MAPM: MULTI-MODAL AUTO-AGILE PROJECT RISK MANAGEMENT AND PREDICTION FOR COLLABORATION PLATFORM USING AI/ML

Agile software development tools have been created to assist project management and enhance productivity. However, it may be challenging to properly employ those tools, especially in a hybrid work environment. Techniques are presented herein, which may be referred to herein as a MaPM system, that leverage a conferencing platform to offer a real-time agile project risk management and prediction framework that utilizes multi-modal collaboration data sources. Aspects of the presented techniques encompass an artificial intelligence (AI)-backed summarization model that may be utilized to extract project details and an auto-agile that model may consume those loggings and generate the predicted project sprint backlogs and their risks. Further aspects of the presented techniques support an optimization module that may jointly update the predicted sprint backlogs and the estimated task risks to realize the finalized backlog sequences. In summary, an MaPM system, according to the presented techniques, offers four novelties compared with conventional agile project management tools – a conferencing platform-centralized solution for automatic agile project risk prediction and management, a real-time multi-modal-based project risk monitoring and prediction system, the generation of sprint backlogs based on fully evaluated contexts that are collected from all of a project’s participants, and the liberation of project contributors from having to conduct manual project tracking and recording

SELF-ADAPTIVE ANOMALY DETECTION WITH DEEP REINFORCEMENT LEARNING AND TOPOLOGY

In the networking field, network topology is one of the most important perspectives as it can bring additional insights to the modeling process. Existing anomaly detection approaches do not take topology information into consideration. To address such limitations techniques are presented herein that support deep Convolutional Neural Network (CNN) modeling with Reinforcement Learning (RL) employing, for example, an Advantage Actor Critic (A2C) algorithm. Additionally, aspects of the techniques presented herein support an innovative new way to model a customer profile that leverages topology information

CROSS CUSTOMERS SMART NETWORK INVENTORY PLANNER (SNIP) AND OPTIMIZATIONS USING DEEP REINFORCEMENT LEARNING

Optimal inventory upgrade planning is one of the most challenging tasks in managing network assets. A Smart Network Inventory Planning (SNIP) architecture or framework is presented herein that leverages a deep reinforcement learning (DRL) framework to enable network inventory upgrade planning for different scenarios. As a foundation for the DRL framework, techniques herein provide for establishing a network inventory environment through which interaction with a supply chain can be used to allow the SNIP architecture to incrementally optimize upgrading sequences for multiple customers. To further optimize inventory upgrades via the DRL framework, the SNIP architecture may employ a multi-objective reward function. Additionally, a transformer can be utilized as a policy network to capture long-term correlations in the inventory upgrading sequence. By incorporating weighting coefficients into both the reward function and a multi-agent actor network, the SNIP architecture can provide customized inventory task scheduling within an optimal framework

SEMI-SUPERVISED DEVICE TAG PREDICTION FOR AUTOMATIC NETWORK PROVISIONING

Device tagging is an important element in the world of network administration, offering an efficient way to organize network and computation resources (such as, for example, network devices, virtual machines, instances, etc.) and support efficient device provisioning and network segmentation (e.g., firewall rules, routing rules, etc.). The manual selection of tags and labelling of individual devices may be error prone and quite time consuming, particularly as the scale of a network grows. To address such challenges, techniques are presented herein that leverage aspects of Graph Convolutional Network (GCN) theory to offer a GCN-based approach for the accurate and automatic tagging of network devices employing a semi-supervised deep learning approach and requiring only minimal human expert knowledge (e.g., for training)

