Abstract: The present disclosure describes systems and methods for acquiring structural attributes of a building. One such method comprises acquiring, by one or more cameras of an unmanned aerial vehicle, infrared thermal and visible RGB aerial images of a building; identifying structural components of the building based on an infrared thermal aerial image, wherein a floor structure of the building is identified from a color change in the infrared thermal aerial image between the building and a foundation structure of the building; positioning a bounding box around the floor structure of the building present in the infrared thermal aerial image; and estimating a lowest floor elevation of the building by calculating a lowest floor elevation value based on an amount of image pixels representing a distance between a top of the bounding box and a top of the foundation structure of the building. Other methods and systems are also provided.

Abstract: In some embodiments, apparatuses and methods are provided to support individuals in a retail environment. Some embodiments, provide systems, comprising: a plurality of cameras positioned in a retail store; an image processing control circuit configured to determine a customer field of view; a product identification control circuit configured to identify a set of multiple products within the identified customer field of view, assign a viewing probability to each product without the first customer having to touch the first product; a product selection control circuit configured to determine a purchase probability of the first product; add the first product to a virtual electronic cart when the purchase probability of the first product exceeds a purchase probability threshold; and communicate instructions to control the worker personal computing device to instruct a worker to retrieve the first product.

Abstract: Systems and methods electronically produce a local estimate of less-than-critical resources based on a client-side version of digital rules and coarse values received from an online service provider. Although using the client-side version of digital rules and the coarse values may not include all the parameters and values needed to provide a fully accurate estimate of the resource, the ability to locally estimate resources without having to make network calls to the online service provider, such as when there are unfavorable conditions or latency of the network, imminent overloading of the online service provider or other operating conditions or demands on the online service provider preventing it from producing a timely more accurate estimate, provides a faster and more efficient way of obtaining a potentially useful estimate of resources.

Abstract: In some examples, a software agent executing on a server receives a communication comprising a first utterance from a customer and predicts, using an intent classifier, a first intent of the first utterance. Based on determining that the first intent is order-related, the software agent predicts, using a dish classifier, a cart delta vector based at least in part on the first utterance and modifies a cart associated with the customer based on the cart delta vector. The software agent predicts, using a dialog model, a first dialog response based at least in part on the first utterance and provides the first dialog response to the customer using a text-to-speech converter.

Abstract: Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.

Abstract: Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.

Abstract: An Online Software Platform (OSP) may receive a first dataset from a remote client device, and compute an estimated resource, from the first dataset and also per stored digital rules. The OSP may then communicate an aspect of the resource to the remote client device and receive a confirmation of the first dataset. The OSP may then process the first dataset, for matching it with another dataset, either internally or by outsourcing. Either way, the OSP may establish that the first dataset has been matched with a compatible second dataset. The OSP may then input a resource for the first dataset, which is determined according to stored digital rules. The OSP may further create one or more notifications and documents about the matching and the resource, and make them available to the remote client device or even to the device of another remote entity.

Abstract: A computer system may connect to various customer-facing devices and manage or automate the order process between a retail store and the customer. The computer system may perform the dialogue and receive an order for items from the retail store and may perform quality control monitoring of the dialogue between customers and employees taking orders. The ordering system may utilize the ordered items in combination with various contextual cues to determine a customer identity which may then be linked to past orders and/or various order preferences. Based on the determined customer identity, the system may provide recommendations of additional order items or order alterations to the customer before personally identifying information has been collected from the customer. The determination of the customer identity and the determination of recommendations may be performed by machine learning algorithms that were trained on customer data and the retail store products.

Abstract: Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.

Abstract: Systems and methods electronically produce a local estimate of less-than-critical resources based on a client-side version of digital rules and coarse values received from an online service provider. Although using the client-side version of digital rules and the coarse values may not include all the parameters and values needed to provide a fully accurate estimate of the resource, the ability to locally estimate resources without having to make network calls to the online service provider, such as when there are unfavorable conditions or latency of the network, imminent overloading of the online service provider or other operating conditions or demands on the online service provider preventing it from producing a timely more accurate estimate, provides a faster and more efficient way of obtaining a potentially useful estimate of resources.

Abstract: In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

Abstract: Systems and methods electronically produce a local estimate of less-than-critical resources based on a client side version of digital rules and a coarse values received from an online service provider. Although using the client side version of digital rules and the coarse values may not include all the parameters and values needed to provide a fully accurate estimate of the resource, the ability to locally estimate resources without having to make network calls to the online service provider, such as when there are unfavorable conditions or latency of the network, imminent overloading of the online service provider or other operating conditions or demands on the online service provider preventing it from producing a timely more accurate estimate, provides a faster and more efficient way of obtaining a potentially useful estimate of resources.

