Abstract: The system and method for generating realistic lip synchronization for an AI avatar. The lip synchronization process begins by receiving input data. The input data can either be text input or audio stream. If the input data is text input, a text-to-speech (TTS) module converts it into speech while generating word-level timestamps. If the input data is the audio stream, an automatic speech recognition (ASR) module transcribes the spoken content and provides word-level timestamps. The transcribed text is transformed into a sequence of phonemes using a grapheme-to-phoneme conversion system. The phonemes are mapped to visemes based on their corresponding mouth shapes using a predefined phoneme to viseme mapping table. Then the visemes are mapped to the blendshapes of the AI avatar using image similarity comparison. The selected blendshapes are animated to generate synchronized animation of the AI avatar, with transitions smoothed to ensure natural lip movements and facial expressions.

Abstract: A real-time AI-based cheating detection system and process for detecting unauthorized assistance during online learning sessions. The process begins by receiving input data from user device. The input data is pre-processed to ensure user is on a valid online learning platform and enrolled in the correct program. If validation is successful, the data is sent for further analysis. Proving one or more prompts a plurality of AI tools for specific tasks. The system and process employ visual recognition algorithm to detect additional individuals in the webcam feed, audio analysis algorithm to identify multiple voices of unauthorized help, and screen activity analysis algorithm to uncover anomalies during the session. Upon detecting unauthorized assistance, AI-based cheating detection systems and process compiles evidence, including video feeds and textual AI-generated transcripts of the session.

Abstract: A system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine provide real-time tutors in an online learning platform to enable the AI tutors to interact with the user and relevantly respond to user queries. The method includes receiving a request from the user for guidance during an online learning session or practice test via an interactive button on the user interface. The AI engine then accesses the user profile and educational database using a collector, gathering details of ongoing and past sessions, performance data, and interaction data. Utilizing this information, a prompt generator creates prompts to direct the AI engine, which then produces a detailed, personalized response. This response is generated in correlation to the context of the learning session and user data, explains concepts, corrects misunderstandings, and provides guidance, enhancing the educational experience for the user.

Abstract: A system and method guide an Artificial Intelligence engine to determine the semantic and grammatical correctness of a user-expanded sentence in real-time. The sentence validation process involves receiving input from the user, the input includes sentence fragment that the user wishes to expand and user-expanded sentence that the user constructs on the fragment provided. The inputs are broken down into tokens. The word-level tokenization algorithm is used, which identifies tokens by splitting the text into spaces, punctuation marks, and other delimiters. Further, a token comparison algorithm is used to assess the relationship between the sentence fragment and the user-expanded sentence to analyze order and placement. Once the token comparison is complete, a prompt is generated using prompt generator to evaluate grammatical and semantic evaluation of the user-expanded sentence. Real-time feedback is provided to the user based on grammatical and semantic evaluation.

Abstract: A system and method combine programmatic control and a guided and constrained Artificial Intelligence (AI) engine to generate dialogues for historical figures is disclosed. Historical data associated with historical figures including dates, events, and achievements are collected. Profiles of historical figures involved in the dialogues including biographical information, known speeches, writings, and significant actions are gathered. A content generation module is used to identify the context of the dialogues and generate historical content to maintain historical accuracies by verifying dates, events, and historical figures' specific details. The generated historical content is analyzed with educational standard to identify modern figure for integration with historical content. A prompt is generated to guide and constrain the AI engine to generate dialogues for historical figures based on the historical content aligned to educational standards.

Abstract: An educational standards alignment system and process automatically evaluate whether an input sentence aligns with predefined educational standards is disclosed. The educational standards alignment system receives an input sentence from a database that stores sentences associated with various educational standards. The system accesses a mapping function library, which contains multiple sets of functions designed to check the sentence against individual educational standards. Each set of functions can include programmatic checks, large language model (AI) checks, and natural language processing (NLP) checks. The functions are called and executed to determine if the input sentence aligns with a specific language standard. If confirmed, the sentence is tagged accordingly and passed for further evaluation against additional standards.

