TECH OFFERS

In dental treatment, clear aligners are successful alternatives to the conventional fixed appliances or braces in achieving physiological orthodontic tooth movement (OTM). However, its control of dental movement is not absolute and attachments, usually made of tooth-coloured dental composite resins, are inserted at precise locations to allow the aligners to grip the teeth and guide them into their new locations. This procedure takes up clinical time and increases the cost to the clinicians. Moreover, these attachments protrude off from the surface of the teeth making the appliance obviously visible and may also potentially increase patient’s discomfort as they scratch the insides of the patient’s mouth. Upon completion of treatment, these attachments need to be removed and the enamel surfaces of the teeth may potentially be scratched or damaged. This invention introduces a Biogel material that will be applied and act as an interface layer between the clear aligner and the clinical crowns of the teeth. As a base and catalyst, the Biogel, a 2-part mixture sets into a semi-solid form after the clear aligner is inserted onto the teeth. The Biogel is a thin interface that engages the undercuts of the teeth, grips the dentition to enhance the transfer of active orthodontic forces from the clear aligners onto the teeth without the need for placement of attachments. The Biogel does not adhere to the teeth but rather the internal surfaces of the clear aligners and can be easily peeled off clean and replaced as required. The Biogel is a 2-part mixture that chemically sets within minutes upon contact. It provides sufficient curing time for the user to insert the gel onto the internal surfaces of the clear aligners. It is packaged into a dispenser with a partition that separates the 2-part mixture. A mixing tip allows the correct ratio and homogenous mix. Upon insertion of the clear aligners carrying the Biogel, a cotton bud or tissue may be used to clean off the excess that extrudes beyond the edges of the clear aligners. The final setting is achieved after a few minutes and the clear aligner is now activated with the Biogel attachments. Typically, clear aligners are worn full time except for brushing and eating, at least 20 hours a day and changed every 1- 2 week.  The Biogel can withstand normal biting forces within the clear aligners, and it does not absorb any oral fluids, food or drinks when consumed. Although the end users are orthodontic patients who are prescribed clear aligners as their modality of orthodontic treatment, the Biogel is prescribed by the dental clinicians.. The Biogel may be purchased by the clinician to be resold to the patient or may be factored into the cost of the overall treatment fee. The Biogel may also be packaged to be sold directly to the consumer whereby the dental professional provides a prescription to direct the patient to acquire it by themselves. This Biogel is compatible with any clear aligner orthodontic systems in the market. At least 80% of dentists and orthodontists worldwide provide orthodontic treatment with some form or brand of clear aligners, and this number is still growing. Attachments are required in most of these clear aligner systems, and they have various disadvantages in terms of aesthetics, costs, time consumption and potential damage to the enamel upon polishing and/or removal. This Biogel is compatible with any clear aligner orthodontic systems currently in the market and negates the need to have attachments placed. This potentially levels the playing field between leading brands of clear aligners and other newer aligner systems. Other active ingredients may also be incorporated into the Biogel. These may include fluoride releasing, anti-bacteria, and dental whitening properties that promotes oral health, reduces dental decay, and whitens teeth while undergoing orthodontic treatment. Different flavouring may also be added. According to Fortune Business InsightsTM , the global clear aligners market size was USD 2.41 billion in 2020. The market is projected to grow from $2.85 billion in 2021 to $10.04 billion in 2028. About 60-70% of the global population suffers from misaligned teeth. In North America, 40% of children are estimated to suffer from malocclusion, and more than three out of five teenagers have severe tooth displacement. The growing demand for aesthetics and surge in demand for clear aligners in orthodontic treatment is likely to fuel the clear aligners market growth. Significant technology advancement, improvement in per capita spending, the betterment of economic indicators, and increased penetration by key companies in developing regions are contributing to the markets growth rate. Developed countries are also seeing huge growth in adults seeking orthodontic treatment, which is mainly driven by the availability of products such as clear aligners. The British Orthodontic Society (BOS) reports that 75% of their members have seen an increase in patients opting for clear aligner treatment. In 2018, it was estimated that 60% of the worldwide population