Systems and methods for augmented-reality tutoring can utilize optical character recognition, natural language processing, and/or augmented-reality rendering for providing real-time notifications for completing a determined task. The systems and methods can include utilizing one or more machine-learned models trained for quantitative reasoning and can include providing a plurality of different user interface elements at different times.

Methods and systems are provided for labeling text data using one or more machine learning (ML) models. In one embodiment, a method for training an ML model to label text data comprises manually labeling one or more words in a portion of a first set of text data as instances of a predefined entity of interest; extracting one or more example phrases from the labeled portion of text data, submitting an instruction to a Large Language Model (LLM) to label instances of the predefined entity of interest in the first set of text data, the instruction including the one or more example phrases; and training an ML model to label instances of the predefined entity of interest in a second set of text data, using training data including labeled text data of the first set of text data, the labeled text data outputted by the LLM.

Speed of first work is compared with speed of second work based on a first working period when a worker is caused to perform the first work of setting annotation data to first image data and a second working period when the worker is caused to perform the second work of correcting advance annotation data set based on a recognition result obtained by causing a predetermined recognizer to recognize the first image data, and, in a case where the first work is faster than the second work, the worker is requested to correct second image data in which advance annotation data is not set, while, in a case where the second work is faster than the first work, the worker is requested to correct advance annotation data set based on a recognition result obtained by causing the recognizer to recognize the second image data.

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes generating an interactive classification user interface concurrently displaying a first group of medical images and a second group of medical images, each group depicting objects associated with a respective classification. User input indicating movement of medical images from the first group to the second group is detected. The moved medical images are classified according to the second group. The re-classified medical images are provided to a machine learning system, with the machine learning system updating based on analysis of object characteristics of the re-classified medical images to increase accuracies associated with automated assignment of classifications.

Conventional whiteboards or chalkboards on which writing is effected using Dry-Erase or chalk markers are enabled for digital conversion of the writing into the digital domain. An indoor positioning subsystem determines the absolute position of a marker with a dry-erase or another conventional writing tip within a local coordinate system to track the location of the marker on the board. Ultra wide band (UWB) is an example technology that may be used for this purpose. An inertial sensor in the marker captures sensor data generated by handwriting by sensing the movement of the marker, essentially tracking the relative positioning and motion of the handwriting. The absolute position and relative positions are then processed to generate digital handwriting which may be refined using deep learning subsystem(s) for handwriting recognition and classification for the data captured by the inertial sensor to determine the most appropriate character or shape of the writing.

Learning data is generated so as to correspond to documents in various layouts. An information processing apparatus generates layout data indicating a layout of a character string based on template data to define a layout of a document, and generates learning data based on the generated layout data, wherein the generated learning data are used for generating a learned model that extracts a named entity from a document image.

Systems and methods for augmented-reality tutoring can utilize optical character recognition, natural language processing, and/or augmented-reality rendering for providing real-time notifications for completing a determined task. The systems and methods can include utilizing one or more machine-learned models trained for quantitative reasoning and can include providing a plurality of different user interface elements at different times.

A graphic recognition-based interactive method and electronic device. The interactive method comprises: acquiring a first image to be analyzed (101); recognizing the first image to be analyzed to obtain a recognition result (102); playing a narration audio file associated with the recognition result (103); playing a question audio file associated with the interactive content of the first image to be analyzed based on the recognition result after the narration audio file is played (104); acquiring a second image to be analyzed (105); determining whether a feature graphic exists in the second image to be analyzed (106); if the feature graphic exists in the second image to be analyzed, determining whether the feature graphic overlaps with an interactive region, wherein the interactive region is an area corresponding to an answer to the question in the question audio file (108); playing a correct audio file if the feature graphic and the interactive region are overlapped (109); and playing a wrong audio file if the feature graphic and the interactive region are not overlapped (110). The said method provides increased interactivity.

An information processing apparatus comprising: at least one processor programmed to cause the apparatus to: hold label information regarding presence of a target object, the label information being set for the target object in an image; obtain a reliability of the label information; cause a display apparatus to display the label information and an image corresponding to the label information in the image, based on the reliability; accept an operation made by a user; and modify the label information based on the operation.

Conventional whiteboards or chalkboards on which writing is effected using Dry-Erase or chalk markers are enabled for digital conversion of the writing into the digital domain. An indoor positioning subsystem determines the absolute position of a marker with a dry-erase or another conventional writing tip within a local coordinate system to track the location of the marker on the board. Ultra wide band (UWB) is an example technology that may be used for this purpose. An inertial sensor in the marker captures sensor data generated by handwriting by sensing the movement of the marker, essentially tracking the relative positioning and motion of the handwriting. The absolute position and relative positions are then processed to generate digital handwriting which may be refined using deep learning subsystem(s) for handwriting recognition and classification for the data captured by the inertial sensor to determine the most appropriate character or shape of the writing.