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.

Abstract: A method and a system are provided for training a machine-learning (ML) system to function as a chatbot. According to one embodiment, a method for training and ML system includes providing to the machine-learning system: in a first iteration, a first input-output pair that includes a first input and a first output; and, in a second iteration, a second input-output pair that includes a second input and a second output, where the second input includes the first input-output pair and the second output is different from the first output, so that a context for the second input-output pair is stored in the memory of the ML system.

Abstract: A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

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 for providing a data consolidation for improved customer communication and agent performance evaluation in a multi-channel contact center are provided. The method includes receiving interaction data of an agent with customers across different communication channels within the contact center, consolidating the interaction data received across the different communication channels, aggregating the consolidated data to generate a set of metrics indicative of agent performance, inputting the set of metrics into an agent performance measurement framework, and determining a performance score for the agent based on an output of the agent performance measurement framework.

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 assessing damage to a structure. According to one embodiment, the method includes using a classifier that is trained to associate respective portions of in an image of a structure with respective external parts of the structure to detect several of external parts of a structure in a first image of the structure. The method also includes using a first machine learning system, trained to perform segmentation of an image, to identify one or more image segments in a second image, where each image segment represents damage of a particular type and, using a second machine learning system, trained to associate image segments with external parts identified in an image, to associate a first image segment with a first external part, indicating damage to the first external part.

Abstract: Systems and methods for sensing emotion in voice signals and dynamically changing suggestions in a call center are disclosed. According to one embodiment, a computer-implemented method comprises receiving a call from a customer at a call center. A sample of the call is recorded and the Mel-Frequency Cepstral coefficient is extracted from the sample. A machine learning model predicts an emotion of the customer and generates a confidence score for the emotion. A script of a call center agent is modified based on the emotion and the confidence score.

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: 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: 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.