Abstract: An automatic sentence mapping system analyzes one or more input sentences to identify one or more associated education language standards such as Common Core English Language Arts (CCELA). One of the main components of the automatic sentence mapping system is mapping functions library. The mapping functions library includes information regarding various educational language standards. For each educational language standard, a set of functions within the standards are defined for validating the input sentences against a specific standard. Most functions defined within an educational language standard are designed for grammatical checks within a sentence against the associated educational language standard while some functions are designed for complex linguistic analysis that leverage artificial intelligence, such as large language models, for performing checks on the sentence for the desired associate language standard.

Abstract: A sensitive content detection system and method to enhance the accuracy and reliability of sensitive content detection by analyzing videos using multiple AI engines is disclosed. The sensitive content detection method receives video data from a cloud database, where all recorded videos are stored. A video extractor extracts video frames at pre-defined intervals, each representing a video segment for analysis. A batch of frames is sent to a primary AI engine utilizing machine learning algorithms to detect sensitive content. If sensitive content is found, the corresponding frames are marked positive and sent to secondary AI engines, each specialized in detecting specific types of sensitive content. The results from the primary and secondary AI engines are then aggregated using a consensus mechanism, with the final result based on a predefined agreement threshold.

Abstract: A talking avatar generation system and method featuring one or more avatars, using natural language inputs received from the user to guide an artificial intelligence (AI) engine in generating talking avatar applications using natural language input from a user is disclosed. The method involves receiving a natural language input or prompt from the user which is then analyzed and parsed to convert it into a structured API request with commands and parameters. Using these commands, the application code is generated that integrates interactive avatars with the functionalities specified by the user. The avatar's personalities are created using prompt engineering techniques, enabling them to display distinct behaviors, emotions, and conversational traits. The generated code undergoes automated testing in a simulated environment to ensure reliability. Finally, the talking avatar application is deployed and displayed to the user, allowing real-time communication with the avatars.

Abstract: A personalized coaching advice generation system and method collects media stream data and user interaction data. Machine learning algorithms analyze media stream data and user interaction data to identify specific learning patterns, which are then classified into positive (posi-patterns) and negative (anti-patterns) categories. The personalized coaching advice generation system and method generate coaching prompts based on these patterns and a predefined prompt structure created by the prompt engineer. An AI engine uses these prompts to create personalized coaching advice, which is then displayed to the user through an online learning platform. The personalized coaching advice addresses specific behaviors detected during the learning session and suggests corrective actions to improve learning efficiency. The personalized coaching advice generation system and method also include features for calculating learning time wastage.

Abstract: A virtual character identification system for guiding an AI engine to identify a virtual character based on AI-generated dialogues and images on an online learning platform is disclosed. The virtual character identification method involves collecting input data from user profiles and data models. The collected data is analyzed to generate insights that are used for prompt creation. These prompts are then used to guide the AI engine to generate text dialogues, which include an introduction, hints, and learning content. The AI engine also converts these dialogues into speech and dynamically generates images of the virtual character, where the image is progressively revealed based on user interaction. Furthermore, interactive audio-visual feedback, including educational videos with the virtual character, is provided to enhance the learning experience, allowing real-time engagement between the user and the platform.

Abstract: A real-time focus score calculation and visualization system and method for guiding an Artificial Intelligence (AI) Engine to generate a real-time focus score and visualize it for a user are disclosed. The real-time focus score calculation and visualization process involves receiving input data from multiple sources including webcam, screen content, and app usage, and providing the analyzed data to the Artificial Intelligence (AI) Engine to determine the presence, idleness, and focus of the user by utilizing a plurality of multiple machine learning algorithms. The focus score is recalculated every second by the AI Engine to determine the user's current level of engagement and is presented to the user in real-time along with an issue count, current status, or ongoing issue related to the user's current level of engagement, visually color-coded feedback.

Abstract: A stimulus image generation system and method provide access to a data model containing educational standards and stimulus types, mapping educational standards to relevant stimulus types. The stimulus image generation system and method also offers access to a repository of JSON schemas and Python functions, linking stimulus types to specific JSON schemas and functions. Upon receiving an input query, the stimulus image generation system selects a relevant JSON schema and Python functions based on the stimulus type. The system then generates prompts to guide an AI engine in populating the chosen schema, incorporating inputs from the data model and query. The system transfers the prompts to the AI engine for schema population. A python function module then calls python functions to generate stimulus images, utilizing various rendering libraries.