suffers from problems associated with misalignment of teeth, and around 300 million people could benefit from straightening their teeth. With the clear aligner market showing rapid signs of growth now and in the immediate future, this Biogel, which is compatible to any clear aligner systems in the market will see its potential growth and uptake mirrored. Current leading clear aligner brands require the placement of attachments on the teeth for it to work effectively. The Biogel interface: Improves the aligner fit but engaging the natural undercuts of the dentition Removes or reduces the need to have attachments placed. This improves aesthetics, reduces clinical chair time and clinical costs, diminishes pain from scratching of the insides of the mouth, and improves the ease of insertion and removal of the clear aligners. The reduced need for “attachment” placement and removal also reduces any potential damage to the enamel surfaces of the dentition. May contain fluoride releasing and anti-bacterial properties to prevent and reduce the incidences of dental decay while undergoing orthodontic treatment. May contain teeth whitening properties to safely whiten teeth while undergoing orthodontic treatment. Patents granted in US and China, and patent pending in Australia and EU countries. Clear aligners, physiologic tooth movement, biogel, attachments Materials, Bio Materials

To date, the current primary feedstock for plastic production is oil, which accounts for more than 850 million metric tons of greenhouse gases emissions per year. Hence, there has been an increasing demand for green plastics, which are plastic materials produced from renewable sources. This technology offer is a synthesis method of green plastics from carbon dioxide (CO2) and renewable feedstock. The green plastics produced are non-isocyanate polyurethanes (NIPUs) and can be actively tuned to be anionic, cationic, oil-soluble and cross-linkable which enables a wide range of applications. These NIPUs are non-skin irritant, have high bio-content and can possibly be made to be bio-degradable. This technology owner is looking for partners in various industries such as personal and consumer care, coatings and lubricant additives (to name a few) for further co-development of the solution. The technology owner is keen to license this technology as well. This technology offer is a synthesis process of making green plastics from CO2 and other renewable feedstock. The technical features & specifications are as follows: Non-isocyanate polyurethanes (NIPUs) Mild synthesis conditions CO2 and renewable feedstock Tuneable properties (film forming, adhesion, emulsion stabilisation, anti-redeposition, reversible cross-linking, wax inhibition and pour point depression) Non-skin irritant High bio-content (possible to be bio-degradable) This technology is applicable for those looking for green plastics. The potential applications are: Coatings (self-healable coatings and adhesives) Consumer and personal care products (oil-based film formers, pigment dispersions, wax inhibitors) Lubricant additives In the Industry there is a trend towards the avoidance of toxic chemicals such as isocyanates and phosgene in the production processes. Incorporation of renewable feedstock, CO2 and having higher bio-content in the final product and tuneable functionality will be added advantages to capture market opportunities. Use CO2 and renewable feedstocks (decarbonisation and sustainability) Mild manufacturing conditions Higher bio-content for possible biodegradation Superior and tuneable properties Renewable feedstock, Green plastics, CO2, Polyurethanes, Bio-derived Materials, Plastics & Elastomers, Chemicals, Polymers, Sustainability, Low Carbon Economy

Most existing training applications offer good programmes for guiding users to achieve individual fitness goals, some even come with guided video workouts led by professional trainers. However, such applications lack or have limited capability to assess whether the correct posture is maintained during exercise - poor posture can reduce exercise effectiveness and may even cause injury, e.g. arched back during push-ups. This solution is a synergistic combination of video/image processing, human pose recognition, and machine learning technologies to deliver a solution that addresses the twin challenges of accurate count and correct execution of exercises in an automated manner, without having to wear any additional hardware/sensors. The software-only solution is able to advise users on the correct execution of repetitive movement sequences, e.g. sit-ups, and push-ups, and is deployable on a wide range of affordable camera-enabled hardware devices such as mobile phones, tablets, and laptops, and it can be easily integrated into existing applications to enhance functionality. It is applicable to the sports and healthcare industry to help users perform exercises correctly and effectively in an unencumbered manner. This software-only solution is able to identify the correct or incorrect execution of repetitive movement sequences using camera