Abstract: A computing system for generating a quantum circuit. The computing system samples a search space for candidate quantum circuits for a circuit layer of a quantum circuit design. The computing system evaluates performance of the candidate quantum circuits for the circuit layer. The computing system selects one of the candidate quantum circuits for the circuit layer based on the evaluated performance, adds an additional circuit layer based on the quantum circuit design to the selected one of the candidate quantum circuits.

Abstract: A recording medium stores a program for causing a computer to execute processing including: acquiring a first quantum circuit by nodes that performs simulation of quantum calculation; detecting a first quantum gate including, as an operation object, a first qubit that causes transmission and reception of state data for each combination of states of respective qubits indicated in the first quantum circuit when storage destinations of the state data are allocated to the nodes based on qubit numbers of qubits by a quantum gate in the first quantum circuit; inserting a second quantum gate indicating a swap operation between a state of a second qubit by the first quantum gate and a state of a third qubit that is operatable without the transmission and reception of the state data between the nodes; and changing the operation object of the first quantum gate from the second qubit to the third qubit.

Abstract: Systems and methods for quantum machine learning are described. A plurality of qubits can be entangled to create a cluster state. The plurality of qubits can include at least an input qubit, an output qubit, and at least one ancilla qubit. The input qubit can represent data among a training data set of a machine learning model represented by a unitary operation. Sequential local measurements of the cluster state can be performed to generate a plurality of measurement outcomes. At least one of the plurality of qubits can be rotated according to the plurality of measurement outcomes and rotation parameters of the unitary operation. The sequential local measurements and rotation of the plurality of qubits can transform an input state of the input qubit into an output state of the output qubit. The machine learning model can be trained based on the output state of the output qubit.

Abstract: Aspects of the present disclosure relate generally to systems and methods for use in the implementation and/or operation of quantum information processing (QIP) systems including an ion trap and a multiple-zone addressing system, and more particularly, to a large field-of-view Raman system having a micro-or metalens array. In some aspects, the ion trap is configured to confine at least a first trapped ion chain and a second trapped ion chain. In some aspects, the multiple zone addressing system includes a first optical addresser, a second optical addresser, and a combining region.

Abstract: A method for executing quantum network applications comprises receiving a quantum code block associated with a first quantum network application by an operating system of a first quantum network node, the quantum code block comprising quantum operations, the quantum operations including local quantum operations not related to entanglement generation and at least an entanglement generation operation for entanglement generation between the first quantum network node and a second quantum network node; executing at least part of the quantum operations on the quantum computing system via a first subsystem of the operating system, wherein if a quantum operation is relates to an entanglement generation operation, sending the entanglement generation operation to a second subsystem of the operating system, the second subsystem preparing execution of the entanglement generation operation as a background process of the operating system, while the first subsystem continues executing local quantum operations.

Abstract: Method for determining a lower bound of a fidelity of an approximated final state, comprising: receiving an initial state in a matrix product representation; receiving a quantum circuit comprising gates; iterating over the gates: applying a current gate to the initial state; if the current gate is a two-qubit gate, factorizing a portion of the updated state by SVD into a product of a unitary matrix, a diagonal matrix, and a unitary matrix; if a bond dimension of the diagonal matrix exceeds a threshold: truncating the diagonal matrix such that the bond dimension does not exceed said threshold, and determining a truncation fidelity; in a next iteration, using the updated state as the initial state; determining a lower bound of the fidelity of the approximated final state as a product of the truncation fidelities.

Abstract: The invention relates generally to systems and methods estimating a target outcome using a combination of quantum computing and a Monte Carlo simulation. A quantum processor loads variables and distributions into a quantum system, begins a quantum walk, performs arithmetic operations with the variables and distributions to initiate the steps in the quantum walk, and ultimately performs a quantum estimation of the quantum state to estimate a target variable.

