Abstract: A field programmable gate array (FPGA) may be used for inference of a trained deep neural network (DNN). The trained DNN may comprise a set of parameters and the FPGA may have a first precision configuration defining first number representations of the set of parameters. The FPGA may determine different precision configurations of the trained DNN. A precision configuration of the precision configurations may define second number representations of a subset of the set of parameters. For each precision configuration of the determined precision configurations a bitstream file may be provided. The bitstream files may be stored so that the FPGA may be programmed using one of the stored bitstream files for inference of the trained DNN.

Abstract: A radar device may include a radar transmitter to output a radio frequency (RF) transmission signal including a plurality of frequency-modulated chirps. The radar device may include a radar receiver to receive an RF radar signal, and generate, based on the RF radar signal, a dataset including a set of digital values, the dataset being associated with a chirp or a sequence of successive chirps. The radar device may include a neural network to filter the dataset to reduce an interfering signal included in the dataset, the neural network being a convolutional neural network. At least one layer of the neural network may be a complex-valued neural network layer includes complex-valued weighting factor, where the complex-valued neural network layer is configured to perform one or more operations according to a complex-valued computation.

Abstract: Hardware for implementing a Deep Neural Network (DNN) having a convolution layer. A plurality of convolution engines each perform a convolution operation by applying a filter to a data window. Each of the plurality of convolution engines includes multiplication logic that combines a weight of a filter with a respective data value of a data window; control logic that receives configuration information identifying a set of filters for operation on a set of data windows at the plurality of convolution engines; determines a sequence of convolution operations for evaluation at the multiplication logic; requests weights and data values for at least partially applying a filter to a data window; and causes the multiplication logic to combine the weights with their respective data values. Accumulation logic accumulates the results of a plurality of combinations performed by the multiplication logic to form an output for a convolution operation of the determined sequence.

Abstract: An analog neuromorphic circuit is disclosed, having input voltages applied to a plurality of inputs of the analog neuromorphic circuit. The circuit also includes a plurality of resistive memories that provide a resistance to each input voltage applied to each of the inputs so that each input voltage is multiplied in parallel by the corresponding resistance of each corresponding resistive memory to generate a corresponding current for each input voltage and each corresponding current is added in parallel. The circuit also includes at least one output signal that is generated from each of the input voltages multiplied in parallel with each of the corresponding currents for each of the input voltages added in parallel. The multiplying of each input voltage with each corresponding resistance is executed simultaneously with adding each corresponding current for each input voltage.

Abstract: A method includes: providing input information in an electronic format; converting the electronic input information into an optical input vector; optically transforming the optical input vector into an optical output vector based on an optical matrix multiplication; converting the optical output vector into an electronic format; and electronically applying a non-linear transformation to the electronically converted optical output vector to provide output information in an electronic format. For example, a set of input values are encoded on respective optical signals. For each of at least two subsets of optical signals, a copying module splits the subset into multiple copies of the optical signals. For each copy of a first subset of optical signals, a corresponding multiplication module multiplies the optical signals of the first subset by matrix element values using optical amplitude modulation. A summation module produces an electrical signal representing a sum of the results of the multiplication modules.

Abstract: A method includes receiving input data including a plurality of feature vectors and labeling each feature vector based on a temporal proximity of the feature vector to occurrence of a fault. Feature vectors that are within a threshold temporal proximity to the occurrence of the fault are labeled with a first label value and other feature vectors are labeled with a second label value. The method includes determining, for each feature vector of a subset, a probability that the label associated with the feature vector is correct. The subset includes feature vectors having labels that indicate the first label value. The method includes reassigning labels of one or more feature vectors of the subset having a probability that fails to satisfy a probability threshold and, after reassigning the labels, training an aircraft fault prediction classifier using supervised training data including the plurality of feature vectors and the labels.

Abstract: A method for establishing a data-recognition model includes: generating (Z) number of Y-combinations of dithering algorithms from (X) number of dithering algorithms; for each Y-combination, performing a dithering operation on a to-be-processed data group, so as to obtain, in total, (Z) number of size-reduced data groups; performing training operations on a deep neural network using the size-reduced data groups, respectively, so as to generate, for each training operation, a DNN model and a steady deviation; and selecting the Y-combination corresponding to the size-reduced data group that results in the smallest steady deviation as a filter module, and selecting the corresponding DNN model as the data-recognition model.

Abstract: Given an input image, an image enhancement task, and no external examples available to train on, an Image-Specific Deep Network is constructed tailored to solve the task for this specific image. Since there are no external examples available to train on, the network is trained on examples extracted directly from the input image itself. The current solution solves the problem of Super-Resolution (SR), whereas the framework is more general and is not restricted to SR.

