Abstract: Machine learning techniques are used in combination with graph data structures to perform automated classification of accounts. Graphs may be constructed using a seed node and then expanded outward to second-degree nodes and third-degree nodes that are connected to a seed user account node via direct interaction between the accounts. Characterization information regarding the interaction between accounts can be stored in the graph (e.g., quantity of interactions, types of interactions) as well as other metrics and metadata. A classifier, using random forest or another technique, may be trained using a number of different graphs that can then be used to reach a determination as to whether a user account falls into one particular category or another. These techniques can identify accounts that may be violating terms of service, committing a security violation, and/or performing illegal actions in a way that is not ascertainable from human analysis.

Abstract: Machine learning techniques are used in combination with graph data structures to perform automated classification of accounts. Graphs may be constructed using a seed node and then expanded outward to second-degree nodes and third-degree nodes that are connected to a seed user account node via direct interaction between the accounts. Characterization information regarding the interaction between accounts can be stored in the graph (e.g., quantity of interactions, types of interactions) as well as other metrics and metadata. A classifier, using random forest or another technique, may be trained using a number of different graphs that can then be used to reach a determination as to whether a user account falls into one particular category or another. These techniques can identify accounts that may be violating terms of service, committing a security violation, and/or performing illegal actions in a way that is not ascertainable from human analysis.

Abstract: Methods and systems are presented for classifying a particular user account as a fraudulent user account by analyzing links between the user account and two or more known fraudulent user accounts collectively. Attributes of the particular user account are compared against attributes of a plurality of known fraudulent accounts to determine that the particular user account has shared attributes with a first known fraudulent account and a second known fraudulent account. The shared attributes with the first known fraudulent account and the second known fraudulent account are analyzed collectively to determine a risk level for the particular user account. The risk level may indicate a likelihood that the particular user account corresponds to a fraudulent account.

Abstract: The present invention is a Distally Assembled Steerable Cannula (DASC), a robotically-manipulated device that can be deployed and extended within a patient's body by growing from its distal end, or can be used in non-medical applications. In some embodiments, growth occurs by sequentially assembling segments that interlock to form a rigid tube with a complex 3-D shape. The segments are individually transported through the growing cannula, and then assembled at the distal end. A segment can be wedge-shaped in profile, allowing adjustment of the local radius and plane of curvature of the cannula to be controlled by relative segment orientation.

Abstract: Various systems, mediums, and methods may involve a data engine with various components. For example, a system with the data engine may include a segmentation component, an asset preparation component, a clustering component, a variable generation component, and classification component. As such, the system may determine a number of assets associated with a number of activities of one or more accounts. Further, the system may determine various links associated with the number of assets. As such, the system may detect an attack and/or an attack trend associated with the one or more accounts based on the various links associated with the number assets. Further, the system may generate a notification that indicates the attack and/or the attack trend detected.

Abstract: The present invention is a Distally Assembled Steerable Cannula (DASC), a robotically-manipulated device that can be deployed and extended within a patient's body by growing from its distal end, or can be used in non-medical applications. In some embodiments, growth occurs by sequentially assembling segments that interlock to form a rigid tube with a complex 3-D shape. The segments are individually transported through the growing cannula, and then assembled at the distal end. A segment can be wedge-shaped in profile, allowing adjustment of the local radius and plane of curvature of the cannula to be controlled by relative segment orientation.

Abstract: Placing an event into a particular cluster can allow various inferences about the event. A new payment transaction that looks similar to a previously identified cluster of mostly fraudulent payment transactions, for example, may be higher risk. The present disclosure includes structural data improvements to the way that online clustering of events (which may include web events and not just payment transactions) occurs. A new event can be classified into a particular segment very quickly using feature table searching, which can allow for better decision making when a short timeframe is required (e.g. transaction processing, online advertising, etc.).

Abstract: Methods and apparatus for automatically preparing food for consumption in which preparation comprises dispensing, manipulation, heating, and other operations using a wide variety of ingredients. The methods and apparatus described use ingredients efficiently and maintain their quality, while avoiding contact between ingredients and apparatus to minimize the risk of system contamination.

Abstract: Methods and apparatus for automatically preparing food for consumption in which preparation comprises dispensing, manipulation, heating, and other operations using a wide variety of ingredients. The methods and apparatus described use ingredients efficiently and maintain their quality, while avoiding contact between ingredients and apparatus to minimize the risk of system contamination.

Abstract: A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.

Abstract: A computer system receives a goal for an environment, wherein the environment corresponds to at least one webpage. The computer system receives one or more classifiers corresponding to the environment, wherein the one or more classifiers provide information corresponding to a current webpage and information corresponding to one or more previous actions taken by a web crawler. The computer system identifies a recommended next action based on the one or more classifiers. The computer system transmits the recommended next action to the web crawler to cause the web crawler to perform the recommended next action.

Abstract: Placing an event into a particular cluster can allow various inferences about the event. A new payment transaction that looks similar to a previously identified cluster of mostly fraudulent payment transactions, for example, may be higher risk. The present disclosure includes structural data improvements to the way that online clustering of events (which may include web events and not just payment transactions) occurs. A new event can be classified into a particular segment very quickly using feature table searching, which can allow for better decision making when a short timeframe is required (e.g. transaction processing, online advertising, etc.).

Abstract: The present invention includes a process, system and apparatus for multi-material additive manufacturing process comprising: extruding an extrudable material through a nozzle capable of moving along one or more axis and concurrently extruding one or more filaments, wherein the filament is embedded in, on or about the extrudable material from the nozzle.

Abstract: Methods, systems, and computer programs, for predicting a likely outcome for an entity. A method includes obtaining a first data structure that includes data that represents a transaction, determining a similarity level of the transaction to each of a plurality of categories, determining a transaction category based on the determined similarity level, generating an input data structure that includes data representing (i) at least a portion of the data representing the transaction, and (ii) data describing the determined category, providing the input data structure to a predictive algorithm trained to determine a value that represents a likely outcome for an entity that initiated a transaction, obtaining output generated by the predictive algorithm based on the predictive algorithm's processing of the input data structure, the output including a value that represents a likely outcome for the entity, and determining a likely outcome for the entity based on the obtained output.

Abstract: A method is provided that includes accessing, by a server provider server of a service provider, a database storing associations between network addresses and locations. Additionally, the method includes determining a subset of the database corresponding to a first network address, each association included in the subset corresponding to an association between the first network address and a respective location. The method also includes in response to determining that the subset of the database satisfies one or more clustering criteria, calculating a representative location corresponding to the first network address, and storing an association between the first network address and the representative location in a second database.