166 results about "Interactive graphics" patented technology

A system for monitoring hemodynamic status of a patient can include a transducer, an adapter and one or more monitor devices. The adapter may be in communication with the transducer and the one or more monitor devices. The adapter can be configured to receive and process data from the transducer such as unprocessed physiological data. The adapter can be configured to transmit data to the monitor device(s) such as processed and / or unprocessed physiological data. The adapter can be configured to generate, and transmit to the monitor devices(s), user interface data for rendering interactive graphical user interfaces to display information such as physiological information relating to a hemodynamic status of the patient. The adapter can be configured to receive and process, from the monitor device(s) user commands or instructions to control an operation of the system or its components.

A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and / or location data. The system receives data from multiple vehicles in a fleet. The vehicle gateway device gathers vehicle metric data at a high frequency. Instead of transmitting a large amount of vehicle metric data at a fine level of granularity, the vehicle gateway device aggregates and buckets the vehicle metric data over a period of time (such as, every five minutes). The system uses the bucketed data for fleet management analysis.

The invention discloses a three-dimensional Gaussian sputtering method for sparse visual angle semantic priori. The method comprises the following steps of: 1, acquiring a target scene image, constructing a real image set as a data set, and manually selecting an interested object in a target scene to perform semantic three-dimensional reconstruction to obtain an initial semantic image of a sparse view angle; a multi-view image is collected, camera external parameters and scene sparse point clouds are obtained, a plurality of views are selected and input into the SAM2 segmentation model, and a view set Is with semantic images is obtained; 2, using a pre-trained SAM2 segmentation model as an interactive image sequence segmentation model, and initializing a three-dimensional Gaussian primitive according to the scene sparse point cloud; and step 3, training parameters of semantic three-dimensional Gaussian sputtering based on the trained three-dimensional Gaussian sputtering model and the interactive image sequence segmentation model, and reconstructing a target scene. According to the method, the high-quality semantic model is efficiently reconstructed. And the reconstruction result can be easily corrected through the interactive graphical interface in the training process.

An apparatus and method for generating an interactive graphical user interface are disclosed. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive first input data and second input data, identify at least a target datum from the first input data, determine at least an operating parameter from the second input data as a function of the at least a target datum, generate a plurality of tasks as a function of the at least an operating parameter, generate an interactive graphical user interface, wherein generating the interactive graphical user interface includes receiving concurrent user actions for the plurality of task nodes from a plurality of user devices and updating the interactive graphical user interface as a function of the concurrent user actions using an adaptive mechanism.

A system and method for accuracy-enhanced processing of query data within a multi-digital agent architecture may include receiving unrefined query data including textual data; retrieving historical user dialogue data from a computer database associated with the unrefined query data; transforming the unrefined query data to refined query data based on the retrieved historical user dialogue data; generating at least one digital agent classification inference using the refined query data; routing the refined query data to a digital agent based on the at least one digital agent classification inference; generating, by the digital agent, a digital response to the unrefined query data; and returning the digital response to an interactive graphical user interface.

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

A computer system and method for presenting interactive graphical user interface (GUI) widgets are provided. Profile information associated with a user of the computer system is determined and a page is presented in a GUI. Based on the profile information associated with the user, a first template is determined that indicates a first arrangement of interactive GUI widgets to be presented in the page, whereby the first arrangement is different from that of a second template of a plurality of templates, each indicating an arrangement of a plurality of interactive GUI widgets. Based on the first template, the interactive GUI widgets are presented in the page arranged according to the first arrangement.

Systems and methods for generation and use of intellectual-property (IP) landscaping platform architectures are disclosed. A landscaping component may be utilized to produce refined clusters of IP assets using user seeded searches in varying areas of interest, such as, for example, target technical fields, targeted publications, targeted products, and / or competitor entity portfolios. The landscaping component may be further utilized to produce an interactive graphical element including a spatial representation of the clusters of IP assets. The interactive graphical element may include a slider filter control that may be configured to receive user input representing a lower bound and / or an upper bound associated with an actual date (e.g., a year, a month, a day, etc.) associated with the IP assets included in the clusters of a selected result set.

