Abstract: A wearable device may include a head-mounted display (HMD) for rendering a three-dimensional (3D) virtual object which appears to be located in an ambient environment of a user of the display. The relative positions of the HMD and one or more eyes of the user may not be in desired positions to receive, or register, image information outputted by the HMD. For example, the HMD-to-eye alignment may vary for different users and may change over time (e.g., as a user moves around and/or the HMD slips or is otherwise displaced). The wearable device may determine a relative position or alignment between the HMD and the user's eyes. Based on the relative positions, the wearable device may determine if it is properly fitted to the user, may provide feedback on the quality of the fit to the user, and may take actions to reduce or minimize effects of any misalignment.

Abstract: A system, device, and method for sensing air quality with a sensor platform is disclosed. The method includes (i) directing an air quality measurement system to a predicted high information region wherein the predicted high information region is determined based on high variance of prior air quality measurements taken at different times, (ii) directing the air quality measurement system to a predicted low information region wherein the predicted low information region is determined based on low variance of prior air quality measurements taken at different times. The air quality measurement system is preferentially directed to the predicted high information region.

Abstract: A system, device, and method for sensing air quality with a sensor platform is disclosed. The method includes (i) directing a set of mobile sensors to a coarse region wherein the coarse region has coarse size based on variance of prior air quality measurements taken at locations within a vicinity of the coarse region, and (ii) directing the set of mobile sensors to a fine region wherein the fine region has a fine size based on variance of prior air quality measurements taken at locations within a vicinity of the fine region. The variance of air quality measurements associated with the fine region is greater than a variance of air quality measurements associated with the coarse region.

Abstract: Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Abstract: An I-beam clamp (10) is provided that has a first clamping member (26) having a first flange engagement surface (28) configured for engagement with a first flange surface of a flange of an I-beam. The first clamping member has a first web engagement surface (30) configured for engagement with a first web surface of a web of the I-beam. A second clamping member (32) is provided and configured for engagement with the first flange surface and a second web surface of the web. The first and second clamping members are configured for movement relative to one another in a direction (28) perpendicular to the first and second web surfaces. A third clamping member (40) is present and has a third flange engagement surface (42) configured for engagement with a second flange surface of the flange. The third clamping member is configured for movement toward the second flange surface of the flange of the I-beam.

Abstract: The system receives a base machine learning model trained using a generic dataset. For example, the base machine learning model may be an off-the-shelf machine learning based model. The base machine learning model is trained to receive an input and generate a feature vector representing the input. The input may be a natural language expression, an image, or any other type of input. The system receives a domain specific training dataset based on known categories for input values. The system determines an orthogonal transformation for reducing the dimensions of the base machine learning model using on the domain specific training dataset. The system applies the orthogonal transformation to the base machine learning model to obtain a domain specific machine learning model. The system uses the domain specific machine learning model for processing inputs, for example, in a production environment.

Abstract: Described herein are systems, apparatus, methods and computer program products for machine learning intent classification. In various embodiments, historical utterances provided by users may be utilized for bot training. Context and personally identifiable information may be removed from the utterances. The utterances may be associated with vectors. The utterances and vectors may be used to determine recommendations.

Abstract: An apparatus (10) is provided, configured for use with a trailer (48) of a tractor trailer (44), that includes forward and rearward axles (12, 28) that each have a central body. The forward axle (12) has first and second mounting portions (16, 20) that are configured to receive first and second wide base tires (24, 26). The rearward axle (28) has first and second rearward mounting portions (32, 36) configured to receive third and fourth wide base tires (40, 42). The first forward mounting portion axis (18) is oriented at an angle (56) from +0.75-+0.95 degrees to the lateral direction, and the second forward mounting portion axis (22) is oriented at an angle (58) from +0.08-+0.28 degrees to the lateral direction. The first rearward mounting portion axis (34) is oriented at an angle (60) from +0.65-+0.85 degrees to the lateral direction, and the second rearward mounting portion axis (38) is oriented at an angle (62) from ?0.05-+0.15 degrees to the lateral direction.

Abstract: A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.

Abstract: Methods and systems for answering frequently asked questions are described. An utterance is received. A decision score that is indicative of the likelihood that the utterance is answerable according to a set of frequently asked questions and associated answers is determined for the utterance. A candidate answer from the associated answers and a selection score for the candidate answer are determined for the utterance. A total score for the candidate answer is determined based on the decision score and the selection score. The total score is indicative of the likelihood that the candidate answer is a correct answer for the utterance according to the set of frequently asked questions and associated answers.

