Various embodiments of the present disclosure provide an imaging system that includes an imaging device, a height detection sensor, and a programmable logic controller configured to receive sensor data from the height detection sensor, modify a focus level of the imaging device to one of a plurality of calibrated focus levels based on the sensor data; and initiate a validation image at the focus level.
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing an inclusion of an entity for an event. In accordance with one embodiment, a method is provided that includes: determining whether a graph representation data object comprises an inbound edge connecting an entity node representing the entity with an event node representing the event; and responsive to determining the graph representation data object comprises the inbound edge, performing an action involving inclusion of the entity for the event. The inbound edge is generated via an inbound edge generator machine learning model configured to: traverse entity and/or inclusion edges of the graph representation data object to identify inclusion and entity edges connected, generate an entity score data object for the entity based at least in part on the inclusion edges, and responsive to the data object satisfying a threshold, generate the inbound edge.
Various embodiments of the present disclosure provide automated message processing techniques that improve traditional communication systems, such as those that interface between a user and a plurality of requesting entities. The techniques include identifying a message that (i) is directed to a user inbox, (ii) is associated with an automated task category of a plurality of different automated task categories, and (iii) comprises message text data reflective of the automated task category. The techniques include generating a coded model output (i) based on the message text data and a domain knowledge index and (ii) that comprises a semantic intent classification and a shared embedding code and identifying the automated task category based on the semantic intent classification and the shared embedding code. The techniques include generating, using the domain knowledge index, a predicted response for the message based on the automated task category and modifying message with the predicted response.
Various embodiments of the present disclosure provide an asynchronous imaging technique. The asynchronous imaging technique may include identifying, from a local object queue buffer including a portion of container data from a remote system, a queued container data object that corresponds to a container located on a conveyance line. In addition, the process may include generating, using an imaging device, an imaging response for the container and a verification event for the container based on the imaging response. The process may listen for a heartbeat and identify a heartbeat anomaly with the remote system. In response to the heartbeat anomaly, the imaging system may generate a connection loss alarm tag, store the verification event in a local image buffer with the connection loss alarm tag, and initiating one or more conveyance line operation instructions based on a position of the queued container data object within the local object queue buffer.
Various embodiments of the present disclosure provide an adaptive imaging process that includes receiving a measured fill level for a container based on a distance reading measurement, selecting a focus level from a plurality of calibrated focus levels based on the measured fill level, providing, to an imaging device, one or more imaging instructions to trigger a validation image at the focus level, receiving, from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image, generating a verification event based on the image classification, and storing the verification event in association with the container.
G06K 7/10 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation électromagnétique, p. ex. lecture optiqueMéthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
6.
CHUNKING, POOLING, AND LABEL ATTENTION TECHNIQUES FOR GENERATING EXPLAINABLE PREDICTIONS
Embodiments of the present disclosure provide systems and methods for generating explainable predictions. One method may include generating a plurality of overlapping data chunks from an input data object, generating, using a machine learning class-agnostic model, a plurality of intermediate feature representations respectively corresponding to the plurality of overlapping data chunks, generating, using a machine learning class-specific model, a plurality of chunk-based classification probabilities from the plurality of intermediate representations that correspond to a particular prediction class, generating, using the plurality of chunk-based classification probabilities, a plurality of class scores for the plurality of overlapping data chunks, and, providing, by the one or more processors, a classification output that is based on the plurality of class scores and comprises a class prediction for the input data object and an overlapping data chunk from the plurality of overlapping data chunks that corresponds to the class prediction.
Techniques for efficient data categorization are disclosed herein. An example computer-implemented method includes receiving (i) a data set including a plurality of data points that each include at least one data line and (ii) a rule group including a plurality of rules and a plurality of rule sets. The example method further includes applying a categorization algorithm to the data set and the rule group that includes: generating a rule signature for each data line in each data point, identifying a set of unique rule signatures within the generated rule signatures, and determining a categorization for each unique rule signature of the set of unique rule signatures. The example method further includes storing a data object indicative of the determined categorizations.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
8.
SYSTEM AND METHOD FOR VALIDATING A CLASSIFICATION CODE ASSIGNED TO A DATA OBJECT BY A FIRST ARTIFICIAL INTELLIGENCE (AI) MODEL USING A SECOND AI MODEL
Systems and methods for validating a classification code assigned to a data object by a first artificial intelligence (AI) model using a second AI model are provided. A data object associated with an entity including a classification code that is assigned to the data object via the first AI model can be received. The classification code for the data object that is assigned to the data object via the first AI model can be validated using the second AI model. The second AI model can be trained using positive data objects that include assigned classification codes that are correct and negative data objects that include assigned classification codes that are incorrect. A validation result can be transmitted to a user device based on validating the classification code for the data object using the second AI model.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
9.
