Various embodiments of the present disclosure provide parameter optimization and collaborative networking techniques for improving traditional disparate computing ecosystem. The techniques may include identifying a condition-specific entity cohort for a data entity that is associated with (i) a condition and (ii) a primary computing entity within a computing entity ecosystem. The techniques include generating a real-time optimization model for the condition using the condition-specific entity cohort and, using the real-time optimization model, generating an optimized entity parameter sequence for the data entity. The techniques include initiating the performance of a prediction-based action and, responsive to the prediction-based action, may include receiving a parameter modification for the data entity, generating a simulated recovery feature for the data entity, and provide access to data indicative of the simulated recovery feature to the computing entity ecosystem.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable software for tracking, reporting, reviewing, and sharing health care data analytics; Providing on-line non-downloadable software for aggregating and consolidating health information
3.
SYSTEMS AND METHODS FOR DETERMINING CONDITION OF AN ENTITY VIA MACHINE LEARNING TECHNIQUE
Systems and methods are disclosed for analyzing real-time data utilizing machine learning for determining the condition of an entity. The method includes receiving a control dataset and a system dataset or a non-system dataset for a first entity; determining, via input of a first subset of the control dataset into a first machine learning model, classification of the first entity; determining, via input of the system dataset into a second machine learning model or the non-system dataset into a third machine learning model, system score or non-system score, respectively; determining, via input of the system score, the non-system score, or a second subset of the control dataset into a fourth machine learning model, composite score; determining lateral score or longitudinal score based on the classification of the first entity or the composite score; and comparing the lateral score or the longitudinal score with a pre-determined threshold for initiating mitigation action(s).
A computing system may produce standardized compliance reports that may contains change requirements, test and security results, and approvals with validation and attaching the compliance report to change tickets. Compliance Automation application programming interface (API) into a software change workflow interface may enable the compliance reports to be constructed by software engineering teams while they use the software change workflow interface. The system may enable software engineering teams to generate a standardized software change management compliance reports that have a uniformity that helps auditors reviewing process.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for (i) generating document-topic-entity relationship features that are associated with a plurality of topics, a plurality of entities, and a plurality of documents, (ii) generating knowledge graph data objects based on the document-topic-entity relationship features, (iii) generating prompt elements based on a query input, the prompt elements comprising (a) context data associated with one or more query topics, one or more query entities, or one or more query documents and (b) the knowledge graph data objects, (iv) generating, using a natural language processing machine learning model, one or more subgraph data objects based on the prompt elements, and (v) providing, one or more answer outputs based on the one or more subgraph data objects.
Techniques for hierarchical clustering with tiered specificity are disclosed herein. An example computer-implemented method includes receiving data points that each include data corresponding to a feature set. The example computer-implemented method further includes applying, a machine learning model to the data points to: cluster (i) a first portion of the data points into a first cluster set based on similarity values computed using a first subset of the feature set and (ii) a second portion of the plurality of data points into a second cluster set based on similarity values computed using a second subset of the feature set that is different from the first subset. The example computer-implemented method further includes generating a data object indicating a course of action for an entity associated with a first data point based on the first data point being included in the first cluster set or the second cluster set.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
9.
SYSTEMS AND METHODS FOR PREDICTIVE ANALYSES WITH MACHINE LEARNING SYSTEMS
A method includes receiving, by one or more processors, a dataset including transition data and factor data. The method includes generating a feature for a machine learning model based on the transition data, generating, via input of at least the feature into the machine learning model, one or more data objects indicative of a transition prediction for a transition from the first stage to the second stage, the machine learning model having been trained: with data sources including training factor data having information other than a chemical constituent of blood, and to output information associated with a transition prediction. The method further includes initiating performance of one or more remedial or analytical actions in response to generating the one or more data objects indicative of the transition prediction.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
10.
SECURE AND AUTONOMOUS DATA ENCRYPTION AND SELECTIVE DE-IDENTIFICATION
Various embodiments of the present disclosure provide automated encryption and data de-identification techniques for improving computer security. The techniques apply machine learning and encryption techniques to transform input data objects to tagged data objects that may be locally decrypted using encrypted element representation stored within the tagged data objects. The techniques may include determining a protected data element from an input data object based on privacy criteria and generating the tagged data object from the input data object by replacing the protected data element with an anonymized privacy tag that identifies a privacy type of the protected data element. The techniques may further include generating an encrypted element representation of the protected data element and inserting the encrypted element representation to a portion of the tagged data object to enable decryption of the tagged data object by authorized entities.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for receiving training data comprising data records with identified presence of modalities, training a multi-modal generative model based on the training data, and imputing missing modalities of input data records using the multi-modal generative model, wherein the multi-modal generative model comprises (i) a modality-agonistic latent variable encoder and (ii) one or more modality-specific latent variable encoders configured to receive output of the modality-agonistic latent variable encoder as input.
Systems and methods for routing data using an artificial intelligence (AI) model are disclosed. The method includes receiving a data request associated with one or more data gaps, determining, by an AI model, a plurality of ranking values for a plurality of candidate data sources respectively based on one or more attributes, each of the plurality of ranking values indicative of a likelihood of filling the one or more data gaps associated with the data request; and routing, over a network, the data request to a first candidate data source of the plurality of candidate data sources based on a first ranking value of the plurality of ranking values; and blocking routing of the data request over the network to a second candidate data source of the plurality of candidate data sources based on a second ranking value of the plurality of ranking values.
Various embodiments of the present disclosure provide methods, apparatuses, systems, devices, computing entities for evaluating a medical encounter between a healthcare provider and a patient. Various embodiments evaluate a medical encounter to determine a classification of the medical encounter. An example method comprises receiving a claim data object comprising a plurality of code portions, each code portion corresponding to a dimension of the medical encounter; processing the claim data object to extract a plurality of code character strings, each code character string extracted from a corresponding code portion of the claim data object; generating a claim classification for the claim data object based at least in part on evaluating the plurality of code character strings with respect to at least one dimension relating to the provider's contribution to the encounter and at least one dimension relating to the patient's contribution to the encounter; and performing at least one classification-based action.
