Abstract: Systems and devices for producing biological products and methods of using the same are provided. An example universal skid includes a rigid support and a plurality of flow-channel hardware sets. Each flow-channel hardware set couples with a respective flow channel of a plurality of flow channels for unit operations. The flow-channel hardware sets allow the flow channels to operate in parallel or in series for continuous processing over the unit operations. An example modular assembly includes one or more single-use kits for one or more unit operations and a universal skid coupled with the one or more single-use kits. An example manufacturing system includes multiple modular assemblies coupled with each other allowing continuous processing within each modular assembly, in between the modular assemblies, fully continuous processing across multiple unit operations.

Abstract: The present disclosure relates to methods and systems for the continuous production of recombinant proteins. In particular embodiments, the disclosure relates to methods and systems using capture chromatography, post-capture chromatography, virus filtration, and ultrafiltration/diafiltration for the continuous production of recombinant proteins.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: Micro-donations are received from users for various items of content published by different content providers. The micro-donations are aggregated into the user accounts and the content provider accounts until a transaction condition is satisfied. When the transaction condition is satisfied for the user account, a process to collect the micro-donation amount contributed by a respective user to different content providers is effectuated. When the transaction condition is satisfied for a content provider account, the micro-donation amount received from different users and aggregated within the content provider account is provided to the respective content provider. Various user and content provider incentives are implemented to encourage participation by the users and the content providers.

Abstract: Micro-donations are received from users for various items of content published by different content providers. The micro-donations are aggregated into the user accounts and the content provider accounts until a transaction condition is satisfied. When the transaction condition is satisfied for the user account, a process to collect the micro-donation amount contributed by a respective user to different content providers is effectuated. When the transaction condition is satisfied for a content provider account, the micro-donation amount received from different users and aggregated within the content provider account is provided to the respective content provider. Various user and content provider incentives are implemented to encourage participation by the users and the content providers.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: Embodiments of the present invention provide for methods, systems and computer program products for utilizing a lattice structure in an advertisement serving system. The method according to one embodiment of the present invention comprises storing one or more advertisement contracts in a lattice structure. A request for one or more advertisements that satisfy one or more constraints is then received and within the lattice structure, a lowest level of one or more pools of advertisement contracts satisfying the one ore more constraints is located. A set of advertisement contracts is generated, wherein the set of advertisement contracts comprise one or more advertisement contracts from lowest level of the lattice structure. The method further comprises adding to the set of advertisement contracts advertisement contracts from the pools of advertisement contracts that are parent to the pools of contracts in the lowest level of the lattice structure.

Abstract: A method for indexing advertising contracts for rapid retrieval and matching in order to match only the top N satisfying contracts to advertising slots. Descriptions of advertising contracts include logical predicates indicating weighted applicability to a particular demographic. Descriptions of advertising slots also contain logical predicates indicating weighted applicability to particular demographics, thus matches are performed on the basis of a weighed score of intersecting demographics. Disclosed are structure and techniques for receiving a set of contracts with weighted predicates, preparing a data structure index of the set of contracts, receiving an advertising slot with weighted predicates, and retrieving from the data structure only the top N weighted score contracts that satisfy a match to the advertising slot predicates.

Abstract: A method for indexing advertising contracts for rapid retrieval and matching in order to match satisfying contracts to advertising slots. The descriptions of the advertising contracts include logical predicates indicating applicability to a particular demographic. Also, the descriptions of advertising slots contain logical predicates indicating applicability to a particular demographic, thus matches can be performed using at least matches on the basis of intersecting demographics. The disclosure contains structure and techniques for receiving a set of contracts with predicates, preparing a data structure index of the set of contracts, receiving an advertising slot with predicates, and structure and techniques for retrieving from the data structure contracts that satisfy a match to the advertising slot predicates.

Abstract: A system and method to facilitate automatic categorization of events in a network, wherein one or more keywords are retrieved from a keyword database, each retrieved keyword matching a corresponding event unit of an event input by a user over a network. A dominant keyword corresponding to a highest parameter value calculated for each retrieved keyword is then selected. Finally, the event is categorized based on one or more categories associated with the dominant keyword. The dominant keyword may be selected based on one or more keyword categories associated with each retrieved keyword and an ambiguity parameter value calculated for each keyword. Alternatively, the dominant keyword may be selected based on a highest-ranked output value calculated for each retrieved keyword. One or more categories associated with the dominant keyword are subsequently retrieved from the keyword database and the event is categorized based on the category associated with the dominant keyword.

Abstract: The present invention is directed to systems and methods for identifying compatible advertisement contracts to satisfy an advertisement opportunity. The systems and methods include receiving a number of attribute values associated with a user accessing a network-based location. The systems and methods include sorting an index of lists for a plurality of advertisement contracts on the basis of a number of contract term values. The systems and methods include traversing the index of lists to return a set advertisement contracts compatible with the number of attribute values associated with the user. The systems and methods include selecting a given advertisement contract from the set of advertisement contracts compatible with the number of attribute values associated with the user. The systems and methods include presenting an advertisement associated with the given advertisement contract on a display viewable by the user accessing the network-based location.

Abstract: Subject matter disclosed herein relates to a system for managing online resources, and in particular, to a system using an offline process to optimize the management of such online resources.

Abstract: Generally, embodiments of the present invention provide for methods, systems and computer program products for utilizing date constraints in an advertisement serving system. The method according to one embodiment of the present invention comprises receiving a request for one or more advertisements that satisfy one or more constraints. A set of advertisement contracts is generated, wherein the set of advertisement contracts comprise one or more advertisement contracts that satisfy the one or more constraints. The contractual time period for a given advertisement contract in the set of advertisement contracts is then determined. A subset of advertisement contracts is then generated, wherein the subset of advertisement contracts comprises the advertisement contracts in the set of advertisement contracts that are active on a given date. The subset of advertisement contracts is then inputted into an optimization algorithm and one or more advertisement contracts are selected by the optimization algorithm.

Abstract: The present invention is directed to a system and method for optimizing an advertisement plan for allocating advertisements to a contract in a network-based environment. The system and method includes determining a shadow price for an advertisement placement where the advertisement placement is associated with a user having defined characteristics. The system and method includes determining a contract price for the advertisement placement relative to a plurality of advertisement contracts for the user having the defined characteristics. The system and method includes determining an advertisement plan for allocating an advertisement to a given contract on the basis of the shadow price and the contract price. The system and method includes presenting a given advertisement associated with the given contract according to the advertisement plan on network-based output.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the pre-processed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive mode. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A system and method to facilitate automatic weighting of events in a network and targeting of advertising information to users within the network based on assigned event weights are described. Multiple events associated with a user are retrieved from a data storage module. Each event is further analyzed to extract one or more event features. A weight parameter value is further calculated for each retrieved event. Each event is further assigned to a predetermined category based on the calculated weight parameter value. Finally, each event and the associated weight parameter value are stored within the data storage module in connection with the predetermined category.