12244results about "Editing/combining figures or text" patented technology

An example operation may include one or more of storing a generative artificial intelligence (GenAI) model configured to create images, displaying a plurality of prompts on a user interface of a software application, receiving an identifier of a goal of the user and attributes of the goal via the plurality of prompts on the user interface, executing the GenAI model on the identifier of the goal of the user and the attributes of the goal to generate a custom image of the goal for the user, and displaying the custom image of the goal via the user interface of the software application.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on architecture diagrams of a software architecture and descriptions of the architecture diagrams, displaying one or more prompts on a user interface, receiving one or more natural language responses associated with the software architecture in response to the one or more prompts, generating a text-based response to the natural language query submitted via the user interface based on execution of the GenAI model on the one or more prompts and the one or more natural language responses associated with the software architecture, and displaying the text-based response via the user interface.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model to generate images based on user data using a dataset of images, executing the GenAI model based on input data from a user interface of a software application to generate an image corresponding to the input data, and displaying the image via a user interface of a software application, receiving feedback about the image via the user interface; andretraining the GenAI model based on the generated image and the received feedback about the image via the user interface.

An example operation may include one or more of generating a unique image comprising a plurality of image segments via an image generating model and displaying the unique image via a user interface of a software application, installing a smart contract associated with the unique image on a blockchain ledger, detecting a command to move an image segment from among the plurality of image segments based on user inputs via the user interface of the software application, removing the image segment from the unique image and adding the image segment to a different image based on the detected command, and transmitting the different image to a user account of the software application.

An example operation may include one or more of generating an image of an object based on execution of a generative artificial intelligence (GenAI) model and displaying the image via a user interface of a software application, receiving inputs via the user interface, determining that a new feature of the software application has been activated by a user account of the software application based on the received inputs, and in response to the activation of the new feature, adding additional content to the image of the object based on execution of the GenAI model on information associated with the new feature and refreshing a display of the image of the object within the user interface of the software application.

An example operation may include one or more of establishing a network connection between the computing system and a data source, iteratively performing a sequence of steps comprising collecting user data from the data source, executing a generative artificial intelligence (GenAI) model on the collected user data to generate a different image segment, and filling in a different subset of pixels of an image with the generated image segment and display the partially-filled in image on a user interface.

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

One embodiment of a method for determining user intent includes receiving user input that comprises first natural language text, performing one or more operations to map the user input to one or more classes of intents included in a plurality of classes of intents, and responsive to determining that the user input does not map to any class of intents, generating, via a first trained language model, second natural language text requesting additional user input.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enabling artificial intelligence to generate new images based on contextual data and to generate digital components based on the images. In one aspect, a method includes receiving one or more queries from a client device of a user. A digital component is selected based on the one or more queries. A customized digital component is generated by obtaining an image of an object corresponding to the selected digital component and generating, using a language model, an image editing prompt for editing the image based on digital component data related to the digital component and query data including the one or more queries and contextual data. The image and the image editing prompt are provided to an image editing model. An edited image is received and used to generate the customized digital component.

The present disclosure generally describes user interfaces related to time. In accordance with embodiments, user interfaces for displaying and enabling an adjustment of a displayed time zone are described. In accordance with embodiments, user interfaces for initiating a measurement of time are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface using a character are described. In accordance with embodiments, user interfaces for enabling and displaying a user interface that includes an indication of a current time are described. In accordance with embodiments, user interfaces for enabling configuration of a background for a user interface are described. In accordance with embodiments, user interfaces for enabling configuration of displayed applications on a user interface are described.

Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

Systems and methods are described for inputting text input to a trained machine learning model; generating, using the trained machine learning model and based on the text input, a single-layer image comprising a plurality of objects; segmenting the single-layer image to generate a plurality of images, each image of the plurality of images comprising a depiction of a respective object of the plurality of objects of the single-layer image; extracting, from the text input, a portion of the text input describing a background portion of the single-layer image; generating, using the trained machine learning model and based on the extracted portion of the text input, a background image; and generating the multi-layer image based on the plurality of images and the background image.