Various embodiments of the present disclosure provide a contextualized task-specific graphical visualization related to one or more third-party data sources. The techniques may include generating a defined data object by transforming a plurality of third-party data elements from one or more third-party data sources to a defined first-party format, generating a structured data object from the defined data object based on a data structure format comprising a set of format features defined by a machine learning formatting prompt, generating a task-specific data object from the structured data object and corresponding to a defined domain task by filtering a plurality of task-agnostic features of the structured data object using a task-specific prompt that defines a set of task-related features, and initiating a rendering of a contextualized task-specific graphical visualization that is based on the task-specific data object and comprises a set of interactive graphical elements for the defined domain task.

A network computer system provides interactive graphic design system instructions for performing fragment-based design search. The network computer system receives a search query specifying a representation of one or more design elements. The network computer system matches one or more embeddings associated with the representation to a set of embeddings for a set of design fragments, wherein each of the one or more design fragments corresponds to a subset of one or more hierarchical structures representing one or more design interfaces. The network computer system generates a set of search results using the set of embeddings and outputs the set of search results in a response to the search query.

A cabin management control device is disclosed. In embodiments, the cabin management control device includes a touchscreen (e.g., either a fixed panel or a mobile computing device) and processors. The computing device is in communication with aircraft-based networks or controllers configured for control of cabin features or functions. The computing device displays, via the touchscreen, an interactive graphical user interface (GUI) in the form of a high resolution “virtual cabin”, e.g., a graphical representation of the aircraft cabin or a zone thereof. The GUI incorporates active touch areas (ATA) corresponding to visible cabin features (e.g., lighting, windows, inflight entertainment systems (IFE)). Similarly, the GUI displays direct access controls (DAC) allowing for similar control of non-visible cabin features (e.g., volume settings, climate settings), When an ATA or DAC is selected, the GUI displays a textless control interface enabling the user to control the corresponding feature via an optimally intuitive interface.

Systems and methods for betting on a live sporting event are disclosed. At least one input capture device and at least one user device are in network communication with a server platform. The at least one input capture device collects and transmits live raw data relating to the live sporting event. The server platform receives and aggregates the live raw data relating to the live sporting event from the at least one input capture device. The server platform accesses personal and / or emotional factors for each player in the live sporting event. The at least one user device places at least one bet on at least one aspect of the live sporting event to the server platform via an interactive graphic user interface (GUI) at least based on the live raw data. The server platform determines at least one betting outcome relating to the live sporting event.

The invention relates to the technical field of industrial data processing, in particular to a PDF drawing vector review method and system based on the creative technology, and the method comprises the steps: receiving a PDF drawing data stream through a creative security communication channel, and converting the PDF drawing data stream into a standardized graphic primitive containing a two-dimensional coordinate and engineering metadata through a sandbox security parser; and on the basis, a spatial semantic data structure integrating quadtree spatial indexes and semantic grouping information is constructed. According to the method, a dynamic performance benchmark test data model is also constructed; and during rendering, predicting and selecting an optimal rendering scheme capable of meeting preset target performance based on the model, efficiently generating an interactive graphic review interface by utilizing the data structure, and finally receiving and storing a user review instruction associated with the graphic object. By constructing the adaptive rendering model and fusing a data structure of space and semantic information, high-performance and safe vectorization interactive review of the PDF drawing in a credential environment is realized.

A system receives vehicle metric data from a gateway device connected to a vehicle. The vehicle gateway device gathers data related to operation of the vehicle and / or location data. The system receives data from multiple vehicles in a fleet. The vehicle gateway device gathers vehicle metric data at a high frequency. Instead of transmitting a large amount of vehicle metric data at a fine level of granularity, the vehicle gateway device aggregates and buckets the vehicle metric data over a period of time (such as, every five minutes). The system uses the bucketed data for fleet management analysis.