inputs from devices such as mobile phones, tablets, or laptops. The solution can be deployed on any Python and JavaScript-capable platform; providing flexibility for deployment across a range of devices and operating systems, including web browsers. Video/Image Processing Pre-processing of video frames as inputs for Human Pose Estimation and Machine Learning Built-in algorithm used to assess real-time movement direction, i.e., up, down, right and left, thereby providing inputs for a more accurate count Human Pose Estimation Based on BlazePose, OpenCV and TensorFlow for real-time tracking of up to 32 human pose keypoints, e.g. shoulder, wrist, hip, knee, ankle, etc Measurement of angles between pose keypoints as input parameters to determine correct posture, e.g. is the user's back straight, are the knees bent Enhances original image from camera with accentuated pose keypoints and connections between keypoints, i.e., stickman diagram, used as input AI classification to determine correct posture. Machine Learning Train and test AI models using human subject video footage for correct and automatic classification of exercises AI models classify exercise based on angles and distance of pose landmarks and enhanced images with accentuated pose landmarks and connections between landmarks   Primarily for fitness and healthcare (rehabilitation) industries - can also be applied for any activity that requires assessment of posture Repetitive exercise-specific counting e.g. push-up, sit-up, additional exercises can be included, requiring specific customisation Posture assessment and analysis of the movement of human subjects - personalised calibration to initial posture can be explored  Enables intelligent coaching functionality in fitness/healthcare applications to promote healthy or active living Enables real-time posture assessment and monitoring Does not require additional body-worn sensors or wearables; simply requires a camera-enabled mobile device Provides value added-service to an existing healthcare/fitness application to help end-users perform exercises correctly and effectively in an unencumbered manner  Provides remote assessment of exercise without any over-reliance on the expertise of a professional coach The technology partner is looking for test-bedding opportunities in the fitness and rehabilitation industries, which would involve studying specific exercises individually. Additionally, the technology owner is looking for co-development with companies that have adjacent use for this posture assessment software, e.g. heavy load lifting, combining the software with additional body-worn sensors to detect uneven weight distribution on the workman's back. Human Pose Estimation, OpenCV, Tensorflow, Dense Optical Flow, Sports and Leisure, Rehabiliation Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence, Personal Care, Wellness & Spa, Data Processing

While interest and demand for blockchain-related technologies continue to gain popularity and spurs the exponential growth in adoption of blockchain in areas such as payment, documents and digital identities, not just in finance, but in industries such as logistics, supply chain as well. Many emerging areas that rely on blockchain as a core technology lack the manpower needed to sustain budding development, this lack of technical skillset required for blockchain development is the primary hurdle to successful blockchain application development - less than 1% of the tech workforce is skilled or competent in blockchain-related development. The characteristic of blockchain technology, which enables a permanent record of digital information such that it cannot be modified by any single entity renders it well suited as a digital ledger of online transactions. As such, blockchain is a core technology for many emerging areas such as Decentralised Finance (DeFi) and Decentralised Autonomous Organisation (DAO). This technology offer consists of a platform tool and a set of zero-configuration REST APIs that abstract away the complexity of blockchain technology and enables any developer to easily build blockchain-based applications or integrate blockchain functionality into their existing systems. Intended as a low-code platform, it addresses the skills gaps traditionally required for blockchain development and deployment and allows companies to realise their blockchain ideas, enhance business operations and expand solution offerings.   