Abstract: A dataset of classification orderings is created based on previously observed interface elements in interfaces of third-party interface providers. A request to evaluate a sequence of predicted classifications is received. The dataset is queried to determine a value derived from a frequency of the sequence of predicted classifications occurring in the dataset. A client device is caused, by responding to the request with the value, to autocomplete input to a plurality of elements corresponding to the sequence of predicted classifications if the value reaches a value relative to a threshold cause.

Abstract: A sequence of interface elements in an interface is determined, where the sequence includes a first element that immediately precedes a second element in the sequence. A first set of potential classifications for the first element is obtained. A set of local confidence scores for a second set of potential classifications of the second element is obtained. A set of sequence confidence scores is obtained by obtaining, for each second potential classification of the second set of potential classifications, a set of scores indicating probability of the second potential classification being immediately preceded in sequence by each first potential classification of the first set of potential classifications. A classification assignment for the second element is determined based on the set of local confidence scores of the first element and the set of sequence confidence scores. An operation is performed with the second element in accordance with the classification assignment.

Abstract: Source code of a form element of a web form and a predetermined data classification of the form element is obtained. A vector is generated based at least in part on a transformation of a set of keywords derived from the source code. A machine learning model is trained to predict data categories of form elements by providing, to the machine learning model, the predetermined data classification and the vector.

Abstract: A first hierarchy path associated with a first interface of a first provider is determined, with the first interface being of a first type. A second hierarchy path associated with a second interface of the first provider is determined, with the second interface being of a second type different from the first type. The first hierarchy path is determined to be a mismatch from the second hierarchy path. A third hierarchy path corresponding to another interface of the first type is obtained from a second provider. A dictionary is generated based on a determination that the third hierarchy path matches the first hierarchy path. A vector that corresponds to a third interface is generated based on the dictionary. As a result of the third interface being determined to be of the first type based on the vector, a device is caused to perform an operation against the third interface.

Abstract: A first path associated with a first interface of a first provider is determined, with the first interface being of a first type. A second path associated with a second interface of the first provider is determined, with the second interface being of a second type. The first path is determined to be a mismatch to the second path. A third path corresponding to an additional interface of the first type is obtained from a second provider. A dictionary is generated based on the third path matching the first path. Vectors corresponding to the first and second types are generated based on the dictionary. A machine learning algorithm is trained based on the vectors with the first and second types as a ground truth values.

Abstract: A first hierarchy path associated with a first interface of a first provider is determined, with the first interface being of a first type. A second hierarchy path associated with a second interface of the first provider is determined, with the second interface being of a second type different from the first type. The first hierarchy path is determined to be a mismatch from the second hierarchy path. A third hierarchy path corresponding to another interface of the first type is obtained from a second provider. A dictionary is generated based on a determination that the third hierarchy path matches the first hierarchy path. A vector that corresponds to a third interface is generated based on the dictionary. As a result of the third interface being determined to be of the first type based on the vector, a device is caused to perform an operation against the third interface.

Abstract: A first path associated with a first interface of a first provider is determined, with the first interface being of a first type. A second path associated with a second interface of the first provider is determined, with the second interface being of a second type. The first path is determined to be a mismatch to the second path. A third path corresponding to an additional interface of the first type is obtained from a second provider. A dictionary is generated based on the third path matching the first path. Vectors corresponding to the first and second types are generated based on the dictionary. A machine learning algorithm is trained based on the vectors with the first and second types as a ground truth values.

Abstract: Embodiments described herein simplify ordering of products from online shops. This may involve an agent server receiving captured contents of a product webpage from a client device and autonomously deriving product information therefrom. Alternatively, the agent service can receive a web address corresponding to a product webpage, and the agent server can visit the product webpage and capture its contents. The agent server then uses the derived product information to autonomously control ordering of the product for the user from an online shop, without requiring that the user have an account with the online shop, without requiring that the user enter personal and payment information in a check-out webpage of the online shop, without requiring that the user select a buy or shopping cart button associated with the online shop, and without requiring the online shop utilize an API to enable the agent server to interact with the online shop.

Abstract: The invention relates to a method for enabling order handling via the Internet, wherein the method is performed in a computer system comprising at least a client, an ordering server and an agent server connected to the Internet, each of the client, the ordering server and the agent server comprising at least one processor and a memory storing one or more programs for execution by the at least one processor. The invention discloses a method comprising: capturing at least a part of the contents of a website retrieved by a web browser on the client from the ordering server; transferring the captured contents to the agent server; deriving object information from the captured contents on the agent server to identify an object to be ordered; controlling the ordering server by the agent server to order the object.

Abstract: Embodiments described herein simplify ordering of products from online shops. This may involve an agent server receiving captured contents of a product webpage from a client device and autonomously deriving product information therefrom. Alternatively, the agent service can receive a web address corresponding to a product webpage, and the agent server can visit the product webpage and capture its contents. The agent server then uses the derived product information to autonomously control ordering of the product for the user from an online shop, without requiring that the user have an account with the online shop, without requiring that the user enter personal and payment information in a check-out webpage of the online shop, without requiring that the user select a buy or shopping cart button associated with the online shop, and without requiring the online shop utilize an API to enable the agent server to interact with the online shop.