Methods and system are disclosed to implement an interactive companion table alongside graphical data visualizations within a computing environment, aimed at enriching data analysis and interpretation. The underlying numerical data of the graphs are displayed through a specialized table and responses to user interactions between the graphs and the table are highly synchronized with the establishment of the bidirectional links between each graph and its corresponding cells in the table. The method encompasses several key procedures: initializing a table object, accessing graphs in the active data visualization, incorporating cell blocks to represent data elements of graphs, adding bidirectional links, refreshing the table display, and handling user interactions. The methods and system enhance the data analysis experience by closely integrating visual and numerical information and is especially useful in areas that demand an in-depth examination of data alongside a broad visualization, including financial analysis, scientific research, and business intelligence.

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

A computing device displays a graphical user interface (GUI) comprising a navigator interface component. The navigator interface component comprises an interactive graphical representation of an asset. The device, responsive to detecting a predefined event, generates an alert. The device updates the navigator interface component to indicate the alert in association with the asset in the interactive graphical representation. The device receives a selection of the asset via the interactive graphical representation. The device displays, in the GUI, a second interactive graphical representation specific to the asset. The second interactive graphical representation of the asset comprises a task control element for controlling tasks specific to management of the asset. The device receives, using the task control element: a task for resolving the alert; and an association with the task and the asset. The device updates the navigator interface component to indicate the association with the task and the asset.

The invention provides a project visualization full-process management method and system combined with a knowledge graph, and the method comprises the steps: firstly gathering multi-source project information units which cover project tasks, resources, progress and participant information in a project implementation process, and then carrying out the correlation analysis of the multi-source project information units; the method comprises the following steps: generating association relationship data including relationships such as inter-task preposition dependency, resource and task allocation and use, progress and task completion states, participants and task responsible execution and the like, establishing a project association map according to the association relationships, and describing association paths and interaction modes of all elements of a project; the project association graph is displayed on a visual management interface including graph display, information details, relation analysis and an operation control area in an interactive graph form, a user management instruction is responded, the project association graph and a corresponding multi-source project information unit are adjusted, updated and refreshed and displayed in real time, and the project management efficiency and quality are improved.

An apparatus and method for generating an interactive graphical user interface are disclosed. The apparatus includes a memory communicatively connected to at least a processor, wherein the memory contains instructions configuring the at least a processor to receive first input data and second input data, identify at least a target datum from the first input data, determine at least an operating parameter from the second input data as a function of the at least a target datum, generate a plurality of tasks as a function of the at least an operating parameter, generate an interactive graphical user interface, wherein generating the interactive graphical user interface includes receiving concurrent user actions for the plurality of task nodes from a plurality of user devices and updating the interactive graphical user interface as a function of the concurrent user actions using an adaptive mechanism.

A system may comprise a data interpolation platform that comprises a data collector, a pre-processor, an artificial intelligence (AI) engine, an interactive graphical user interface (GUI) engine, a data monitor and one or more servers. The data collector may be configured to collect, from among one or more data sources, data and information (“collected data”). The AI engine may be configured to generate, train, validate, test and / or deploy one or more AI models. The interactive GUI engine may be configured to generate and dynamically update an interactive GUI. The one or more servers may comprise one or more processors, a memory and computer-readable instructions that, when executed by the one or more processors, cause the data interpolation platform to determine, display and dynamically update predictive and interpolated pricing data.

An interactive graphing user interface is described. In one or more implementations, a user interface of an interactive graphing application is displayed. The user interface includes a graph having a first axis and a second axis. User input to assign a first set of data values to the first axis of the graph and to assign a second set of data values to the second axis of the graph is received via the user interface. In response to the user input, a visualization of the first set of data values and the second set of data values is displayed in the graph. The visualization includes data points that plot the first set of data values against the second set of data values.

A system and method for accuracy-enhanced processing of query data within a multi-digital agent architecture may include receiving unrefined query data including textual data; retrieving historical user dialogue data from a computer database associated with the unrefined query data; transforming the unrefined query data to refined query data based on the retrieved historical user dialogue data; generating at least one digital agent classification inference using the refined query data; routing the refined query data to a digital agent based on the at least one digital agent classification inference; generating, by the digital agent, a digital response to the unrefined query data; and returning the digital response to an interactive graphical user interface.