The core of this technology is a chain-agnostic, node-wrapping, enterprise-grade middleware that includes a suite of documented REST APIs, developer tools, and frameworks required for the development of blockchain-based applications and their integration into a company’s existing systems. It includes the following features: Supports both permissioned (private) and permissionless (public) blockchain networks Orchestration between the nodes and synchronisation are handled by the platform Flexible Scalability - enabling scale in/out, scale up/down Integrates with a decentralised peer-to-peer file storage system to ensure data is stored off-chain (InterPlanetary File System, IPFS) Integrated development environment (IDE) for custom code port-in from an external repository Library of ready-to-use smart contracts (fungible tokens, non-fungible tokens) Selectable blockchain protocol (Ethereum, Ethereum Mainnet, Corda, Hyperledger Fabric) Decentralised application can be deployed on any cloud provider (Google Cloud Platform, Amazon Web Services and Microsoft Azure) This technology allows for the development and deployment of blockchain use cases for any industry. The following areas have benefited from this technology, and are readily available as working smart contracts for use on the platform: Food and vaccine safety/quality/authenticity traceability Circular economy, digital asset tokenization Green financing/loans, green bonds, carbon credit tokenization Any use-case which is able to leverage on blockchain's inherent properties of immutable traceability and auditable trails will benefit from this technology, including the following benefits: Low-code platform simplifies blockchain application development, deployment and integration, especially for businesses that want to use blockchain but don't necessarily have the know-how Automatically encompasses the best practices for blockchain development  Eliminates the repetitive tasks and overhead required to build robust blockchain applications Reduces the time from use-case ideation, to development and PoC deployment in just 1~2 months - speeding up the testing of an actual use case allows businesses to quickly gauge the blockchain use-case’s impact and make data-driven decisions Industry-agnostic - build any use-case with a library of ready-to-use smart contract templates, enabling rapid development, configuration and deployment in a single platform Applicable for asset tokenization, supply chain traceability, trade finance, etc. that would benefit from immutable blockchains and auditable trails including, but not limited to sustainability initiatives blockchain, low code, traceability, esg, sustainability Infocomm, Blockchain & Other Distributed Ledgers

The current clean meat technologies grow lab meat with conventional 2D cell culture. However, the conventional cell culture technique has an overall low yield of cells, as the cells are restricted to growth on surface areas.  A new 3D scaffolding method has been developed to overcome this problem with the use of microcarrier beads that provide cells with additional surface area to attach onto and proliferate. The microcarrier beads are suspended in the cell culture thus maximizing the 3D volume of the cell culture, leading to an increased yield. A microcarrier type has been identified to yield the highest number of porcine cells. The conditions of the cell culturing process have been optimised to improve the cell viability in a 3D environment Companies interested in cell-cultured meat development could consider using this method to grow cell-cultured meat at a larger scale with a potentially lower cost of production. The technology developer is seeking companies that are keen to scale up lab-grown meat applications.  The technology owner uses microcarrier beads, which are small (approx. 75 um diameter) polymer beads. They collectively provide a significantly larger surface area than standard tissue culture flasks for cell growth. Typically used in the pharmaceutical industry to scale up cells for protein expression, the use of microcarrier beads for the scale-up of cell-cultured meat is a relatively new application.   Some key features of the technology: Increases cell yield by 7-fold from lab-scale Microcarrier beads hold their shape throughout Made up of inert and food grade material  Different cell types require specific types of microcarrier beads to optimize growth The method is suitable for:   Clean meat industry / Cultured meat companies Pharmaceutical companies that apply such technologies towards protein manufacturing, drug discovery or antibody production. Other potential clean meat applications using other cell line sources. With the increasing demand for meat production for population consumption, there is a need to look at other more sustainable meat source. Lab grown ‘clean meat’ can be an alternative to livestock farming. The development of ‘clean-meat’ can help to reduce carbon footprint, improve animal welfare and prevent foodborne illnesses. The cultured meat market size was valued at $1.6 million in 2021 and is projected to reach $27 billion by 20301.   1Cultured meat market size, share - global industry report, 2022-2030. Allied Market Research. April 2021.   Increased yield of viable cells as compared to 2D cell culture Scalable Up to 7x lower in cost of production at a larger scale Pathogen-free source of meat produced High reproducibility Low usage of microcarrier beads: 10g microcarriers/20 litres of cell culture clean meat, microcarrier, dynamic culture, cultured, meat, scaffolding, 3d, scaffold Chemicals, Polymers, Foods, Ingredients, Processes

