Accenture Global Solutions Limited

Edouard Mathon

Christian Souche

Richard Vidal

Alexandre Naressi

Ji Tang

Victor Chevalier

Robi Fazio

Habibou Sissoko

Chongmo Liu

Lucia Gattoni

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.

Precision color application system and method

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.

Disability-oriented font generator

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.

Utilizing a neural network model to predict content memorability based on external and biometric factors

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.

Utilizing a neural network model to generate a reference image based on a combination of images

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. The output interface may transmit, to a user device, the new design or graphical layout to a user device or a publishable medium.

Visual content optimization system using artificial intelligence (AI) based design generation and validation

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.

Dynamic content generation

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.

Visual and digital content optimization

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.

Heat ranking of media objects

A system for rendering media having content displayed comprises a receiving unit to receive media, a scanner to detect an object in the media for placing a placeholder, a checker to provide the placeholder to a user for feedback and receive the feedback. The system also includes a template generator to generate a template indicating the placeholder in the media. The system also includes a rendering unit in communication with the template generator to render, upon receiving a request for watching the media, the media having content to display to the user, based on at least one of a storage space and a processing capacity.

Dynamic content rendering in media

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.

Dynamic media placement in video feed

A system for media placement comprises a receiving unit to receive media, a scanner to detect an object in the media for placing a placeholder, a checker to provide the placeholder to a user for feedback and receive the feedback. The system also includes an instruction receiving unit to receive instructions for adding special effects to the placeholder, a template generator to generate a template indicating the placeholder in the media, and a machine learner to store the feedback for the placeholder and selection of the special effects for the placeholder. Further, the scanner detects another object in the media or another media and determines a position of another placeholder, based on the stored feedback. The scanner also suggests special effects for addition to the other placeholder, based on the stored selection of the special effects.

Growing community of inventors

Antibes, France

Edouard Mathon