Abstract: Methods and systems are provided for utilizing enhanced SIM cards with integrated SIM cards. An example SIM card may comprise an integrated SIM card that is smaller than the SIM card; with the SIM card comprising a plurality of punched cutouts adjacent to the integrated SIM card. The punched cutouts may be arranged such that the integrated SIM card can be detached from the SIM card. Each one of the punched cutouts may line up with at least one electrical contact of the integrated SIM card, and a width of each one of the punched cutouts may be set based on at least one dimension of the electrical contact of the integrated SIM card. The punched cutouts may be arranged based on a direction of insertion of the integrated SIM card. The SIM card may comprise engraved lines arranged based on the punched cutouts.

Abstract: A radio frequency identification (RFID)-enabled article includes a housing formed from a first material and having an RF chamber, and an RFID transponder arranged within the chamber, the RFID transponder including an antenna for transmitting and receiving data. The RF chamber is dimensioned to form a pocket around the antenna such that the antenna is separated by inner surfaces of the chamber by a predefined minimum distance.

Abstract: An insert for a chip card includes a body provided with a cavity in which is inserted an electronic module provided with a microelectronic chip connected to an inductive or capacitive coupling. The body has a stack of layers at least a first layer of which comprises a first booster antenna and a second layer of which comprises a second booster antenna, the various booster antennas being coupled together inductively and/or capacitively, and at least one of the booster antennas being coupled inductively and/or capacitively with the coupling of the module. The body furthermore comprises at least one metal plate disposed between two layers of ferrite, the first and second booster antennae and the metallic plate being arranged in such a way that at least one of the two booster antennas and the electronic module remain coupled together inductively and/or capacitively, despite the presence of the metal plate.

Abstract: A method and an arrangement for detecting and counting articles are disclosed, performing the steps of: providing a detecting station arranged along a passageway for the articles, the detecting station being adapted to establish at least a scanning radiation beam at a cross section of the passageway; passing articles past the detecting station, the detecting station detecting an article upon sensing at least a partial interruption of the scanning radiation beam; and obtaining a count of the detected articles through the passageway, wherein the detecting station include first and second detecting assembly arranged at a predetermined distance from each other along the direction of travel of the articles, which first and second detecting assembly establish a respective scanning radiation beam on a first, respectively second scanning plane at a corresponding cross section of the passageway, and wherein at least a first predetermined reference section of the articles is detected at each scanning plane; a travel

Abstract: The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

Abstract: Methods, systems, and apparatus for efficiently performing a computation of a convolutional neural network layer. One of the methods includes transforming a X by Y by Z input tensor into a X? by Y? by Z? input tensor, wherein X? is smaller than or equal to X, Y? is smaller than or equal to Y, and Z? is larger than or equal to Z; obtaining one or more modified weight matrices, wherein the modified weight matrices operate on the X? by Y? by Z? input tensor to generate a U? by V? by W? output tensor, and the U? by V? by W? output tensor comprises a transformed U by V by W output tensor, wherein U? is smaller than or equal to U, V? is smaller than or equal to V, and W? is larger than or equal to W; and processing the X? by Y? by Z? input tensor using the modified weight matrices to generate the U? by V? by W? output tensor, wherein the U? by V? by W? output tensor comprises the U by V by W output tensor.

Abstract: There is disclosed a novel system and method for building artificial neural networks for a given task. In an embodiment, the method utilizes one or more network models that define the probabilities of nodes and/or interconnects, and/or the probabilities of groups of nodes and/or interconnects, from sets of possible nodes and interconnects existing in a given artificial neural network. These network models can be constructed based on the properties of one or more artificial neural networks, or constructed based on desired architecture properties. These network models are then used to build combined network models using a model combiner module. The combined network models and random numbers generated by a random number generator module are then used to build one or more new artificial neural network architectures. New artificial neural networks are then built based on the newly built artificial neural network architectures and are trained for a given task.

Abstract: Methods, systems, and apparatus for efficiently performing a computation of a convolutional neural network layer. One of the methods includes transforming a X by Y by Z input tensor into a X? by Y? by Z? input tensor, wherein X? is smaller than or equal to X, Y? is smaller than or equal to Y, and Z? is larger than or equal to Z; obtaining one or more modified weight matrices, wherein the modified weight matrices operate on the X? by Y? by Z? input tensor to generate a U? by V? by W? output tensor, and the U? by V? by W? output tensor comprises a transformed U by V by W output tensor, wherein U? is smaller than or equal to U, V? is smaller than or equal to V, and W? is larger than or equal to W; and processing the X? by Y? by Z? input tensor using the modified weight matrices to generate the U? by V? by W? output tensor, wherein the U? by V? by W? output tensor comprises the U by V by W output tensor.