AUTONOMOUS AND SEMANTICALLY CONSISTENT MESSAGE AUGMENTATION PIPELINES
Various embodiments of the present disclosure provide automated message processing techniques that improve traditional communication systems, such as those that interface between a user and a plurality of requesting entities. The techniques include identifying a message that (i) is directed to a user inbox, (ii) is associated with an automated task category of a plurality of different automated task categories, and (iii) comprises message text data reflective of the automated task category. The techniques include generating a coded model output (i) based on the message text data and a domain knowledge index and (ii) that comprises a semantic intent classification and a shared embedding code and identifying the automated task category based on the semantic intent classification and the shared embedding code. The techniques include generating, using the domain knowledge index, a predicted response for the message based on the automated task category and modifying message with the predicted response.
Various embodiments of the present disclosure provide predictive mapped formatting for data. The techniques may include receiving an input structured data object, identifying a format inconsistency error for the input structured data object, generating a predictive mapped format data object for an input data format of the input structured data object by using a predictive machine learning model, initiating a presentation of a validation user interface that reflects the predictive mapped format data object, and storing the predictive mapped format data object in response to a confirmation input to the validation user interface.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
11.
MULTI-DIMENSIONAL EVALUATIONS FOR THE CLASSIFICATION OF DATA OBJECTS
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.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing digital image processing operations. For example, as described herein, various embodiments of the present invention relate to performing digital image processing operations using at least one of using bounding box precision models to generate an optimal object differentiation kernel, using an optimal object differentiation kernel to generate/detect optimal bounding boxes of an image set, and using an image classification machine learning model to generate bounding box classifications for the optimal bounding boxes of an image set.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
G06V 30/40 - Reconnaissance des formes à partir d’images axée sur les documents
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
13.
Methods, systems, and computer program products for selecting criteria subsets for performing a medical necessity review for a patient care plan
A method includes receiving input information associated with a health record of a patient, the input information comprising information associated with a plurality of input variables; embedding the information associated with the plurality of variable to generate a plurality of input variable vectors, respectively; aggregating the plurality of input variable vectors to generate a patient health record vector; generating, using a knowledge graph, a first ranking of a plurality of subsets of criteria used for determining an appropriateness of a care plan for the patient based on the patient health record vector; generating, using an artificial intelligence engine, a second ranking of the plurality of subsets of the criteria used for determining the appropriateness of the care plan for the patient based on the patient health record vector; and generating a final ranking of the plurality of subsets of the criteria used for determining the appropriateness of the care plan for the patient based on the first ranking and the second ranking.
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Pharmacy benefit management services Providing temporary use of on-line non-downloadable software for tracking, reporting, reviewing and sharing health care data analytics Providing healthcare information; Healthcare
15.
NATURAL LANGUAGE PROCESSING TECHNIQUES FOR MACHINE-LEARNING-GUIDED SUMMARIZATION USING HYBRID CLASS TEMPLATES
As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations for generating guided summaries using summarization templates that are mapped to hybrid classes of a hybrid classification space for a hybrid classification machine learning model. In some embodiments, by using summarization templates, a proposed summarization framework is able to vastly reduce the computational complexity of performing summarization on an input document data object, such as an input multi-party communication transcript data object, by defining the set of dynamic data fields that apply to the input document data object based at least in part on an assigned class/category of the input document data object.
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing temporally dynamic location-based predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform temporally dynamic location-based predictive data analysis by using at least one of cohort generation machine learning models and cohort-based growth forecast machine learning models.
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 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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
19.
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 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
25.
SYSTEMS AND METHODS FOR AUTHENTICATING A RESOURCE SYSTEM
Systems and methods are disclosed for determining authenticity of a resource system. The method includes receiving a dataset that includes a first subset and a second subset associated with a first resource system; down-sampling the first subset but not the second subset; generating a first feature for a machine learning model based on the down-sampled first subset; generating a second feature for the machine learning model based on the second subset; generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure; and initiating performance of one or more prediction-based actions in response to the generating.
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 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
27.
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.
36 - Services financiers, assurances et affaires immobilières
Produits et services
Pharmaceutical cost management services and drug utilization review services Pharmacy benefit management services; Providing counseling and consulting in the field of healthcare insurance benefits; Providing insurance information in the field of employee pharmacy benefit plans insurance
29.
MACHINE LEARNING TECHNIQUES FOR SYNTHESIZING MULTI-MODAL DATASETS
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.
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.
To automate a pricing strategy for an otherwise unpriced service or item, prices may be generated through a plurality of different pricing models, via a pricing engine passing input data to a plurality of discrete pricing models. Those pricing models may pass data back to the pricing engine, which then adjudicates the results of the pricing models to identify a most-relevant pricing model for the particular unpriced service or item.
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.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
33.
Multi-dimensional evaluations for the classification of data objects
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.
36 - Services financiers, assurances et affaires immobilières
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Insurance services, namely, underwriting, issuance and administration of health insurance insurance; Providing insurance information in the field of health insurance Providing healthcare information; Healthcare
35.
SYSTEMS AND METHODS FOR INTELLIGENT MODEL TRAINING USING RELEVANT DATA OBJECTS
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 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
G16H 10/65 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients stockées sur des supports d’enregistrement portables, p. ex. des cartes à puce, des étiquettes d’identification radio-fréquence [RFID] ou des CD
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
G16H 70/40 - TIC spécialement adaptées au maniement ou au traitement de références médicales concernant des médicaments, p. ex. leurs effets secondaires ou leur usage prévu
40.