Systems and methods are described for training and/or using a machine-learning model. A first set of textual data is received. Using a trained machine-learning model that is applied to the first set, a classification of the first set is generated. The trained machine-learning model has been trained based on a subset of textual data that resulted from filtering a set of training textual data. The filtering of the set of training textual data to generate the subset of textual data is based on a comparison between the training textual data and a second set of textual data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing temporary use of on-line non-downloadable software for tracking, reporting, reviewing and sharing health care data analytics; Providing temporary use of on-line non-downloadable software for providing users access to health and healthcare related documents, health and medical provider data
Various embodiments of the present disclosure provide machine learning and rules-based recommendations for user interface workflows. In one example, an embodiment provides for generating a set of recommendation data objects for a user identifier associated with a user interface based on a set of predefined rules associated with input data provided via a user interface workflow associated with the user interface, generating a ranked version of the set of recommendation data objects using a machine learning model, and initiating a rendering of a set of selectable graphical elements via the user interface based on the ranked version of the set of recommendation data objects.
Hierarchical data objects are generated via a computer-based system for applying a series of rules to establish episode-specific data objects reflecting a plurality of discrete claim records before further dissecting the generated episode-specific data objects prior to finalization of those episode-specific data objects to identify claim records within the episode-specific data objects that are eligible for generation of one or more sub-episodes within the episode-specific data objects. The identified sub-episodes are reflected within the episode-specific data object to designate complete episodes of care that additionally reflect interactions with the corresponding parent episode.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/65 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 70/40 - ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
19.
DATASET LABELING USING LARGE LANGUAGE MODEL AND ACTIVE LEARNING
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for labeling data by (i) generating, using a natural language machine learning model, a labeled dataset from unlabeled data, (ii) training one or more instances of a classification machine learning model based on the labeled dataset, (iii) generating, using the one or more instances of the classification machine learning model, a plurality of validation classifications, and (iv) generating a refined labeled dataset that is based on the labeled dataset and a plurality of uncertainty scores associated with the plurality of validation classifications.
G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for labeling data by (i) generating, using a natural language machine learning model, a labeled dataset from unlabeled data, (ii) training one or more instances of a classification machine learning model based on the labeled dataset, (iii) generating, using the one or more instances of the classification machine learning model, a plurality of validation classifications, and (iv) generating a refined labeled dataset that is based on the labeled dataset and a plurality of uncertainty scores associated with the plurality of validation classifications.
Various embodiments of the present disclosure provide production line conformance measurement techniques using intelligent retraining of machine learning models. The techniques may include receiving, using a performance metric event stream associated with a categorical validation ensemble model, a performance metric event associated with a categorical validation machine learning model of the categorical validation ensemble model. In response a determination that the performance metric event satisfies a defined performance metric threshold, the techniques may also include identifying a training dataset for the categorical validation machine learning model and generating, and using the training dataset, an updated version of the categorical validation machine learning model. The training dataset may include a plurality of training production line images each associated with an object identifier, a site identifier, and/or a fill level.
Various embodiments of the present disclosure provide a contextualized task-specific graphical visualization related to one or more third-party data sources. The techniques may include generating a defined data object by transforming a plurality of third-party data elements from one or more third-party data sources to a defined first-party format, generating a structured data object from the defined data object based on a data structure format comprising a set of format features defined by a machine learning formatting prompt, generating a task-specific data object from the structured data object and corresponding to a defined domain task by filtering a plurality of task-agnostic features of the structured data object using a task-specific prompt that defines a set of task-related features, and initiating a rendering of a contextualized task-specific graphical visualization that is based on the task-specific data object and comprises a set of interactive graphical elements for the defined domain task.
Various embodiments of the present disclosure provide machine learning architectures and training techniques for improving predictive functionality of a computer. The techniques apply a multi-layered machine learning model to a target prediction domain to generate a model prediction for an input data object. The techniques may include inputting a vector to layer models of the multi-layered machine learning model to generate a layer code predictions for a code defined within a target coding domain. The techniques include inputting the layer code predictions to a layer metamodels of the multi-layered machine learning model to generate intermediate code predictions for the code. The techniques include inputting intermediate outputs to a fusion model of the multi-layered machine learning model to generate a fused code prediction for the code and outputting a model prediction for the code based on the fused code prediction.
Systems and methods are disclosed for determining unnecessary internal system utilization. A method includes receiving a first data object and generating an entity data object for each entity of the plurality of entities based on at least a portion of the first data object. The method further includes generating a usage indicator for each entity of the plurality of entities by applying a machine-learning model to at least a portion of the entity data object for the entity, the usage indicator associated with a pre-determined time period. The method further includes generating a utilization data object based on the entity data object and the usage indicator generated for each entity, and causing the utilization data object to be displayed on a Graphical User Interface (GUI).
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
26.
SYSTEMS AND METHODS FOR PREDICTING UNNECESSARY RESOURCE UTILIZATION
Systems and methods are disclosed for predicting unnecessary resource utilization. A processor receives a first data object and generates for each member of a plurality of members a usage indicator for a pre-determined time period and a usage rate for the pre-determined time period. The processor generates each member of the plurality of members, based at least on the first classification data set, the second classification data set, the usage indicator, and the usage rate, a member optimization parameter. The processor generates based at least on the usage indicator, the usage rate, and the member optimization parameter for each member of the plurality of members, a plurality of cluster data objects, where members of each cluster data object are unique from members of any other cluster data object. The processor causes at least one of the plurality of cluster data objects to be displayed on a Graphical User Interface (GUI).