Abstract: A quantum modulation classifier. The quantum modulation classifier includes a trained quantum variational classifier including a plurality of qubits. The quantum variational classifier includes an embedding stage operable to apply a quantum embedding technique to embed a modulated radio signal, a variational stage operable to receive the modulated radio signal from the embedding stage and pass the modulated radio signal through a plurality of variational layers, and a measurement stage operable to receive the modulated radio signal from the variational stage and extract measurement results to classify the modulated radio signal.

Abstract: The present invention provides an improved method and system for variational quantum algorithms (VQA). Procedures set out herein include receiving a quantum circuit for implementing the VQA, the quantum circuit being parameterised by a set of quantum circuit parameters. A cost function for the circuit is formulated as a non-convex polynomial optimisation problem. Next moment/Sums of Squares, SOS, relaxations are applied to the non-convex polynomial optimisation problem to generate a hierarchy of semidefinite programming (SDP) relaxations that approximate the non-convex polynomial optimisation problem. These SDP relaxations are then solved using classical optimisation algorithms. The solutions are used to update the quantum circuit parameters, thereby providing an improved VQA circuit. Upon repeated iterations of the procedure, this provably converges toward the optimal VQA circuit for the problem at hand.

Abstract: A system may receive a function ƒ(x) describing a value of an object, values of x, and probabilities p(x) for the values of x. The system may determine a quantum operator U+{right arrow over (?)} that, when executed by a quantum computing system, encodes an approximation of the function ƒ(x) in an amplitude of a quantum state without calculating |ƒ(x) for any of the values of x. The system may instruct the quantum computing system to execute quantum operators (including U+{right arrow over (?)}) to generate a quantum state on a register of qubits, where one of the amplitudes of the generated quantum state includes probabilities p(x) for the values of x and output values of the approximation of the function ƒ(x) for the values of x. The system may determine the value of the object based on the generated quantum state.

Abstract: Method for determining an approximated final state having a fidelity above a bound, comprising: receiving an initial state; receiving a quantum circuit comprising gates; defining a lower bound of the fidelity; iterating over the gates: applying a current gate to the initial state; if the current gate is a two-qubit gate, factorizing a portion of the updated state by SVD into a product of a unitary matrix, a diagonal matrix, and a unitary matrix; truncating the bond dimension of the diagonal matrix to a target bond dimension such that a product of a truncation fidelity of the truncated matrix, truncation fidelities of previously truncated matrices and future target truncation fidelities is greater than the bound; in a next iteration, using the updated state as initial state; defining an approximated final state equal to the updated state of a last iteration.

Abstract: A microfluidic product utilizing gradient surface energy coatings for fluid control comprising a plurality of fluid passages wherein at least one fluid passage comprises a coating configured to control liquid flow wherein the coating configured to control liquid flow comprises a gradient surface energy coating from a proximal location to a distal location on a surface of the fluid passage. The product can include uniform regions and surface gradient regions in the same passage. Coating compositions and product dimensions can be selected to provide control over different flow properties including fluid velocity, reduction and acceleration of fluid flow, and starting and stopping fluid flow.

Abstract: The present invention provides for a system and a method for implementing artificial intelligence-based optimised data stewardship. The system comprises a memory for storing program instructions, a processor executing instructions stored in the memory and a digital data stewardship engine executed by the processor. One or more events are identified based on nature of the events and a sequence is determined for invoking one or more units of the digital data stewardship engine based on the identified event. Machine learning-based intelligent analysis is performed on additional information obtained through third-party websites associated with the identified event. Rules are applied on the results of the intelligent analysis for augmenting the results as per pre-defined requirements and outcome generated based on application of rules are delivered as an executable file.

Abstract: A distribution of values of time-series data is obtained. Based on the distribution of the values, the time-series data is sampled to generate an anomaly preserving version of the time-series data. Via a trained machine learning model, a reconstructed version of the time-series data is generated based on the anomaly preserving version of the time-series data.

