Abstract: An aircraft includes a fuselage that includes a nose portion, a cabin portion, an underwing portion, and an aft portion. The nose portion includes sensors that generate sensor data. The cabin portion is aft of the nose portion and includes a passenger cabin. The underwing portion is aft of the cabin portion and includes a wing attachment region and a battery bay. The aft portion is aft of the underwing portion. A wing assembly including motor mounts and control surfaces is attached to the wing attachment region such that the underwing portion of the fuselage is located under the wing assembly. A tail assembly is attached to the aft portion. The electric motors are attached to motor mounts such that the propellers are in a pusher configuration facing rearward. An autonomous control system controls the electric motors and control surfaces based on the sensor data.

Abstract: Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.

Abstract: Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.

Abstract: Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.

Abstract: A system and method for automatically submitting an online order from a customer to a restaurant. Input data and customer data is used by an order engine to select a deployment platform, such as social media networks, search engines, mobile applications, and related websites, for a user interface. The user interface automatically populates the restaurant's menu options and business data, allowing the customer to build an order. The order engine submits the order to the restaurant via a non-verbal communication platform. An automated confirmation call is generated to the restaurant confirming receipt of the order. From the confirmation call the restaurant may choose to repeat the message, accept the order, connect to the customer, connect to the service provider, decline the order, or opt-out. The order engine allows the restaurant to monitor online orders and to enroll in the above services for subsequent online orders.

Abstract: A system and method for automatically submitting an online order from a customer to a restaurant. Input data and customer data is used by an order engine to select a deployment platform, such as social media networks, search engines, mobile applications, and related websites, for a user interface. The user interface automatically populates the restaurant's menu options and business data, allowing the customer to build an order. The order engine submits the order to the restaurant via a non-verbal communication platform. An automated confirmation call is generated to the restaurant confirming receipt of the order. From the confirmation call the restaurant may choose to repeat the message, accept the order, connect to the customer, connect to the service provider, decline the order, or opt-out. The order engine allows the restaurant to monitor online orders and to enroll in the above services for subsequent online orders.

Abstract: A system and method for automatically determining an amount of review a crowd-sourcing task needs after an initial review has been completed by a processing worker. An evaluation metric is automatically assigned to the work performed by the processing worker to determine the appropriate amount of human review required for a particular task. The evaluation metric may be calculated by accessing and evaluating a plurality of transaction categories related, but not limited to, worker characteristics, document characteristics and processing characteristics. Additionally, the evaluation metric may be used to determine compensation of the processing worker and whether a promotion or demotion is necessary. The system is also capable of balancing individual workloads based upon the evaluation metric.

Abstract: A method for generating a website includes obtaining a seed input associated with an entity. The seed input may include one or more keywords, such as a business name. Obtaining the seed input may include receiving the seed input from the user, or the seed input may be obtained without input from the user. The seed input is used to identify the entity. The method further includes retrieving, using at least one of the seed input and the identification of the entity, content relevant to the entity from one or more data stores. Retrieving the content may include using one or more categories relevant to the entity to identify the content. The website is generated without an input from the entity, and includes at least a portion of the content. Generating the website may include identifying a template having a plurality of content regions for containing the content.

Abstract: A method for generating a website includes obtaining a seed input associated with an entity. The seed input may include one or more keywords, such as a business name. Obtaining the seed input may include receiving the seed input from the user, or the seed input may be obtained without input from the user. The seed input is used to identify the entity. The method further includes retrieving, using at least one of the seed input and the identification of the entity, content relevant to the entity from one or more data stores. Retrieving the content may include using one or more categories relevant to the entity to identify the content. The website is generated without an input from the entity, and includes at least a portion of the content. Generating the website may include identifying a template having a plurality of content regions for containing the content.

Abstract: A method for generating a website includes obtaining a seed input associated with an entity. The seed input may include one or more keywords, such as a business name. Obtaining the seed input may include receiving the seed input from the user, or the seed input may be obtained without input from the user. The seed input is used to identify the entity. The method further includes retrieving, using at least one of the seed input and the identification of the entity, content relevant to the entity from one or more data stores. Retrieving the content may include using one or more categories relevant to the entity to identify the content. The website is generated without an input from the entity, and includes at least a portion of the content. Generating the website may include identifying a template having a plurality of content regions for containing the content.

Abstract: Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.