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 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: Techniques described herein relate to a method for managing financial service requests. The method includes obtaining, by a prediction manager, a financial service request (FSR) from a client; obtaining historical information associated with the FSR; generating prediction model inputs using the FSR and the historical information; identifying FSR agent comments associated with the FSR; generating a request vector using the FSR agent comments; generating an FSR authenticity index using the request vector and the prediction model inputs; applying a first machine learning model to the FSR authenticity index and the prediction model inputs to generate a first prediction; providing the first prediction to a first approver; and obtaining first approver comments from the first approver.

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 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 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, 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: 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.

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: 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: 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: 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: A method, system and computer readable medium for performing a purchase prediction operation. The purchase prediction operation includes: selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser; capturing a product term associated with a most recent purchase period of the target purchaser; performing a sequential recommendation operation, the sequential recommendation operation providing a sequence recommendation score; and, generating a purchase pattern prediction for the target user based upon the sequential recommendation score.

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 medium are disclosed for performing a recommendation operation, comprising: optimizing a product list to provide an optimized product list for use when generating a recommendation for an account; optimizing a neighbor set to provide an optimized neighbor set for use when generating the recommendation for the account; boosting a self-cosine similarity metric to provide a boosted self-cosine similarity metric, the self-cosine similarity metric corresponding to the account; and, providing a recommendation for the account, the recommendation being based on the optimized product list, the optimized neighbor set and the boosted self-cosine similarity metric.

Abstract: A method, system and computer readable medium for performing a purchase prediction operation. The purchase prediction operation includes: selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser; capturing a product term associated with a most recent purchase period of the target purchaser; performing a sequential recommendation operation, the sequential recommendation operation providing a sequence recommendation score; and, generating a purchase pattern prediction for the target user based upon the sequential recommendation score.

Abstract: A system, method, and computer-readable medium are disclosed for performing a recommendation operation, comprising: optimizing a product list to provide an optimized product list for use when generating a recommendation for an account; optimizing a neighbor set to provide an optimized neighbor set for use when generating the recommendation for the account; boosting a self-cosine similarity metric to provide a boosted self-cosine similarity metric, the self-cosine similarity metric corresponding to the account; and, providing a recommendation for the account, the recommendation being based on the optimized product list, the optimized neighbor set and the boosted self-cosine similarity metric.