Abstract: A method for automated root cause analysis via one or more structured data sources and one or more unstructured data sources includes identifying an error in a process or an environment. The method also includes searching the one or more unstructured data sources via a retrieval-augmented generation (RAG) component based on an error description corresponding to the identified error. The method further includes generating, via the RAG component, a hypothesis for the error in accordance with searching the one or more unstructured data sources. The method also includes searching the one or more structured data sources based on one or more queries generated via a text-to-SQL component in accordance with the hypothesis. The method still further includes autonomously adjusting one or more parameters of one or more devices associated with the process or the environment in accordance with searching the one or more structured data sources.

Abstract: A diagnostic service is selectively started at opportune times, such as when the electrical vehicle is initially plugged in. When the electrical vehicle is turned off, there may not be any messages being sent on the communication bus since most vehicle electronic controllers are disabled to conserve power. But, when someone plugs in a charger, there is a surge of bus traffic for a short period of time. The system parses through the bus traffic and looks for a particular communication bus address. The presence of this address can be used as an indicator for an opportune time to start the diagnostic service to confirm whether the electric vehicle is charging or not and to log the state of charge of the electric vehicle.

Abstract: The present application discloses a method, system, and computer system for identifying function signatures that are used to detect certain types of malware. The method includes: (a) performing disassembly of a plurality of input binaries to generate a set of function signatures, (b) determining a ranking of function signatures for the set of function signatures, and (c) automatically selecting a subset of function signatures for detecting a type of file, wherein the subset of function signatures is selected based at least in part on the ranking of function signatures.

Abstract: The technology relates to cybersecurity attacks and cloud-based security, and specifically to detecting malicious embeddings in document destined for a networked system. Such embeddings can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology intercepts a document that is compatible with an MS Office file format, finds embedded code, engineers at least five features that characterize the embedded code. The technology inputs the engineered features to a trained machine learning model and determines, as a threat level, a likelihood of malicious embedding from at least the engineered features of the embedded code. Based on the threat level, the technology can block the document with a malicious threat level, accept the document with a non-malicious threat level, and or isolate the document with a suspicious threat level.

Abstract: A system and method for exploring security rule chains in a security platform. The method includes displaying a first plurality of graphical elements of a graphical user interface (GUI), each graphical element of the first plurality of graphical elements referencing a respective chained outcome of a plurality of chained outcomes of a respective chained rule, The respective chained rule includes two or more security rules that are linked based on their respective security outcomes, receiving, via the GUI, a selection of a first graphical element of the first plurality of graphical elements, the first graphical element corresponding to a first chained outcome of the plurality of chained outcomes, and displaying a second plurality of graphical elements in a visual association with the first element, each element of the second plurality of elements referencing a respective security outcome of the two or more security rules that are serially linked.

Abstract: An analytics reporting system to perform operations that include: aggregating sensor data collected from a plurality of sensor devices within a database, the sensor data comprising a set of values that correspond with a metric; generating a threshold value based on the set of values that correspond with the metric; accessing a portion of the sensor data based on an identifier associated with the portion of the sensor data; determining the portion of the sensor data transgresses the threshold value; and generating a report that comprises a display of the portion of the sensor data based on the determining that the portion of the sensor data transgresses the threshold value.

Abstract: A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

Abstract: The technology disclosed relates to cybersecurity attacks and cloud-based security, and specifically to the detection of malicious code that is obfuscated within document files. Such malicious code can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology disclosed detects obfuscated and malicious code using a trained machine learning model to predict which documents include malicious code, despite such malicious not having a known signature. The technology disclosed can thus predict which documents include malicious code lacking a known signature. Safe documents are allowed into the network. Suspicious documents are subjected to additional processing, including quarantining or sandboxing methods. Malicious documents are rejected from the network.

Abstract: A system receives historical vehicle battery data from a gateway device connected to a vehicle. Some vehicles with plug-in rechargeable batteries recommend/require that the vehicle computer be turned off when recharging. Thus, obtaining a current state of charge while a vehicle is charging can be difficult because the vehicle computer can be off. While the vehicle/gateway device is unable to transmit current battery data, the systems estimate a battery charge from the historical data.

