Talent Unlimited Online Services Private Limited

Talent Unlimited Online Services Pvt. Ltd.

Ankit Prasad

Rahul Prasad

Sumegha Yadav

Abhishek R Sharma

Lovely Kukreja

Abhik Kapoor

Kaushik Parashar

Gaurav Srivastava

Rajendra Engla

A method for converting speech in one of a plurality of input languages into text using machine transliteration and transfer learning is disclosed. The method includes a training stage. The training stage includes receiving a training set of a plurality of audio files and an input text corresponding to the audio input in any input language using the speech recognition engine; transliterating the training set to transform the input text into transliterated text that includes characters of a base language and training acoustic model with the plurality of audio files and corresponding transliterated text using transfer learning. The method further includes an inference stage. The inference stage includes performing decoding on output of the trained acoustic model to generate text includes characters of the base language at inference and transliterating the generated text to output text includes characters in input language using reverse transliteration.

System and method for speech recognition using machine transliteration and transfer learning

The present invention provides systems and methods for dynamic word prediction and suggestion. The method includes an AI engine configured to render a touch-enabled keyboard interface on a display unit of the electronic device, identify a plurality of elements of an input message of a user on a messaging application and transform the input message into an array of a specific numbers based on the plurality of elements. The method further includes converting the array into a 2D matrix of embeddings by the AI engine, wherein the embeddings include semantic information of the input message to identify a context of the input message and converting the matrix into an output array with a changed dimension. The AI engine is further configured to convert the output array into probabilities of one or more words corresponding to the input message of the user on the messaging application.

Artificial intelligence based hybrid system and method for generation of word predictions based on language modelling

A system including an artificial intelligence (AI) engine and an AI-based method are provided for dynamically predicting and suggesting emojis on a quick access emoji interface. The AI engine detects an input message in an input field on a graphical user interface of a user application. The AI engine processes the input message to identify elements, for example, character elements, image elements, patterns, etc., of the input message. The AI engine analyzes and determines a context and factors, for example, emoji positions, one or more languages, intent, user and global preferences, etc., associated with the input message for each element of the input message. The AI engine dynamically generates predictions of emojis based on the context and one or more factors in real time. The AI engine renders one or more grouped sets of emojis on the emoji interface based on the dynamically generated predictions.

Artificial intelligence-based system and method for dynamically predicting and suggesting emojis for messages

A system for generating a contextualized text using a character-based convolutional neural network architecture (CNN) is disclosed. The system includes a memory, a character-based CNN engine having executable modules and a processor configured to execute the character-based CNN engine and the executable modules for generating contextualized text using a character-based convolutional neural network architecture (CNN). The executable modules includes a data processing module for pre-processing an input conversational text data, a corpus builder unit for developing a corpus of most frequent n-grams, a dataset generation module for performing logarithmic mapping of a frequency of a respective n-gram in the corpus for a dataset prepared for training, a training module for training an artificial intelligence (AI) based model with datasets for a predetermined range of epochs and a prediction module for executing the trained AI-based model for predicting characters contextually till an end-of-sentence (EOS) token is found.

System and method for generating contextualized text using a character-based convolutional neural network architecture

An integrated payment system (IPS) and a method are provided for managing payment transactions within an input interface. A payment system is integrated within a keyboard, independent of a messenger application. The IPS renders a payment interface element (PIE) on the keyboard. In response to a user input action on the PIE, the IPS automatically detects a recipient for a payment transaction during an interaction between a sender and the recipient, through the messenger application. The IPS automatically determines an identifier of the detected recipient and account information linked to the detected identifier for executing a payment transaction between the sender and the recipient. The IPS generates and renders transaction information within the keyboard for convenient user access.

Integrated payment system and method for managing payment transactions within an input interface

An actionable content generation system (ACGS) and a method for generating and rendering intent-based actionable content in real time are provided. The ACGS, integrated within an input interface, for example, a keyboard, detects and simultaneously analyzes one or more messages being entered by a user in an input field of a user application using the input interface in real time. The ACGS generates tokens from the message(s) and determines intent with a confidence element from the tokens in real time based on a stored mapping and a confidence computation. The ACGS generates actionable content based on the intent using campaign data. The ACGS renders at least one element containing the actionable content on a graphical user interface and/or the input interface for interaction by the user and performance of one or more recommended actions.

System and method for generating and rendering intent-based actionable content using input interface

A system including an avatar generation engine (AGE) () and a method for automatically generating an avatar with pronounced features are provided. The AGE () extracts a primary image of a primary component, for example, a face component, and a secondary image of a secondary component, for example, a hair component, from an input image of a target object. The AGE () normalizes and processes the primary image for extracting a feature image corresponding to a feature indicating a distinct characteristic of the target object. The AGE () processes the extracted feature image for graphically pronouncing the features. The AGE () generates a primary canvas including a predicted tone of the primary component. The AGE () generates an avatar with pronounced features by merging a primary graphical image generated by merging the primary canvas with the graphically pronounced features, with a secondary graphical image of the secondary component.

System and method for automatically generating an avatar with pronounced features

A system including a direct message sending engine (DMSE) and a method for directly sending messages with minimal user input are provided. When a user invokes an input interface proximal to an input message field (IMF) of a user application, the DMSE renders a list of preconfigured messages on the input interface. When the user performs a selection action on at least one of the preconfigured messages on the input interface, the DMSE receives the selection action and in response, the DMSE transforms properties of the IMF, commits the preconfigured message(s) to the IMF, and simultaneously and directly sends the preconfigured message(s) to a recipient device. On committing the preconfigured message(s) to the IMF, the DMSE simultaneously executes a programmatic simulation of an action key press, thereby directly sending the preconfigured message(s) to the recipient device free of a manual user input.

System and method for directly sending messages with minimal user input

A system to measure latency in displaying a key tap event corresponding to a touchscreen keyboard is disclosed. The system includes a text input module, configured to capture pre-defined text for latency measurement. The system includes a keyboard selection module, configured to select a virtual keyboard for the latency measurement with respect to the pre-defined text. The system includes a keyboard configuration module, configured to capture positional details of each text character of the pre-defined text with respect to registered virtual keyboard. The execution module, configured to execute the key tap event for each of the text character of the pre-defined text for latency measurement, to measure time gap of each text character of the pre-defined text between the key tap event and corresponding text display event and also configured to calculate average latency measurement of the pre-defined text from a measured time gap for each of the character.

System and method to measure latency in displaying a key tap event

A system and a method are provided for enhancing an expression of a digital pictorial image on a graphical user interface (GUI) of an electronic device. An expression enhancing engine (EEE) in the system receives a user selection of the digital pictorial image via an input interface, in communication with a sensing unit. The EEE enhances the expression of the digital pictorial image in real time by modifying a size of the digital pictorial image to a selected scaling size; retrieving media content associated with the user selection of the digital pictorial image from a data storage device; and reconstructing the digital pictorial image of the modified size with the media content. The EEE, in operable communication with a media playback device, renders the reconstructed digital pictorial image with the media content on the GUI in real time.

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Ankit Prasad