Abstract: A computer-implemented method for processing images to determine EI site status is provided. The method includes image processing of an aerial image by two EI feature recognition models. A first EI feature recognition model recognizes a first EI feature and a second EI feature recognition model recognizes a second EI feature. The results of each model are further used to determine a composite indication of EI site status.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions from large discrete action sets. One of the methods includes receiving a particular observation representing a particular state of an environment; and selecting an action from a discrete set of actions to be performed by an agent interacting with the environment, comprising: processing the particular observation using an actor policy network to generate an ideal point; determining, from the points that represent actions in the set, the k nearest points to the ideal point; for each nearest point of the k nearest points: processing the nearest point and the particular observation using a Q network to generate a respective Q value for the action represented by the nearest point; and selecting the action to be performed by the agent from the k actions represented by the k nearest points based on the Q values.

Abstract: An image recognition method includes: obtaining an image; extracting a target image region corresponding to a target part from the image, wherein the target image region includes a target object; determining a location of the target object in the target image region (i) according to pixel values of pixels in the target image region and a location relationship between the pixels, or (ii) inputting the target image region to a trained segmentation model to obtain the location of the target object in the target image region; and displaying a recognition result of the image, wherein the recognition result indicates the location of the target object in the target image region.

Abstract: Systems and methods combine an input image with an edited image generated using a generator neural network to preserve detail from the original image. A computing system provides an input image to a machine learning model to generate a latent space representation of the input image. The system provides the latent space representation to a generator neural network to generate a generated image. The system generates multiple scale representations of the input image, as well as multiple scale representations of the generated image. The system generates a first combined image based on first scale representations of the images and a first value. The system generates a second combined image based on second scale representations of the images and a second value. The system blends the first combined image with the second combined image to generate an output image.

Abstract: A device may receive historical data and real-time data associated with a troubleshooting service, identify, using a machine learning model, an optimal resolution based on the historical data and the real-time data, and identify, using a graph analytics model, an optimal path of actions based on the optimal resolution. The machine learning model may be trained to identify one of the set of historical issues associated with the unresolved issue, and identify the optimal resolution based on one of the set of historical resolutions associated with the one of the set of historical issues. The graph analytics model may be trained to generate a set of paths of actions based on the historical data, and identify the optimal path based on respective numbers of actions associated with the set of paths. The device may identify optimal action based on the optimal path and the prior action.

Abstract: A machine learning based system and method for automated recognition of consumer products in images and video using a camera system, a neural network using a ranked tagging system, a two-stage recognition application, and a training module. Training image sets of items captured by the camera system are assigned identification tags through template matches to training sets within the neural network. Tags are assigned from various levels of specificity to identify exact product matches. A user recognition application captures images and generates bounding boxes for detected objects and assigns a general classification within the image using a single, fast, convolutional neural network (CNN) layer. General classification narrows subsets for each generated bounding box and multi-scale template matching is applied to achieve detailed identification of single or multiple items detected within single images or video. The training module adjusts smart camera systems and the neural network based on accuracy feedback.

Abstract: A system comprises a memory that stores computer-executable components; and a processor, operably coupled to the memory, that executes the computer-executable components. The system includes a receiving component that receives a corpus of data; a relation extraction component that generates noisy knowledge graphs from the corpus; and a training component that acquires global representations of entities and relation by training from output of the relation extraction component.

Abstract: Systems, apparatuses and methods may provide for conducting an importance measurement of a plurality of parameters in a trained neural network and setting a subset of the plurality of parameters to zero based on the importance measurement. Additionally, the pruned neural network may be re-trained. In one example, conducting the importance measurement includes comparing two or more parameter values that contain covariance matrix information.

Abstract: The present disclosure provides a processing device including: a coarse-grained pruning unit configured to perform coarse-grained pruning on a weight of a neural network to obtain a pruned weight, an operation unit configured to train the neural network according to the pruned weight. The coarse-grained pruning unit is specifically configured to select M weights from the weights of the neural network through a sliding window, and when the M weights meet a preset condition, all or part of the M weights may be set to 0. The processing device can reduce the memory access while reducing the amount of computation, thereby obtaining an acceleration ratio and reducing energy consumption.

Abstract: Some embodiments of the present invention are directed to techniques for training teacher neural networks (TNNs) and student neural networks (SNNs). A training data set is received with a lossless set of data and a corresponding lossy set of data. Two branches of a TNN are established, with one branch trained using the lossless data (a lossless branch) and one trained using the lossy data (a lossy branch). Weights for the two branches are tied together. The lossy branch, now isolated from the lossless branch, generates a set of soft targets for initializing an SNN. These generated soft targets benefit from the training of lossless branch through the weights that were tied together between each branch, despite isolating the lossless branch from the lossy branch during soft-target generation.