Abstract: Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: An analog neuromorphic circuit is disclosed having resistive memories that provide a resistance to each corresponding input voltage signal. Input voltages are applied to the analog neuromorphic circuit. Each input voltage represents a vector value that is a non-binary value included in a vector that is incorporated into a dot-product operation with weighted matrix values included in a weighted matrix. A controller pairs each resistive memory with another resistive memory. The controller converts each pair of resistance values to a single non-binary value. Each single non-binary value is mapped to a weighted matrix value included in the weighted matrix that is incorporated into the dot-product operation with the vector values included in the vector. The controller generates dot-product operation values from the dot-product operation with the vector and the weighted matrix where each dot-product operation is a non-binary value.

Abstract: A memory control unit for handling data stored in a memory device includes a first interface to an interconnection with at least one memory bank; a second interface for communicating with a data requesting unit; and a memory quantization unit. The memory quantization unit is configured to: obtain, via the first interface, a first weight value from the at least one memory bank; quantize the first weight value to generate at least one quantized weight value having a shorter bit length than a bit length of the first weight value; and communicate the at least one quantized weight value to the data requesting unit via the second interface.

Abstract: Systems, methods, and apparatuses are provided for permeability prediction. The method acquires data associated with one or more geological formations, calculates, using processing circuitry and a trained Hidden Markov model, log-likelihood values to group the data into a plurality of clusters, and trains an artificial neural network for each of the plurality of clusters when the mode of operation is training mode. Further, the method acquires one or more formation properties corresponding to a geological formation, determines using the trained Hidden Markov model, a log-likelihood score associated with the one or more formation properties, identifies a cluster associated with the one or more formation properties as a function of the log-likelihood score, and predicts a permeability based at least in part on the one or more formation properties and a trained artificial neural network associated with the identified cluster when the mode of operation is forecasting mode.

Abstract: A platform for providing a task is provided based on a deep learning neural network. A method implemented by a computer includes extracting conversation information from at least one message application installed in an electronic device, extracting task information from the conversation information by using a convolutional neural network (CNN) learning model for natural language processing and a recurrent neutral network (RNN) learning model for creating a sentence, and providing the task information through a task providing application installed in the electronic device.

Abstract: An individual ising device connected to common buses includes neuron circuits, a memory, and a router. The memory holds connection destination information per neuron circuit. An individual item of connection destination information includes first address information identifying one of a plurality of connection destination neuron circuits of a neuron circuit and second address information identifying a first ising device including at least one of the connection destination neuron circuits, the first and second address information being correlated.

Abstract: Various systems and methods are provided that identify prior art patent references for a subject patent application. For example, the system preprocesses a corpus of patent references to identify keywords that are present in each of the patent references, n-grams present in the corpus, and a weighting associated with the identified n-grams. To identify prior art patent references, the system requests a user to provide a patent application. The system extracts n-grams found in the provided patent application and orders the n-grams based on the assigned n-gram weights. The system compares the top Y-rated n-grams with the identified keywords and retrieves patent references that include a keyword that matches one of the top Y-rated n-grams. The system re-ranks the retrieved patent references using, for example, artificial intelligence. The top Z-ranked retrieved patent references are transmitted to a user device for display in a user interface.

Abstract: The semiconductor device comprises a controller, a frame memory, and a register. The controller comprises a control circuit and a prediction circuit. The frame memory comprises a memory device and a monitor circuit. The register comprises a first memory circuit and a second memory circuit. The second memory circuit comprises a transistor which includes a metal oxide in a channel formation region. The prediction circuit is configured to predict the necessity of power supply to the register using a neural network and outputting a first signal corresponding to a result of the prediction to the control circuit. The control circuit is configured to save data stored in the first memory circuit to the second memory circuit on the basis of the first signal. The monitor circuit is configured to output a second signal containing information on power consumption of the memory device to the prediction circuit.

Abstract: A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. A method creates a set of k indicators, each indicator associated with one multi-bit binary number in a large dataset of multi-bit binary numbers. The method includes arranging the multi-bit binary numbers such that each bit n of each said multi-bit binary number is located in a different row n of an associative memory array, starting from a row storing a most significant bit (MSB), adding an indicator to the set for each multi-bit binary number having a bit with an extreme value in the row and continuing the adding until said set contains k indicators.

