Abstract: Methods and systems are provided for blockchain-based social media commerce referral tracking system (BRTS). In one novel aspect, the blockchain is used to support traceability and transparency for the referral tracking system that monitors transactions of one or more user generated contents (UGCs) with one or more actionable items. Traceability and transparency foster trust among participants. In one embodiment, the BRTS tracks transactions of a UGC, updates a UGC record upon detecting one or more predefined transactions at a receiving node, detects one or more exit transactions at an ending node, and adds a UGC tuple of transactions from the originator node to the ending node based on the UGC record to a blockchain database upon detecting the one or more exit conditions. In another embodiment, the UGC record is updated using a recursive push procedure. In one embodiment, the BRTS generates one or more referral rewards for each UGC tuple record based on a configured rewarding scheme.

Abstract: Methods and systems are provided for blockchain-based credential vault system (CVS). In one novel aspect, the CVS identifies a set of credential of a trustor, verifies each credential, and stores the verified credentials to a CVS blockchain database such that the authorized beneficiary can obtain the trustor credentials from the CVS. In one embodiment, the CVS authenticates a trustor request from a trustor, wherein a trustor record in the CVS is uniquely identified by a trustor identification in a blockchain-based database of the CVS, processes submission from the authenticated trustor to generate a set of canonical credentials using a recurrent neural network (RNN) model, performs credential verification for each generated canonical credential in the authenticated trustor submission, and appends each verified canonical credential to the trustor record in the blockchain-based database of the CVS.

Abstract: Methods and systems are provided for the AI-based computer-aided persuasion system (CAPS). The CAPS obtains inputs from both the target and the agent for an object, dynamically generates persuasion references based on analysis of the input. The CAPS obtains content output by analyzing the agent audio stream and the target audio stream using a recurrent network (RNN) model, obtains sentiment classifiers based on a convolutional neural network (CNN LSTM) model, updates a conversation matrix, and generates a persuasion reference based on the updated conversation matrix. The persuasion reference is based on an acceptance likelihood result generated from the conversation matrix using the RNN model. The CAPS further generates a target profile using DNN (deep neural net) with input of target Big Data, wherein the target profile includes one or more objects, and wherein the agent is selected based on the generated profile and one or more selected objects.

Abstract: Methods and systems are provided for adaptive recruitment computer system. In one novel aspect, the adaptive recruitment computer system generates a question bank based on a job description, selects adaptively questions from the question bank for an interview, and generates a feedback report for the candidate based on the evaluation of the candidate's answer. In one embodiment, the computer system categorizes a job requirement based on a body of knowledge (BOK) skill knowledge base, generates a question bank, selects adaptively a subset of questions from the generated question bank, wherein each question selected is based on evaluations of candidate's answers to corresponding prior questions using a recurrent neural network (RNN) model, and generates a feedback report for the candidate, wherein the feedback report using the RNN model based on evaluations of answers and a BOK candidate knowledge base, wherein the BOK candidate knowledge base receives updates from the computer system.

Abstract: Methods and systems are provided for AI-based robotic automation for persuasive references. In one novel aspect, a robotic persuasive reference is generated based on a prospect product-service (P_PS) matrix, which is generated based on predictive analysis using DNN model and dynamically obtained feedbacks. In one embodiment, the DNN model is trained with customer personal profiles against associated PS revenues for each customer data set. In one embodiment, the predictive analysis uses a decision tree classifier. In one embodiment, the computer system detects one or more predefined triggering events comprising feedback information for the robotic persuasive reference and one or more predefined lifetime events, updates the P_PS matrix based and the robotic persuasive reference accordingly. In one embodiment, the feedback information is a sentiment analysis on responses from the prospect.

Abstract: Methods and systems are provided for skill-based/JHE credential verification using robotic contact and analysis, social media search, and/or peer affirmation within a blockchain-based database that holds verified trustor records. In one embodiment, a credential vault system (CVS) identifies a set of skill-based canonical JHEs for a trustor based on a description of job history and experience (JHE) of the trustor using a recurrent neural network (RNN) model with natural language process/natural language understanding (NLP/NLU), wherein a trustor record in the CVS is uniquely identified by a trustor identification in a blockchain-based database of the CVS, collects one or more corroborations for each canonical JHE by the CVS, evaluates each canonical JHE based on the collected one or more corroborations using a deep learning model to verify the corresponding canonical JHE, and appends each verified canonical JHE to the trustor record in a blockchain-based database of the CVS.

Abstract: Methods and systems are provided for the AI-based computer-aided persuasion system (CAPS). The CAPS obtains inputs from both the target and the agent for an object, dynamically generates persuasion references based on analysis of the input. The CAPS obtains content output by analyzing the agent audio stream and the target audio stream using a recurrent network (RNN) model, obtains sentiment classifiers based on a convolutional neural network (CNN) model, updates a conversation matrix, and generates a persuasion reference based on the updated conversation matrix. The persuasion reference is based on an acceptance likelihood result generated from the conversation matrix using the RNN model. The CAPS further generates a target profile using CNN with input of target Big Data, wherein the target profile includes one or more objects, and wherein the agent is selected based on the generated profile and one or more selected objects.

Abstract: Methods and systems are provided for adaptive recruitment computer system. In one novel aspect, the adaptive recruitment computer system generates a question bank based on a job description, selects adaptively questions from the question bank for an interview, and generates a feedback report for the candidate based on the evaluation of the candidate's answer. In one embodiment, the computer system categorizes a job requirement based on a body of knowledge (BOK) skill knowledge base, generates a question bank, selects adaptively a subset of questions from the generated question bank, wherein each question selected is based on evaluations of candidate's answers to corresponding prior questions using a recurrent neural network (RNN) model, and generates a feedback report for the candidate, wherein the feedback report using the RNN model based on evaluations of answers and a BOK candidate knowledge base, wherein the BOK candidate knowledge base receives updates from the computer system.

Abstract: Methods and systems are provided for speech emotion recognition interview process. In one novel aspect, in addition to contents assessment to an answer audio clip, the concurrent sentiment classifier is generated based on emotion classifier of the answer audio clip. In one embodiment, the computer system obtains a sentiment classifier of an audio clip of a first answer to the first question, wherein the sentiment classifier is derived from an emotion classifier resulting from a convolutional neural network (CNN) model analysis of the audio clip; obtains an assessment result to the first question by analyzing the audio clip of the first answer to the first question using a recurrent neural network (RNN) model; and generates a first emotion response result to the first question based on the sentiment classifier and the assessment result, wherein the first emotion response result presents a sampling experience factor to the response assessment result.