A network computer provides interactive graphic design system instructions for execution at a user computing device. The user computing device determines that a main component in a design interface includes a freeform content area and rendering, on a canvas, a visual representation of the freeform content area within an instance of the main component. The user computing device further receives, via an input interface, a first set of user input specifying content to be inserted in the freeform content area of the instance and populates the freeform content area within the instance with the content.

Systems and methods for generating a data aggregator interactive graphical user interface. An interactive graphical user interface (GUI) includes a selectable symbol region, a query region and a data results region. The selectable symbol region displays predefined symbols. The query region displays user input fields for generating queries. The system receives user input associated with the user input fields of the query region to form a filter set. The data results region is automatically updated responsive to the user input, to display one or more data values from among one or more databases associated with the filter set. The system receives a subscription request to save the filter set as a user-customized query. A custom symbol is created responsive to the subscription request that is associated with the filter set. The selectable symbol region is updated to display the custom symbol together with the predefined symbols.

Systems and methods are provided for activating a card using augmented reality. The systems and methods may include capturing a real-time image of the card using a customer device, and extracting card information from the real-time image. The card information may be processed and used by a customer device or a financial provider server to activate the card. The customer device may display interactive graphics overlaid on the real-time card image to guide the customer through the activation process, and to educate the customer about the components of the card and aspects of the financial service associated with the card, such as a credit card service. The customer may interact with the overlaid graphics such as by selecting icons or portions of the card to trigger tutorials or to set preferences related to the card and / or financial service.