Abstract: Provided is an apparatus including a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to: acquire a candidate for a solution of an optimization problem for optimizing a third objective function based on a first objective function and a second objective function; obtain, as another candidate for the solution of the optimization problem, a solution that optimizes the second objective function under a constraint corresponding to a value of the first objective function for the acquired candidate; and select the solution of the optimization problem from among the plurality of candidates for the solution of the optimization problem. Also provided as the first aspect are a method and non-transitory computer readable storage medium.

Abstract: An assistance strategy may be generated with a generating apparatus including a processor, and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to create a reward estimation model for estimating a reward for assisting at least one subject by analyzing a history of input by the subject, create a decision making model including a plurality of forms of assistance and estimated rewards for each form of assistance based on the reward estimation model and the history of input by the subject, and generate an assistance strategy based on the decision making model.

Abstract: Methods, computing systems and computer program products implement embodiments of the present invention that include identifying a set of the items in the transactions, and executing, using input including the transactions, an implicit feedback collaborative filtering model to compute, for each of the users, a predicted rating for each of the items. Using input including the transactions and the predicted ratings as labels, a sentiment analysis model is executed to compute, for each given user, an opinion for each of the given items in the transactions for the given user, and using input including the transactions and the opinions as factors for the predicted ratings, an explicit feedback collaborative analysis model is executed to update, for each of the users, the predicted ratings for each of the items.

Abstract: Change points of a system represented by a plurality of time series are detected more appropriately. An information processing system includes means for learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and means for detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point.

Abstract: A computer implemented method is disclosed of providing an artificial intelligence architecture for controlling data and performing decisions relating to an object and/or an environment of the object. The method comprises executing on one or more processors the steps of: processing data from one or more sensors to identify first and second states of the object and/or the object's environment; analyze the first state and second state of the object and/or the object's environment to discover an apparent causal relationship between the first and second states of the environment; and making a change to the data relating to an object and/or the object's environment based on the apparent causal relationship to affect subsequent states of the object and/or the object's environment.

Abstract: The present application provides a method, apparatus, device, and system for predicting future travel volumes of geographic regions based on historical transportation network data. In one embodiment, the disclosure describes a method comprising receiving first historical travel data associated with a plurality of users, the first historical travel data including a plurality of first historical travel bookings for a plurality of regions of a map; predicting user travel information in a selected region of the plurality of regions in a future time range based on the first historical travel data, the user travel information including, within the future time range for the selected region, a future travel booking quantity and a future travel booking response quantity; and transmitting the user travel information to one of a service device or a user device.

Abstract: A system and method for making entailment inferences are disclosed. Entailment inferences are computed between semantic representations of text objects which, for a set of features, indicate whether the feature is known or unknown about the text object. A function of the semantic representations of first and second text objects is computed with an asymmetric vector space operator which differs depending on the entailment relationship.

Abstract: Methods and apparatus are provided for estimating anomalous sensors. The apparatus includes a target data acquiring to acquire a plurality of sets of target data serving as an examination target, each set of target data being output by a plurality of sensors. The apparatus further includes a calculating section calculate, for each of a plurality of sensor groups such that each sensor group includes at least two sensors among the plurality of sensors, a degree of difference of a target data distribution of the plurality of sets of target data relative to a reference data distribution of output from the sensor group. The apparatus additionally includes an estimating section to estimate one or more sensors among the plurality of sensors to be a source of outlierness, based on a calculation result of the calculating section.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: A mechanism is provided in a data processing system for text classifier training. The data processing system receives a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier. Each text and class specification pair comprises a text and a corresponding class specification. A domain terms selector component executing on a processor of the data processing system identifies at least one domain term in the texts of the training set. A domain terms replacer component executing on a processor of the data processing system replaces the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs. A text classifier trainer component executing on a processor of the data processing system trains the text classifier machine learning model using the revised set to form a trained text classifier machine learning model.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: Provided is an apparatus, method, and computer-readable storage medium for acquiring learning data that includes an evaluation of evaluation targets; generating a constraint condition to be satisfied by a value of an evaluation function that includes a weighting for each of a plurality of evaluation criteria of the evaluation target and an unknown term for each evaluation target corresponding to an unknown evaluation criterion that is not among the plurality of evaluation criteria, based on the learning data; determining a value of the unknown term and the weighting for each evaluation criterion in the evaluation function in a manner that satisfies the constraint condition; extracting a set of evaluation targets for which an evaluation of each evaluation criterion is opposite of an evaluation based on the unknown term, from among the plurality of evaluation targets; and outputting the extracted set of evaluation targets.