Abstract: In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

Abstract: Systems and methods electronically generate sample dots, produce resources associated with the dots and estimate a resource for a target point from known resources of dots near the target point based on client side version of digital rules, cataloged data and coarse values previously received from the online service platform. A client receives cataloged data of a cataloged domain in which the cataloged data includes data representing a plurality of dots and a respective computed resource value for each of the dots and each dot of the plurality of dots represents a point in the cataloged domain. In response to confirming a target point is in the cataloged domain, the system discovers a closest one or more dots to the target point based on the cataloged data, estimates a statistic for a resource for the target point based on the respective computed resource values of the closest one or more dots, stores the estimated statistic in a memory, and produces the local estimate based on the estimated statistic.

Abstract: In some examples, a server may receive an utterance from a customer. The utterance may be included in a conversation between the artificial intelligence engine and the customer. The server may convert the utterance to text and determine a customer intent based on the text and a user history. The server may determine a user model of the customer based on the text and the customer intent. The server may update a conversation state associated with the conversation based on the customer intent and the user model. The server may determine a user state based on the user model and the conversation state. The server may select, using a reinforcement learning based module, a particular action from a set of actions, the particular action including a response and provide the response to the customer.

Abstract: An immersive device and method for streaming of immersive media are provided. The method includes receiving body motion coordinates of a user in each of three dimensions and determining deviation in the body motion by a first immersive device. A dominant direction of motion based on the deviation calculated is then determined. A request by the first immersive device for at least one media frame to be displayed is received by a second immersive device. The received request is processed by the second immersive device and the media frame to be displayed is segmented into a plurality of tiles by the second immersive device. One tile with the first pixel density and a remainder of the tiles of the plurality of tiles with a second pixel density are stitched by the second immersive device to render one immersive media frame which is displayed to the user.

Abstract: In some aspects, an edge appliance is placed in an active mode and causes a software agent that is based on a machine learning algorithm to engage in a conversation to take an order from a customer that is located at an order post. The edge appliance provides, using a communication interface, audio data that includes the conversation, to a communications system of a restaurant. The edge appliance provides, using the communication interface, a content of a cart associated with the order to a point-of-sale terminal of the restaurant. If the edge appliance determines, using the communication interface, that a microphone of the communication system is receiving audio input from an employee, the edge appliance automatically transitions the edge appliance from the active mode to an override mode, enabling the employee to receive a remainder of the order from the customer.

Abstract: In some aspects, a computing device receives a scan of a code displayed on an order post located near a restaurant, determines that the code is associated with the restaurant, and automatically opens a software application and navigates the software application to an ordering page associated with the restaurant. The computing device initiates receiving, via the software application, input associated with an order, sends the input to a machine learning based software agent executing on a server, receives a predicted response to the input, provides the predicted response as audio output and/or displays the predicted response on the touchscreen display device. After the order is complete, the computing device sends order data associated with the order to the restaurant. After receiving an indication from the restaurant that the order is ready, the computing device indicates that the order is ready to be picked up.

Abstract: A software agent, comprising a machine learning algorithm trained to engage in a conversation with a customer to take an order, receives an utterance from a customer. The utterance is converted to text and an analysis of the text performed. If the software agent determines, based on the analysis, that the software agent is untrained to respond to the text, the software agent establishes a connection to a point-of-sale device associated with a human agent. The human agent may perform a modification (e.g., an edit to the text, a modification to a cart, or provide input) to a modifiable portion displayed by the point-of-sale device. The software agent, based at least in part on the modification, resumes the conversation with the customer. The human agent does not directly interact with the customer during the conversation between the software agent and the customer.

Abstract: A computer system may connect to various customer-facing devices and manage or automate the order process between a retail store and the customer. The computer system may perform the dialogue and receive an order for items from the retail store and may perform quality control monitoring of the dialogue between customers and employees taking orders. The ordering system may utilize the ordered items in combination with various contextual cues to determine a customer identity which may then be linked to past orders and/or various order preferences. Based on the determined customer identity, the system may provide recommendations of additional order items or order alterations to the customer before personally identifying information has been collected from the customer. The determination of the customer identity and the determination of recommendations may be performed by machine learning algorithms that were trained on customer data and the retail store products.