Abstract: One embodiment of the present invention sets forth a technique for performing spatial propagation. The technique includes generating a first directed acyclic graph (DAG) by connecting spatially adjacent points included in a set of unstructured points via directed edges along a first direction. The technique also includes applying a first set of neural network layers to one or more images associated with the set of unstructured points to generate (i) a set of features for the set of unstructured points and (ii) a set of pairwise affinities between the spatially adjacent points connected by the directed edges. The technique further includes generating a set of labels for the set of unstructured points by propagating the set of features across the first DAG based on the set of pairwise affinities.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: This application provides a method for training a pose recognition model performed at a computer device. The method includes: inputting a sample image labeled with human body key points into a feature map model included in a pose recognition model, to output a feature map of the sample image; inputting the feature map into a two-dimensional (2D) model included in the pose recognition model, to output 2D key point parameters used for representing a 2D human body pose; input a target human body feature map cropped from the feature map and the 2D key point parameter into a three-dimensional (3D) model included in the pose recognition model, to output 3D pose parameters used for representing a 3D human body pose; constructing a target loss function based on the 2D key point parameters and the 3D pose parameters; and updating the pose recognition model based on the target loss function.

Abstract: An information processing system includes a storage device that stores therein a trained model, and a processor. The trained model is trained to output a position and shape of an object in a training image based on training data. The training data is data in which the training image is provided with an annotation indicating the position and shape of the object. The training image is an image captured with an angle of view including the object whose position and shape are not clearly displayed in an image. The processor executes detection processing on a detection image to output detected information indicating the position and shape of the object. The processor then causes a display device to display the detected information superimposed on the detection image.

Abstract: A method includes obtaining a source training dataset that includes a plurality of source training images and obtaining a target training dataset that includes a plurality of target training images. For each source training image, the method includes translating, using the forward generator neural network G, the source training image to a respective translated target image according to current values of forward generator parameters. For each target training image, the method includes translating, using a backward generator neural network F, the target training image to a respective translated source image according to current values of backward generator parameters. The method also includes training the forward generator neural network G jointly with the backward generator neural network F by adjusting the current values of the forward generator parameters and the backward generator parameters to optimize an objective function.

Abstract: Embodiments of this application disclose an image description generation method performed at a computing device. The method includes: obtaining a target image; generating a first global feature vector and a first label vector set of the target image; generating a first multi-mode feature vector of the target image through a matching model, the matching model being a model obtained through training according to a training image and reference image description information of the training image; and applying the first multi-mode feature vector, the first global feature vector, and the first label vector set to a computing model, to obtain the target image description information, the computing model being a model obtained through training according to image description information of the training image and the reference image description information.

Abstract: The disclosure relates to a system and a method for generating a neural network model for image processing by interacting with at least one client terminal. The method may include receiving via a network, a plurality of first training samples from the at least one client terminal. The method may also include training a first neural network model based on the plurality of first training samples to generate a second neural network model. The method may further include transmitting, via the network, the second neural network model to the at least one client terminal.

Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.

Abstract: A device, system, and method is provided to mimic a pre-trained target model without access to the pre-trained target model or its original training dataset. A set of random or semi-random input data may be sent to randomly probe the pre-trained target model at a remote device. A set of corresponding output data may be received from the remote device that is generated by applying the pre-trained target model to the set of random or semi-random input data. A random probe training dataset may be generated comprising the set of random or semi-random input data and corresponding output data generated by randomly probing the pre-trained target model. A new model may be trained with the random probe training dataset so that the new model generates substantially the same corresponding output data in response to said input data to mimic the pre-trained target model.

Abstract: A computer implemented method of storing and retrieving feature map data of a neural network the method comprising receiving a first portion of feature map data from local storage, selecting a first set of subportions of the first portion of feature map data, compressing the subportions to produce a first plurality of sections of compressed feature map data and instructing the storage of the sections into external storage. The method also comprises receiving a second plurality of sections of compressed feature map data from the external storage, decompressing the sections to produce a second set of subportions of the second portion of feature map data and storing the second portion of feature map data in local storage. The first and second sets of subportions each correspond to a predetermined format of subdivision and the method comprises selecting the predetermined format of subdivision from a plurality of predetermined formats of subdivision.