The invention discloses an advertisement pushing method and system based on user behavior analysis, and relates to the technical field of advertisement pushing, and the method comprises the following specific steps: data collection and preprocessing: employing a web crawler technology, setting corresponding capture rules and ranges for a social media platform, a professional interest forum and an e-commerce platform, and carrying out the data collection and preprocessing; according to the method, the whole process of user interest evolution is deeply tracked, the real-time behavior data of the user is combined, and the advertisement content is adaptively adjusted, so that the advertisement highly matched with the current interest and behavior of the user can be accurately pushed, meanwhile, a mixed feature fusion and dynamic weight distribution mechanism is introduced, and the user experience is improved. According to the method, user interest features are extracted more comprehensively, and feature weights are dynamically adjusted according to real-time feedback of user behaviors, so that feature vectors capable of reflecting real-time interests of users more accurately are constructed, and advertisement pushing accuracy is greatly improved.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that edit digital images using combinations of speech input and gesture interactions. For instance, in some embodiments, the disclosed systems receive speech input from a client device displaying a digital image within a graphical user interface, the digital image portraying an object. Additionally, the disclosed systems detect, via the graphical user interface, one or more gesture interactions with respect to the object of the digital image. Based on the speech input, the disclosed systems determine an edit for the object of the digital image indicated by the one or more gesture interactions. Further, the disclosed systems modify the object within the digital image using the edit indicated by the one or more gesture interactions.

The present disclosure generally relates to user interfaces for adjusting simulated image effects. In some embodiments, user interfaces for adjusting a simulated depth effect is described. In some embodiments, user interfaces for displaying adjustments to a simulated depth effect is described. In some embodiments, user interfaces for indicating an interference to adjusting simulated image effects is described.

The present application provides an image generation method and apparatus based on artificial intelligence, an electronic device, a computer readable storage medium, and a computer program product. The method comprises: acquiring a content text and acquiring a style image having a target style; carrying out text encoding processing on the content text to obtain a content text code of the content text, and extracting a style code from the style image; and on the basis of a dual-path cross attention mechanism corresponding to the style code and the content text code, carrying out inverse diffusion processing on a noise image to obtain a target image, wherein the target image is matched with the content of the content text, and the target image has the target style.

Approaches presented herein are directed to generating synthetic images with one or more augmentations realistically added to objects in the images, while ensuring the preservation and integrity of semantic or contextual information within the image. A synthetic augmentation system may identify and extract foreground image data (e.g., text), and a version of the image with the foreground image data removed can be processed by a generative diffusion model. One or more inputs can be provided to specify aspects such as a type or strength of augmentation to be performed. After an augmented image is generated using the generative diffusion model, the previously removed text can be blended back into the image. A synthetic augmentation system may use one or more blending weights for the text, such as a defect blending weight and a letter blending weight. The final result is a synthetic image with added realistic augmentations that preserves the semantic content.

A method, apparatus, and non-transitory computer readable medium for image generation are described. Embodiments of the present disclosure obtain a content input and a style input via a user interface or from a database. The content input includes a target spatial layout and the style input includes a target style. A content encoder of an image processing apparatus encodes the content input to obtain a spatial layout mask representing the target spatial layout. A style encoder of the image processing apparatus encodes the style input to obtain a style embedding representing the target style. An image generation model of the image processing apparatus generates an image based on the spatial layout mask and the style embedding, where the image includes the target spatial layout and the target style.

The invention discloses an advertisement copywriting generation method and device based on multi-modal fusion, equipment and a medium, and relates to the technical field of advertisement marketing. The method comprises the steps of obtaining multi-modal data of a target video, the multi-modal data comprising visual data, auditory data and related metadata of the video, and extracting associated data from a local knowledge base; and preprocessing the multi-modal data and the local knowledge base data, and converting the multi-modal data and the local knowledge base data into feature forms which can be used for analysis. According to the method, semantic calibration is carried out on multi-modal input by means of a local knowledge base, it is ensured that generated content strictly follows domain knowledge constraints, the problem of deviation caused by the fact that a traditional model depends on implicit knowledge is solved, multi-modal information ambiguity is eliminated, collaborative semantic generation of texts, images and structured data is achieved, content dimensions are enriched, and the method has the advantages of being high in practicability and easy to popularize. And an efficient solution is provided for the landing of the intelligent generation technology in the vertical field.

