Abstract: An infection risk prediction system may receive a query associated with an infection risk assessment for a geographic location for an infectious disease and obtain associated infectious disease data. The system detects a plurality of objects at the geographical location and a plurality of material categories for the plurality of objects associated. The system determines a usage frequency indicator and spatial-temporal features for each object and creates a risk prediction model based on the lifespan of the virus on the plurality of material categories for each of the plurality of objects based on the viral disease data and the spatial-temporal features for each object. A risk assessment score for each object is determined and an associated heat map is provided to alert a user of potential virus exposures or to provide a routing application to generate a route that minimizes potential pathogen exposure.

Abstract: A device may receive complex data from a user device and may provide multiple images to the user device based on receiving the complex data. The device may receive, from the user device, a selection of two or more images from the multiple images, and may determine whether a combination of the two or more images is stored in a data structure. The device may determine a mapping of information identifying the two or more images with the complex data, based on the combination of the two or more images not being stored in the data structure, and may store the information identifying the two or more images, the complex data, and the mapping in the data structure. The device may process the two or more images to generate a reference image that satisfies a memorability score threshold and may provide the reference image to another user device.

Abstract: Examples of a system for intelligent communication management for content summarization are provided. In an example, the system receives a summary generation requirement. The system establishes a question database for generation a set of questions for a user corresponding to the summary generation requirement. The system implements an artificial intelligence component to sort the answers obtained from the question and generates a user-specific knowledge database. The system may use the user-specific knowledge model for generation of further questions for the user. The system may assist a user with collecting information in a conversational manner mode and to automatically produce intelligible deliverables. The system may accept multiple modes of input while collecting information from a user. The system may be used for an automatic summary generation for customer service conversations, interviews, conferences and presentations, a person's holiday chronicles, and the like.

Abstract: The present disclosure describes a system and method for using image processing to check color application and to indicate to a user where color application is erroneous. The system and method may include capturing a first digital image of an object before color is applied and a second digital image after color is applied. Then, the first and second digital images may undergo editing processes to prepare the digital images for analysis, analysis to determine colors in the digital images, and the generation of heatmaps in a third digital image showing errors in the application of color.

Abstract: Systems and methods for disability simulations and accessibility evaluations of content is disclosed. A disclosed system runs using an information loss determination engine via a processor, for a given disability, at least one simulation to simulate how a content is experienced by a user having such disability. The system computes information loss based on comparison of the simulated content with desired original content. Further, the system transmits data packets indicative of a content optimization strategy that is determined based on the determined information loss.

Abstract: A system for providing visual content optimization is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with a design or graphical layout from a data source, and receive priority parameters. The processor may identify a plurality of discrete design elements from the design or graphical layout. The processor may create a new design or graphical layout based on the plurality of discrete design elements and on priority parameters. The processor may also evaluate the new design or graphical layout based on an evaluation technique. In some examples, the evaluation technique may include a visual attention prediction subsystem to determine the most visually appealing design using artificial intelligence (AI) or machine learning. The processor may also select the new design or graphical layout based on a selection technique.

Abstract: In some implementations, a font generator device may receive input text that includes one or more characters. The font generator device may generate a disability-oriented font based on one or more parameters that relate to a vision deficiency, wherein the disability-oriented font is readable by users that have the vision deficiency and unreadable by users that do not have the vision deficiency. The font generator device may transform the one or more characters included in the input text into the disability-oriented font. The font generator device may generate an output that represents the input text using the disability-oriented font based on transforming the one or more characters included in the input text into the disability-oriented font.

Abstract: A system comprises a receiver to receive a main content. The system further comprises a detector to detect a placeholder in the main content for placement of a media object. Further, the system comprises an obtainer to obtain a plurality of media objects having placement attributes corresponding to the placeholder in the main content, where a placement attribute is indicative of characteristics of a media object compatible with the placeholder. The system further comprises a selector to select one of the plurality of media objects for being placed in the placeholder of the main content, based on a user profile. Further, the system comprises a generator to generate a final content indicative of the selected media object embedded in the main content.

Abstract: A device may receive target user category data identifying a target user, daily user data associated with the target user, real-time user data associated with the target user, and content data. The device may convert the target user category data, the daily user data, the real-time user data, and the content data into embedded target user category data, embedded daily user data, embedded real-time user data, and embedded content data. The device may process the embedded target user category data, the embedded daily user data, and the embedded real-time user data, with a neural network model, to determine a real-time user state and may determine a real-time user memory score. The device may process the embedded content data, the real-time user state, and the real-time user memory score, with the neural network model, to determine a memorability score and may perform one or more actions based on the memorability score.

Abstract: A device may receive complex data from a user device and may provide multiple images to the user device based on receiving the complex data. The device may receive, from the user device, a selection of two or more images from the multiple images, and may determine whether a combination of the two or more images is stored in a data structure. The device may determine a mapping of information identifying the two or more images with the complex data, based on the combination of the two or more images not being stored in the data structure, and may store the information identifying the two or more images, the complex data, and the mapping in the data structure. The device may process the two or more images to generate a reference image that satisfies a memorability score threshold and may provide the reference image to another user device.