Systems and methods are disclosed for determining unnecessary internal system utilization based on protocol adherence. A method includes receiving a first data object, generating an entity data object, and generating a verified entity data object based on comparing one or more metrics of the entity data object against one or more predetermined threshold values, wherein entities of the verified entity data object are a subset of the entities of the entity data object. The method further includes generating a compliance indicator for each entity of the verified entity data object. The method furthermore includes generating a utilization adjustment data object and causing the utilization adjustment data object to be displayed on a Graphical User Interface (GUI).
G06Q 10/0639 - Performance analysis of employeesPerformance analysis of enterprise or organisation operations
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
28.
METHODS, SYSTEMS, AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING WHEN PRIOR AUTHORIZATION IS REQUIRED FOR A HEALTH CARE PROCEDURE USING STATISTICAL ANALYTICS
A method includes processing, by one or more processors, historical claim and claim remittance information to extract prior authorization data; performing, by the one or more processors, a probabilistic analysis on the prior authorization data for a procedure to generate a first metric and a second metric, the first metric comprising a percentage of claims including the procedure in which prior authorization is applied for and the second metric comprising a percentage of claims including the procedure in which prior authorization was not applied for and were denied payment for not applying for prior authorization; determining, by the one or more processors, whether the first metric satisfies a first authorization threshold and the second metric satisfies a second authorization threshold, the first and second thresholds being associated with a prior authorization rule corresponding to the procedure; and generating, by the one or more processors, a recommendation on whether to obtain prior authorization before performing the procedure based on whether the first metric satisfies the first authorization threshold and the second metric satisfies the second authorization threshold.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
29.
METHODS, SYSTEMS, AND COMPUTER PROGRAM PRODUCTS FOR FLAGGING DENTAL CLAIMS FOR FURTHER SCRUTINY BASED ON PROCESSING OF DENTAL CLINICAL IMAGES AND PERIODONTAL CHARTS USING MULTIPLE ARTIFICIAL INTELLIGENCE (AI) MODELS
A method includes receiving, by one or more processors, a clinical image associated with a dental procedure; identifying, by the one or more processors, one or more dental procedure codes based on processing the clinical image using a plurality of AI models; receiving, by the one or more processors, a periodontal chart image; processing, by the one or more processors, the periodontal chart image using optical character recognition to obtain pocket measurements associated with a plurality of teeth along with positional coordinates of each of the pocket measurements; identifying, by the one or more processors, a submitted dental procedure code in a dental claim for the dental procedure; determining, by the one or more processors, whether the submitted dental procedure code corresponds to a visibly detectable procedure; determining, by the one or more processors, whether the submitted dental procedure code matches any of the one or more dental procedure codes based on processing the clinical image when the submitted dental procedure code corresponds to a visibly detectable procedure; and flagging, by the one or more processors, the dental claim when at least one of the submitted dental procedure code does not match any of the one or more dental procedure codes based on processing the clinical image, the submitted dental procedure code does not correspond to a visibly detectable procedure, or the pocket measurements do not support the submitted dental procedure code.
Various embodiments of the present disclosure provide machine-learning question resolution techniques for improving question response outputs. The techniques may include receiving a plurality of evidence passages from a document set corresponding to an input question. The techniques may include generating, using a retrieval ensemble model, a plurality of evidence predictions for an evidence passage of the plurality of evidence passages based on the input question. The techniques may include generating, using the retrieval ensemble model, a weighted aggregate prediction for the evidence passage based on the plurality of evidence predictions. The techniques may include selecting, a set of input passages from the plurality of evidence passages based on the weighted aggregate prediction. The techniques may include generating, using a machine learning aggregation model, a question response based on the set of input passages and the input question. The techniques may include providing the question response.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Systems and methods are disclosed for detecting unnecessary resource re-utilization. A method includes receiving a first data object, the first data object including an entity data set containing a plurality of entities; a first data set including request data associated with the plurality of entities; an event data set; and a plurality of data sets associated with one or more performance metrics. The method further includes generating an entity data object for each of the plurality of entities and applying a machine-learning model to the entity data objects generated for the plurality of entities. The method further includes determining a prediction indicator for each entity of the plurality of entities, generating a re-utilization offset data object for each of the plurality of entities, and causing the re-utilization offset data object for each entity to be displayed on a Graphical User Interface (GUI).
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06F 16/2457 - Query processing with adaptation to user needs
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
32.
PRODUCTION LINE CONFORMANCE MEASUREMENT TECHNIQUES USING INTELLIGENT IMAGE CROPPING AND CATEGORICAL VALIDATION MACHINE LEARNING MODELS
Various embodiments of the present disclosure provide image and prediction processing techniques for providing improved image-based prediction. The techniques may include generating a transformed image from a production line image corresponding to a primary orientation and the generating one or more derivative transformed images for the production line image, each corresponding to one of one or more derivative orientations from the primary orientation. The techniques may include generating, using a categorical validation machine learning model, a plurality of validation predictions for the production line image based on the transformed image and the one or more derivative transformed images. The techniques include generating an aggregate validation prediction based on the plurality of validation predictions and initiating the performance of the prediction-based action based on the aggregate validation prediction.
Embodiments address various deficiencies and provide technical advantages with respect to evaluating performance of topic models, particularly LLMs that perform topic modeling. Embodiments utilize a second LLM for evaluation, where the LLM is specially configured utilizing a particular evaluation rubric and domain-specific contextual data that enables accurate and automatic use of the configured LLM for topic model evaluation within particular domains.
Embodiments of the present disclosure provide techniques and systems for intelligently routing linked objects. One technique may include receiving, at a conveyer assembly, a first carrier container pair. The first carrier container pair may be moved, responsive to a first distance-based stimulus, to a first recirculation system within a first transportation portion of the conveyer assembly. The first recirculation system may recirculate the first carrier container pair until a first distance threshold is achieved. Responsive to a second distance-based stimulus, the first carrier container pair may be moved to a second recirculation system within a second transportation portion of the conveyer assembly. The second recirculation system may recirculate the first carrier container pair until a second distance-based stimulus is achieved. The first carrier container pair and a second carrier container pair may then be moved to a packaging portion of the conveyer assembly based on the second distance threshold.