STRATIFIED INVESTIGATION OF LOW-PERFORMING NETWORK ARCHITECTURE (SINA) USING GRAPH NEURAL NETWORKS AND PEER BENCHMARKING

Monitoring and troubleshooting networks to improve their performance and reliability is a complicated task, not only because it requires the checking of every single network device but also because it involves understanding the connections between those devices. To address that complexity, techniques are presented herein that support a Stratified Investigation of Low-Performing Network Architecture (SINA) system. Such a system is a framework that identifies any low-performing network architecture areas and makes improvements on a subnetwork level. Such a system may automatically identify low-performing areas of a customer’s network based on information about similar networks and expert knowledge with solutions. Such a system may employ a graph neural network (GNN) to identify areas of a customer’s network that need improvement based on a calculated performance score while considering the interaction between devices and the topology of networks. Further, such a system may leverage network performance metrics from many customers to create a performance benchmark and then evaluate where a customer’s network’s performance lies within that benchmark. Still further, such a system may employ a transformer-based natural language processing (NLP) model (that understands key semantic knowledge from documents, device configurations, and logs) to help generate solutions to network issues. Finally, based on high-performing customers relative to the benchmark and documents with best practices for configuring networks, a SINA system may provide solutions to a customer’s network that will help optimize network performance

HIMEG: HIERARCHICAL MEETING NOTE GENERATION USING TEXT SEGMENTATION AND ATTENTION CORRELATION

Techniques are presented herein that support a HiMEG system, a framework that helps create meeting notes of multiple granularities for meeting invitees so that they can refresh their memory or catch up on any meeting. Such a system comprises a Segmentation Engine that may divide a meeting transcript into separate sections representing the different topics that were covered during a meeting. Such a system also comprises an Attention Correlation Analyzing Model that may be used to capture the attention correlation between different meeting notes that were generated from the discovered topics, which is useful in a Meeting Note Summarization Model that may assess which meeting notes are most similar. Under such a system, one effective summary may be formed based on the most similar meeting notes and the process may be repeated until there is one overall summary of a meeting. In the end, a user may read the high-level summary of a meeting and then dive further into the specific contents of the general meeting note based on their interests and needs. While the above-described framework was originally developed for generating meeting notes, it may also be applied to any text input such as speeches, action scripts, and training scripts

ENHANCED ENGAGEMENT AND PRODUCTIVITY IN ONLINE MEETING WITH INTELLIGENT REAL-TIME CONTENT-BASED QUESTION AUTO-GENERATOR

The host of an online meeting may frequently wonder if the meeting attendees understand the message that they are trying to deliver. Additionally, an attendee of an online meeting may be confused by the content of a meeting or a webinar and need clarification. Techniques are presented herein that support a real-time, intelligent, content-based automatic question generator that enhances meeting engagement and productivity. The presented techniques can perform all of the capabilities of the current question generator tools but, most importantly, they can also automatically generate and rank relevant questions during a meeting based on the content of that meeting. After receiving the results of their auto-generated quiz or poll, a host may check the attendees’ understanding during a meeting and reiterate previous content if necessary. After a meeting ends, an automated message (including the generated quiz or poll questions, along with the correct answers) may be sent to all of the meeting attendees while a host may receive the meeting’s statistics (so that they can pinpoint the key areas they need to emphasize in future meetings)

DEEPSORTING: CUSTOMER-CENTRIC SEQUENCE-TO-SEQUENCE NOTIFICATION PRIORITIZATION

Network operators may control many thousands of devices and, as a consequence, they may be bombarded with notifications (e.g., posture assessments and exceptions for devices that do not comply with standards or which require remediation actions to circumvent security issues) and can be discouraged by unimportant or irrelevant information. To address the challenge that was described above, techniques are presented herein that support a process for intelligently filtering and prioritizing notifications to reduce noise and deliver recommendations that will provide the greatest impact to an environment. Aspects of the presented techniques encompass a smart recommendation notification system that prioritizes network actions based on multiple embedding spaces and dimensions; the use of business and financial logic, a persona, and a network operating state for reducing network actions into a prioritized output; the use of click-through and sequence mining to establish a ground truth of event prioritization of a network operator; an adaptive learning method for tracking proposed network recommendations to the final action that may be executed by a network operator; and a method for reducing multi-step recommendations based on an identification of the most efficient sequence of events based on a network operator implementation

MEASURING AND VISUALIZING USER SENTIMENT CHANGES OVER MULTIPLE CHANNELS

Techniques are provided to simultaneously infer approximately where a speaker is looking and the speaker\u27s emotion during a conversation. Due to privacy concerns, only the speaker\u27s approximate facial features may be estimated. The inferred face may be converted into a cartoon face that retains the main facial features. This may enhance user interaction experience even when a speaker does not turn on video in teleconference