AI-Powered Virtual Shopping Marketplace Platform for the Hair Integrations Industry
The average wig industry revenue over the last five years has a steady growth to $415.2 million per year. This industry caters to four distinct consumer groups: 1) individuals that purchase wigs for aesthetic purposes, 2) those that have lost their hair due to a medical condition or treatment, 3) those that follow their religious practice for specific hair restrictions, and 4) film/theatre directors who purchase wigs as part of character costumes. A wig costs from $600 to $1500 or more. In addition, with the COVID-19 outbreaks, online shopping inevitably became the leading trends.
However, shopping for a perfect wig online is not an easy task. We will build an AI-powered marketplace to solve the problem. In the AI-powered marketplace, customers get expert advice from AI as if customers are served by domain experts. AI will extract customers’ head shape, skin tone, and personality from the image and video, and make the best recommendations.
An intelligent immersive content creation platform for the non-programmer for training, maintenance and assembly using AR and VR.
OVA’s StellarX is an uncompromisingly good virtual and augmented reality software. Purpose-built for teams that want to collaborate and create in those new paradigms.
Cloud Native Big Data Engineering and Automation
XLScout is a startup engaged in democratizing Innovation and connecting research and development with intellectual property (IP) departments across the world. The company is investing in developing proprietary algorithms, using Artificial Intelligence and Machine learning, to mimic the behaviour of an expert searcher. XLScout focus is on creating a sustainable and adaptive text mining framework that will provide natural Language Processing (NLP)based research outputs for IP in different domains. Various data points have been secured for our solution and they include patent data, litigation data, corporate data, reassignment data, examination data, and other patient-related data. XLScout hosts a data vault of about 130+ million patent documents which occupies approximately 8TB of storage. Document searching, in general ,is a cumbersome process, and specifically, searching the patent-related documents requires advanced strategies that a novice searcher might not be aware of. Therefore, this type of search requires extensive effort and time. The main objective of this project is to automate the patent and non-patent search effectively by allowing machines to understand users’ queries and thus, creating a sustainable and adaptive text mining framework that will provide NLP-based research outputs for IP search in different domains. Furthermore, this project aims to develop scalable solutions for XLScout’s data vault on which the company will run proprietary AI and ML models and generate high-value analytic solutions to help the customers make informed decisions.
Computer vision powered digital twin for tracking manual manufacturing processes
Over 70% of tasks in manufacturing are still manual; therefore, over 75% of the variation in manufacturing comes from human beings. Human errors were the primary driver behind $22.1 billion in vehicle recalls in 2016. Currently, when plant operators want to gain an understanding of their manual processes, they send out their highly paid industrial engineers to run time studies. These studies produce highly biased and inaccurate data that provides minimal value to manufacturing teams. This project aims to develop a computer vision powered digital twin prototype that is ready to test on the client’s site, which helps manufacturing plant operators gain unprecedented visibility into their manual production operations, allowing them to optimize their worker efficiency while maximizing productivity. This will be done by automated data generation using computer vision, conversion of raw data into useable information, visualization of information using standard Business Intelligence methodologies and lastly, prediction of future plant performance based on historical information, as well as information about other market drivers.
End-to-End Machine Learning Based Demand Forecasting
This project aims to implement an end-to-end (E2E) Artificial Intelligence (AI) / Machine Learning (ML) driven demand forecasting predictive model pipeline to increase process efficiencies, help improve forecast accuracy, forecast bias and forecast value added and unlock trapped capacity in the business ensuring improved service, optimum inventory, better production scheduling and better P&L forecasting. As participants in this consortium, Unilever, Larus and SOSCIP bring unique contributions that augment the benefits of the initiative from both scientific and business perspectives. More specifically, Unilever brings unique knowledge of business and operational landscape, curated and diverse data sets and analytics expertise in story building and turning data into insights, Larus brings unique expertise in AI/ML decision making and optimization systems and SOSCIP provides the required high performance computing platforms to run ML algorithms utilizing large data sets with optimal performance.
Workplace of the Future
Ambient factors such as light, heat, and music can significantly impact physical comfort in employees. Additionally, affective states and cognitive load of workplace occupants can affect their quality of life, general happiness, and cognitive capacity. This research aims to design and develop a smart workplace capable of detecting, analyzing, and acting on the environmental ambient factors, thus optimizing both physical comfort and happiness in employees in the workplace. To achieve this goal, this project targets the development of tools and methods using artificial intelligence, machine learning, and deep learning for ubiquitous monitoring, analysis, and interpretation employee happiness, cognitive load, and physical comfort based on information obtained from a variety of different wearable and non-contact sensors. Moreover, algorithms and models will be developed for aggregation of individual preferred and perceived ambient conditions, and cognitive/affective states. Finally, the obtained information will be used to autonomously and intelligently tune ambient and organizational parameters for optimum employee happiness, comfort, and performance. With the recent COVID-19 pandemic and the necessity for organizations to adopt to new workplace models, particular attention will be paid to distributed and remote working conditions. Machine learning will be used to understand affective and cognitive factors such as happiness, stress, and cognitive load from remote audio and video calls in addition to wearable sensors where possible.