Abstract: A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

Abstract: During a software development lifecycle of a software application, application library versions are modified and multiple versions are built and packaged to be installed on different computing systems. An intelligent prediction and capture of runtime errors that may be encountered during a runtime of a software application provides a system-agnostic intelligent software code compiler/build system that intelligently identifies runtime errors at a build stage using artificial intelligence (AI) and automatically encapsulates specific software code to auto-heal runtime exceptions caused by updates to library files. The system may optionally also output a solution to the runtime error to the graphical user interface (GUI) of a software developer capable of performing another software application build.

Abstract: A method includes accessing a first instance of a first web application screen and a second instance of the first web application screen, generating key-value pairs for each web element identified in the first instance and the second instance, and generating a first element map based on the key-value pairs. The method further includes accessing a third instance of the first web application screen, and generating an updated element map based on the first element map and a second element map generated based on the third instance. The method includes executing a machine-learning model trained to generate a prediction of key-value pairs for instances of a second web application screen based on the updated element map and a wireframe of the second web application screen, and generating a final element map based on the prediction of key-value pairs and the wireframe of the second web application screen.

Abstract: Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: An apparatus configured to be worn on a head of a user, includes: a screen configured to present graphics to the user; a camera system configured to view an environment in which the user is located; and a processing unit configured to determine a map based at least in part on output(s) from the camera system, wherein the map is configured for use by the processing unit to localize the user with respect to the environment; wherein the processing unit of the apparatus is also configured to obtain a metric indicating a likelihood of success to localize the user using the map, and wherein the processing unit is configured to obtain the metric by computing the metric or by receiving the metric.

Abstract: Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.

Abstract: Embodiments of the present invention provide a system for facilitating a secure way to allow primary users and secondary users to perform interactions. In particular, the system may be configured to receive an interaction request from a primary user, where the interaction request comprises location of an automated machine, initiate and lock a session between the automated machine and the entity application located on the user device of the primary user, identify a trigger and display a code on the automated machine, wherein the code is scannable by the user device of the primary user or a user device of an authorized secondary user, receive a scanned code from the user device of the primary user or the user device of the authorized secondary user, determine a match between the scanned code and the code displayed on the automated machine, and complete the interaction.

Abstract: An apparatus configured to be worn on a head of a user, includes: a screen configured to present graphics to the user; a camera system configured to view an environment in which the user is located; and a processing unit configured to determine a map based at least in part on output(s) from the camera system, wherein the map is configured for use by the processing unit to localize the user with respect to the environment; wherein the processing unit of the apparatus is also configured to obtain a metric indicating a likelihood of success to localize the user using the map, and wherein the processing unit is configured to obtain the metric by computing the metric or by receiving the metric.

Abstract: An apparatus configured to be worn on a head of a user, includes: a screen configured to present graphics to the user; a camera system configured to view an environment in which the user is located; and a processing unit configured to determine a map based at least in part on output(s) from the camera system, wherein the map is configured for use by the processing unit to localize the user with respect to the environment; wherein the processing unit of the apparatus is also configured to obtain a metric indicating a likelihood of success to localize the user using the map, and wherein the processing unit is configured to obtain the metric by computing the metric or by receiving the metric.

Abstract: A detection system detects malfunctions in an autonomous farming vehicle during an autonomous routine using one or more models and data from sensors coupled to the autonomous farming vehicle. The models may include machine-learned models trained on the sensor data and configured to identify objects indicative of an operational or malfunctioning component within a tilling assembly such as a tilling shank or sweep. Additionally, a machine-learned model may be trained on sensor data to detect whether debris has plugged the tilling assembly of the autonomous farming vehicle. In response to detecting a malfunction or a plug, the detection system may modify the autonomous routine (e.g., pausing operation) or provide information for the malfunction to be addressed (e.g., the likely location of a malfunctioning sweep that has detached from the tilling assembly).

Abstract: Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

Abstract: A farming machine moves through a field and performs one or more farming actions (e.g., treating one or more plants) in the field. Portions of the field may include moisture, such as puddles or mud patches. A control system associated with the farming machine may include a traversability model and/or a moisture model to help the farming machine operate in the field with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the farming machine attempting to traverse an untraversable portion of the field, and the control system may employ the moisture model to reduce the likelihood of the farming machine performing an action that will damage a portion of the field.

Abstract: A farming machine moves through a field and performs one or more farming actions (e.g., treating one or more plants) in the field. Portions of the field may include moisture, such as puddles or mud patches. A control system associated with the farming machine may include a traversability model and/or a moisture model to help the farming machine operate in the field with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the farming machine attempting to traverse an untraversable portion of the field, and the control system may employ the moisture model to reduce the likelihood of the farming machine performing an action that will damage a portion of the field.

Abstract: A vehicle moves through an environment (e.g., a farming, construction, mining, or forestry environment) and performs one or more actions in the environment. Portions of the environment may include moisture, such as puddles or mud patches. A control system associated with the vehicle may include a traversability model or a moisture model to help the vehicle operate in the environment with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the vehicle attempting to traverse an untraversable portion of the environment, and the control system may employ the moisture model to reduce the likelihood of the vehicle performing an action that will damage a portion of the environment.

Abstract: Apparatus and methods for a smart glasses device are provided. The smart glasses device may execute a prediction model on video data captured by the smart glasses device to retrain the prediction model. The prediction model may be retrained in response to a data synchronization between an output of the prediction model and a gesture captured by the smart glasses device.

Abstract: Embodiments of the present invention provide a system for facilitating a secure way to allow primary users and secondary users to perform interactions. In particular, the system may be configured to receive an interaction request from a primary user, where the interaction request comprises location of an automated machine, initiate and lock a session between the automated machine and the entity application located on the user device of the primary user, identify a trigger and display a code on the automated machine, wherein the code is scannable by the user device of the primary user or a user device of an authorized secondary user, receive a scanned code from the user device of the primary user or the user device of the authorized secondary user, determine a match between the scanned code and the code displayed on the automated machine, and complete the interaction.