Abstract: A method of using a computing device to provide non-functional requirement (NFR) fulfilment based technical disposition including identifying, by the computing device, optimal solutions based on overlaying prioritized NFRs for at least one target system as supported by each candidate system of multiple candidate systems. Supportable technical NFRs are used for each combination of options under the multiple candidate systems. Identifying further includes generating a cognitive processing model using machine learning adaptability for extracting NFRs for options for existing system designs. Classification overlay of each individual candidate system is provided across the NFRs prioritized based on business requirement. Unbiased decision processing utilized based on discord, exclusion and similarity as functions of the cognitive processing model. A sub-optimal solution space and adaptability for the sub-optimal solution space in absence of an optimal solution is identified.

Abstract: A wearable electronic device that uses artificial intelligence, trained to recognize the play calls, formations, team roles, player audibles and player position changes. This device is worn by every role on a team. On command from a responsible party, it will generate a display for the play call and individual player expectations to the individual players. Each device has its own identifiable ID and functions according to the role of the individual wearer from coaches to players. The commands can be audio, visual or text. Artificial intelligence interprets commands and communicates with each player's device on the team which then displays the change to the formation or play and highlights the individual player's expectations matching the role of the receiving device. The device may give GPS feedback for statistics and utilize cloud processing. The devices communicate using 4G, 5G or any cellular technology method not yet discovered.

Abstract: Methods and systems are described herein for updating machine learning models based on the impact of features on predictions. The system inputs, into a machine learning model, a dataset including entries and features to obtain predictions. The machine learning model is trained to generate predictions for entries based on features. The system generates, for each entry, feature impact parameters indicating a relative impact of each feature on each prediction. The system determines a feature impact threshold for assessing which features have contributed to each prediction and generates, using the feature impact parameters and the feature impact threshold, a sparsity metric for each prediction. The sparsity metric indicates which features have relative impacts that meet the feature impact threshold for the prediction. The system generates a global sparsity metric for the machine learning model and updates the machine learning model based on the global sparsity metric.

Abstract: Methods and systems are described herein for updating machine learning models using weights. The system inputs, into a machine learning model, a dataset including entries and features to obtain a relative impact of each feature. The system generates, using the relative impacts, a sparsity metric for each entry, each sparsity metric indicating a measure of a number of features used to generate a corresponding prediction. The system retrieves a sparsity threshold for assigning weights to the plurality of entries. The system generates an updated dataset based on assigning, to each entry within the dataset, a corresponding weight. Each corresponding weight is determined based on a relation of the sparsity metric to the sparsity threshold. The system inputs, into the machine learning model, the updated dataset to update the machine learning model based on the corresponding weights, where the machine learning model relies more heavily on entries with higher corresponding weights.

Abstract: Embodiments of the invention are directed to a computer system including a memory communicatively coupled to a processor system. The processor system is operable to perform processor system operations that include using a first machine learning (ML) algorithm to convert to-be-classified-data (TBC-data) from a TBC-data format to a second data format; and extract features from the TBC-data in the second data format. A second ML algorithm is used to perform a task that includes determining, based at least in part on the features of the TBC-data in the second data format, that the TBC-data having the second data format is an outlier.

Abstract: An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item/item map pair.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to training an AI model to predict status of a DBMS. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a data ingestion component that can use testing data of an AI model to generate ingested data by randomly changing one or more records of at least one feature comprised in the testing data, wherein the ingested data can be used to compute a first ratio indicative of inequity of the at least one feature. The computer executable components can further comprise a training component that can train the AI model using at least the first ratio to predict a status of system.

