Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: Systems and methods for determining plans for ridesharing vehicle shifts for a plurality of ride requests that can provide an optimal plan are provided. The systems and methods can involve iteratively planning shifts such that a maximal number of rides can be schedules within a shift within one or more limitations.

Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: Computer implemented method for detecting a hand gesture of a user, comprising: (a) Receiving sequential logic models each representing a hand gesture. The sequential logic model maps pre-defined hand poses and motions each represented by a hand features record defined by discrete hand values each indicating a state of respective hand feature. (b) Receiving a runtime sequence of runtime hand datasets each defined by discrete hand values scores indicating current state hand features of a user's moving hand which are inferred by analyzing timed images depicting the moving hand. (c) Submitting the runtime hand datasets and the pre-defined hand features records in SSVM functions to generate estimation terms for the runtime hand datasets with respect to the hand features records. (d) Estimating which of the hand gestures best matches the runtime sequence depicted in the timed images by optimizing score functions using the estimation terms for the runtime hand datasets.

Abstract: The distribution of media clips stored on one or more servers is controlled using updateable permissions or rules defined by a content owner. The clip is made available from a server via a website, app or other source, for an end-user to view; the permissions or rules stored in memory are then updated; the permissions or rules are reviewed before the clip is subsequently made available, to ensure that any streaming or other distribution of the clip is in compliance with any updated permissions or rules.

Abstract: Online selling mechanism creation is described, for example, whereby a retailer with business goals and constraints benefits from automatic generation and execution of software which controls generation of online offers so as to meet the business goals within the constraints. For example, as business goals and constraints change over time, bespoke selling mechanisms may be automatically updated. In various examples business goals are used to select selling mechanisms from a plurality of available online selling mechanisms; properties of the selling mechanisms may be taken into account. In examples, generic software implementing the selling mechanisms is used to instantiate a bespoke selling mechanism according to particular business goals and constraints. For example, a bespoke selling mechanism may be executed at a commerce server so as to control dynamic generation of offers.

Abstract: An advertisement system which uses a retailer inventory is described, for example, to dynamically generate advertisements with data from the retailer inventory. In various examples product data from a retailer inventory such as numbers of remaining units of a product, is available to an advertising server by querying a retailer inventory. In various examples a retailer is able to create an advertisement design for a selling period; the design is used to dynamically generate advertisements in response to advertisement impression requests from information retrieval systems, dedicated sales sites, and other entities. In various examples, prices, minimal bids and other data is calculated using data retrieved from a retailer inventory and input to advertisements generated for display at end user devices. In an example group buying is facilitated by generating advertisements stating a current number of participants out of a target number of participants.

Abstract: A computerized method of adapting an event management framework comprising providing an event processing network (EPN) which models processing of a plurality of incoming events by the event management framework, providing at least one goal specifying a target value of at least one measurable attribute of the event management framework, performing a plurality of simulations on the EPN, each simulation of the processing of the plurality of incoming events according to a different set of a plurality of control values defining a behavioral pattern of at least one event processing agent of the EPN, selecting a control values set from the plurality of control values sets according to a match between an outcome of the plurality of simulations and the at least one target value, and adapting the event management framework according to the selected control values set.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: A user utility function is implemented in allocating advertisement space to one or more potential advertisers. The user utility function allows advertisement space to be allocated based upon, among other things, the expected utility or usefulness that a proposed advertisement will have to a user. The user utility function, for example, compares proposed advertisements to historical user actions to generate respective user utility values for advertisements (e.g., based upon user responses to advertisements for particular types of product, responses to advertisements from particular types of sellers, etc.). The user utility values can then be applied to bids submitted by advertisers for advertisement space for particular advertisements to obtain modified bids. The modified bids thus reflect, among other things, the expected utility of an advertisement to a user, and thus allow an advertisement host to allocate advertisement space accordingly.