Embodiments address various deficiencies and provide technical advantages with respect to evaluating performance of topic models, particularly LLMs that perform topic modeling. Embodiments utilize a second LLM for evaluation, where the LLM is specially configured utilizing a particular evaluation rubric and domain-specific contextual data that enables accurate and automatic use of the configured LLM for topic model evaluation within particular domains.
A computer-implemented method includes detecting, by one or more processors, a presence of an event written to a blockchain as one or more data objects, the event being associated with an entity; determining, by the one or more processors, one or more digital resource object categories to be mapped to the event based on one or more factors; determining, by the one or more processors, at least one magnitude value associated with each of the one or more digital resource object categories based on an evaluation of the event, the evaluation including an analysis of one or more parameters associated with the event; and generating, by the one or more processors, an aggregate digital resource object based on each determined magnitude value.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06Q 40/04 - Trading Exchange, e.g. stocks, commodities, derivatives or currency exchange
38.
REAL-TIME MACHINE LEARNING TECHNIQUES FOR PROACTIVELY GENERATING AND ACTING ON USER-SPECIFIC ACTIVITY INSIGHTS
Various embodiments of the present disclosure provide real-time machine learning techniques for proactively generating and acting on user-specific activity insights. One technique may include generating a predictive activity sequence for a user that includes multiple activity predictions corresponding to multiple activity time segments within an evaluation time period. The technique may also include generating, based on the predictive activity sequence, a personalized activity sequence for the user that includes multiple activity subgoals corresponding to one or more activity time segments of the multiple activity time segments within the evaluation time period. The technique may also include identifying an occurrence of an activity time segment corresponding to an activity subgoal of the multiple activity subgoals. The technique may also include, in response to the occurrence of the activity time segment, providing data indicative of the activity subgoal.
Embodiments provide processing of time-series data for improved embedding and processing, specifically using dimension attention for contrastive learning. The improved embedding enables the creation of more accurate embeddings within an embedding space, including an embedding space shared between the data types, via contrastive learning and dimension attention.
Various embodiments of the present disclosure provide message filtering and routing techniques for intelligently routing messages within a complex network domain. The techniques may include receiving, originating from a provider system, a provider-platform request message that identifies a provider service request and a designated coverage platform corresponding to the provider service request. In response to an identification of a designation error associated with the designated coverage platform, the techniques may include providing to a primary coverage platform, using a cross-platform interface, a platform-platform request message that identifies the provider service request; and providing an alert message to the provider system.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing suggestion keywords based on historical search data of a user by: generating one or more keyword feature vectors associated with a plurality of keywords from a list of suggestion keywords, generating one or more personalized feature vectors associated with the user based on search session data, generating a plurality of predictions of the user selecting the plurality of keywords based on the one or more keyword feature vectors and the one or more personalized feature vectors, assigning a plurality of rankings to the plurality of keywords based on the plurality of prediction probabilities, and generating one or more typeahead suggestion keywords based on the plurality of rankings.
Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques may include generating an intake data object including an intake identifier and an object identifier respectively corresponding to a plurality of intake objects. The intake data object is used to generate transitioning data objects corresponding to a subset of the intake objects. The intake data object may be modified with a transition identifier to link the objects. In addition, one or both identifiers may be added to a backstop data object to link the subset of intake objects to a backstop location. In response to a request, the subset of intake objects may be moved from the backstop location to conveyor pallet, and, in response, the transition and intake identifier may be removed from the backstop data object and added to a conveyor pallet data object.
Various embodiments of the present disclosure provide complex data processing pipeline configuration and anomaly detection techniques for developing, maintaining, and tracking metrics for time-based execution workflows. The techniques may include generating, using a current pipeline version of a data processing pipeline, a time-dependent output for a current data version of a dynamic input dataset at a current time and then generating a current compliance data object that is indicative of the current pipeline version, the current data version, and the time-dependent output to holistically record one or more aspect of the execution of the data processing pipeline. The techniques may include identifying a performance anomaly based on a comparison between the current compliance data object and a plurality of historical compliance data objects and initiating the performance of a predictive action based on the project segment.
Various embodiments of the present disclosure provide contextually aware debiasing techniques for debiasing a document. Some embodiments generate one or more document segments that each comprise a sequence of terms from a syntactic debiased document, identify one or more candidate semantic bias terms from a document segment of the one or more document segments based on a semantic bias corpus in response to the identification of the one or more candidate semantic bias terms, generate, using a classification model, a bias classification for the document segment, and in response to a positive bias classification, provide, using a semantic debiasing model, one or more replacement tokens for the one or more candidate semantic bias terms.
Various embodiments of the present disclosure provide techniques and systems for improving efficiency of fulfillment systems. One technique may include moving a plurality of container carriers to one or more carrier recirculation systems. The technique may include moving a plurality of item containers to a container recirculation system, positioned at least partially parallel to a carrier recirculation system of the one or more carrier recirculation systems. The technique may include moving, using a container partitioning assembly of the container recirculation system, an item container of the plurality of item containers into an induction system. The technique may include moving, using a carrier partitioning assembly of the one or more carrier recirculation systems, a container carrier of the plurality of container carriers to a loading position relative to the induction system. The technique may include loading the item container into an interior portion of the container carrier.
Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques of the present disclosure may include receiving a transition identifier corresponding to a transitioning data object that include (a) a first object count of a plurality of transitioning objects within a transitioning container, (b) an intake identifier corresponding to the plurality of transitioning objects, and (c) a pallet identifier for a conveyor pallet configured to move the transitioning container. The techniques may include receiving a canister identifier corresponding to a distribution canister and in response to receiving the transition identifier and the canister identifier, (a) linking the canister and transition container, (b) determining, a second object count corresponding to the distribution canister, and (c) initiating a compliance operation based on a comparison between the first object count and the second object count.