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 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
41.
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.
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.
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.
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 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
48.
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 - Analyse des performances des employésAnalyse des performances des opérations d’une entreprise ou d’une organisation
G06Q 10/0635 - Analyse des risques liés aux activités d’entreprises ou d’organisations
G16H 20/10 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients
50.
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 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
51.
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 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
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 - Opérations mathématiques complexes pour l'évaluation de données statistiques
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G16H 40/20 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
54.
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.
Certain embodiments are directed to systems and methods for automatically providing data indicative of one or more characteristics of services that may be recommended to a particular patient, wherein the services are executable at least in part electronically based on data generated and provided by a system for facilitating access to the services. The generated data may be utilized for generating one or more user interfaces providing data regarding derived standard pricing data that is automatically assigned to the referred services and which may be attributable to a patient based at least in part on the patient's usage of the services.
G16H 20/00 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 15/00 - TIC spécialement adaptées aux rapports médicaux, p. ex. leur création ou leur transmission
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 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G06Q 40/04 - TransactionsOpérations boursières, p. ex. actions, marchandises, produits dérivés ou change de devises
61.
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.
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
65.
MACHINE LEARNING TECHNIQUES FOR PREDICTING AND RANKING SUGGESTIONS BASED ON USER ACTIVITY DATA
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating (i) a first label set representative of a selection of a content item based on search session data and (ii) a second label set representative of one or more transactions associated with the content item based on transaction data. A dominant label set is determined from the plurality of label sets based on an occurrence frequency associated with the first label set and the second label set. Based on an occurrence of an event associated with the dominant label set, either a first label associated with the dominant label set is assigned to first search query-content item record pairs associated with a training dataset, or one or more stochastic labels from the plurality of label sets are assigned to second search query-content item record pairs associated with the training dataset.
Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing content item suggestions based on historical search data of a user by: generating one or more content item feature vectors associated with a plurality of content items from a list of suggestions, generating one or more personalized feature vectors associated with the user based on user activity data, generating a plurality of predictions for the plurality of content items 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 content items based on the plurality of prediction probabilities, and generating one or more suggestions based on the plurality of rankings.
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.
G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
71.
CONTINUOUS MOTION SYSTEMS AND METHODS FOR AUTOMATIC OBJECT LOADING AND TRANSPORTATION
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 - TIC spécialement adaptées aux thérapies ou aux plans d’amélioration de la santé, p. ex. pour manier les prescriptions, orienter la thérapie ou surveiller l’observance par les patients concernant des médicaments ou des médications, p. ex. pour s’assurer de l’administration correcte aux patients delivrés par des distributeurs
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 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique caractérisée par la commande de vitesse, d'accélération ou de décélération
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation électromagnétique, p. ex. lecture optiqueMéthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p. ex. lecture de la lumière blanche réfléchie
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 for determining fraudulent entities. The method includes retrieving characteristics data associated with known fraudulent entities. A first graph is generated based on the characteristics data associated with the known fraudulent entities, the first graph represents relationships among the fraudulent entities and related entities of the known fraudulent entities. Identification data associated with a target entity is received. Characteristics data associated with the target entity is retrieved using the identification data. A second graph is generated based on the characteristics data associated with the target entity, the second graph represents relationships among the target entity and related entities of the target entity. The first graph and the second graph are compared to generate an association score for the target entity. Investigative targets are determined based on the association score. A presentation of the investigative targets is displayed via a graphical user interface of a device.
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.
G06V 30/416 - Extraction de la structure logique, p. ex. chapitres, sections ou numéros de pageIdentification des éléments de document, p. ex. des auteurs
G06F 40/106 - Affichage de la mise en page des documentsPrévisualisation
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
78.
SYSTEMS AND METHODS FOR SERIALIZING DATA EXTRACTED FROM DATA OBJECTS
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.
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
79.
REINFORCEMENT LEARNING FOR MACHINE LEARNING MODELS USING DYNAMIC CONFIDENCE THRESHOLDS
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 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et 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 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour la gestion ou l’administration de ressources ou d’établissements de soins de santé, p. ex. pour la gestion du personnel hospitalier ou de salles d’opération
G16H 50/70 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres 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.
G06F 16/951 - IndexationTechniques d’exploration du Web
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable services, namely, coordination of the procurement and distribution of in-kind donations of children's books, toys and games from public donors to hospitalized children Charitable foundation services, namely, providing financial support to to families with children for healthcare services that are not covered by health insurance
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable services, namely, coordination of the procurement and distribution of in-kind donations of children's books, toys and games from public donors to hospitalized children Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
92.
PREDICTIVE MONITORING OF THE GLUCOSE-INSULIN ENDOCRINE METABOLIC REGULATORY SYSTEM
There is a need for more effective and efficient predictive data analysis, such as more effective and efficient data analysis solutions for performing predictive monitoring of the glucose-insulin endocrine metabolic regulatory system.
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.
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
36 - Services financiers, assurances et affaires immobilières
Produits et services
Charitable foundation services, namely, providing financial support to families with children for healthcare services that are not covered by health insurance
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.