Abstract: A training process a predictive model uses a dataset of features and an outcome. The method generates a table for a dataset comprising multiple features, the table contains values for each pair of features in the dataset, randomly selects features from the dataset, thereby creating a first subset of features, operates a propensity score matching using the randomly selected features to identify cases and controls using the outcome variable, rewards one or more features of a second subset of features in the multiple features that were not selected randomly, each feature of the second subset addresses a statistical significance criteria, updating each entry in the table with a reward distance between each pair of features, calculates a cumulative reward measure, iterating the steps until convergence, selects a final subset of features when a variability criteria of the cumulative reward measure addresses convergence criteria, and trains the predictive model.

Abstract: Provided is a computing system for implementing a system model using big data machine learning, which is intended to build a hypothetical model, calculate verified parameter values by performing machine learning on big data acquired from an actual system, and apply the verified parameter values to the hypothetical model. A system modeling method of completing a simulation model for a target system by causing a hypothetical model defined by acquiring knowledge about the target system to perform machine learning on big data acquired by running and observing the target system includes defining a hypothetical model for a target system by finding acquirable information related to the target system.

Abstract: Embodiments analyze an operating state of a physical asset. An embodiment first acquires, based on one or more predetermined criteria, data measurements from one or more preselected sensors configured to sense one or more respective aspects of the physical asset. The data measurements correspond to one or more time periods, the one or more preselected sensors are preselected by correlating data measurements from a plurality of sensors of the physical asset to one or more operating states of the physical asset, and the one or more predetermined criteria are predetermined by identifying one or more data output patterns of the one or more preselected sensors. Then, via a first model, one or more operating states of the physical asset are determined based on the acquired data measurements.

Abstract: According to a present invention embodiment, a system identifies a plurality of configurations for machine learning models. Each configuration indicates a machine learning model and a corresponding technique to determine parameters for the machine learning model. The plurality of configurations are evaluated by training the machine learning model of the plurality of configurations according to the parameters determined by the corresponding technique. Performance of the machine learning models of the plurality of configurations is monitored, and resources used for evaluating at least one configuration are adjusted based on the performance of the machine learning model for the at least one configuration relative to the performance of the machine learning models of others of the plurality of configurations. Embodiments of the present invention further include a method and computer program product for training machine learning models in substantially the same manner described above.

Abstract: A method comprises determining whether a decision can be determined for the request based on a current information available; when the decision can be determined, utilizing a first model to determine a set of questions corresponding to the request, the first model previously trained using training data comprising a set of questions associated with a set of requests; utilizing a second model to determine one or more predicted answers for the set of questions, the second model ingesting the set of questions determined by the first model and at least one attribute associated with the request to generate the one or more predicted answers; and utilizing a third model to determine the decision for the request.

Abstract: Methods and systems for generating federated learning models. In some aspects, the system receives, from each client device, user data profiles that are anonymized with respect to users associated with user data stored locally at a client device. The system processes the user data profiles to generate a plurality of clusters. For each cluster, the system transmits, to one or more client devices corresponding to a cluster, a first instruction to train a machine learning model on user data corresponding to user data profiles included in the cluster and a second instruction to validate the machine learning model with respect to user data corresponding to one or more clusters of the plurality of clusters other than the cluster to generate a prediction accuracy metric. The system determines, from the plurality of clusters, a first cluster based on associated prediction accuracy metrics.

Abstract: A system and method includes obtaining an incumbent model and a candidate model, generating a plurality of synthetic model input datasets, computing, for each synthetic model input dataset, a model performance efficacy metric and a model fairness efficacy metric for the incumbent model based on assessing model output data of the incumbent model that corresponds to each respective synthetic model input dataset of the plurality of synthetic model input datasets, computing, for each synthetic model input dataset, a model performance efficacy metric and a model fairness efficacy metric for the candidate model based on assessing model output data of the candidate model that corresponds to each respective synthetic model input dataset of the plurality of synthetic model input datasets, computing, for the candidate model, a disparity-mitigating model viability score, and displaying, via a graphical user interface, a representation of the candidate model in association with the disparity-mitigating model viability sc

Abstract: The present embodiments relate to systems and methods to intelligently handle different types of concept drift scenarios in a machine learning operations framework with minimal/no down time or loss in processing data. Particularly, the present embodiments enable a system to generate AI model specific instructions from raw data automatically and use the generated instructions to generate code blocks through which the machine learning system can intelligently come up with challenges of AI Model quality drop scenarios. The present embodiments can solve the problem of automatic concept drift resolution in machine learning operations systems. For instance, the present embodiments can recommend the best possible code blocks for data pipelines and ML pipelines based on data distributions. Further, the embodiments can improve and maintain the AI model quality in a production environment, and also improve the code quality based on validation from an operator.

