Eureka-AI is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Eureka AI

2300 results about "A domain" patented technology

A domain name is an identification string that defines a realm of administrative autonomy, authority or control within the Internet. Domain names are used in various networking contexts and for application-specific naming and addressing purposes.

Site acceleration with content prefetching enabled through customer-specific configurations

A CDN edge server is configured to provide one or more extended content delivery features on a domain-specific, customer-specific basis, preferably using configuration files that are distributed to the edge servers using a configuration system. A given configuration file includes a set of content handling rules and directives that facilitate one or more advanced content handling features, such as content prefetching. When prefetching is enabled, the edge server retrieves objects embedded in pages (normally HTML content) at the same time it serves the page to the browser rather than waiting for the browser's request for these objects. This can significantly decrease the overall rendering time of the page and improve the user experience of a Web site. Using a set of metadata tags, prefetching can be applied to either cacheable or uncacheable content. When prefetching is used for cacheable content, and the object to be prefetched is already in cache, the object is moved from disk into memory so that it is ready to be served. When prefetching is used for uncacheable content, preferably the retrieved objects are uniquely associated with the client browser request that triggered the prefetch so that these objects cannot be served to a different end user. By applying metadata in the configuration file, prefetching can be combined with tiered distribution and other edge server configuration options to further improve the speed of delivery and/or to protect the origin server from bursts of prefetching requests.

Scheme of sending email to mobile devices

A method, apparatus, client and server are directed at providing a simplified scheme to deliver email messages that include text message body, and/or MIME attachments from desktop computing devices to messaging (such as SMS) and wireless internet capable phones. A web form is provided for users to compose messages and/or adding MIME attachments from their PC. Also provided in the form are input fields for users to enter device numbers for recipients. Upon submitting the form, message body and MIME attachments are uploaded and stored on the server. An email WAP page that contains the message body and the links to all the MIME attachments is created dynamically and stored on the server. The server then queries a service database and looks up a domain routing table to build the email like messaging addresses for recipients. The server sends a notification message with an embedded link to the email like messaging addresses. The link, such as a URL, a script, an executable, a program, and the like, pointing to the email WAP page, can be invoked from mobile devices. When the link is invoked on the mobile device, it sends a request to the server for the email WAP page. Upon receiving the request from the mobile device, the server collects the information about the mobile device; queries a device database for formats, display and capabilities; locates and loads the email WAP page; converts the MIME attachments to the formats supported by the mobile device; formats the email WAP page for display on the mobile device; delivers the formatted email WAP page to the mobile device. The email WAP page can be viewed, downloaded, and played on the mobile device. The scheme of the present invention supports a device number based authentication. The scheme of the present invention can also be implemented to deliver email messages to multiple mobile devices. The scheme of the present invention can expand the PC to SMS capabilities by enabling text messaging with arbitrary message length.
Owner:DENG LI +1

Interactive tool for semi-automatic creation of a domain model

A method, system and program product 100 usable by domain developers having any experience level in creating domain models. A representation of domain model knowledge is derived from a domain specification. The domain specification includes multiple potential domain objects, e.g., tables of APIs functional arguments, and each of the potential domain objects include one or more attributes. Potential domain objects are selected one at a time 102 from the specification and offered to the developer. The developer decides 104 whether or not to include the potential domain object in the domain model. If the developer decides to include the potential domain object 106, then the system provides a default name 108, i.e., the table name or argument name, and allows the developer to rename the selected domain object 110. Then, after having selected the object, potential attributes 112, e.g., table columns 1122, are selected from the object and offered to the developer 116. If the developer decides to include a potential attribute, then a default name, i.e., the column name or name extracted from an API function, is offered 1126 for the selected attribute and the developer is allowed to rename attributes 1128. Once all the potential domain objects have been offered 118 to the developer and the developer has either decided to include the potential objects or not, the system checks the domain model for nesting structure 200. If domain objects include attributes that are shared with other domain objects 2006, then those domain objects may be reorganized such that some domain objects include instances of identically named attributes from other domain objects.

Unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products