicle and the retrieved data is data that is at least provided by data providing units included in the mobile device; adding a driver-specific identifier to the retrieved data which identifies the driver of the vehicle irrespective of the vehicle that is used; performing analysis of the retrieved data in the mobile device, and/or sending the retrieved data to a remote computer and performing analysis of said data in the remote computer, wherein the analysis includes extracting driver-specific vehicle operation schemes and/or calculating a driver-specific safety factor.

It is described at least a method for performing vehicle operation analysis of a driver, comprising retrieving data related to the operation of the vehicle via a mobile device, wherein the mobile device is placed inside the vehicle and the retrieved data is data that is at least provided by data providing units included in the mobile device; adding a driver-specific identifier to the retrieved data which identifies the driver of the vehicle irrespective of the vehicle that is used; performing analysis of the retrieved data in the mobile device, and/or sending the retrieved data to a remote computer and performing analysis of said data in the remote computer, wherein the analysis includes extracting driver-specific vehicle operation schemes and/or calculating a driver-specific safety factor.

A method and system for intelligently provisioning resources in storage systems. Specifically, the method and system disclosed herein entail throttling the allocation of resources aiding in the performance of background service tasks on a backup storage system. That is, throughout a predicted span of a background service task, resources may be dynamically allocated towards the performance of the background service task at discrete time intervals within the predicted span, thereby improving overall system utilization.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.

We propose a neural network-implemented method for base calling analytes. The method includes accessing a sequence of per-cycle image patches for a series of sequencing cycles, where pixels in the image patches contain intensity data for associated analytes, and applying three-dimensional (3D) convolutions on the image patches on a sliding convolution window basis such that, in a convolution window, a 3D convolution filter convolves over a plurality of the image patches and produces at least one output feature. The method further includes beginning with output features produced by the 3D convolutions as starting input, applying further convolutions and producing final output features and processing the final output features through an output layer and producing base calls for one or more of the associated analytes to be base called at each of the sequencing cycles.

Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.