The images represented in the image diagrams are interactive graphical user interfaces (GUIs) that visually convey information to a user related to systems for (i) creating and / or modifying and / or analyzing unit operations and data generated therefrom in manufacturing processes, such as cell and gene therapy manufacturing processes, (ii) combining live data from databases with textual information, and (iii) interactive analysis of data (e.g., patient data visualization systems) and analysis of data related to manufacturing processes. (A. Graph-Based Visualization and Interactive Data Views for Pharmaceutical Manufacturing Process Data Reconciliation) For example, in certain embodiments, the images represented in the image diagrams are associated with graph-based visualization tools that enable a user to analyze data related to one or more experiments and / or manufacturing processes used to produce pharmaceutical products and / or variations thereof. In particular, the images for graph-based visualization and data analysis GUI tools represented in the image diagrams can be used in association with GUIs that enable a user to automatically and / or semi-automatically (e.g., in conjunction with user review and / or input) generate visualizations that facilitate the examination of experiments and / or manufacturing processes and reconcile data generated across multiple processes and / or process runs. For example, among other things, the interactive graph-based visualization tools and their images may be provided (e.g., rendered) individually or together via one or more GUIs or windows, sub-windows, panels, etc. (Process Graph) For example, the image diagram illustrates a GUI that provides a visualization of a process graph. In one embodiment, the graph-based visualization tool represented in the image diagram includes generating and / or rendering a process graph that displays an experimental and / or manufacturing process for the production of pharmaceutical products, including biologics such as cell-based therapeutics and biologic drugs. The process graph includes multiple nodes, each representing a data point corresponding to a unit operation in a particular experimental and / or manufacturing process where information is collected and / or an action is performed.The image diagrams further illustrate various approaches for capturing and / or displaying these data points and how users interact with them. (Comparing and Reconciling Multiple Processes) In certain embodiments, the images depicted in the image diagrams are used as GUIs to provide graph-based visualization tools that provide comparison and / or reconciliation of data from multiple experiments and / or manufacturing processes, e.g., in an automated and / or semi-automated manner (e.g., coupled with user interactions such as review and selection actions). Among other things, the images depicted in the image diagrams include designs that overlay multiple graph processes, structurally compare them, and reconcile them based on data points. For example, the images depicted in the image diagrams may provide rendering data created by process comparison and reconciliation tools that (e.g., automatically) identify and address discrepancies, missing data, or anomalies, which can be used to generate a harmonized data set for further analysis, such as mathematical modeling. Thus, among other things, the graph-based visualization tools provided in the images depicted in the image diagrams address the challenges presented by unreconciled data across multiple experiments and / or manufacturing processes and / or within a single process that may be performed under varying conditions. Achieving automated reconciliation of such data is a significant challenge that, if not addressed, hinders effective and accurate analysis, which, in turn, can dramatically impact a user's and / or organization's ability to optimize and / or maintain manufacturing process quality and / or develop new processes. In particular, the images presented in the graph-based visualizations and the graphical representations that present them aid in adjusting sampling points to maximize overlay for effective comparisons and / or, in certain embodiments, identify overlapping points in existing data for effective real-time analysis. For example, the graphical representations show examples of multiple levels of data views and clickable / expandable pop-ups. In particular, in certain embodiments, the graphical representations are associated with GUIs that integrate the ability to collect and link actual data with process diagrams.For example, multiple levels of data can be viewed in an interactive and dynamic manner. For example, images presented in pictorial diagrams provide a high-level view, and while not all data may be directly visible, they provide clickable / expandable, customized, dynamic nodes for each node type, allowing users to simultaneously inspect and analyze collected and / or input data. In this manner, images presented in pictorial diagrams facilitate the identification and analysis of differences between processes. In some embodiments, structural differences between different processes indicate variations in conditions (e.g., temperature, duration, chemical concentrations), as well as the sequence and / or presence of certain steps. While unit operations (basic steps or stages in a process) may be generally similar to other processes, unique conditions and / or sequences can significantly affect the outcome, or the nature of the product or result. Thus, images presented in pictorial diagrams that provide visual display tools can be extremely useful in process optimization, troubleshooting, and ensuring that a process meets desired specifications. For example, changing the device that performs a unit operation and / or certain parameter values ​​within the unit operation (e.g., rotation speed, total volume, duration, etc.) can have a significant impact on the quality, recovery, efficacy, etc. of the output of that unit operation, which in turn can affect characteristics such as the biological / potency of the product, and additionally or alternatively, factors such as the cost of production, the number of doses produced per manufacturing run, and the like. Thus, among other things, images depicted in pictorial diagrams can be associated with and provided with GUIs that present generated data, such as automatically generated data (e.g., using ontologies), which helps to identify commonalities and / or differences between studies and facilitates performing analyses to determine, for example, whether device or parameter changes have significant impacts.In particular, as described herein, a graph-based visualization tool represented in a pictorial diagram provides a user with a visual representation of one or more manufacturing processes via a graph-based approach that easily communicates and highlights differences in conditions and unit operations, as well as their nature. For example, an image represented in a pictorial diagram may include visual features that highlight unreconciled data points. This approach facilitates the identification and correction of data reconciliation issues, thereby streamlining data analysis for research and process development conducted in the creation and production of pharmaceuticals, such as cell-based therapeutics and biologic drugs. (i. Process Graph with Interactive Nodes) The pictorial diagram illustrates an example of a graph-based visualization of a manufacturing process. As shown in the pictorial diagram, a manufacturing process can be represented and displayed via a process graph. Each node in the process graph displays a data point corresponding to a unit operation in the particular manufacturing process that it (e.g., the process graph) represents. The node may include information about the current data state and content (e.g., in real time) of the data point it represents. Within a graph-based visualization, nodes can