Abstract: A machine learning development support system includes: a data storage unit that stores training data; a data attribute extraction unit that refers to definition information whereby a conversion condition for converting the training data into an attribute thereof is defined and extracts data acquisition conditions from the training data; and an experiment record information storage unit that stores experiment record information., The machine learning product including a machine learning program and a trained model is divided into versions and recorded, and an accuracy of an inference obtained by each version is recorded in association with the version, the trained model, and the training data; and a processing specification unit that refers to the experiment record information, and specifies, for each of the data acquisition conditions, a machine learning product in which the accuracy of the inference is improved between old and new versions of the machine learning product.

Abstract: A method including receiving a configuration indicating one or more rules for switching between a set of operational modes associated with at least one of: inference, data collection or training of a machine learning model, the machine learning model being associated with a network optimization function; determining, based on the one or more rules, whether to switch from a current operational mode to another operational mode from the set of operational modes; and performing the current operational mode or the other operational mode based on the determination.

Abstract: Systems and methods described herein can improve typicality of batches for machine learning. The systems and methods can include obtaining a corpus of training data, the corpus of training data including one or more training examples. The systems and methods can include generating a first batch set including a plurality of batches from the corpus of training data, each of the batches including a subset of the one or more training examples. The systems and methods can include determining a batch distribution of a first batch of the first batch set. The systems and methods can include determining that the first batch is an atypical batch based on the batch distribution of the first batch. The systems and methods can include, in response to determining that the first batch is an atypical batch, shuffling the training examples of the first batch and one or more second batches of the first batch set to generate a second batch set.

Abstract: A method for training and using a field machine learning (ML) model to classify emission data is presented. The method includes generating synthetic data by a large language model (LLM) by prompting the LLM with emission classes and few shot examples. The synthetic data includes multiple synthetic data instances and corresponding instance labels. A training dataset is obtained from the synthetic data. The method further includes training the field ML model with training instances which are synthetic data instances from the training dataset and corresponding training labels. The field ML model generates a predicted probability distribution of a training output class corresponding to a training instance. The method further includes adjusting a model parameter weight of the field ML model to minimize a categorical cross-entropy loss function calculated based on the generated predicted probability distribution. The trained field ML model is used to classify emission data.

Abstract: A system and method of for providing assistance to complete machine learning on workflow engines that deal with machine learning flows comprising operators configured in a coordinate grid. The process analyzes the positions and composition of operators, branches, inconsistencies, collisions and redundancy in the workflow in order to suggest to the user which changes should be made to the workflow.

Abstract: An information processing device includes: a first feature value extracting unit extracting a feature value of input data; a first probability calculating unit performing inference on the input data based on the feature value, and calculates a probability to classify the input data into each of a first number of classes; and a first classification unit classifying the input data into at least one of the first number of classes based on the probability. The first classification unit rearranges the input data so that the probability is in ascending or descending order, extracts a label having a maximum probability from the rearranged input data, compares the label having the maximum probability with a correct answer label, stores a class in which the labels coincide with each other, stores a class in which the labels do not coincide with each other, and statistically processes the stored classes.

