An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.

An information processing apparatus includes a controller that causes learning data learned by an artificial intelligence to be recorded in a recording unit in such manner that influencing learning data that has influenced performance of an artificial intelligence and non-influencing learning data that has not influenced performance of an artificial intelligence are distinguishable.

Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.

There is provided a method and server for estimating an uncertainty parameter of a sequence of computer-implemented models comprising at least one machine learning algorithm (MLA). A set of labelled digital documents is received, which is to be processed by the sequence of models. For a given model of the sequence of models, at least one of a respective set of input features, a respective set of model-specific features and a respective set of output features are received. The set of predictions output by the sequence of models is received. A second MLA is trained to estimate uncertainty of the sequence of models based on the set of labelled digital documents, and the at least one of the respective set of input features, the respective set of model-specific features, the respective set of output features, and the set of predictions.

The present disclosure provides method and apparatus for providing a response to a user in a session. At least one message associated with a first object may be received in the session, the session being between the user and an electronic conversational agent. An image representation of the first object may be obtained. Emotion information of the first object may be determined based at least on the image representation. A response may be generated based at least on the at least one message and the emotion information. The response may be provided to the user.

A system and a method for training an inference model using a computing device. The method includes: providing a text-to-vector converter; providing the inference model and pre-training the inference model using labeled fashion entries; providing non-labeled fashion entries; separating each of the non-labeled fashion entries into a target image and target text; converting the target text into a category vector and an attribute vector using the text-to-vector converter; processing the target image using the inference model to obtain processed target image and target image label; comparing the category vector to the target image label; when the category vector matches the target image label, updating the target image label based on the category vector and the attribute vector to obtain updated label; and retraining the inference model using the processed target image and the updated label.

Techniques are provided for customer support ticket aggregation. One method comprises obtaining a customer support ticket; extracting a topic of the customer support ticket using a topic model based on natural language processing techniques; converting the customer support ticket to a topic vector representation that identifies the extracted topic and comprises a list of words describing the topic based on a collection of processed customer support tickets; extracting features from the customer support ticket; generating a fingerprint for the customer support ticket that comprises the topic vector representation and the extracted features; applying the fingerprint to a machine learning similarity model that compares the fingerprint to fingerprints of processed customer support tickets from the collection of processed customer support tickets; and identifying a processed customer support ticket from the collection of processed customer support tickets that is related to the customer support ticket.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.

An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

According to one embodiment of the present invention, a symbol analysis device included in a facility floor plan can perform the operations of: acquiring a plurality of facility floor plans; detecting a rectangle included in each of the plurality of facility floor plans and an arc connected to the rectangle; specifying a window area and a door area on the basis of the rectangle and the arc; labeling pixels of the specified window area as the class of a window, and labeling pixels of the specified door area as the class of a door; and inputting the plurality of facility floor plans and data labeled in pixel units into a neural network model designed on the basis of a predetermined image segmentation algorithm, so as to learn the weight of the neural network model that derives the correlation between positions of the labeled pixels and the classes of windows and doors included in the plurality of facility floor plans, and thus a neural network model that determines the position and the class of windows and doors included in a facility floor plan is generated on the basis of the correlation.