This technology offer presents an autonomous materials handling system. The technology could reduce manual labor requirements and increase working efficiency. The solution consists of a Lidar Elevator Stand (LES) system, which will trigger the autonomous actuators (e.g., trolley puller, tray return robots) via the robot command center whenever no goods (e.g., trolley, tray, and stock) is detected in the designated area. Currently, the technology has been demonstrated in autonomous trolley return solutions. Generally, trolley replenishment requires deploying manual labour to monitor the available quantity at the trolley bay and replenishing it by physically operating an electric trolley puller to transport the new stack of trolleys. Therefore, the system was developed to solve the problem by triggering an autonomous trolley puller to replenish the new stack of trolleys whenever the trolley quantity is depleting. The system can be further customized and repositioned based on clients’ requirements. The  Machine-to-Machine communication protocol for Lidar Elevator Stand system (Client), remote autonomous trolley puller (Client) and Robot Command Centre (Server). The 2D Lidar sensor has a field-of-view in Lidar Elevator Stand (LES) system with detection range from 0.05m to 10m, and a horizontal aperture angle of 200 degree, which is suitable for deployment in the application at airport Ability to avoid false triggering requests for trolleys replenishment from LES System. LES system’s operations and processing are able to work under different lighting conditions. Replenishment of trolleys can be customised for multiple location points. Material handling with delivery in airports, supermarkets, warehouses, logistics, hotels and factories. Improve and simplify work operations. Free up existing manpower to take up new higher value-added tasks. Seamless communication between monitoring and replenishment of trolley. Safe mobility replenishment of trolleys, trolleys, material handling, autonomous trolley puller, trolley puller, trolley Infocomm, Networks & Communications, Mobility, Robotics & Automation, Logistics, Inventory Management, Transportation

The proliferation of e-commerce, ride-hailing and food-delivery services have fueled the need for more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA) which relies on route distance that is calculated between the origin and the desired destination. It only considers the duration from pick up to drop off, and does not consider the additional time needed for preparing and offloading the goods. This technology offer is a Machine Learning (ML) model that is able to calculate the stop duration (job completion duration), which together with the ETA, provides the Estimated Time of Completion (ETC). This ML model is for Singapore use only. For the Machine Learning (ML) model to calculate the stop duration, users will need to key in the input parameters such as building name, block number, road name, postal code, day of week, day of month and time. The system will predict the stop duration (job completion duration) in minutes. ML model enables prediction of ETC based on historical data and a small set of input parameters Highly correlated with ETA which can be easily obtained from API services such as Google or Onemap Takes in consideration of temporal data including hours of the day and day of the week. Can be integrated with existing web/mobile based solutions. Apart from being a productive tool for route planning systems, the software can also be used in various situations such as: Customer Services/Call Centres Fleet Management  Loading Bay Assignment This model can be used to improve the existing route planning systems as it provides additional job completion duration prediction on top of estimated ETA.  Low cost - the model is easy to implement and incorporate into other applications. Simple to use - only a small set of input parameters are required. Able to predict the stop duration (job completion duration) with reasonable accuracy. The technology owner is keen to out-license this technology to collaborators in the field of last mile-logistics, or collaborators who are providers of software for last-mile logistics. This ML model is for Singapore use only. Last Mile Delivery, Estimated Time of Completion (ETC) Infocomm, Artificial Intelligence, Logistics, Delivery & Distribution, Value-Added Services

There are 30,000 occupational drivers in Singapore, out of which 13,500 are 45 years old and above. The risk of acquiring cardiovascular disease increases with age and is potentially exacerbated by low physical activity and high emotional stress levels, which are two typical characteristics of occupational drivers arising from their work environment. Low level of physical activity and high stress levels have been shown to have significant relationship with heart rate variability, one of the indicators of cardiovascular disease. This technology is developed to help drivers to monitor their stress level, provide them with instantaneous feedback and the necessary alerts for a timely intervention. This technology offer presents a cross-platform AI system that estimates the stress levels