Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: When a chatbot application is in a mode of live chat conversation with a user, a trained intent classifier determines an intent that underlies a first live question received by the trained intent classifier from the user. A trained next predictor receives the intent from the intent classifier. The trained next intent predictor generates a set of predicted next intents responsive to receiving the intent. A trained re-ranker selects at least one of the predicted next intents responsive to receiving the set of predicted next intents. A question selection engine sends at least one suggested question to the user responsive to receiving the at least one predicted next intent. As a result of the above, the chatbot application provides to the user at least one suggested next question the user may wish to ask in response to the first question from the user.

Abstract: Systems and methods for grouping telematics vehicle data in one or more zones are provided herein. Systems and methods for generating zones and providing vehicle information associated with the one or more zones are also provided. Systems and methods for parallelizing zone generation and addressing zones disposed adjacent a boundary of a geographical are also provided.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Disclosed are systems and methods relating to defining a road network used by vehicles for movement and/or parking. A classifier may be employed for identifying portions of the road network via machine learning techniques and processing of historical telematic data.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: A method, system, and computer program product for routing queries to answer resources based on component parts and intents of a received query is provided. The method receives a query from a user. The query is analyzed to identify a set of entities associated with the query and generate an utterance representing the query. The method generates an intent classification for the utterance and a vector for the query. The vector is generated based on the set of entities, the utterance, and the intent classification. The method determines an answer resource for the query based on the vector and the intent classification of the query. In response to determining the answer resource, the method provides an answer interface based on the query, the vector, and the intent classification. The answer interface dynamically provides a response to the query.

Abstract: Provided herein are peptide amphiphiles (PAs). In some embodiments, provided herein are PEGylated PAs (e.g. nonionic PAs). In some embodiments, the peptide amphiphiles are assembled into nanofibers. The nanofibers may further comprise a growth factor protein, which may bind to a growth factor binding sequence presented on the nonionic PA. In some embodiments, the nanofibers further comprise a charged peptide amphiphile. The charged peptide amphiphile may be bound to a growth factor protein. Further provided herein are methods of use of the peptide amphiphiles and compositions comprising the same, such as for regenerative therapy.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: For a chat bot, first intent data is provided, including a first instance of sample utterances in a first natural language and intents corresponding to the respective sample utterances. The sample utterances are translated by a computer system to a second natural language as a second instance of the sample utterances. The sample utterances of the second instance are translated by a computer system back to the first natural language as a third instance of the sample utterances. Second intent data is provided including the third instance of the sample utterances and the corresponding intents. An intent classifier of the chat bot is trained to identify respective intents of real time utterances, wherein the identifying is responsive to the intent classifier receiving the real time utterances from a user when the chat bot is in an operating mode and the training includes training the intent classifier on the first and second intent data.

Abstract: For a chat bot, first intent data is provided, including a first instance of sample utterances in a first natural language and intents corresponding to the respective sample utterances. The sample utterances are translated by a computer system to a second natural language as a second instance of the sample utterances. The sample utterances of the second instance are translated by a computer system back to the first natural language as a third instance of the sample utterances. Second intent data is provided including the third instance of the sample utterances and the corresponding intents. An intent classifier of the chat bot is trained to identify respective intents of real time utterances, wherein the identifying is responsive to the intent classifier receiving the real time utterances from a user when the chat bot is in an operating mode and the training includes training the intent classifier on the first and second intent data.

Abstract: A computer-implemented method, a computer system, and a computer program product for automatically generated conversation output is provided. The present invention may include training an answer-intent classifier to associate an intent expressed in an example question with an example answer that is responsive to the example question. The present invention may further include classifying, using the trained answer-intent classifier, a content transmitted to the trained answer-intent classifier with the intent expressed in the example question in response to determining, using the trained answer-intent classifier, that the content includes relevant information for generating the example answer that is responsive to the example question.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: When a chatbot application is in a mode of live chat conversation with a user, a trained intent classifier determines an intent that underlies a first live question received by the trained intent classifier from the user. A trained next predictor receives the intent from the intent classifier. The trained next intent predictor generates a set of predicted next intents responsive to receiving the intent. A trained re-ranker selects at least one of the predicted next intents responsive to receiving the set of predicted next intents. A question selection engine sends at least one suggested question to the user responsive to receiving the at least one predicted next intent. As a result of the above, the chatbot application provides to the user at least one suggested next question the user may wish to ask in response to the first question from the user.

Abstract: Disclosed herein are embodiments of a clamp assembly for attaching a tonneau cover to a vehicle. The clamp assembly can include a bow hanger comprising a main body extending between first and second ends and a protrusion extending away from the main body and along a portion of a length thereof. The clamp assembly can have a bow slide comprising a head configured to surround and move along the bow hanger. The clamp assembly can have a slot configured to receive the protrusion of the bow hanger, wherein engagement between the protrusion and the slot of the bow slide prevents the bow slide from rotating around the bow hanger. In some embodiments, the clamp assembly includes a handle coupled to the bow slide and a cam follower. The cam follower can engage rails of the handle which can be disposed either internally or externally of the handle.

Abstract: Disclosed are systems and methods relating to determining geographic locations of vehicle ways which are employed by vehicles for movement and/or parking. A classifier may be defined for identifying portions of the vehicle ways via machine learning techniques and processing of historical telematic data.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.