Abstract: An AI-based response generation chatbot system that acts as a digital replica of a person or expert, rather than being the expert itself, interacts with a user while being entirely guided by the information provided to it, without revealing its AI nature and the source of information. The AI-based response generation chatbot system includes a knowledge database initialized with knowledge documents containing expert knowledge with a specific viewpoint. The knowledge documents are compiled into a vector database through chunking and embedding techniques and are converted into unique topic-specific knowledge chunks in a machine-readable format. The compiled vector database further incorporates the Retrieval Augmented Generation (RAG) framework, enabling the retrieval of relevant information from the vector database and then using the retrieved information to frame accurate and contextually relevant responses aligned with user queries.

Abstract: The system and method for managing asynchronous communication between an AI avatar and a human representative upon detection of a network failure to establish a real-time connection. The asynchronous communication management process comprises the AI avatar that is configured to detect a connection failure when unable to establish or maintain a real-time connection with the human representative. The AI avatar sends a first notification message to prompt the human representative to provide the necessary information asynchronously. The AI avatar receives an asynchronous response message from the human representative including information relevant to the context of the interactive session. Updating a knowledge base of the AI avatar by parsing the received asynchronous response message. The AI avatar sends a second notification to the user through the communication channel to inform the user that the knowledge base has been successfully updated with the new information provided by the human representative.

Abstract: A fill-in-the-blank generation system and method integrates programmatic control and a guided and constrained AI engine to generate a fill-in-the-blank (FITB) question aligned with the educational curriculum of the user. The study mode delivery system accesses the data from the educational standards database and historical database. The study mode delivery system receives a standard description and set of key terms from the data accessed from the databases. The analyzer integrated within the study mode delivery system analyzes the course, standard description, and key terms relevant to the educational curriculum and standards.

Abstract: A system and method for guiding an AI engine to generate a response by an AI avatar for a user. The response generation process includes an AI guidance and control system configured to facilitate the communication of the user to the AI avatar. The response generation process receives real-time inputs from a human representative via a mobile application which helps the AI engine to provide the response to the user when the query of the user is new to the AI avatar. A specially generated, technical prompt guides the AI engine to enable dynamic and continuous interaction. The AI engine ingests both user and real-time inputs, using a natural language processing algorithm to analyze and update a knowledge base of the AI avatar. A multimodal processing engine on a cloud-based server utilized by AI engine further enhances by handling diverse input types to provide personalized responses to the user.

Abstract: A system and method for guiding an Artificial Intelligence (AI) engine creates and operates a real-time, personalized and dynamically adapting avatar that mimics a human representative. The real-time adaptive avatar generation process receives initial human representative data human data such as video, images, or audio recording through an AI guidance and control system 110. The human representative data is analyzed to generate a prompt by a prompt generator to capture the physical and vocal characteristics of the human representative. The AI engine uses generative algorithms to produce a three-dimensional model reflecting unique attributes like facial structure and skin tone. It also employs voice synthesis algorithms to replicate the vocal properties of the human representative, including pitch, tone, and accent.

Abstract: A comic generation system based on user profile details, proficiency and educational standard selects a learning topic for the comic generation by accessing the user profile details and one or more curriculum graphs stored in a curriculum database. One or more learning topics are generated that are relevant to the user profile details and the one or more curriculum graphs. The user profile details include one or more user interests obtained through the interaction between the user and a chatbot and user details stored in the user profile. The one or more curriculum graphs are generated based on curriculum obtained from the one or more educational standards.

Abstract: An avatar generation system and method generate dynamic and interactive three-dimensional avatars for educational interaction and a personalized virtual assistant. The avatar generation system utilizes a combination of 3D modeling, face reenactment, and text-to-speech module to produce 3D avatars with realistic movements and interactions. The avatar generation system utilizes prompts to guide an artificial intelligence (AI) engine in generating the avatars. The avatar generation system improves the scalability and personalization of avatars. Furthermore, the avatar generation system aims to provide a more efficient and quality-effective way for the generation of avatars. The use of algorithms for the automatic generation of movements and behaviors is introduced to produce more realistic and personalized animations.