G16H 20/13 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques may include receiving a real time fill request associated with a robotic distribution device and that includes a target object identifier. The techniques include accessing a conveyor dataset corresponding to a conveyance assembly and that includes a pallet data object including one or more intake identifiers and a conveyor location identifier. The techniques include selecting the pallet data object from a plurality of other pallet data objects of the conveyor dataset based on the target object identifier, the one or more intake identifiers, and the conveyor location identifier and initiating the performance of a fill operation for the robotic distribution device based on a conveyor pallet of the conveyance assembly that corresponds to the pallet data object.
G05B 19/416 - Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control of velocity, acceleration or deceleration
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
Various embodiments of the present disclosure object provide tracking and monitoring techniques for implementing improved distribution systems in various environments. The techniques may include generating an intake data object including an intake identifier and an object identifier respectively corresponding to a plurality of intake objects. The intake data object is used to generate transitioning data objects corresponding to a subset of the intake objects. The intake data object may be modified with a transition identifier to link the objects. In addition, one or both identifiers may be added to a backstop data object to link the subset of intake objects to a backstop location. In response to a request, the subset of intake objects may be moved from the backstop location to conveyor pallet, and, in response, the transition and intake identifier may be removed from the backstop data object and added to a conveyor pallet data object.
Systems and methods are disclosed processing a document. A method includes receiving, by a processor coupled to a memory, layout information for two or more layouts of a document, each layout of the two or more layouts having a layout bounding box. The method includes identifying, by the processor, one or more areas of overlap between the layout bounding boxes of each layout, respectively. The method includes identifying, by the processor, content associated with each area of overlap. Further, the method may include use the layout information, the areas of overlap, and the identified content associated with each area of overlap as inputs to a machine learning model. Further, the method may include receiving, by the processor from the machine-learning model, a layout bounding box configuration for one or more of the layouts of the document. Further, the method may include apply the layout bounding box configuration to the document.
Disclosed are systems and methods for detecting a table from a document, classifying each cell of the table as one of: a value cell, a row header cell, or a column header cell, and performing a bounding box elongation operation to match each cell that is classified as a value cell to a first corresponding cell that is identified as a row header cell and a second corresponding cell that is identified as a column header cell. For each cell classified as a value cell, a data tuple is generated comprising a row header element, a column header element, and a value element, wherein the row header element corresponds to a first value in the first corresponding cell, the column header element corresponds to a second value in the second corresponding cell, and the value element corresponds to a third value in the value cell.
Various embodiments of the present disclosure provide reinforcement learning for machine learning using dynamic confidence thresholds. In one example, an embodiment provides for generating a plurality of training datasets for a machine learning model by augmenting a labeled dataset for the machine learning model with a synthetic labeled dataset, generating a plurality of retrained model versions of the machine learning model based on the plurality of training datasets, generating a reward indicator for a retrained model version of the plurality of retrained versions of the machine learning model based on a comparison between a validation dataset for the machine learning model and a respective output dataset for the retrained model version, and modifying the defined confidence threshold based on the reward indicator for the retrained model version to generate a modified confidence threshold for the machine learning model.
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, receive, from a match result serialization queue, a selected unprocessed match result entry that corresponds to an unprocessed concurrent match result determination originating from one of a plurality of processing nodes, receive, from a local storage medium, a set of recently-processed match result entries that respectively correspond to a set of concurrent match result determinations assigned an affirmative match result validation during a defined recency period, generate a serialized match result determination for the selected unprocessed match result entry based on the unprocessed concurrent match result determination and the set of recently-processed match result entries, and initiate a performance of a concurrent write request for the unprocessed concurrent match result determination based on the serialized match result determination.
A computer-implemented method includes receiving, by one or more processors, Process Instruction (PI) information containing instructions for adjudicating a healthcare service request based on a primary fact source; dividing, by the one or more processors and using an Artificial Intelligence (AI) model, the instructions into one or more instruction sets; generating, by the one or more processors and the AI model, an input-output mapping for first ones of the one or more instruction sets having a complexity that does not satisfy a complexity threshold; generating, by the one or more processors and the AI model, code for second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold; generating, by the one or more processors and the AI model, a validation input set for the input-output mapping and for the code; and applying, by the one or more processors and the AI model, the validation input set to the input-output mapping and the code to generate a validation output for the input-output mapping and for the code.
Various embodiments of the present disclosure provide machine learning training techniques for implementing a multi-modal interpretation process to generate holistic outputs for an event. The techniques may include generating, using first layers of a multi-modal machine learning model, text-based intermediate representations for an entity based on textual input data. The techniques include generating, using second layers of the multi-modal machine learning model, image-based intermediate representations for the entity based on the text-based intermediate representations and input images for the entity. The techniques include generating, using one or more third layers of the multi-modal machine learning model, an entity representation summary based on the image-based intermediate representations and an image narrative summary for the input images. The techniques include initiating the performance of a prediction-based action based on the entity representation summary.
Various embodiments of the present disclosure provide machine learning training techniques for implementing a multi-phase training process to holistically train a machine learning summarization model. The multi-phase training process may include generating, using the machine learning summarization model, a training summary for a training transcript. A first, second, and/or a third reward metric may be generated based on the training summary. Each reward metric may be tailored to a different aspect of the machine learning summarization model. For example, the first reward may be based on a comparison between the training summary and a target summary corresponding to the training transcript. The second reward may be based on the training summary and a positive/negative summary. The third reward may be based on training key phrases from the training summary. The model may be trained by optimizing an aggregated reward metric derived from the first, second, and/or third reward metrics.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Business services provided to the healthcare industry, namely, the collection, reporting, and analysis of healthcare quality data for business purposes; Business management consulting and advisory services for the healthcare industry Providing temporary use of on-line non-downloadable software for tracking, reporting, reviewing and sharing health care data analytics
Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment. Some embodiments store machine learning model(s) trained utilizing third-party workspace(s), initiate a deployed instance of a selected machine learning model, receive data artifact(s) in response to operation of the deployed instance, generate updated evaluation data associated with the deployed instance, determine that the updated evaluation data does not satisfy model maintenance threshold(s), and trigger a process that terminates access to at least the deployed instance.
Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment. Some embodiments store machine learning model(s) trained utilizing third-party workspace(s), initiate a deployed instance of a selected machine learning model, receive data artifact(s) in response to operation of the deployed instance, generate updated evaluation data associated with the deployed instance, determine that the updated evaluation data does not satisfy model maintenance threshold(s), and trigger a process that terminates access to at least the deployed instance.
Embodiments provide for improved model maintenance utilizing third-party workspaces. Some embodiments receive data artifact(s) associated with training of a machine learning model, generate model keyword(s) based on the data artifact(s), and store the machine learning model linked with the model keyword(s). Some embodiments receive data artifact(s), generate an embedded representation based on the data artifact(s), and store the embedded representation of the machine learning model in an embedding space. The stored data is then searchable to identify relevant models for deployment. Some embodiments store machine learning model(s) trained utilizing third-party workspace(s), initiate a deployed instance of a selected machine learning model, receive data artifact(s) in response to operation of the deployed instance, generate updated evaluation data associated with the deployed instance, determine that the updated evaluation data does not satisfy model maintenance threshold(s), and trigger a process that terminates access to at least the deployed instance.
A method includes: receiving a first set of data associated with an element during a first stage of a plurality of stages; applying a first stage machine learning model to the first set of data to generate a prediction value, wherein the first stage machine learning model is trained with a first set of feature data that is available during the first stage; updating the prediction value by: receiving a second set of data associated with the element during a second stage of the plurality of stages; and applying a second stage machine learning model to the second set of data, wherein the second stage machine learning model is trained with (i) the first set of feature data, and (ii) a second set of feature data that is available during the second stage; and initiating performance of a mitigation action based on the updated prediction value.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion. Example embodiments are also configured for initiating performance of a search query resolution based on the SCP.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for analyzing performance and operation of one or more downstream applications by: (i) asynchronously capturing one or more event message payloads, (ii) providing, using a producer client, the one or more event message payloads as an input topic to a streaming server that is configured to provide, to one or more consumer clients, (1) one or more input topic streams that comprises the one or more event message payloads and (2) one or more output topic streams, (iii) applying a transformation to the one or more event message payloads received from the input topic by a consumer client of the one or more consumer clients, and (iv) providing, using the producer client, one or more transformed event message payloads as an output topic to the streaming server.
Systems and methods are disclosed for processing forms to automatically adjudicate religious exemptions. The method includes receiving an input from a user to data fields of forms associated with a religious exemption request, wherein the input is in a first data format and includes location information, religious information, employment information, or demographic information associated with the user. Exemption-relevant features are determined from the input. A data object including the exemption-relevant features is generated. The exemption-relevant features are transformed into corresponding embeddings in a second data format, wherein the embeddings represent semantic relations between the exemption-relevant features. The authenticity of the data object is determined based on the embeddings using a classification model that has been trained using a plurality of embeddings representative of a plurality of exemption-relevant features. A notification is transmitted indicating an approval or a disapproval of the religious exemption request.
Various embodiments of the present disclosure provide federated query processing techniques for remote query processing for a federated query system based on predicted query processing duration. The techniques include identifying an identifier from a federated query that references one or more data segments from a plurality of third-party data sources, identifying an execution plan for executing the federated query via one or more executable tasks with respect to the plurality of third-party data sources, predicting a query processing duration for the federated query based on a mapping between the identifier and the execution plan, and/or executing the one or more executable tasks based on the query processing duration.
A system and methods for multimodal patient management in a healthcare facility are disclosed. The method can include determining that a patient is within a threshold distance of a healthcare facility based on geolocation data for a wearable device associated with the patient, automatically checking-in the patient for an appointment; transmitting, to the wearable device, a notification indicating: i) that the patient ready to be seen the healthcare provider for the scheduled appointment, and ii) a target area in the healthcare facility to which the patient is to navigate for the scheduled appointment; continuously tracking a position of the patient within the healthcare facility using the wearable device, in part to determine when the patient has exited the healthcare facility; and updating a status of the patient in a digital patient tracker based on the tracked position of the patient within the healthcare facility.
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
66.
ORDERED CODE SEQUENCES USING A COMPOSITE MACHINE LEARNING MODEL
Various embodiments of the present disclosure provide techniques for generating an ordered code sequence. For example, the techniques may include generating a predictive group code and an anchor code for an entity based on entity data. The techniques may include generating, using the first portion of the composite machine learning model, an unordered code sequence comprising one or more predicted codes based on the entity data, the predictive group code, and the anchor code. The techniques may include generating, using a second portion of the composite machine learning model, an ordered code sequence based on the unordered code sequence. The techniques may include providing the ordered code sequence.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Various embodiments of the present disclosure provide federated query processing techniques for monitoring data access patterns through federated queries and intelligently caching disparate data segments based on the data access patterns. The techniques include determining a relative priority for a data segment of a third-party data source based on a plurality of federated queries. Each of the plurality of federated queries may reference data segments from a plurality of disparate data sources. The relative priority may be indicative of a priority of the data segment relative to other data segments. In response to a determination that the relative priority satisfies a priority threshold, the techniques include storing a data segment in an intermediary local data source and initiating the performance of a data segment redirect configured to redirect a subsequent federated query associated with the data segment to the intermediary local data source.
Embodiments of the present disclosure provide for improved data imputation and use of imputed data in processing of downstream models. Some embodiments specially train a model that performs improved data imputation utilizing a specially-configured attention mechanism. Some embodiments train a model utilizing stratified masking. Some embodiments train a particular pre-processing layer of a downstream task-specific model to adaptively learn threshold values for imputing particular data. The pre-processing layer is usable to improve accuracy training and/or use of a downstream task-specific model based at least in part on the imputed data.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Pharmacy benefit management services Providing temporary use of on-line non-downloadable software for providing users access to health and healthcare related documents, health and medical provider data
71.
