Abstract: Facilitating reduction of noise in non-standard printed circuit board assembly component descriptions using a zero-shot model to identify salient component class descriptions is presented herein.

Abstract: A computer system and method for managing simulation artifacts is disclosed herein. The method includes parsing at least one first workspace associated with simulation of at least one first product to generate one or more first simulation artifacts. Further, at least one knowledge graph is generated by mapping the one or more first simulation artifacts corresponding to the at least one first product to a predetermined ontology model. Further, a user's intent to modify at least one second simulation artifact in a second workspace is captured based on interaction of the user with a simulation modelling user interface. The second workspace is associated with simulation of a second product. Further, one or more recommendations for modifying the at least one second simulation artifact are generated, based on one or more properties of a contextually similar first simulation artifact, and displayed on the simulation modelling user interface.

Abstract: At least one embodiment is directed to a computer-implemented method for using machine vision to categorize a locality to conduct product positioning analyses, the method including: generating locality profile scores for each locality of a plurality of localities using deep learning networks, where the locality profile score includes distributions of entity classes within the locality; extracting a set of entities having the same entity class from a group of localities; retrieving historical purchasing data for the entity set; and generating a sequence of products likely to be purchased by a target entity as a function of: the similarity of purchasing characteristics of the target entity with respect to other entities, product sequences found in product purchase of other entities, and entity profile weights extracted from the locality profile scores of other entities that have purchased one or more of the same products as the target entity.

Abstract: An apparatus comprises a processing device configured to obtain a first image, to detect one or more designated types of objects in the first image, at least a given one of the designated types of objects comprising at least a given portion of a computing device associated with a service request and, responsive to detecting the given one of the designated types of objects, to identify features of the given portion of the computing device in the first image. The processing device is also configured to perform an augmentation of the identified features of the given portion of the computing device to generate a second image, the second image comprising a modified version of the first image that contains the given portion of the computing device and excludes one or more other portions of the first image, and to process the service request utilizing the second image.

Abstract: A corpus of textual data records, labeled by experts as corresponding to a defined characteristic, that comprise descriptions of problems with an item are collected. A language model generates a plurality of n-grams from the corpus. Frequently occurring n-grams are analyzed using a zero-shot learning model to determine similarity to the defined characteristic. N-grams highly similar to the defined characteristic may be selected as defined phrases. N-grams highly similar to another characteristic may also be selected to reduce false positives. The zero-shot model may also be used to determine a weighting factor for each defined phrase for each record. A relevance score is determined for a record by multiplying the weighting factors for each phrase that has a similarity score relative to the record above a threshold based on the expert labeling. The relevancy score may be used to automatically diagnose problems with the item.

Abstract: One example method includes accessing input data elements from logs that identify user problems with computing system components, the data elements each associated with a respective original class label that identifies a class of computing system components to which the data element relates, the respective original class labels forming a group of class labels, and a first of the original class labels is overrepresented in the group, and reducing overrepresentation of the first original class label in the group by creating an arbitrary aggregation of some of the class labels that includes the first original class label. The method includes creating, based on a hierarchical modeling structure, prepared data in which an original class label is replaced by the aggregation. Next a hierarchical model and benchmark model are trained, and each model generates respective predictions for comparison. An inferencing process is performed to determine which predicted label will be used.

Abstract: An apparatus comprises a processing device configured to obtain a plurality of images each comprising at least a given portion of an instance of a computing device and to detect, utilizing an image classification machine learning model, at least a subset of the plurality of images having one or more designated failure characteristics. The processing device is also configured to create, utilizing a semantic segmentation machine learning model, a mask for each image in the subset of the plurality of images, the created masks characterizing locations of the designated failure characteristics in each image in the subset of the plurality of images. The processing device is further configured to generate an aggregated view of locations of the designated failure characteristics for the subset of the plurality of images utilizing the created masks, and to perform failure analysis for the computing device utilizing the generated aggregated view.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: A multimodal PET (positron emission tomography)/MRI (magnetic resonance imaging) contrast agent, a process of synthesizing said PET/MRI contrast agent, and a pharmaceutical formulation comprising said PET/MRI contrast agent are disclosed. The PET/MRI contrast agent comprises a magnetic signal generating core, and a coating portion formed at least partially over a surface of said magnetic signal generating core, wherein the coating portion comprises a plurality of layers, including an inner layer having a functionalized surface, and an outer layer in the form of a radionuclide electrolessly plated layer formed on said functionalized surface.