be dynamic, such that, for example, a user interaction with a particular node (e.g., a mouse hover, click, touchscreen tap or long press, etc.) triggers the display of a tooltip indicating the current data state for the displayed data point, including data collected at various stages of the manufacturing process. Process graphs can be rendered and used for experiment design and process execution to outline complex manufacturing experiments over multiple days of manufacturing procedures and to visualize and track different stages of a real-time experiment.Experimental Overview: In some embodiments, a graph is displayed as an experimental overview, providing a high-level overview of an experiment, including the process steps and (e.g., approximate) order in which they were performed, the dates particular process steps were performed, how the process steps relate to one another (e.g., the top half of FIG. 1A ), and an overview of the various states / arms evaluated during the experiment and how those states relate to one another (e.g., the bottom half of FIG. 1A ). Timeline: In some embodiments, a process graph may include a timeline, displaying multiple timepoints, such as the days, during which a particular experiment or manufacturing process represented by the graph was performed. As shown in the image depicted in the pictorial diagram, the timeline may be displayed along the horizontal axis, with labeled circular icons used to visually represent individual timepoints (e.g., days of execution from day 1 through day 21). Other ways of visually displaying the timeline may be used, for example, along the vertical axis and / or using other shaped icons, other units (e.g., hours, weeks, etc.). Process Unit Operations: In some embodiments, a process graph may visually identify individual process unit operations performed during a manufacturing process. Individual process unit operations may be displayed, for example, through a combination of text labels and icons or markings that convey the particular unit operations performed and, optionally, the time at which they are carried out and / or their (e.g., temporal) relationship with respect to other unit operations. For example, the graph-based visualization shown in the image depicted in the pictorial diagram includes a series of text labels along the top row (of nodes) identifying various unit operations such as "Material Preparation," "Start-Up," "Transduction," and "Compounding." In one embodiment, as shown in the image depicted in the pictorial diagram, for example, the text labels also include a numerical component and identify the particular day on which each unit operation is performed, and the text labels are arranged sequentially from left to right along the horizontal axis to mark the order in which the operations are performed over time.The vertical dotted lines extending downward from each text label provide a visual guide for the expected schedule for each unit operation and / or a temporal mapping of the manufacturing process. (Data Points (Nodes)) As shown in the images depicted in the pictorial diagrams, data points in the manufacturing process associated with a particular unit operation and from which pertinent information (measurements and / or recorded observations) is collected are represented via nodes. Nodes may be rendered as icons such as filled circles as shown in the images depicted in the pictorial diagrams. In the images depicted in the pictorial diagrams, each circle is a node and is positioned so as to be visually aligned with a particular unit operation. That is, in the images depicted in the pictorial diagrams, it is located on a vertical dotted line extending downward from a text label identifying a particular unit operation, thereby identifying the data point related to that particular unit operation. (Connectivity) In some embodiments, the process graph may display the dependency or sequence of material and / or data flow from one unit operation to another via rendered connections between the various nodes. For example, as shown in the image depicted in the pictorial diagram, node connectivity may be rendered as lines connecting nodes across a timeline. (Data Connection Points) Specific points along the process where data is collected are marked, such as important checkpoints for quality control or measurements required for process evaluation. (Arms) In some embodiments, a process graph may include and / or display one or more (e.g., separate) arms, each representing a different experimental condition and / or baseline process variation. For example, in the image depicted in the pictorial diagram, the arms are rendered as various smaller graphs positioned below the timeline. Each line in the arms section indicates a different arm / condition as defined by the operator.Labels are provided to help clarify the focus of these steps and / or to distinguish and identify nodes as belonging to one condition, so that corresponding data collected from the laboratory are placed in the correct node and we do not mix data across conditions. In this case, the base process may be the step following a standard or control process, while other processes are steps that make experimental changes to that process or supplement the base process (e.g., making media, preparing materials, etc.). (Baseline Process and Base Process Graph) A baseline process may be a control and / or standard procedure and may be rendered as a base process graph. Text labels, color schemes, icon styles, positioning, and the like may be used to visually identify the baseline process, such as in graph-based visualizations. For example, in images presented in pictorial diagrams, the baseline process is identified and displayed as a node line directly below the timeline. In process graphs of images presented in pictorial diagrams, the baseline process may be the standard process against which other variations are compared. Variations and Outputs / Endpoints In some embodiments, a graph-based visualization can include one or more auxiliary subgraphs, each corresponding to and representing variations to experimental conditions and / or baseline versions of a manufacturing process. For example, the image depicted in the image diagram shows auxiliary subgraphs displaying variations labeled as "Ver1.1," "Ver1.2," "Ver2.1," "Ver2.3," etc., and "Condition 1," "Condition 2," "Condition 3," etc., representing different experimental arms or conditions. In this manner, the graph-based visualization tool can be used to communicate variations in materials used, process conditions, or specific unit operations performed. Additionally or alternatively, endpoints and / or outputs of different processes can also be displayed (e.g., via endpoint graphs).For example, as shown in the image depicted in the pictorial diagram, a series of nodes labeled "Out.1" and "Out.2" represent different endpoints and / or outputs of a process, such as different ways of processing or storing a final product. For example, in the exemplary subgraph shown in the image depicted in the pictorial diagram, lines refer to experimental conditions or interrelated subsets of a process, such as preparing materials (e.g., media) to feed a cell-containing process. Arm / condition sections help orient operators to the specific task being performed, and by identifying various arms in a descriptive manner, data collected in the lab are entered into corresponding nodes, and numbers / quantities, etc., are not mixed between conditions. Process graphs therefore facilitate the visualization and management of complex manufacturing processes. Among other things, they allow researchers to track the progress of experiments, compare different conditions or variations side by side, and ensure that data is systematically collected at designated points throughout the process. The ability to visualize the entire process in this way helps identify bottlenecks, ensure consistency, and facilitate data-driven decision-making.

Disclosed are various data processing systems, user interfaces, application programming interfaces, and related operations for commodity financial data processing scenarios. In some implementations, a system may provide a graphical user interface (GUI) to customize a financial strategy associated with a commodity delivery contract. The GUI may include an interactive graph representation having a vertical axis and a horizontal axis, with the vertical axis corresponding to price values and the horizontal axis corresponding to time values of the financial strategy. The interactive graph representation may provide a vertical user interface control which is capable of horizontal movement to select a time value, and a horizontal user interface control which is capable of vertical movement to select a price value of the financial strategy. An accompanying information display of the GUI may display the time value and the price value and related information obtained for the strategy.

The images shown in the diagram are interactive graphical user interfaces (GUIs) that visually communicate to the user information related to (i) unit operations in manufacturing processes such as cell and gene therapy manufacturing processes and the creation and / or modification and / or analysis of data generated therefrom, (ii) combinations of live data and textual information from databases, and (iii) interactive data analysis (e.g., patient data visualization systems) and systems for analyzing data related to manufacturing processes. For example, in one embodiment, the images shown in the diagram provide the user with multiple levels of detail about the experiment and manufacturing process, and such details can be visually presented to the user in various forms including specific combinations of charts and / or dynamic elements, which can be manipulated by the user in an interactive manner. (C. Data Display and Interactive Images) (Cell Data Images) In one embodiment, the data display image includes cell data images that track changes in various metrics (e.g., performance metrics) for multiple cell lines over the course of the manufacturing and / or experimental process. The cell data images can be presented as an organized dashboard, as shown in the diagram. In one embodiment, an interactive dashboard for viewing cell data displays data for one or more lots. In another embodiment, data for multiple lots may be displayed and presented visually to the user. In yet another embodiment, a specific lot may be automatically viewed.

A method includes providing an interactive graphical user interface comprising a first menu providing one or more input options, a second menu providing one or more machine learning models, and a third menu providing one or more output formats. The method also includes generating a graph in a portion of the interactive graphical user interface by detecting one or more user selections of an input option, a machine learning model, and an output format, displaying nodes corresponding to the input option, the machine learning model, the output format, and displaying edges connecting the first node to the second node, and the second node to the third node. The method additionally includes applying the machine learning model to an input associated with the input option to generate an output in the output format. The method further includes providing, by the interactive graphical user interface, the output in the output format.

Methods and systems for facilitating selection of tools for one or more machine vision jobs are disclosed herein. An example method may be performed by one or more processors and includes displaying, via a display screen, an interactive graphical user interface (GUI) of an application, the application configured to generate job scripts for imaging devices to execute and the application operating in a job construction mode. The method also includes displaying an image within the GUI and detecting a selection of a portion of the image. The method further includes analyzing the portion of the image to determine a corresponding tool for processing the portion of the image and adding the corresponding tool to a job flow of the machine vision job.