QUALITY ASSURANCE FOR MACHINE LEARNING USING DISTRIBUTION PATTERNS RELATED TO TRAINING DATASETS
Various embodiments of the present disclosure provide quality assurance for machine learning using distribution patterns related to training datasets. In one example, an embodiment provides for generating an augmented training dataset of a plurality of augmented training datasets for a machine learning model by randomly modifying a subset of binary labels of a training dataset for the machine learning model based on a probability value, generating a graphical distribution pattern for the training dataset based on a plurality of accuracy scores of the plurality of augmented training datasets, and generating a quality score for the training dataset based on a comparison between the graphical distribution pattern and a predefined graphical distribution pattern.
Various embodiments of the present disclosure provide a refined query resolution based on relevant search clustering using real time data. The techniques may include receiving a prefix text input associated with a search query, identifying a preceding text input associated with a historical search query preceding the search query, identifying a plurality of relevant search clusters from a clustered hierarchical tree based on the prefix text input and the preceding text input, identifying one or more search labels for the search query from the plurality of relevant search clusters, and initiating the performance of a query resolution operation for the search query based on the one or more search labels.
Various embodiments of the present disclosure provide automated data compliance techniques for complex access controlled datasets subject to a plurality of data access constraints. Some of the techniques may include generating, using one or more natural language models, entity-relationship data for an access controlled dataset and generating a knowledge graph based on the entity-relationship data. The knowledge graph includes a plurality of vertices connected by a plurality of edges that may be traversed to identify a data access condition indicative of a data access violation or a data coverage violation. Some of the techniques may include generating, using the knowledge graph, a natural language condition description based on the data access condition and providing a condition alert indicative of the natural language condition description.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age/time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encounters data records.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying one or more evidence text portions comprising one or more bases relied on by a generative machine learning model for assigning a plurality of model-assigned categorical identifiers to a plurality of text segment data objects associated with a document data object, and verifying the one or more evidence text portions with a verifier machine learning model to generate one or more classifications of the document data object and provide the one or more verified evidence text portions along with the one or more classifications.
G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
76.
MACHINE LEARNING TECHNIQUES FOR GENERATING DOMAIN-AWARE QUERY EXPANSIONS
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating domain-specific queries that are semantically similar to a search query by spell-correcting and tokenizing a search query, and then generating, using an embeddings dictionary data object associated with one or more domain vocabulary data objects, queries semantically related to the search query based on proximity of one or more similar embeddings to an embedding associated with the tokenized query within a domain vector space.
Various embodiments of the present disclosure provide query processing techniques for resolving queries in a complex search domain to improve upon traditional search resolutions within such domains. The techniques may include generating a keyword and an embedding representation for an agnostic search query. The keyword representation may be compared against source text attributes within one or more domain channels to generate a plurality of keyword similarity scores between the search query and features within a search domain. The embedding representation may be compared against source embedding attributes within the one or more domain channels to generate a plurality of embedding similarity scores between the search query and the features within the search domain. The keyword and embedding similarity scores may be aggregated to generate aggregated similarity scores for identifying an intermediate query resolution for the search query. The intermediate query resolution may be leveraged to resolve the query.
Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include receiving a plurality of interaction data objects comprising a plurality of assessment codes and a plurality of intervention codes. The techniques may include generating a frequency distribution comprising a plurality of code pairs based on a plurality of cooccurrences of the plurality of assessment codes and the plurality of intervention codes within the plurality of interaction data objects. The techniques may include generating, using the frequency distribution, a cross-code dataset comprising one or more mapped code pairs from the plurality of code pairs based on a threshold cooccurrence value. The techniques may include initiating the performance of a query resolution operation for a search query based on the cross-code dataset.
Various embodiments of the present disclosure provide an interactive map-based visualization system related to multi-channel search for search domains to improve upon traditional search resolutions within such domains. The techniques may include receiving a user interface request that comprises (i) character-level text input related to a search query via a user interface of a user device and (ii) filter metadata for a user identifier associated with the user interface request, generating a set of query result data objects for the user interface request by correlating the character-level text input to at least one domain knowledge profile, and generating a set of filtered query result data objects for the user interface request by filtering the set of query result data objects using the filter metadata. In some examples, the techniques may include initiating a rendering of a set of selectable graphical element options that are correlated to a real-time map visualization.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06F 16/387 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
Various embodiments of the present disclosure provide model-based domain-aware autocomplete techniques for generating autocomplete suggestions in a complex search domain. Example embodiments are configured to generate, using a domain-aware autocomplete model, a label for an autocomplete suggestion based on a set of keywords within an autocomplete suggestion training dataset associated with a target domain source. Example embodiments are also configured to generate, using a weak-labeling model, an updated label for the autocomplete suggestion by decorrelating the set of keywords from the label. Example embodiments are also configured to generate, using a sentence classification model, a category for the autocomplete suggestion based on the updated label. Example embodiments are also configured to, using the domain-aware autocomplete model, generate a suggestion-category pair (SCP) based on the autocomplete suggestion and the category for the autocomplete suggestion. Example embodiments are also configured for initiating performance of a search query resolution based on the SCP.
Various embodiments of the present disclosure provide machine learning configuration techniques for seamlessly leveraging compute functionalities from across a plurality of disparate third-party computing resources. The configuration techniques include receiving a first-party workspace request that identifies a third-party computing resource and in response to the first-party workspace request: generating a compute agnostic project workspace hosted by a first-party computing resource, initiating the generation of a third-party workspace hosted by the third-party computing resource, and initiating the configuration of a first-party routine set within the third-party workspace. The first-party routine set includes a plurality of webhooks that facilitate communication between the first-party computing resource and the third-party computing resource, thereby enabling a first-party to leverage multiple different, traditionally incompatible, computing functionalities from one centralized location.