Abstract: An online system stores objects representing potential transactions of an enterprise. The online system uses predictor models to determine an aggregate score based on values of the objects associated with a time interval, for example, a month. Each object is configured to take one of a plurality of states. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data for generating the predictor models. The online system categorizes the objects into bins based on states of the objects. The online system may generate different predictions for each category. The online system may use machine learning based models as predictor models. The online system extracts features describing potential transaction objects and provides these as input to the predictor model.

Abstract: An apparatus (10) is provided, configured for use with a trailer (48) of a tractor trailer (44), that includes forward and rearward axles (12, 28) that each have a central body. The forward axle (12) has first and second mounting portions (16, 20) that are configured to receive first and second wide base tires (24, 26). The rearward axle (28) has first and second rearward mounting portions (32, 36) configured to receive third and fourth wide base tires (40, 42). The first forward mounting portion axis (18) is oriented at an angle (56) from +0.75-+0.95 degrees to the lateral direction, and the second forward mounting portion axis (22) is oriented at an angle (58) from +0.08-+0.28 degrees to the lateral direction. The first rearward mounting portion axis (34) is oriented at an angle (60) from +0.65-+0.85 degrees to the lateral direction, and the second rearward mounting portion axis (38) is oriented at an angle (62) from ?0.05-+0.15 degrees to the lateral direction.

Abstract: An online system stores objects representing potential transactions of an enterprise. The online system uses predictor models to determine an aggregate score based on values of the objects associated with a time interval, for example, a month. Each object is configured to take one of a plurality of states. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data for generating the predictor models. The online system categorizes the objects into bins based on states of the objects. The online system may generate different predictions for each category. The online system may use machine learning based models as predictor models. The online system extracts features describing potential transaction objects and provides these as input to the predictor model.

Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata.

Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a record associated with a conversation involves obtaining a plurality of utterances associated with the conversation, identifying, from among the plurality of utterances, a representative utterance for semantic content of the conversation, assigning the conversation to a group of semantically similar conversations based on the representative utterance, and automatically updating the record associated with the conversation at a database system to include metadata identifying the group of semantically similar conversations.

Abstract: Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a group of records involves determining, based on one or more fields of metadata associated with the records, a plurality of candidate names, wherein each candidate name of the plurality of candidate names corresponds to semantic content of the one or more fields of a respective record of the group of records, for each candidate name, assigning a name relevance score based on respective word relevance scores assigned to respective words of the respective candidate name based on usage, selecting a candidate name in a manner that is influenced by the respective name relevance scores assigned to the respective candidate names and automatically assigning a name to the group of records using the candidate name.

Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for extracting topics from a corpus of exchanges. The system generates vector representations of utterances of an entity common to the exchanges and uses the vector representations to cluster the utterances. The system labels the clusters and uses the labeled clusters to generate an exchange label sequence for each of the exchanges, where each exchange label sequence corresponds to a sequence of utterances generated by the entity. The system processes the exchange label sequences to generate one or more subsets of the utterances, where each of the subsets corresponds to a particular topic.

Abstract: A computing system may receive a corpus of training data including a plurality of data entity schemas. A first data entity of a first set of data entities corresponding to a first data entity schema is associated with a topic characteristic based on a first set of attributes defined by the first data entity schema, and a first attribute of the first set of attributes is associated with a structural characteristic that is common across each of the first set of data entities. The system may identify a respective attribute type identifier for each attribute of the first set, generate an attribute embedding for each attribute using the attribute value and the identifier, generate an entity embedding based on each attribute embedding and parameterize the topic characteristic for each data entity and the structural characteristic for each attribute.

Abstract: Methods and systems for answering frequently asked questions are described. An utterance is received. A decision score that is indicative of the likelihood that the utterance is answerable according to a set of frequently asked questions and associated answers is determined for the utterance. A candidate answer from the associated answers and a selection score for the candidate answer are determined for the utterance. A total score for the candidate answer is determined based on the decision score and the selection score. The total score is indicative of the likelihood that the candidate answer is a correct answer for the utterance according to the set of frequently asked questions and associated answers.

Abstract: A system uses conversation engines to process natural language requests and conduct automatic conversations with users. The system generates responses to users in an online conversation. The system ranks generated user responses for the online conversation. The system generates a context vector based on a sequence of utterances of the conversation and generates response vectors for generated user responses. The system ranks the user responses based on a comparison of the context vectors and user response vectors. The system uses a machine learning based model that uses a pretrained neural network that supports multiple languages. The system determines a context of an utterance based on utterances in the conversation. The system generates responses and ranks them based on the context. The ranked responses are used to respond to the user.