Abstract: An apparatus comprises at least one processing device configured to receive a query to determine associations between named entities and aspect terms for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying associations of the words, and to determine, for a given aspect term, attention weights for a given subset of the words surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, predictions of association between the given aspect term and named entities recognized in the given subset of the words, and to provide a response to the query comprising at least one of the predicted associations.

Abstract: An apparatus comprises at least one processing device configured to receive a query to generate a summary of a document, to perform two or more iterations of filtering the document to produce a current version of the summary of the document, wherein each of the iterations comprises determining similarity between a first vector representation of the current version of the summary and second vector representations of respective ones of two or more portions of the unstructured text data of the document not yet added to the current version of the summary. The processing device is also configured to generate, following identification of one or more designated stopping criteria in a given iteration, a final version of the summary based at least in part on the current version of the summary produced in the given iteration, and to provide a response to the query comprising the final version of the summary.

Abstract: An apparatus comprises at least one processing device configured to receive a query to perform sentiment analysis for a document, to generate, utilizing a first machine learning model, a first set of encodings classifying words of the document as being aspect or non-aspect terms, to generate, utilizing a second machine learning model, a second set of encodings classifying sentiment of the words of the document, and to determine, for a given aspect term, attention weights for a given subset of the words of the document surrounding the given aspect term. The processing device is also configured to generate, utilizing a third machine learning model, a given sentiment classification of the given aspect term based on the attention weights and a given portion of the second set of encodings for the given subset of the words, and to provide a response to the query comprising the given sentiment classification.

Abstract: An apparatus comprises at least one processing device configured to receive a query to generate a summary of a document, to perform two or more iterations of filtering the document to produce a current version of the summary of the document, wherein each of the iterations comprises determining similarity between a first vector representation of the current version of the summary and second vector representations of respective ones of two or more portions of the unstructured text data of the document not yet added to the current version of the summary. The processing device is also configured to generate, following identification of one or more designated stopping criteria in a given iteration, a final version of the summary based at least in part on the current version of the summary produced in the given iteration, and to provide a response to the query comprising the final version of the summary.

Abstract: A system can receive first input data indicative of first user input identifying a first diagnostic state of a computing system. The system can, based on the first input data, determine a second diagnostic state of the computing system, the second diagnostic state comprising information of the first diagnostic state. The system can present a first indication of the second diagnostic state via a user interface. The system can receive second input data indicative of second user input confirming the second diagnostic state. The system can determine a recommended action based on the second diagnostic state. The system can present a second indication of the recommended action via the user interface.

Abstract: An apparatus comprises a processing device configured to obtain a first image, to detect one or more designated types of objects in the first image, at least a given one of the designated types of objects comprising at least a given portion of a computing device associated with a service request and, responsive to detecting the given one of the designated types of objects, to identify features of the given portion of the computing device in the first image. The processing device is also configured to perform an augmentation of the identified features of the given portion of the computing device to generate a second image, the second image comprising a modified version of the first image that contains the given portion of the computing device and excludes one or more other portions of the first image, and to process the service request utilizing the second image.

Abstract: A system, method, and computer-readable medium are disclosed for management of a distributed ledger technology customer loyalty program, by establishing a distributed ledger technology network of customer and entity nodes. The entity nodes support products and/or services purchased by customers, providing a distributed ledger technology platform accessible by the nodes. Transactions between nodes go through the distributed ledger technology platform. A distributed ledger technology ledger which tracks the transactions. Coins/credits are provided to customers based on their transactions.

Abstract: A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

Abstract: An apparatus comprises a processing device configured to receive a query to extract information from a document, and to perform two or more iterations of utilizing a machine learning-based information extraction model to extract portions of unstructured text data from the document. In each iteration, a portion of the unstructured text data extracted from the document and an associated relevance score are output. In a first iteration, the query and document are input while in subsequent iterations the query and modified versions of the document are input, the modified versions having previously-extracted portions of the unstructured text data removed therefrom. The processing device is also configured to generate a response to the query comprising a subset of the portions of the unstructured text data extracted from the document determined to have associated relevance scores exceeding a threshold relevance score and at least a threshold level of similarity to the query.

Abstract: At least one embodiment of the disclosed system is directed to computer-implemented method for using machine vision to categorize a locality to conduct lead mining analyses. Embodiments of the method may include: generating locality profile scores and economic categorization for each locality of a plurality of localities, the locality profile score for each locality being derived through neural network analyses of map images of the locality, the economic categorization being derived through neural network analyses of images of entities within the locality; and generating a lead score for each entity in the locality group as a function of the locality profile score for the locality in which the entity is located, the economic categorization of the locality in which the entity is located, and campaign vehicles used in the locality in which the entity is located.

Abstract: A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.