Abstract: A device may receive activity data identifying activities of a process performed by users via user devices. The device may receive baseline data identifying baselines associated with the process and variant data identifying variants from the baselines. The device may apply a sequence alignment model, to the activity data and the baseline data, to determine first similar sequences associated with the activities and the baselines and may apply the sequence alignment model, to the activity data and the variant data, to determine second similar sequences associated with the activities and the variants. The device may determine, based on the first similar sequences, first label data identifying first labels for the activities and may determine, based on the second similar sequences, second label data identifying second labels for the activities. The device may generate a process diagram based on the activity data, the first label data, and the second label data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: A device may receive activity data identifying activities of a process performed by users via user devices. The device may receive baseline data identifying baselines associated with the process and variant data identifying variants from the baselines. The device may apply a sequence alignment model, to the activity data and the baseline data, to determine first similar sequences associated with the activities and the baselines and may apply the sequence alignment model, to the activity data and the variant data, to determine second similar sequences associated with the activities and the variants. The device may determine, based on the first similar sequences, first label data identifying first labels for the activities and may determine, based on the second similar sequences, second label data identifying second labels for the activities. The device may generate a process diagram based on the activity data, the first label data, and the second label data.

Abstract: A system for object recognition and segmentation from digital images provides an intelligent object recognition and segmentation using one or more multilayer convolutional neural network (CNN) models trained in multiple-stages and in a parallel and distributed manner to improve training speed and efficiency. The training dataset used in each of the multiple training stages for the CNN models are generated, expanded, self-validated from a preceding stage. The trained final CNN models are augmented with post-model filters to enhance prediction accuracy by removing false positive object recognition and segmentation. The system provides improved accuracy to predict object labels to append to unlabeled image blocks in digital images. In one embodiment, the system may be useful for enhancing a digital landmark registry by appending identifying labels on new infrastructure improvements recognized in aerial or satellite land images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a self-learning robotic process automation are disclosed. In one aspect, a method includes receiving an automated script that includes one or more commands and that is configured to interact with graphical elements that appear on a user interface. The method further includes executing a command of the one or more commands of the automated script. The method further includes determining that an error occurred during execution of the command of the one or more commands of the automated script. The method further includes determining a modification for the command by applying a script repair model. The method further includes executing the modified command. The method further includes determining whether the error or another error occurred during execution of the modified command. The method further includes determining whether to update the automated script with the modified command.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a cognitive robotics analyzer are disclosed. In one aspect, a method includes the actions of receiving, for each user characteristic of a plurality of user characteristics, first data that identifies one or more first actions that perform a first process and second data that identifies one or more second actions that perform a second process that is labeled as similar to the first process. The actions further include training a predictive model. The actions further include receiving data that identifies actions performed by a user. The actions further include applying the predictive model to one or more of the actions. The actions further include classifying a process performed by the one or more actions as similar to a particular process. The actions further include associating the user with the particular user characteristic.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: A system for object recognition and segmentation from digital images provides an intelligent object recognition and segmentation using one or more multilayer convolutional neural network (CNN) models trained in multiple-stages and in a parallel and distributed manner to improve training speed and efficiency. The training dataset used in each of the multiple training stages for the CNN models are generated, expanded, self-validated from a preceding stage. The trained final CNN models are augmented with post-model filters to enhance prediction accuracy by removing false positive object recognition and segmentation. The system provides improved accuracy to predict object labels to append to unlabeled image blocks in digital images. In one embodiment, the system may be useful for enhancing a digital landmark registry by appending identifying labels on new infrastructure improvements recognized in aerial or satellite land images.

Abstract: This document describes systems, methods, devices, and other techniques for accessing a first video showing a first two-dimensional scene of an environment and captured by a first camera located in the environment having a first field of view; detecting one or more objects shown in the first video; analyzing the first video to determine one or more features of each of the detected objects shown in the first video; accessing a second video showing a second 2D scene of the environment and captured by a second camera located in the environment having a second field of view; detecting one or more objects shown in the second video; analyzing the second video to determine one or more features of each of the detected objects shown in the second video; and correlating one or more objects shown in the first video with one or more objects shown in the second video.