Abstract: In some examples, a computer system may receive sensor data and failure data for equipment. The system may determine, for the equipment, a plurality of time between failure (TBF) durations that are longer than other TBF durations for the equipment. The system may determine, from the sensor data corresponding to operation of the equipment during the plurality of TBF durations, a plurality of measured sensor values for the equipment. Additionally, the system may determine a subset of the measured sensor values corresponding to a largest number of the TBF durations of the plurality of TBF durations. The system may further determine at least one operating parameter value for the equipment based on the subset of the measured sensor values. The system may send a control signal for operating the equipment based on the operating parameter value and/or a communication based on the operating parameter value.

Abstract: First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

Abstract: One or more computing devices, systems, and/or methods for entity disambiguation are provided. For example, a document may be analyzed to identify a first mention and a second mention. One or more techniques may be used to select and link a candidate entity, from a first set of candidate entities, to the first mention and select and link a candidate entity, from a second set of candidate entities, to the second mention.

Abstract: Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: Systems and methods for sports data crowdsourcing and analytics is provided. In one embodiment, a method for at least one server for generating verified sports data of a sporting event is provided, the method comprising: receiving, at the server(s), first audio data captured by a microphone of a first client device; receiving, at the server(s), second audio data captured by a microphone of a second client device; synching the first audio data by arranging the first audio data based on at least one parameter; synching the second audio data by arranging the second audio data based on the at least one parameter; and generating the verified sports data by comparing, based on the at least one parameter, the first audio data and the second audio data to determine that a stat within the sporting event is verified.

Abstract: Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

Abstract: Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for improving performance of a dialog system employing an automated virtual dialog agent. Embodiments involve utilizing an automated virtual agent to receive a natural language request and generate a corresponding response, automatically identifying and resolving a corresponding knowledge gap between the request and response, and refining the automated virtual agent with the resolved knowledge gap.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: A method can be used to determine a security-constrained optimal power flow (SC-OPF) of a power grid. Input data associated with a power grid scenario is obtained. The input data defines a security-constrained optimization problem (SC-OPF problem). The power grid scenario includes a power grid structure, a power demand and a generator capability and/or cost. Power flows in branches of the power grid that are equal to or greater than limits for a contingency associated with the power grid scenario are estimated based on the obtained input data by an AI. A modified optimization problem which is smaller than the SC-OPF problem is solved based on the estimated power flows.

Abstract: A method and system are provided that apply a combination of machine learning and graph techniques to classify and transform sequential event data. In some embodiments, the method and system are applied to generate raw data in the shipping industry to automatically classify a sequence of status codes extracted from EDI data files corresponding to a series of physical events experienced by a shipping container into a sequence of meaningful milestones to provide improved visibility regarding the actual status of the shipping container. The method and system can be applied to classify and transform sequential event data for use in the shipping industry and in other applications.

Abstract: Pure machine learning classification approaches can result in a “black box” solution where it is impossible to understand why a classifier reached a decision. This disclosure describes generating new classification rules leveraging machine learning techniques. New rules may have to meet evaluation criteria. Legibility of those rules can be improved for understanding. A machine learning classifier can be created that is used to identify possible candidate classification rules (e.g. from a group of decision trees such as a random forest classifier). Classification rules generated with the assistance of machine learning may allow for identification of transaction fraud or other classifications that a human analyst would be unable to identify. A selection process can identify which possible candidate rules are effective. The legibility of those rules can then be improved so that they can be more easily understood by humans.

Abstract: Apparatus, systems, processes, and computer-readable mediums for facilitating the use of drones are described. For one embodiment, such a system includes a user element having a user application computer program configured to instruct a user interface device to facilitate use of user data and use of mission parameter(s) for a proposed drone mission. An owner element includes an owner application computer program configured to facilitate use of owner data and use of at least one drone parameter. A fleet system element is communicatively coupled to the user element and to the owner element and includes a computer system processor configured to facilitate use of a fleet record and use of at least one fleet parameter.

Abstract: Implementations of various methods and systems of a network, GPS system, mobile computing devices, servers, forward commodity market servers, grouping software for hubs, transparent open access pricing systems, blockchain audit and safety methods and systems, virtual hub systems, algorithm methods for no arbitrage conditions in a simple easy to use graphical user interface format for mobile or virtual computing over various mediums which are connected via a network to transact and trade transportation seats or capacity units in airline transport, subway transport, train transport, automobile transport, autonomous vehicle transport, taxi transport, space transport, package freight transport, tractor trailer freight transport, cargo freight transport, container freight transport, virtual transport, underground transport, ship or sea transport, public transport, private transport or drone transport on a computer, mobile computer device, audio computer device, virtual reality computer device or mixed reality comp