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective execution of database management operations. For example, various embodiments of the present invention describe techniques for performing one or more database update operations given a concurrent write request group for a database using P concurrent request processor computing nodes, a match result serialization queue, and a centralized match result serializer computing node.
A method performed by one or more processors includes: receiving at least one first data object and at least one second data object for an entity; determining if the at least one first data object includes at least one inclusionary code associated with a fall related injury; determining if the at least one first data object excludes at least one exclusionary code associated with a non-fall related injury; and when it is determined that the at least one first data object includes the at least one inclusionary code and excludes the at least one exclusionary code: determining if the at least one second data object includes at least one fall-related word; and when it is determined that the at least one second data object includes the at least one fall-related word, marking the at least one first data object as being indicative of an actual fall event.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
84.
ATTRIBUTE-LEVEL ACCESS CONTROL FOR FEDERATED QUERIES
Various embodiments of the present disclosure provide access control for data provided by a federated query system based on attributes of a federated query. The techniques include determining a user identifier associated with a federated query, determining a set of access controls for a plurality of third-party data sources based on the user identifier, and generating an execution plan for resolving the federated query via one or more executable tasks with respect to the plurality of third-party data sources. The techniques also include generating, using a first portion of the execution plan, a result set that comprises information aggregated from the plurality of third-party data sources in accordance with the federated query. The techniques also include generating, using a second portion of the execution plan, a user result set that masks a portion of the information for the result set in accordance with the set of access controls.
Various embodiments of the present disclosure provide machine learning model performance monitoring techniques for automatically generating performance metrics for a machine learning model. The techniques may include identifying a generative synthetic data model corresponding to a historical training dataset for a target machine learning model, generating, using the generative synthetic data model, a synthetic dataset for the target machine learning model, generating a performance output for the target machine learning model based on a comparison between the synthetic dataset and a contemporary input dataset, and initiating the performance of one or more model performance-based operations based on the performance output for the target machine learning model.
Various embodiments of the present disclosure provide federated query processing techniques for quality evaluation and augmentation of data provided by a federated query system. The techniques include receiving an execution plan for a federated query. The techniques also include receiving one or more data segments from a plurality of third-party data sources and receiving quality evaluation data using a set of data accessing tasks for the execution plan. The techniques also include generating a result set for the federated query using a set of data processing tasks for the execution plan. Additionally, the techniques include generating quality metrics data for the one or more data segments based on the quality evaluation data and generating an augmented result set for the federated query based on the result set and the quality metrics data.
Various embodiments of the present disclosure provide universal machine learning tracking and control techniques for enforcing universal standards across a plurality of disparate machine learning projects within an enterprise. The techniques include generating a canonical representation of a machine learning model. The techniques include receiving model activity data from a third party computing resource in response to user activity within a third party workspace. The techniques include generating relative progress data for the machine learning model based on the model activity data and modifying the canonical representation of the machine learning model based on the model activity data and the relative progress data. The techniques include generating and providing a model interface point for the machine learning model in response to the canonical representation satisfying a publication threshold.
Various embodiments of the present disclosure provide multi-stage data lineage tracking techniques for automatically generating holistic and accurate data catalog for complex data ecosystems. The techniques may include generating a critical attribute collection for a data-related task based on one or more natural language descriptions for the data-related task. The techniques include receiving a data lineage map that defines a plurality of hierarchical data layers for a data ecosystem associated with the data-related task. The techniques include generating a critical attribute map for the data-related task by identifying a data element for a critical attribute at each of the plurality of hierarchical data layers of the data lineage map. The techniques include identifying noncritical attributes for the data related task generating a task attribute map for the data-related task based on the noncritical attributes and the critical attribute map.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying stale or vibrant open-source packages by training a predictive machine learning model with a labeled dataset, wherein the labeled dataset is created by generating package-basis features and version-basis features based on repository data, generating package-basis clusters based on the package-basis features, generating version-basis clusters based on the version-basis features, and generating labels for the labeled dataset based on the package-basis clusters and the version-basis clusters.
A computing device may receive, from a remote computing device, a request to establish a communication session with the remote computing device and contextual information indicative of an urgency level of the communication session. The computing device may output, at one or more output devices, the contextual information indicative of the urgency level of the communication session and at least one of: haptic output or an audible alert that indicates the request to establish the communication session.
Various embodiments of the present disclosure provide federated query processing techniques for dynamically tailoring the use and parameters of intermediary local sources based on the identification of federated query. The techniques include receiving an execution plan for executing a federated query that include a plurality of executable tasks for generating a result set from a plurality of third-party data sources. The techniques include generating a result set hash for the result set based on the execution plan and determining a query uniqueness status for the federated query based on a comparison between the result set hash and a plurality of historical result set hashes. In response to determining that the federated query is a unique query, the techniques include generating a time interval that is tailored to the unique query.
Systems and methods for providing collision-free unique versioned identifiers using a distributed ledger are provided herein. An example system may include a unique identifier provider that is configured to provide evolving, backward compatible, unique versioned identifiers from semi-structured data such as forms or claims to various in a decentralized fashion.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
93.
Display screen or portion thereof with graphical user interface
A system and related methods for a digital wallet are disclosed. The method includes: establishing a connection between external fund sources and a digital wallet account to facilitate electronic fund transfers, the external funding sources including an external fund source that is directly associated with a primary owner of the digital wallet account and an external fund source that is associated with an entity other than the primary owner; obtaining a set of rules that dictate how funds are disbursed from the external fund sources funds, including a rule defined by the primary owner of the digital wallet system and a rule defined by each of the plurality of external fund sources; receiving a first electronic payment request; identifying an external fund source or sources by evaluating the first electronic payment request with respect to the first set of rules; and disbursing funds to satisfy the first electronic payment request.
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
