Various embodiments illustrated herein disclose a method that includes receiving a plurality of images from an image capturing unit. Thereafter, an image evaluation process is executed on each of plurality of sections in each of the plurality of images. The image evaluation process includes performing optical character recognition (OCR) on each of the plurality of sections in each of the plurality of images to generate text corresponding to the plurality of respective sections. Further, the image evaluation process includes querying a linguistic database to identify one or more errors in the generated text. Further, the method includes modifying one or more image characteristics of each of the plurality of images and repeating the execution of the image evaluation process on the modified plurality of images until at least the calculated statistical score is less than a pre-defined statistical score threshold.

One example method includes generating personalized content for online sales. Recorded calls and other sources are converted to text. The text is semantically processed and correlated with customer data including customer order data to identify content that impacted the purchase of a product. The content may be incorporated into an online product page to facilitate online sales.

One example method includes generating personalized content for online sales. Recorded calls and other sources are converted to text. The text is semantically processed and correlated with customer data including customer order data to identify content that impacted the purchase of a product. The content may be incorporated into an online product page to facilitate online sales.

An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.

A system and method for secure cloud computing. The cloud based processing system comprises a user interface, allowing a user to enter and edit data, a proxy server, and a cloud based processing server. The user interface sends data entered by a user to the proxy server, which sends the encrypted data to the cloud based processing server. The proxy server receives editing commands from the user interface, and sends those commands to the cloud based processing server along with the encrypted data. The cloud based processing server receives the encrypted data and editing commands, applies the editing commands to the encrypted data, and sends the edited encrypted data back to the proxy server.

A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.

A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.

A method of auto-provisioning AI-based dialog services for a plurality of target applications includes storing a plurality of dialog templates, generating a deployment object associating one or more of the dialog templates with a target application from among the plurality of target applications, extracting textual data from the target application, assembling the extracted textual data into inquiries or inquiry responses according to the one or more dialog templates associated with the deployment object, and deploying an AI-based dialog service to the target application based on the assembled inquiries or inquiry responses. Each of the dialog templates may include one or more sets of common inquiries or common inquiry responses.

A method of auto-provisioning AI-based dialog services for a plurality of target applications includes storing a plurality of dialog templates, generating a deployment object associating one or more of the dialog templates with a target application from among the plurality of target applications, extracting textual data from the target application, assembling the extracted textual data into inquiries or inquiry responses according to the one or more dialog templates associated with the deployment object, and deploying an AI-based dialog service to the target application based on the assembled inquiries or inquiry responses. Each of the dialog templates may include one or more sets of common inquiries or common inquiry responses.

A multicore apparatus determines fitness of a candidate for a role. The apparatus includes a multicore system processing device, a plurality of parallel multicore graphics processing devices, a network interface device, a storage device, and a system interface bus. The network interface device provides remote connection to the multicore system processing device. The storage device stores training data including positive and negative examples. The positive examples represent candidates who would be invited to an interview, and the negative examples represent candidates who would not be invited to an interview. The positive and negative examples are used by the plurality of parallel multicore graphics processing devices to train a deep learning model, which is used by the multicore system processing device to determine fitness of the candidate for the role while eliminating unwanted bias.