Abstract: Computer-implemented methods of operating an augmented reality device can involve capturing camera images, processing the camera images, and displaying virtual display images. The camera images can be captured automatically using a camera disposed within an augmented reality device worn by a user. The camera images can be processed automatically using a processor located within the augmented reality device. The virtual display images can be displayed automatically to the user within the augmented reality device while the user is looking through the augmented reality device and simultaneously viewing real objects through the augmented reality device. The virtual display images can be based on the processed camera images. Additional steps can include accepting a first user input, storing camera image(s) on a memory located within the augmented reality device based on the first input, accepting a second user input, and displaying stored image(s) to the user based on the second input.

Abstract: According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.

Abstract: A method for implementing a micro grid within a Smart Distribution Power Grid (SDPG) has steps for connecting a smart meter enabled to sense voltage, to sense current and current direction and to communicate wirelessly to individual ones of consumer sites within an area of the micro grid, determining a set of Smart Distribution Nodes of a number, placement and data packet transfer technology to transfer data from the smart meters to a Micro Grid Controlling Station (MCS), the set determined in a manner to provide cost optimization, where cost is determined form at least original node costs, data packet hops, operating costs and maintenance costs, and implementing the set of SDNs along a power grid topology (PGT) of the micro grid, such that data is transmitted from each smart meter to a proximate SDN and through other SDNs to the MCS.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: Computer-implemented methods of operating an augmented reality device can involve capturing camera images, processing the camera images, and displaying virtual display images. The camera images can be captured automatically using a camera disposed within an augmented reality device worn by a user. The camera images can be processed automatically using a processor located within the augmented reality device. The virtual display images can be displayed automatically to the user within the augmented reality device while the user is looking through the augmented reality device and simultaneously viewing real objects through the augmented reality device. The virtual display images can be based on the processed camera images. Additional steps can include accepting a first user input, storing camera image(s) on a memory located within the augmented reality device based on the first input, accepting a second user input, and displaying stored image(s) to the user based on the second input.

Abstract: According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

Abstract: According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

Abstract: Computer-implemented methods of operating an augmented reality device can involve capturing camera images, processing the camera images, and displaying virtual display images. The camera images can be captured automatically using a camera disposed within an augmented reality device worn by a user. The camera images can be processed automatically using a processor located within the augmented reality device. The virtual display images can be displayed automatically to the user within the augmented reality device while the user is looking through the augmented reality device and simultaneously viewing real objects through the augmented reality device. The virtual display images can be based on the processed camera images. Additional steps can include accepting a first user input, storing camera image(s) on a memory located within the augmented reality device based on the first input, accepting a second user input, and displaying stored image(s) to the user based on the second input.

Abstract: A hierarchical early-warning system for landslide probability issues a first level warning based on measured rainfall amounts exceeding a determined threshold, a second level warning, after the first level warning, based additionally on measured soil moisture content measured at different levels, and Factor of safety derived from forecasted pore pressure (FPP) each exceeding a determined threshold, a third level warning, after the first and the second level warnings, based additionally on ground movement measurements compared to a determined threshold, and a fourth level warning after the first, second and third level warnings, based additionally on data from movement-based sensors including strain gauge data.

Abstract: A hierarchical early-warning system for landslide probability issues a first level warning based on measured rainfall amounts exceeding a determined threshold, a second level warning, after the first level warning, based additionally on measured soil moisture content measured at different levels, and Factor of safety derived from forecasted pore pressure (FPP) each exceeding a determined threshold, a third level warning, after the first and the second level warnings, based additionally on ground movement measurements compared to a determined threshold, and a fourth level warning after the first, second and third level warnings, based additionally on data from movement-based sensors including strain gauge data.

Abstract: Described herein are systems and methods for classifying entities based on their respective attributes using machine learning. In one embodiment, a method of classifying target entities includes retrieving private data and public data for entities; extracting features from the public data and the private data; providing the features to a machine learning model that includes a first submodel, and a second submodel, the first submodel outputting a potential entity value for each entity, and the second machine learning model outputting a likelihood of performing a predetermined action for each entity, generating an entity score; ranking the entities based on the entity scores of the entities; and selecting a predetermined number of top ranked entities.

Abstract: An augmented reality device can include an outer housing, an attachment component, a camera, and a display arrangement. The outer housing can have a transparent front and back arranged to allow a user to see therethrough. The attachment component can removably attach the augmented reality device to user eyewear. The camera can capture images through the outer housing front. The display arrangement can provide a virtual display at an angle to the user looking through the augmented reality device. The display arrangement can include an image emitting component, a prism component, a projecting optic component, a combining optic component, and a unibody optics enclosure. The augmented reality device can also include a processor and a memory configured to store and retrieve buffer clips of captured images, and a wireless communication component configured for communications with outside devices.

Abstract: The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.

Abstract: According to one embodiment, a customer relationship management (CRM) system is queried to obtain a project that is associated with an entity (e.g., a user, a group of users, a client). A first list of one or more contacts (e.g., contact persons, referred to herein target contacts) associated with the project from the CRM system. For each of the contacts in the first list, a domain name is determined based on contact information of the contacts (e.g., emails, Web addresses, name of an account associated with the contacts). A first set of email addresses, referred to as target email addresses, is determined based on the domain name and contacts using a set of email identification rules. An email server is queried to retrieve a list of one or more emails and/or calendar events based on the first set of email addresses.

Abstract: A method for configuring a distribution grid with smart communication devices has steps of assuming specific communication technologies to be considered for intermediate intelligent communication devices in the distribution grid, assuming number and location of smart distribution nodes (SDNs) in the distribution grid, executing a cost optimal calculation model determining exact quantity and specific technology of intelligent communication devices at intermediate nodes, nt=n1; n2:::nj with minimal cost subject to Quality of Service (QoS) parameters of each technology and QoS requirement of a data packet generated by a Smart Consumer Node (SCN) at a smart building in the distribution grid, and placing and powering the determined number and specific communication technology of intelligent communication devices at the SDNs of the distribution grid.

Abstract: In an embodiment, described herein is a system and method for creating a suggested task set to meet a target value. A cloud server, in response to receiving a request specifying a target value, retrieves completed task sets from a database. Each completed task set includes a same set of task categories. The cloud server derives a number of ratios from the retrieved completed task sets, including a composition ratio and a conversion rate for each task category, and an addition ratio for the number of completed task sets. Based on the derived ratios and the specified target value, the cloud server constructs the suggested task set, and displays in real-time the suggested task set together with current values for the task categories. The cloud server alerts users of a discrepancy between a current value and the corresponding suggested value for a task category when the discrepancy reaches a predetermined level.