Abstract: Implementations of various methods and systems of a network, GPS system, mobile computing devices, servers, forward commodity exchanges, grouping software for hubs, transparent open access pricing systems, blockchain audit and safety methods and systems, virtual hub systems, algorithm methods for no arbitrage conditions in a simple easy to use graphical user interface format for mobile or virtual computing over various mediums which are connected via a network to transact and trade transportation seats or capacity units in airline transport, subway transport, train transport, automobile transport, autonomous vehicle transport, taxi transport, space transport, virtual transport, underground transport, ship or sea transport, public transport, private transport or drone transport on a computer, mobile computer device, virtual reality computer device or mixed reality computing device.

Abstract: A generation device that generates a feature amount range visualization element that defines a continuous feature amount range for each of the factors based on time series data including feature amounts of different factors existing in time series and generates an inter-feature amount visualization element that defines relevance between a first feature amount of a first factor and a second feature amount of a second factor that are continuous in time. The device also generates visualization information indicating a relationship of the feature amounts related to the plurality of different factors by associating, by the inter-feature amount visualization element, a first feature amount range visualization element of the first factor with a second feature amount range visualization element of the second factor.

Abstract: An apparatus for success probability determination for a user is provided. Apparatus may include at least a processor and a memory communicatively connected to the processor. The memory may contain instructions configuring the at least a processor to receive a plurality of criteria; generate indicators as a function of the criteria; receive user specifications, the user specifications comprising credentials of a user; and classify the user specifications to a performance category of a plurality of performance categories based on the user specifications and the indicators.

Abstract: Systems, methods, and computer-readable media for matching a job to a plurality of workers are described herein. A user device is associated with a worker from the plurality of workers. A processing circuit is structured to receive an electronic request. The electronic request comprises a data set related to the job. The processing circuit is further structured to generate a local disaster data set and a test requirement data set. The processing circuit is further structured to select at least one worker. The processing circuit is further structured to cause a client application deployed to the user device to display an electronic notification comprising at least in part the data set related to the job, a risk level rating, a risk level pay premium corresponding to the risk level rating, and an accept control structured to allow the worker to accept the job.

Abstract: The invention is directed towards an apparatus and method for generating an action validation protocol. A processor is configured to receiver user data relating to an action datum. The processor is configured to select an action datum validator. The action datum is transmitted to the action datum validator where an action datum label is selected. Once an action datum validator selects an action label, the action is further categorized. If the action datum has not been completed, it will be sent back to the action datum identifier.

Abstract: A collaborative development project having a plurality of contributors is provided. A first contributor of the plurality of contributors as an influencer of the collaborative development project. Contributions of the influencer to a sample project are analyzed to determine a development characteristic of the influencer. One or more development rules are then generated based on the determined development characteristic of the influencer. Further, a second contributor of the plurality of contributors may be identified as a follower of the development project. Identified contributions of the follower may then be modified based on the obtained one or more development rules.

Abstract: An autonomic method for the comprehensive profiling of near or real time representations of time sequenced, synchronized, characterized business activity by any entity within a group of commercially related subscribing business partners, for the purpose of creating a near or real time universal business activity mosaic. An autonomic method within a group of commercially interrelated businesses to utilize a universal business activity mosaic to render a virtual customer storefront, whereby a business may discretely witness in time and character the sale of its own assets and services either fully or partially comprising those sold to anybody of related intermediate or end customers for the purpose of purchasing, selling, financing, warehousing or physically transporting assets on demand.

Abstract: In an approach, a processor receives a request to purchase an item from a retailer, where the request includes a location. A processor receives a request to purchase an item from a retailer, where the request includes location information associated with a buyer. A processor identifies that the item is unavailable from the retailer. A processor determines a plurality of users predicted to have a surplus quantity of the item beyond each respective user's estimated needs at a time of the request. A processor sends an offer to the plurality of users to transfer the item in accordance with the request. A processor, responsive to receiving an acceptance of the offer, selects at least one accepting user, of the plurality of users, based on geographic proximity to the location information. A processor coordinates fulfillment of the request by the at least one user.

Abstract: In the field of government engagement management, an agent guide or script-flow in an employee desktop web client is implemented. In such a system and method, when agents create interactions with clients they can follow a script-flow which will guide the agent through the interaction through a series of menu selections and automated sets of instructions. This feature of the government engagement management system allows existing customer investment from the rich desktop client or non-web client in developing specific scripts, that can also now function in the web client atmosphere. This system and method also enables an agent to handle calls with the web client more efficiently, and allows agents on the web client to automatically classify.