Abstract: A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles and multiple fleets. The system uses machine learning to identify cohorts for fleets. The system calculates metrics for fleets and benchmarks for the cohorts. The system presents the metrics and benchmarks in a graphical user interface.

Abstract: A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles. The vehicle gateway device gathers vehicle metric data and correlates the metric data with location data. The system presents the correlated data in an interactive map graphical user interface.

Abstract: An analytics reporting system to perform operations that include: aggregating sensor data collected from a plurality of sensor devices within a database, the sensor data comprising a set of values that correspond with a metric; generating a threshold value based on the set of values that correspond with the metric; accessing a portion of the sensor data based on an identifier associated with the portion of the sensor data; determining the portion of the sensor data transgresses the threshold value; and generating a report that comprises a display of the portion of the sensor data based on the determining that the portion of the sensor data transgresses the threshold value.

Abstract: A diagnostic service is selectively started at opportune times, such as when the electrical vehicle is initially plugged in. When the electrical vehicle is turned off, there may not be any messages being sent on the communication bus since most vehicle electronic controllers are disabled to conserve power. But, when someone plugs in a charger, there is a surge of bus traffic for a short period of time. The system parses through the bus traffic and looks for a particular communication bus address. The presence of this address can be used as an indicator for an opportune time to start the diagnostic service to confirm whether the electric vehicle is charging or not and to log the state of charge of the electric vehicle.

Abstract: A system receives historical vehicle battery data from a gateway device connected to a vehicle. Some vehicles with plug-in rechargeable batteries recommend/require that the vehicle computer be turned off when recharging. Thus, obtaining a current state of charge while a vehicle is charging can be difficult because the vehicle computer can be off. While the vehicle/gateway device is unable to transmit current battery data, the systems estimate a battery charge from the historical data.

Abstract: A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles and multiple fleets. The system uses machine learning to identify cohorts for fleets. The system calculates metrics for fleets and benchmarks for the cohorts. The system presents the metrics and benchmarks in a graphical user interface.

Abstract: An analytics reporting system to perform operations that include: aggregating sensor data collected from a plurality of sensor devices within a database, the sensor data comprising a set of values that correspond with a metric; generating a threshold value based on the set of values that correspond with the metric; accessing a portion of the sensor data based on an identifier associated with the portion of the sensor data; determining the portion of the sensor data transgresses the threshold value; and generating a report that comprises a display of the portion of the sensor data based on the determining that the portion of the sensor data transgresses the threshold value.

Abstract: An analytics reporting system to perform operations that include: aggregating sensor data collected from a plurality of sensor devices within a database, the sensor data comprising a set of values that correspond with a metric; generating a threshold value based on the set of values that correspond with the metric; accessing a portion of the sensor data based on an identifier associated with the portion of the sensor data; determining the portion of the sensor data transgresses the threshold value; and generating a report that comprises a display of the portion of the sensor data based on the determining that the portion of the sensor data transgresses the threshold value.

Abstract: A system receives historical vehicle battery data from a gateway device connected to a vehicle. Some vehicles with plug-in rechargeable batteries recommend/require that the vehicle computer be turned off when recharging. Thus, obtaining a current state of charge while a vehicle is charging can be difficult because the vehicle computer can be off. While the vehicle/gateway device is unable to transmit current battery data, the systems estimate a battery charge from the historical data.

Abstract: The technology disclosed relates to cybersecurity attacks and cloud-based security, and specifically to the detection of malicious code that is obfuscated within document files. Such malicious code can be delivered in the form of malicious macros and/or malicious OLE objects stored within document files. The technology disclosed detects obfuscated and malicious code using a trained machine learning model to predict which documents include malicious code, despite such malicious not having a known signature. The technology disclosed can thus predict which documents include malicious code lacking a known signature. Safe documents are allowed into the network. Suspicious documents are subjected to additional processing, including quarantining or sandboxing methods. Malicious documents are rejected from the network.

Abstract: A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and/or location data. The system receives data from multiple vehicles. The vehicle gateway device gathers vehicle metric data and correlates the metric data with location data. The system presents the correlated data in an interactive map graphical user interface.