Methods and systems are described for generating memes and enhanced content in electronic communication. The communication is received, analyzed, and assessed for determining whether the enhanced content should be included in the communication. A content, context, intent, and / or mood of the communication is determined. For meme generation implementations, images and captions are generated based on the communication. Some candidate memes incorporate an image of one of the participants in the communication. Enhanced content candidates are generated and presented to the user for inclusion in the communication. Artificial intelligence systems, including neural networks, and models are utilized to improve the enhanced content generation. Related apparatuses, devices, techniques, and articles are also described.

The invention relates to the field of transportation visualization, in particular to a cold chain cargo transportation management visualization method and system. The method comprises the following steps of collecting cold chain cargo state parameters, performing cargo quality trend evaluation, and generating state data streams of a plurality of cargoes; performing dynamic positioning and tracking on the cold-chain transport vehicle, performing space-time mapping processing according to the state data flow, and constructing a real-time tracking trajectory flow; performing future aging deviation prediction on the real-time tracking trajectory flow to generate an aging deviation prediction result; carrying out transportation risk comprehensive assessment according to the aging deviation prediction result, and generating a transportation risk assessment report; and carrying out path planning calculation and client notification pushing according to the transportation risk assessment report so as to execute intelligent transportation management. According to the invention, the transportation energy consumption and time cost are reduced, the resource allocation efficiency is improved, and the cargo real-time tracking visualization effect is improved.

Disclosed in the present disclosure is a rich-media document auxiliary generation apparatus. The apparatus comprises a material extraction module, a theme sorting module, a semantic retrieval module, a structured data text generation module, an illustration recommendation module and a video composition module. The present disclosure uses intelligent means to assist a user to efficiently generate a high-quality rich-media composite document, thereby quickly and accurately describing a theme event in an all-round way.

According to an aspect of the present invention, there is provided an electrical audio signal processing system and device, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

The present disclosure generally relates to navigating, viewing, and sharing activity and workout data and interacting with workout and / or activity applications. In some examples, scrolling of activity data is based on the content being displayed. In some examples, friends' activity data may be viewed. In some examples, a notification and workout data for a friend's completed workout is received and displayed. In some example, the activity data received from friends is viewed and managed. In some examples, workout data for a multi-segment workout is displayed in a three-dimensional stack on a map. In some examples, a workout application operates in a limited mode until a touch input is received with a characteristic intensity that is greater than a threshold intensity.

Systems and methods are described for generating, using the first trained machine learning model and based on text input, a single-layer image comprising a plurality of objects; generating a plurality of masks associated with the plurality of objects; determining a plurality of attributes associated with the plurality of objects; generating, using a second trained machine learning model, a plurality of textual descriptions respectively corresponding to the plurality of objects; inputting the plurality of textual descriptions, and the plurality of attributes, to the first trained machine learning model; generating, using the first trained machine learning model, a plurality of images respectively corresponding to the plurality of textual descriptions; and generating the multi-layer image by combining the plurality of images and by using the plurality of masks, wherein the plurality of images respectively correspond to a plurality of layers of the multi-layer image.

A system leverages machine learning models to generate images, to customize and modify such images, and to generate additional images. The images can be generated within a digital canvas, allowing users to create sets of images, to compare such images, and to use such generated images to generate prompts and additional images. Tools can be provided to allow users to generate images with specific characteristics. For instance a color palette tool can allow users to specify particular colors and color shades for use in generating images. Likewise, a collage tool can allow users to generate and use a seed image to generate images for a collage of images. The system tracks states of images and other media content to promote user interaction with existing media content to generate new media content.

Various embodiments relate to a method and related device and machine-readable storage medium including one or more of the following: receiving, via a first user interface scene, a user specification of a physical structure; committing a data representation of the physical structure to a digital twin; transmitting the digital twin to a remote device; receiving an update to the digital twin from the remote device, producing an updated digital twin; and rendering, via a second user interface scene, a graphical representation of the updated digital twin.

Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.