Abstract: A system for object recognition and segmentation from digital images provides an intelligent object recognition and segmentation using one or more multilayer convolutional neural network (CNN) models trained in multiple-stages and in a parallel and distributed manner to improve training speed and efficiency. The training dataset used in each of the multiple training stages for the CNN models are generated, expanded, self-validated from a preceding stage. The trained final CNN models are augmented with post-model filters to enhance prediction accuracy by removing false positive object recognition and segmentation. The system provides improved accuracy to predict object labels to append to unlabeled image blocks in digital images. In one embodiment, the system may be useful for enhancing a digital landmark registry by appending identifying labels on new infrastructure improvements recognized in aerial or satellite land images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a self-learning robotic process automation are disclosed. In one aspect, a method includes receiving an automated script that includes one or more commands and that is configured to interact with graphical elements that appear on a user interface. The method further includes executing a command of the one or more commands of the automated script. The method further includes determining that an error occurred during execution of the command of the one or more commands of the automated script. The method further includes determining a modification for the command by applying a script repair model. The method further includes executing the modified command. The method further includes determining whether the error or another error occurred during execution of the modified command. The method further includes determining whether to update the automated script with the modified command.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a self-learning robotic process automation are disclosed. In one aspect, a method includes receiving an automated script that includes one or more commands and that is configured to interact with graphical elements that appear on a user interface. The method further includes executing a command of the one or more commands of the automated script. The method further includes determining that an error occurred during execution of the command of the one or more commands of the automated script. The method further includes determining a modification for the command by applying a script repair model. The method further includes executing the modified command. The method further includes determining whether the error or another error occurred during execution of the modified command. The method further includes determining whether to update the automated script with the modified command.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a self-learning robotic process automation are disclosed. In one aspect, a method includes receiving an automated script that includes one or more commands and that is configured to interact with graphical elements that appear on a user interface. The method further includes executing a command of the one or more commands of the automated script. The method further includes determining that an error occurred during execution of the command of the one or more commands of the automated script. The method further includes determining a modification for the command by applying a script repair model. The method further includes executing the modified command. The method further includes determining whether the error or another error occurred during execution of the modified command. The method further includes determining whether to update the automated script with the modified command.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for storing facial recognition image data in a cache. One of the methods includes receiving an image from a camera, detecting, in the received image, a face of a person, searching a biometric data cache based on the detected face, in response to searching the biometric data cache based on the detected face, determining whether the biometric data cache includes data for the person, in response to a determination that the biometric data cache includes data for the person, using the data from the biometric data cache to determine an identifier for the person, and in response to a determination that the biometric data cache does not include data for the person: searching a data storage system based on the detected face of the person to determine whether the data storage system includes data for the person.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying people depicted in images. In one aspect, a process includes receiving an image from a camera. A face of a person is detected in the image. The image is compared to a set of images stored in a local cache. A determination is made whether the face of the person matches a face of a person depicted by at least one image of the set of images. In response to determining that the face of the person does not match a face of a person depicted by at least one image of the set of images, a selection is made of a highest quality image of the face of the person. A server system compares the highest quality image to images from data for the multiple people to identify the person.

Abstract: This document describes systems, methods, devices, and other techniques for accessing a first video showing a first two-dimensional scene of an environment and captured by a first camera located in the environment having a first field of view; detecting one or more objects shown in the first video; analyzing the first video to determine one or more features of each of the detected objects shown in the first video; accessing a second video showing a second 2D scene of the environment and captured by a second camera located in the environment having a second field of view; detecting one or more objects shown in the second video; analyzing the second video to determine one or more features of each of the detected objects shown in the second video; and correlating one or more objects shown in the first video with one or more objects shown in the second video.