Abstract: A method and a machine learning relationship determination system (MLRDS) for determining primary key-foreign key (PK-FK) relationships among data in tables of a target database through machine learning (ML) are provided. The MLRDS selects columns of the tables in the target database and identifies inclusion dependency (ID) pairs from the selected columns. The MLRDS receives training data and validation data from a source database, computes PK-FK features for the inclusion dependency pairs, the training data, and the validation data, and generates trained ML models and validated ML models using the PK-FK features. The MLRDS determines an optimum algorithm decision threshold for a selected machine learning classification algorithm (MLCA), using which the MLRDS determines a resultant on whether the inclusion dependency pair is a PK-FK pair or a non-PK-FK pair. The MLRDS performs majority voting on the resultant for multiple MLCAs to confirm the PK-FK relationships between the inclusion dependency pairs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a machine learning model that has been trained through reinforcement learning to select a content item. One of the methods includes receiving first data characterizing a first context in which a first content item may be presented to a first user in a presentation environment; and providing the first data as input to a long-term engagement machine learning model, the model having been trained through reinforcement learning to: receive a plurality of inputs, and process each of the plurality of inputs to generate a respective engagement score for each input that represents a predicted, time-adjusted total number of selections by the respective user of future content items presented to the respective user in the presentation environment if the respective content item is presented in the respective context.

Abstract: A system for measuring and assigning sentiment using a sentiment analysis measurement module to intercept, collect, evaluate, and assign sentiment ratings to electronically transmitted messages, and deliver said messages with associated indicia of sentiment, be they visual or audible, either within a message delivery framework or as an after-market addition to a messaging system, for the benefit of a recipient.

Abstract: A method and system learns functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: An electronic device includes a memory device storing data and a system-on-a-chip using the memory device as a working memory. The system-on-a-chip performs software training on a second memory area of the memory device by loading a training code to a first memory area of the memory device, and executing the loaded training code.

Abstract: This disclosure relates to learning from distributed data. In particular, it relates to determining multiple first training samples from multiple first data samples. Each of the multiple first data samples comprises multiple first feature values and a first label that classifies that first data sample. A processor determines each of the multiple first training samples by selecting a first subset of the multiple first data samples such that the first subset comprises data samples with corresponding one or more of the multiple first feature values, and combining the first feature values of the data samples of the first subset based on the first label of each of the first data samples of the first subset. The resulting training samples can be combined with training samples from other databases that share the same corresponding features and entity matching is unnecessary.

Abstract: An evaluation platform receives a data set and a description of an outcome, such as predicting results of trends, recognizing patterns, and evaluating options according to specified criteria. The description is evaluated to select candidate evaluators that may be capable of achieving the outcome, and to translate the outcome into a goal for each selected candidate evaluator. The evaluator candidate set is trained using a training data set, and an initial evaluator is selected that exhibits the highest performance to achieve the outcome over the data set. The initial evaluator is applied to achieve the requested outcome over the data set. Optionally, the performance of the initial evaluator may be monitored to detect performance drift. In this event, the evaluator candidate set is reevaluated to identify a substitute evaluator exhibiting higher performance than the initial evaluator, which replaces the initial evaluator in the continued evaluation of the data set.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A method of analysing and tracking machine systems has the steps of sensing operational data from equipment, the operational data comprising at least location, time, and one or more operational condition data related to the equipment; analysing the operational data to identify data patterns; logging the data patterns as events in a database; comparing the events to a database of predetermined patterns to classify each data pattern as a known event or an unknown event; updating the database to include a new data pattern related to any unknown events; and alerting a user to further classify the unknown events manually.

Abstract: A computer-implemented method includes receiving a rule, wherein the rule includes at least one token, and receiving at least two dictionaries, wherein the at least two dictionaries include at least one general language dictionary and at least one domain-specific dictionary for a domain. The computer-implemented method further includes, for each of the at least one token, selecting at least one word at random from at least one of the at least two dictionaries and adding the at least one word to a test data line, such that the test data line includes a candidate statement conforming to the rule. The computer-implemented method further includes filtering the candidate statement based on a domain-specific model for the domain and including the candidate statement in training data provided to a machine learning model. A corresponding computer program product and computer system are also disclosed.