continuously in real time, and can be easily integrated with commercially available photoplethysmography (PPG) wearables, e.g., a PPG wristwatch. In addition, this technology can be adapted for the monitoring of workplace stress with the aim of improving overall mental well-being. The current practice of using electrocardiogram (ECG) to measure the electrical activities of the heart is mainly found in the clinical settings and not easily accessible by the general population due to high costs. The need for an accurate placement of ECG electrodes does not allow the continuous monitoring of the heart condition in a non-clinical setting. It is not easy for individuals to continuously monitor their stress levels at the workplace in daily life, especially for drivers. This technology offer enables continuous, real-time monitoring and is not limited to clinical settings. It is cost-effective without the need to purchase medical grade ECG devices. The solution can be easily customised and adapted to existing and future PPG wearables. It is portable and feasible for all-day monitoring of stress levels and heart conditions in diverse workplace environments. Essentially, it is an algorithm that extracts proprietary features to predict stress levels, with an unique approach, from heart beat signals customised to a given context in the workplace. This facilitates the creation of contextualised stress profiles for specified workplace situations ranging from physical combat conditions to air-conditioned office sedentary conditions. The technology owner is interested to out-license this technology offer. Potential application areas are: Public or private hospitals, clinics, community care centers. Individual drivers and any workers who might be exposed to workplace related stress. Sports sectors including sports training, research and competition. Medical devices, equipment suppliers and manufacturers. Potential products: Existing or future wearables with PPG, e.g., wristwatch, headset, t-shirt, etc. Hardware and platform independent, easy to be incorporated into existing and future wearables. Portable and feasible for 24/7 stress monitoring. Affordable and cost-saving without the need to purchase medical grade ECG devices Real-time monitoring and timely feedback customised for users stress monitoring, ai algorithm, ai system, heart monitoring, workplace stress, wearable technology Infocomm, Artificial Intelligence, Internet of Things, Wireless Technology, Sustainability, Sustainable Living

One use of AI, including deep learning, is in prediction tasks, such as image scene understanding and medical image diagnosis. As deep learning models are complex, heatmaps are often used to help explain the AI’s prediction by highlighting pixels that were salient to the prediction. While existing heatmaps are effective on clean images, real-world images are frequently degraded or ‘biased’-such as camera blur or colour distortion under low light. Images may also be deliberately blurred for privacy reasons. As the level of image clarity decreases, the performance of the heatmaps decreases. These heatmap explanations of degraded images therefore deviate from both reality and user expectations.  This novel technology-Debiased-CAM-describes a method of training a convolutional neural network (CNN) to produce accurate and relatable heatmaps for degraded images. By pinpointing relevant targets on the images that align with user expectations, Debiased-CAMs increase transparency and user trust in the AI’s predictions. Debiased-CAMs are effective in helping users identify relevant targets even on images affected by different clarity levels and multiple issues such as camera blur, poor lighting conditions and colour distortion. The AI’s prediction also becomes more accurate. As the model is trained using self-supervised learning, no additional data is needed to train it.  The training for Debiased-CAM is generalisable, and thus applicable to other types of degraded or corrupted data and other prediction tasks such as image captioning and human activity recognition. Used to train a convolutional neural network (CNN) to produce accurate and relatable heatmaps for degraded images. By pinpointing relevant targets on the images that align with user expectations, Debiased-CAMs increase transparency and user trust in the AI’s predictions. It also increases the ability of meeting regulatory standards to deploy CNN models in the following applications, where explainable AI is required. Healthcare, eg. Radiology Autonomous Vehicles   Produces accurate, robust and interpretable heatmaps for degraded images Works on images with multiple degradation levels and types such as blurring and improper white balance Agnostic to degradation level, so that enhancement can be applied even when the level is unknown Perceived by users to be more truthful and helpful as compared to current heatmaps distorted due to image degradation Method of training can be applied to other degradation types and prediction tasks Explainable AI Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence