Abstract: A method for driving zero time-to-insight and effectiveness of insight-to-nudge in a decision-making process. The method includes identifying a scope associated with a data-to-action loop in the decision-making process. The method further includes determining, by a zero time-to-insight engine, a zero time-to-insight quotient for a data-to-insight loop included in the data-to-action loop. The method additionally includes determining, by an insight-to-nudge engine, an insight-to-nudge quotient for an insight-to-action loop included in the data-to-action loop. The method additionally includes determining, by a predictive model factor component, a data-to-action prediction model factor (D2A PMF) for the data-to-action loop, where the D2A PMF quantifies an incremental zero time-to-insight potential for the data-to-action loop and corresponding attributes.

Abstract: A method for assessing user experience includes determining one or more experience journey maps, identifying personas included in the one or more experience journey maps, and collecting user experience related data from the personas included in the one or more experience journey maps. The method further includes identifying user experience gaps based on an analysis performed on the experience related data collected, and providing a graphical representation that highlights the user experience gaps. The method also includes generating recommendations for execution to mitigate the user experience gaps highlighted in the graphical representation, monitoring and tracking a progress of the executed recommendations, and performing automated monitoring and offset metric tracking of targeted experience-related input metrics used to gauge a performance of the executed recommendations.

Abstract: A method for automatically detecting and evaluating experience data associated with an experience journey to identify and adjust an action that is taken to improve customer experience is provided. In some embodiments, the method includes generating performance data associated with the experience journey from the experience data using a machine learning model. The method further includes determining the action to be taken based on analyzing the performance data. The method further includes collecting new experience data responsive to the action having been taken and training the machine learning model using the new experience data. The method further includes updating the performance data and the action to be taken based on training the machine learning model.

Abstract: Methods and computing apparatus are provided for training AI/ML systems and use of such systems for performing image analysis so that the damaged parts of a physical structure can be identified accurately and efficiently. According to one embodiment, a method includes selecting an AI/ML system of a particular type; and training the AI/ML system using a dataset comprising one or more auto-labeled images. The auto-labeling was performed using the selected AI/ML system configured using a parts-identification model. The configuration of the trained AI/ML system is output as an improved parts-identification model.

Abstract: A method and system for dimension estimation based on duplication identification is disclosed. In some embodiments, the method includes receiving a set of images of an object. The method includes detecting, from each image in the set of images, a respective image segmentation representing a damage of the object. The method then includes determining a respective dimension for the damage represented by each of the image segmentations. The method further includes determining whether two or more of the image segmentations represent a same damage of the object. Responsive to two or more of the image segmentations representing a same damage of the object, the method includes combining the respective dimensions determined for the damage represented by the two or more image segmentations to obtain a final dimension for the same damage.

Abstract: A method and system for processing an audio stream include receiving an audio stream by a node, splitting the audio stream into segments using a producer-consumer algorithm in the memory of the node, where the audio stream is split into the segments based on silence detection, transcribing voice included in a segment into text using a voice-to-text conversion engine, and performing natural language processing on the text to identify situational insights from the segment.

Abstract: A system and method for advanced document redaction are disclosed. According to one embodiment, a system comprises a parser that analyzes documents to identify structured, semi-structured, and unstructured data from a document. A candidates generator generates a list of words for redaction from the structured, semi-structured, and unstructured data. A replacement engine replaces one or more words from the list of words with one or more of a replacement word, random characters, and random numbers.

Abstract: A method and system are provided for matching database records. According to one embodiment, the method includes receiving a record from a first database, and selecting a sequence of characters within the record. The method also includes identifying a key associated with a second, dissimilar database by comparing the selected sequence with a number of historical records. The comparison is performed using one or more analytical processes, where at least one analytical process is a machine-learning (ML) process. The method further includes matching, using the key, the record from the first database with another record from the second database, where the other record includes the identified key.

Abstract: A method and system are provided for assessing damage to a structure. According to one embodiment, the method includes detecting one or more external parts of the structure from a video of the structure using a first machine learning (ML) module trained to identify in one or more frames of a video of a structure an external part of the structure. The method also includes using a second ML module, trained to detect and classify damaged regions of a structure from one or more frames of the video: (i) identifying one or more damaged regions of the structure, and (ii) classifying the one or more damaged regions based on damage types. The method further includes associating the one or more damaged regions and corresponding damage types with the one or more external parts, providing a respective vision-based damage estimate for each of the one or more external parts.

Abstract: A method for identifying shipment shortages on pallets is provided. In some embodiments, the method includes taking one or more digital images of a pallet with a shipment thereon to estimate the shipment's volume. The method further includes estimating an anticipated shipment volume based on the shipment's invoice information, and comparing the estimated shipment volume to the anticipated shipment volume to calculate volume mismatches. The volume mismatches can be used as an indicator of any shipment errors.

Abstract: A method and system are provided to infer and use machine learning algorithms that is applied to financial data and also to determine: (i) compliance with the applied algorithms, and (ii) whether any exceptions to and/or variations from the algorithms are within an organization's norms. A method includes: receiving financial data; applying a set of dynamic or interdependent algorithms to the received financial data to obtain a set of outcomes; and using a machine learning classifier to classify the outcomes as: (i) algorithm compliant, (ii) potentially algorithm non-compliant, or (iii) algorithm non-compliant. Additionally, the method includes generating controllership persona based actionable triggers, which on acceptance corrects the anomalies in financial data, enabling the method and system to perform as a comprehensive, contextual, consistent & machine learning powered, persona based real time controllership engine leading to reduced misstatements in reported financial data.

Abstract: A method for automatically assessing project health and providing persona-based recommendations to improve the project health is provided. In some embodiments, the method includes identifying a set of metrics to be monitored for a project; calculating different levels of scores based on the set of metrics, each score being a health indicator of the project; analyzing the different levels of scores; identifying different levels of actions corresponding to the different levels of scores, an action being taken to improve an improvement area of the project; determining a user role of a user; identifying and providing a type of analytic view for the user based on the user role and analyzing the different levels of scores; and notifying the user to take the action corresponding to a level of scores based on the user role.

Abstract: A method and system are provided for training a machine-learning (ML) system/module and to provide an ML model. In one embodiment, a method includes using a labeled entities set to train a machine learning (ML) system, to obtain an ML model, and using the trained ML model to predict labels for entities in an unlabeled entities set, yielding a machine-labeled entities set. One or more individual ML models may be trained and used in this way, where each individual ML model corresponds to a respective document source. The document sources can be identified via classification of a corpus of documents. The prediction of labels provides a respective confidence score for each machine-labeled entity. The method also includes selecting from the machine-labeled entities set, a subset of machine-labeled entities having a respective confidence score at least equal to a threshold confidence score; and updating the labeled entities set by adding thereto the selected subset of machine-labeled entities.

Abstract: A method for dimension estimation based on duplication identification. In some embodiments, the method includes receiving a set of images of an object. The method then includes detecting, using a first machine learning system trained to perform image segmentation, a first image segmentation representing a damage of the object on a first image and a second image segmentation representing a damage of the object on a second image. The method further includes determining, using a second machine learning system trained to perform dimension estimation, a first dimension for the damage represented by the first image segmentation and a second dimension for the damage represented by the second image segmentation. The method includes determining whether the first and second image segmentations represent a same damage. If these image segmentations represent the same damage, the method intelligently combines the first and second dimensions to obtain a final dimension for the damage.

Abstract: A method and system for generating and correcting chatbot responses based on reinforcement learning (RL) are disclosed. In some embodiments, the method includes receiving user data associated with a user in a chatbot conversation. The method includes providing a first recommendation to the user. The method includes detecting user feedback to the first recommendation in the chatbot conversation. The method then includes determining whether to assign a positive reward or a negative reward to the user feedback based on sentiment analysis performed on the user feedback. If the negative reward is assigned to the user feedback, the method further includes calculating a negative reward score for the first recommendation; retraining the one or more of RL models using one or more of the negative reward score, the user data, the first recommendation, and the user feedback; and determining a second recommendation using the one or more retrained RL models.

Abstract: A method for predicting, assessing, and auditing prices of products or services is provided. In some embodiments, the method includes receiving a product description with one or more terms and obtaining a product list with product descriptions having respective prices, where each product description in the product list includes the one or more terms or combinations thereof from the received product description. The method further includes, training a model to determine how the one or more terms or combinations thereof influence the prices in the product list, using the trained model to make a price prediction for the received product description, and enabling assessment of product prices based on the price prediction made by the trained model.

Abstract: Several computerized tools, methodologies and data structures, according to embodiments of the present invention, are disclosed for collecting data for UX research and development directed to a product, process, and system and for developing personas and scenarios from the data for designing and for measuring the effectiveness of the product, process, and/or systems for use by the personas or real people. The tools, methodologies and data structures may be used individually or in sets.

Abstract: A method and system are provided for training a machine learning (ML) system for predicting delays in processing pipelines. In one embodiment, the method includes receiving labelled historical data pertaining to a pipeline, the labelled data including trigger objects initiating the pipeline and one or more processing times corresponding to one or more stages of the pipeline. The method includes identifying features associated with the trigger objects, formatting the labelled data and, randomly splitting the formatted labelled data into a full training dataset and a testing dataset. Additionally, the method includes distributing the full training dataset into several partial datasets and, in an ensemble ML system, training each of several ML subsystems using a respective partial dataset to provide a respective individual inference model predicting respective processing times, and deriving and storing an ML model for prediction of delays by aggregating the individual inference models.

Abstract: A method for assessing user experience includes determining one or more experience journey maps, identifying personas included in the one or more experience journey maps, and collecting user experience related data from the personas included in the one or more experience journey maps. The method further includes identifying user experience gaps based on an analysis performed on the experience related data collected, and providing a graphical representation that highlights the user experience gaps. The method also includes generating recommendations for execution to mitigate the user experience gaps highlighted in the graphical representation, monitoring and tracking a progress of the executed recommendations, and performing automated monitoring and offset metric tracking of targeted experience-related input metrics used to gauge a performance of the executed recommendations.

Abstract: A method and system are provided for classifying semi-structured documents. According to one embodiment, the method includes accessing a number of documents, and identifying in each of the documents pairs of character segments. The method also includes generating a map of unique identified segment pairs across all documents in the several documents. The method further includes generating for each of the several documents a respective feature vector based on one or more unique pair of segments in the map that are also identified in the document, and clustering the plurality of documents into a plurality of clusters, using the feature vectors.