Abstract: A method comprises receiving an incident report comprising a textual description of an incident; generating a regularized incident report in which out-of-vocabulary terms in the received incident report are replaced with in-vocabulary terms; determining importance measures for a plurality of incident report terms, wherein each of the incident report terms is in the regularized incident report; generating an incident matrix in which similarity values are defined for combinations of terms in the incident report and terms in a predetermined term set; generating an incident vector based on the incident matrix and the importance measures for the terms in the incident report; applying one or more machine learning (ML) models that identify, based on the incident vector, relevant software support records and/or software modules, wherein the relevant software support records and the software modules are potentially relevant to the incident; and outputting data identifying relevant software support records and/or softw

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations configured to integrate distinct clustering schemes given temporal variations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating integrative predicted scores based at least in part on at least one of: within-cluster consistency scores determined for clusters determined using a first clustering scheme (e.g., a service clustering scheme), within-cluster consistency scores determined for clusters determined using a second clustering scheme (e.g., a recipient clustering scheme), cross-cluster consistency scores, and cross-temporal consistency scores.

Abstract: 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).

Abstract: are disclosed. A pill identification request, including one or more images of a pill and a user identifier of a user associated with the pill, is received. A first machine learning system is used to generate one or more image embeddings based on the one or more images. The user identifier is used to retrieve claims data of the user, and the claims data are encoded to generate a claims embedding. A second machine learning system is used to identify the pill based on the one or more image embeddings and the claims embedding. A response to the pill identification request is generated based on the identifying.

Abstract: Systems and methods are disclosed for scheduling appointments based on changing health conditions of users. The method includes inputting the initial health dataset of a user to a machine learning model configured to identify other users with similar health profiles. A frequency of monitoring, a frequency of appointments, a duration between the appointments, or a type and length of the appointments is determined to generate a schedule of appointments. A subsequent health dataset of the user is received, and health scores, rule scores, or medication scores for the user are determined based on the subsequent health dataset. A plurality of risk scores for the user is evaluated based on the health scores, rule scores, or medication scores. A recent risk score is determined based on a change in the plurality of risk scores. The schedule of appointments is adjusted based on the recent risk score and the user is notified.

Abstract: Systems, methods, and apparatuses implementing a display optimization system are provided herein. In some embodiments, an example display optimization system may be configured to perform an optimal anchor-prior matching operation to identify optimal anchor-prior image pairs or series pairs from new medical imaging data (e.g., one or more anchor image series) and historical medical imaging data (e.g., one or more prior image series).

Abstract: Various embodiments of the present invention address technical challenges associated with performing machine learning operations on timeseries/periodic data by introducing a machine learning framework that has a first periodic tier for determining predicted evaluation scores for those predictive entities that are associated with a single evaluation period (e.g., a single year of data) and a second periodic tier for determining predicted evaluation scores for those predictive entities that are associated with multiple evaluation periods. The noted framework addresses the existing shortcomings of machine learning frameworks that operate on timeseries/periodic data with respect to inadequacy of data associated with shorter periods to determine parameters needed to perform comprehensive predictive data analysis with respect to longer periods.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method includes receiving first query data associated with a first query, identifying a system error that is associated with the first unique entity identifier based on an indication that the first query was improperly approved, extracting a cause of the system error from the first query data, determining a machine-readable invalidity code that corresponds to the cause of the system error, updating a system error database with the first unique entity identifier and the machine-readable invalidity code as a first pair, and outputting, the first pair including the first unique entity identifier and the machine-readable invalidity code from the system error database, the first pair being configured to be processed by a front-end system for identifying a potential system error associated with second query data for a second query.

Abstract: Systems and methods are disclosed for identifying relationships between complex datasets and/or high-dimensional datasets for predictive modeling. The method includes clustering data associated with one or more entities in a first dataset based on distance data; clustering the data associated with the one or more entities in a second dataset based on longitudinal data; consolidating the first dataset and the second dataset into a third dataset based on weights assigned to one or more edges between one or more nodes in the first dataset and the second dataset; generating a diagnosis space indicating a condition of the one or more entities based on the third dataset; and determining, via input of the diagnosis space into a machine learning model, an optimization of a weighting scheme for assigning weights to one or more features within the longitudinal data.

Abstract: A computer system and techniques used to support a variety of consumer systems using a variety of versions of an API using step-up request transforms and step-down response transforms. The computer system may be configured to receive a first application programming interface (API) request compatible with a first version of an API and transform, by first step-up request transformer, second step-up request transformer, a single handler, a first step-down response transformer, and a second step-down response transformer, into an API response sent to the requesting consumer system.

Abstract: 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 describe generating data associated with a database association management platform that enables an end user to generate/maintain database association metadata for a database using a sequence of record relationship instructions, such as a sequence comprising at least one of record-structure inclusion (RSI) instructions, record-structure exclusion (RSE) instructions, record-structure non-inclusion (RSNI) instructions, record-structure non-exclusion (RSEI) instructions, and new structure creation (NSC) instructions.

Abstract: Various embodiments of the present disclosure provide summarization techniques for summarizing complex documents, such as long unstructured call transcripts. The summarization techniques include generating a plurality of interaction topics for an interaction transcript and iteratively summarizing each interaction topic based on a preceding partial summary for the interaction transcript that corresponds to a preceding interaction topic that precedes the interaction topic in the interaction transcript. An abstractive summary is generated using a recursive abstractive model that is trained using training data generated based on holistic similarity scores between interaction topics of a call transcript and summary sentences of a corresponding target summary.

Abstract: 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.

Abstract: Techniques for automated query response determination using a hybrid AI are disclosed herein. An example computer-implemented method includes receiving a data file and a request including at least one query associated with the data file and applying a hybrid model to the data file. Applying the hybrid model includes segmenting the data file into one or more portions, embedding the one or more portions into a vector space, extracting, from the data file, data associated with one or more classifications, storing (i) the embedded portions in a first database and (ii) the extracted data in a second database, and determining a response to request queries based on the embedded portions and the extracted data, wherein the hybrid model constrains each response based on a parametric input prompt associated with the extracted data. The example computer-implemented method further includes storing one or more data objects indicating each response.

Abstract: 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.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for revising classifier predictions, wherein classification labels are tokenized according to an index of predefined classification labels. An embedding is created for each token using an embedding model. The embeddings are provided to a machine learning model that accepts a sequence of tokens as input and produces a sequence of tokens as output. Tokens are extracted from the output of the machine learning model and a classification label corresponding to each token is retrieved according to the index of predefined classification labels.

Abstract: 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.

Abstract: Systems and methods for pill identification based on image and user claims data are provided. A pill identification request, including one or more images of a pill and a user identifier of a user associated with the pill, is received. A first machine learning system is used to generate one or more image embeddings based on the one or more images. The user identifier is used to retrieve claims data of the user, and the claims data are encoded to generate a claims embedding. A second machine learning system is used to identify the pill based on the one or more image embeddings and the claims embedding. A response to the pill identification request is generated based on the identifying.