A method and system provide for automating drawing. A drawing of two or more entities is obtained and a resolution is determined. Based on the resolution, the drawing is quantized into a cell map. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data for and provides a smallest tangible unit of information about a corresponding entity. Each of the multiple cells is a number that represents domain specific information about the corresponding entity. The quantizing rounds off of values beyond a specified threshold to a resolution tolerance of the resolution. The cell map is utilized to automate modifications to the drawing.

Embodiments herein provide a system for generating scenic routes with scenic views in a virtual 2D environment using route-generating methods. The system includes a scenic route-finding unit connected to an input data source through a network. The scenic route-finding unit comprises a processor that receives input data, including media content images with points of interest (POIs) and user preferences for the desired route length. It determines scenic points based on the coordinates of POIs, using a condition that each scenic point is equidistant from a randomly selected pair of POIs. The iterative selection continues until the desired route length is achieved. The processor generates scenic paths by forming lines and determining bisectors between POIs, creates a scenic graph by identifying intersection points and edges, and produces the scenic route by applying route-generating methods on the graph, enabling user navigation with corresponding scenic views.

A computer system displays a user interface that includes a data flow region, a data profiling region, and a data preview region. The system displays, in the data flow region, an interactive flow diagram having a plurality of linked nodes, where at least one node of the plurality of linked nodes indicates one or more data transformation operations performed on data of a data source. The system displays, in the data preview region, a sample of data values and concurrently displays, in the data profiling region, statistical information about the sample of data values. The system receives a user interaction to modify at least one data value of the sample of data values. In response to receiving the user interaction, the system modifies the at least one data value and updates the interactive flow diagram in a manner that is responsive to the user interaction.

This invention relates to a comprehensive tool for simulating, analyzing, and reporting on permanent life insurance policy performance under various economic conditions, insurance companies, and stakeholder and policyholder behaviors. The tool enables the creation of baseline policy illustrations and various financial models incorporating life insurance, validates projections against known and hypothetical data, and simulates future scenarios to provide actionable insights for policyholders and advisors.

In some implementations, a monitoring system may capture a set of screenshots that depict behavior associated with a bot during an automation run. The monitoring system may obtain one or more logs that include information describing the behavior of the bot during the automation run. The monitoring system may create, based on a portion of the set of screenshots, video data that includes a sequence of frames related to an anomaly or a failure event that occurred during the automation run. The monitoring system may annotate, based on the information included in the one or more logs, the sequence of frames with metadata describing the behavior of the bot. The monitoring system may provide, to a user device, user interface data that includes the video data and the metadata used to annotate the sequence of frames.

An analysis device according to the embodiment includes a creation unit and an output control unit. The creation unit arranges a line segment indicating a point that is a product of an attribute value of a regression model obtained by regression analysis by secure computation and a regression coefficient in a direction corresponding to a sign of the corresponding regression coefficient and creates a nomogram in which the point is plotted at a position corresponding to a target point on the line segment. The output control unit outputs the created nomogram.

An image processing apparatus includes a processor, in which an evaluation value in accordance with morphological characteristics of a tissue image is estimated using a machine learning model, the machine learning model is trained using a plurality of tissue image sets each including a plurality of training tissue images and provided with a relative rank based on the morphological characteristics of the training tissue image, the training is training in which the machine learning model is caused to estimate the evaluation value in accordance with the morphological characteristics of the training tissue image and the estimated evaluation value is made to conform to the relative rank, and the processor estimates the evaluation value of an acquired evaluation target using a trained machine learning model that has undergone the training.

Proposed concepts thus aim to provide schemes, solutions, concepts, designs, methods and systems pertaining to controlling a display of data. In particular, embodiments aim to provide a method for controlling a display of data by generating one display control signal which defines the display of an avatar of a first patient including a repeating animation, and generating another display control signal which defines the display of an avatar of a second patient using another repeating animation. In order to reduce the potential confusion and distraction of multiple animations (on the same or different display units), at least one of the display control signals is adapted to synchronize the (at least) two animations to one another. In this way, the synchronized animations are less confusing and distracting to look at, allowing a clinician to focus instead on the actual data that they are indicating.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective/relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a plot cart and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep semantic stored procedure subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

Method, system, and computer-readable storage media for generating a data visualization. A plurality of algorithmic visualizations is received for a dataset. Based on the plurality of visualization matrices, a matrix is generated. Each visualization matrix of the plurality of visualization matrices corresponds with an algorithmic visualization of the plurality of algorithmic visualizations. A synthetic matrix is generated by randomly shuffling a plurality of values in each column of the matrix. A random forest classifier is trained to generate a random forest for distinguishing shuffled data of the synthetic matrix from unshuffled data of the matrix. Further, an ensemble visualization graph is generated using the random forest. From the ensemble visualization graph, an embedding vector corresponding to each sample of the dataset is extracted to generate an embedding matrix. Based upon the embedding matrix, the method includes generating the data visualization by harmonizing the plurality of algorithmic visualizations.