Abstract: A device may receive digital content and target user category data identifying target users of the digital content and may modify features of the digital content to generate a plurality of content data. The device may select a neural network model, from a plurality of neural network models, based on the target user category data, and may process the plurality of content data, with the neural network model, to determine first memorability scores for the plurality of content data. The device may process a plurality of areas of the plurality of content data, with the neural network model, to determine second memorability scores for the plurality of areas. The device may perform actions based on the first memorability scores or the second memorability scores.

Abstract: An infection risk prediction system may receive a query associated with an infection risk assessment for a geographic location for an infectious disease and obtain associated infectious disease data. The system detects a plurality of objects at the geographical location and a plurality of material categories for the plurality of objects associated. The system determines a usage frequency indicator and spatial-temporal features for each object and creates a risk prediction model based on the lifespan of the virus on the plurality of material categories for each of the plurality of objects based on the viral disease data and the spatial-temporal features for each object. A risk assessment score for each object is determined and an associated heat map is provided to alert a user of potential virus exposures or to provide a routing application to generate a route that minimizes potential pathogen exposure.

Abstract: A system for providing visual content optimization is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with a design or graphical layout from a data source, and receive priority parameters. The processor may identify a plurality of discrete design elements from the design or graphical layout. The processor may create a new design or graphical layout based on the plurality of discrete design elements and on priority parameters. The processor may also evaluate the new design or graphical layout based on an evaluation technique. In some examples, the evaluation technique may include a visual attention prediction subsystem to determine the most visually appealing design using artificial intelligence (AI) or machine learning. The processor may also select the new design or graphical layout based on a selection technique.

Abstract: A system for visual and digital content optimization may identify a plurality of content creation attributes from multiple data sources and may classify a content record associated with a content creation requirement into a plurality of exhibits. The system may identify a plurality of rules from a rule record and map the plurality of exhibits with the plurality of rules and the plurality of content creation attributes to create a plurality of content models. Each of the plurality of content models may be evaluated for congruence with the plurality of rules and the content creation attributes. Based on the evaluation, an evaluation score for each of the plurality of content models may be determined. A content model having an evaluation score above a threshold evaluation score may be selected and a content creation action may be initiated accordingly.

Abstract: A system for providing visual content optimization is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with a design or graphical layout from a data source, and receive priority parameters. The processor may identify a plurality of discrete design elements from the design or graphical layout. The processor may create a new design or graphical layout based on the plurality of discrete design elements and on priority parameters. The processor may also evaluate the new design or graphical layout based on an evaluation technique. In some examples, the evaluation technique may include a visual attention prediction subsystem to determine the most visually appealing design using artificial intelligence (AI) or machine learning. The processor may also select the new design or graphical layout based on a selection technique.

Abstract: Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items.

Abstract: Examples of a system for intelligent communication management for content summarization are provided. In an example, the system receives a summary generation requirement. The system establishes a question database for generation a set of questions for a user corresponding to the summary generation requirement. The system implements an artificial intelligence component to sort the answers obtained from the question and generates a user-specific knowledge database. The system may use the user-specific knowledge model for generation of further questions for the user. The system may assist a user with collecting information in a conversational manner mode and to automatically produce intelligible deliverables. The system may accept multiple modes of input while collecting information from a user. The system may be used for an automatic summary generation for customer service conversations, interviews, conferences and presentations, a person's holiday chronicles, and the like.

Abstract: A heat map creation engine receives a series of image frames from amongst a plurality of image frames of a media, where the series of image frames includes a target frame and at least one neighboring frame. The heat map creation engine also extracts spatial-temporal features of the image frames, rescales the spatial-temporal features to obtain heat distribution over the target frame, and creates a heat map for the target frame based on the heat distribution. A semantic segmentation engine segments the image frames into multiple media objects based on pre-defined classes, and selects one or more media objects from amongst the media objects based on pre-defined conditions. A heat ranking engine ranks the selected one or more media objects in the media based on the heat scores and the created heat map.

Abstract: A system for providing visual content optimization is disclosed. The system may comprise a data access interface, a processor, and an output interface. The data access interface may receive data associated with a design or graphical layout from a data source, and receive priority parameters. The processor may identify a plurality of discrete design elements from the design or graphical layout. The processor may create a new design or graphical layout based on the plurality of discrete design elements and on priority parameters. The processor may also evaluate the new design or graphical layout based on an evaluation technique. In some examples, the evaluation technique may include a visual attention prediction subsystem to determine the most visually appealing design using artificial intelligence (AI) or machine learning. The processor may also select the new design or graphical layout based on a selection technique.

Abstract: A system for placement of media object in a video feed comprises a receiver to receive the video feed, a tracker to receive a camera tracking data regarding movement of a camera capturing the video feed, a detector to detect a placeholder within the video feed where the media object may be placed and detect an attribute, such as size and position of the placeholder, and a media obtainer to obtain information regarding the media object, the media object having a placement attribute corresponding to the placeholder. The system also comprises an aligner to adjust the placement attribute of the media object with the attribute of the placeholder for positioning the media object within the placeholder, and a content generator to generate a content indicative of the media object embedded within the placeholder of the video feed.