Abstract: A model accuracy determining method includes performing, by a first network element, inference on a task based on a first model; determining, by the first network element, first accuracy corresponding to the first model. The first accuracy is used to indicate accuracy of the first model on an inference result of the task; and in a case that the first accuracy reaches a preset condition, sending, by the first network element, first information to a second network element, where the first information is used to indicate that accuracy of the first model does not meet an accuracy requirement or decreases. The second network element is a network element that provides the first model.

Abstract: Systems, methods, and computer program products for multi-domain ensemble learning based on multivariate time sequence data are provided. A method may include receiving multivariate sequence data. At least a portion of the multivariate sequence data may be inputted into a plurality of anomaly detection models to generate a plurality of scores. The multivariate sequence data may be combined with the plurality of scores to generate combined intermediate data. The combined intermediate data may be inputted into a combined ensemble model to generate an output score. In response to determining that the output score satisfies a threshold, at least one of an alert may be communicated to a user device, the multivariate sequence data may be inputted into the feature-domain ensemble model to generate a feature importance vector, or at least one of a model-domain, a time-domain, a feature-domain, or the combined ensemble model may be updated.

Abstract: Methods and Systems implement patient management. In some cases, a patient management system may include one or more respiratory pressure therapy devices to deliver respiratory pressure therapy to patients, and generate therapy data relating to a therapy session for a patient. The patient management system may include a data server communicating with the therapy device(s). The data server may compute, from therapy data, therapy summary data for the session, the summary data may include one or more statistics summarising therapy data. The patient management system may include a therapy management server communicating with the data server. The therapy management server may apply one or more rules to the summary data, update or generate one or more workflow groups of patients, each workflow group corresponding to a rule, depending on results of the respective rule applications; and/or serve a graphical layout representing one or more workflow groups.

Abstract: Methods are disclosed for booking electrical vehicle charging stations and for managing electrical vehicle battery charging at such stations. The method includes determining the available charging the battery and determining what charges required in order to complete intended journeys. Only charging stations which can provide sufficient charge in the available time are offered and if an excess of charge is available then this can be sold back to the electrical distribution grid and the cost of parking reduced. Only charging stations which can provide sufficient charge in the available time are offered and if an excess of charge is available then this can be sold back to the electrical distribution grid and the cost of parking reduced or other offers made.

Abstract: In one aspect, the present invention provides a computing system for effecting an optimised condition for one or more booking requests in a venue having one or more spaces, comprising an allocation module executing on a processor and arranged to retrieve the booking requests from a database containing a plurality of booking requests, the booking requests including requestor constraint information regarding one or more constraints provided by the booking requestor including a predefined service period, and retrieve venue constraint information from a database, the venue constraint information including venue spatial information and furniture spatial information, wherein the allocation module executes an allocation algorithm that utilises the booking information and the venue constraint information to assess the capacity of the one or more venues and allocate a portion of space for each booking request to satisfy the optimised condition utilising the assessment, to derive an optimised allocation instruction set

Abstract: Computer-implemented systems, methods, and products for enabling one or more nodes of a first electronic ledger platform to carry out operations with respect to one or more records of the first electronic ledger platform. The operations may include receiving an indication from a smart contract to manage a transaction associated with sale of a token by a selling entity; processing information associated with the sale of the token to initiate the transaction, the information including identity of a first entity listing the token for sale and identity of a second entity having record ownership of the token; verifying one or more events or information associated with the transaction to confirm authenticity or transferability of the token; and approving the transaction.

Abstract: A method, computer program product, and computer system for configuring a battery pack. Parcel delivery instructions including parcel level information, at least one electric vehicle property, and a first location and one or more destinations are received. A trained artificial intelligence model is used to extract an expected battery consumption of the electric vehicle for each of the plurality of potential routes, and to identify a delivery route that has a lowest expected battery consumption. Battery service options are mapped along the delivery route. Simulations of the electric vehicle completing the delivery are performed. A size of a battery pack to be used with the electric vehicle at a start of the delivery route at the first location is configured, and a battery pack service schedule for servicing the battery pack between the first location and the one or more destinations is configured, as a function of the multiple simulations.