Abstract: A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.

Abstract: The disclosure includes implementations for providing subscription-based safety features in car sharing. A method may include subscription data describing how to modify an operation of a safety system of a vehicle based on the preferences of a driver. The driver may have reserved the vehicle for their use during a time period via a car sharing service. The safety system of the vehicle may be operable to provide a plurality of safety features. Configuration data included in the safety system may define which of the plurality of safety features are provided during the operation of the vehicle. The subscription data may describe the time period when the vehicle is reserved for the driver and a subset of the plurality of safety features for the vehicle that the driver prefers to be active during the time period. The method may include modifying the configuration data based on the subscription data.

Abstract: Techniques are described for identifying alternative venues for an activity. In one aspect, techniques include receiving information corresponding to an activity at a scheduled location and scheduled time; receiving a current location of the user prior to the scheduled time; calculating a probability of the user arriving at the scheduled location at the scheduled time; and responsive to determining the probability is less than a predetermined threshold, identifying an alternative venue for the activity having resources to support the activity.

Abstract: Systems and methods of the present invention provide for one or more server computers communicatively coupled to a network and configured to: receive a request to purchase admission to an event at a specific venue, a user identification of the user requesting admission and the user's payment method, and a device identifier associated with the user and transmitting the user location; store the request, user/payment data and device identifier in a database; receive, from a sensor within a specific location in the venue, a notification of the device within a proximity to the sensor; and update the payment method to reflect a deduction of the price for the event as determined by the location in proximity to the sensor.

Abstract: Disclosed are a system and method of issuing a key for a door lock at lodging facilities using a smart phone, wherein the system includes: a guest smart phone that makes a request for a reservation and payment at lodging facilities; a door-lock key issuing server that is requested to make a reservation and payment from the guest smart phone, acts as a proxy to make the reservation and payment at the lodging facilities; and a lodging-facilities server that approves the reservation and payment made by the door-lock key issuing server, receives the password of the key for the door lock from the door-lock key issuing server, approves the smart phone when accessed by the smart phone, and permits the door to be open by the Bluetooth communication with the approved smart phone.

Abstract: In one implementation, a method includes receiving, at a computing device, a digital photo of a paper ticket; generating an electronic ticket as a digital version of the paper ticket; outputting, in a user interface on the computing device, selectable options related to the electronic ticket; receiving, through the user interface, user input comprising selection of the option to sell the ticket; transmitting, by the computing device, a request to the computer system to host the electronic ticket for sale; receiving, at the computing device, a unique URL that is associated with an internet-accessible resource for purchasing the electronic ticket over the internet; outputting, in the user interface, selectable features to distribute the URL to other computing devices; receiving selection of one or more of the selectable features that correspond to one or more particular distribution channels; transmitting at least the URL along the one or more particular distribution channels.

Abstract: A system and method for the sale, purchase, and reuse of tickets previously purchased is provided. The system and method provide a tertiary market for used ticket by enabling the tickets of “early exits” to be resold to later attendees who wish to attend the remaining duration of the event. The system and method can be applied to any event in which tickets or passes of some sort are sold, both single-day and multi-day, including but not limited to: sporting events; concerts; festivals; and conferences. Tickets are made available through an electronic system that captures the ticket information either upon departure, or soon after. Upon recapture, tickets are then made available to a new consumer through the same process.

Abstract: A system and method for the sale, purchase, and reuse of tickets previously purchased is provided. The system and method provide a tertiary market for used ticket by enabling the tickets of “early exits” to be resold to later attendees who wish to attend the remaining duration of the event. The system and method can be applied to any event in which tickets or passes of some sort are sold, both single-day and multi-day, including but not limited to: sporting events; concerts; festivals; and conferences. Tickets are made available through an electronic system that captures the ticket information either upon departure, or soon after. Upon recapture, tickets are then made available to a new consumer through the same process.

Abstract: A system and method for the sale, purchase, and reuse of tickets previously purchased is provided. The system and method provide a tertiary market for used ticket by enabling the tickets of “early exits” to be resold to later attendees who wish to attend the remaining duration of the event. The system and method can be applied to any event in which tickets or passes of some sort are sold, both single-day and multi-day, including but not limited to: sporting events; concerts; festivals; and conferences. Tickets are made available through an electronic system that captures the ticket information either upon departure, or soon after. Upon recapture, tickets are then made available to a new consumer through the same process.