Abstract: A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

Abstract: A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

Abstract: A method of identifying successive alerts associated with a detected deviation from an operational state of a device includes receiving feature data corresponding to an alert indication and including time series data for multiple sensor devices associated with the device. The method includes determining, based on a first portion of the feature data, first feature importance data of a first alert associated with the first portion of the feature data and determining a first alert threshold corresponding to the first alert. The method includes determining, based on a second portion of the feature data that is subsequent to the first portion, a metric corresponding to second feature importance data of the second portion. The method includes comparing the metric to the first alert threshold to determine whether the second portion corresponds to the first alert or to a second alert that is distinct from the first alert.

Abstract: In some aspects, a method includes obtaining feature importance data associated with an alert and indicating relative importance of each of multiple sensor devices and of one or more simulated features. The method includes identifying a group of the sensor devices that have greater relative importance than a highest relative importance of any of the one or more simulated features. In some aspects, a method includes obtaining a reference list of alerts that are similar to a reference alert and a list of alerts predicted to be similar to the reference alert and ranked by predicted similarity to the reference alert. The method includes determining a score indicating similarity of the list to the reference list. A contribution of each alert in the list to the score is determined based on whether that alert appears in the reference list and the rank of that alert in the list.

Abstract: A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

Abstract: Methods and systems are provided for personalizing a free-form communicative system. The free-from communicative system allows a service provider to autonomously communicate with a user. The free-form communicative system may use an adaptive communicative application and natural language adaptive components to interact with the user. The communicative system may be configured to receive an inbound message from the user and maintain a conversation with the user in their own natural language. The communicative system may assess the inbound message and generate labels, packages, and blueprint objects related to the input message. The communicative system may generate personalized sentences based on the respective labels, packages, and blueprint objects as well as message templates. In response to the input message, the communicative system sends the personalized sentences as an output message to the user, where the personalized sentences are commensurate to the conversation and natural language of the user.

Abstract: Methods, systems, and computer programs encoded on computer storage medium, for identifying a classification of a first device; comparing the classification of the first device with a device classification listing for zones of control; determining that the classification of the first device matches i) the device classification listing for a first zone of control and ii) the device classification listing for a second zone of control; identifying a classification of a second device; comparing the classification of the second device with i) the device classification listing for the first zone of control and ii) the device classification listing for the second zone of control; determining that both i) the classification of the first device and ii) the classification of the second device matches the device classification listing for only the first zone of control; identifying a gateway of the first zone of control as managing the first and second devices.

Abstract: A method of identifying an unknown device via interrogation may comprise determining a communication address of an unknown device in communication with an information handling system, capturing a digital image of the unknown device, determining the unknown device belongs to a known class of devices via an object recognition algorithm to analyze the digital image, accessing a device class registry listing a plurality of candidate object identifications associated with the known class of devices, identifying a stimulus action or actuation associated with a first of the plurality of candidate device identifications. The method may also comprise performing the stimulus action, receiving an indication via the communication address indicating the unknown device detected the stimulus action, and associating the communication address of the unknown device with the first of the plurality of candidate device identifications.

Abstract: A method of identifying an unknown device via interrogation may comprise determining a communication address of an unknown device in communication with an information handling system, capturing a digital image of the unknown device, determining the unknown device belongs to a known class of devices via an object recognition algorithm to analyze the digital image, accessing a device class registry listing a plurality of candidate object identifications associated with the known class of devices, identifying a stimulus action or actuation associated with a first of the plurality of candidate device identifications. The method may also comprise performing the stimulus action, receiving an indication via the communication address indicating the unknown device detected the stimulus action, and associating the communication address of the unknown device with the first of the plurality of candidate device identifications.

Abstract: Methods, systems, and computer programs encoded on computer storage medium, for identifying a classification of a first device; comparing the classification of the first device with a device classification listing for zones of control; determining that the classification of the first device matches i) the device classification listing for a first zone of control and ii) the device classification listing for a second zone of control; identifying a classification of a second device; comparing the classification of the second device with i) the device classification listing for the first zone of control and ii) the device classification listing for the second zone of control; determining that both i) the classification of the first device and ii) the classification of the second device matches the device classification listing for only the first zone of control; identifying a gateway of the first zone of control as managing the first and second devices.

Abstract: The invention concerns a multiparty accounts receivable and accounts payable system that allows business trading partners to use a single, shared system for both accounts receivable and accounts payable management. The system of the invention forms an electronic “bridge” between a plurality of business trading partners for purposes of invoicing, dispute resolution, financing, and settlement of single and multiple currency debts. As the invoicing and settlement activities of the participants are funneled through a common system, the system allows a participant to aggregate all debts owed to other participants, aggregate all debts owed by the other participants, and net debts owed to other participants with debts owed by these participants. After aggregation and netting, the participant issues a single payment to settle numerous accounts payable items, and receives a single payment that settles numerous accounts receivable items.