Abstract: The disclosed system identifies an unused medication and a current location of the unused medication, and determines that a new medication order can be prepared using the unused medication. Responsive to determining that the new medication order can be prepared using the unused medication, the system provides a preparation instruction to prepare the new medication order using the unused medication, determines a current geolocation of a global positioning system (GPS) receiver of a mobile device associated with a delivery person, the mobile device being remote from the one or more computing devices, determines based on the current geolocation of the GPS receiver of the mobile device and the unused medication, that the unused medication should be retrieved by the delivery person before or instead of a second medication, and provides, to the mobile device, an indication to retrieve the unused medication.

Abstract: A lawn management assistance system 1 includes a communicator 32 and a processor 31. The communicator 32 receives positional information of a lawn mower 10 detected by a position sensor 16 installed at the lawn mower 10 and receives condition information of a lawn detected by a condition sensor 17 installed at the lawn mower 10. The processor 31 analyzes the condition information per piece of the positional information based on information received by the communicator 32 and generates and outputs improvement information indicating a method for improvement of condition of the lawn per piece of the positional information.

Abstract: A computerized-method for trading a scheduled-working-shift, is provided herein. The computerized-method includes operating a trading-shifts module.

Abstract: A system provides a set of data to a user application on a computing device of a user, the set of data being used by the mobile computing device to display a map indicating the current location of the mobile computing device on the map, an estimated arrival time for a vehicle to rendezvous with the user, and a user interface feature operable by the user to request transport. The system can select a driver to fulfill the user's transport request and prior to transport being provided to the user, the system tracks a current location of the selected driver as the selected driver progresses to the pickup location and provides progress information of the selected driver to the user. Upon the user being picked up by the selected driver, the system tracks the selected driver from the pickup location to a drop-off location.

Abstract: Systems and methods are provided for assigning an issue for resolution using natural language processing (NLP) and updating recognition scores for individual/teams accurately redirecting an issue to a different individual/team having a greater ability to resolve it. An issue is analyzed using NLP, and the text is compared to each individual/team's corpus of issues to derive a match percentage. A list is built which ranks individuals/teams by the match percentage. Weights are applied to each individual/team in the list, based on their corresponding recognition scores in their profiles in a profile database. The recognition scores indicate an ability to recognize correct reassignment with a degree of accuracy above a threshold. The list is reordered based on the applied weights, and the issue is assigned to the individual/team having a highest rank.

Abstract: Shift trading systems and methods, and non-transitory computer readable media, include receiving a shift trade request from a source agent, wherein the shift trade request comprises a shift day and a shift time; matching the shift trade request with a plurality of target agents that are available on the shift day and the shift time; for each target agent from the plurality of target agents, calculating a trade index score based on a trade history success index score, a matching skill index score, a skill proficiency index score, and a trade interval index score; ranking the plurality of target agents from highest to lowest trade index score; and displaying the ranked plurality of target agents with the target agent having the highest trade index score at the top of a list.

Abstract: A method for analysis of in-person attendant interactions according to an embodiment includes determining a location of a person within a monitored area based on sensor data generated by one or more sensors, determining a start queue time associated with a time at which the person is located at a start queue position within the monitored area, determining an end queue time associated with a time at which the person is located at an end queue position, recording interaction data of an interaction between the person and an attendant when the person is located at the end queue position, determining a wait time of the person in the queue based on the start queue time and the end queue time, determining an interaction time of the interaction based on the interaction data, and adjusting an attendant schedule for the monitored area to improve the wait time or the interaction time.

Abstract: This application relates to systems and methods for generating desired or optimized event schedules. An example computer-implemented method of dynamically generating an event schedule includes: receiving one or more parameters for a series of live events to be held in a plurality of geographic regions; generating a schedule for the series of live events based on the one or more parameters; and automatically updating the schedule based on a change to the one or more parameters.