TECH OFFERS

Due to social stigma surrounding Sexually Transmitted Diseases (STD), cost barriers, and lack of awareness, a large proportion of men suffering from male health issues avoid accessing in-person medical care, instead, many men turn to online sources such as search engines or discussion forums to seek help. This further compounds the problem as this failure to seek medical attention leads to delays in diagnosis and subsequent treatment, while misinformation and misguidance from the general public can be dangerous and life-threatening. This technology aims to address the gap between crowd-sourced diagnosis and primary healthcare practitioners through a fully anonymous, AI-driven mobile application screening tool that covers 90% of genital pathologies e.g. bumps and lesions, and certain disease and viruses, such as syphilis, herpes, Human Papillomavirus (HPV). This technology offer is Artificial Intelligence (AI) enabled mobile application that utilises a Convolutional Neural Network (ConvNet or CNN) deep learning algorithm equipped with custom-built network layers to screen for male health issues, e.g. genital warts, sexually transmitted diseases (STD) etc. Data augmentation techniques and synthetic data generation methods have been used to vary the dataset and increase and sample size for realistic model training and testing. The highly accurate model has an accuracy of 60-90% for most cases of STI/STDs. This technology offer comprises the following features: Vision-based AI algorithm which analyses a picture and detects/identifies STDs Built-in AI explainability - visualised as heatmaps that highlight the occurrence of recognised abnormalities/pathologies Synthetic data generation pipeline for data augmentation, data bias correction iOS and Android mobile applications with anonymised data acquisition Web-based integration via a suite of REST APIs is also available for developer use. To handle the issue of data privacy, the technology complies with Health Insurance Portability and Accountability Act (HIPAA) requirements and it does not associate any names, emails, or phone numbers with the collected images i.e. screening results are provided directly to the original source of the data. This technology can be applied for use in the following areas: Pre-screening prior to seeking medical attention at a primary care facility Self-serviced recovery monitoring (post-treatment) Preventive/predictive healthcare Telemedicine/remote health monitoring Additionally, the technology is also applicable for the detection and identification of certain visible dermatological conditions and oncological conditions. First in the world application of AI techniques to assist in male STD screening Highly accurate models  Low-latency screening - results returned within 3 seconds Built-in AI explainability presented through heatmaps which increases the confidence level of the end-user The technology owner is keen to work with universities, research institutes, medical institutions, clinics and digital healthcare providers to testbed the technology and provide additional data that will improve the accuracy of the CNN model.   Sexually Transmitted Disease, Convolutional Neural Network, Dermatology, Oncology Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence, Healthcare, Telehealth, Medical Software & Imaging

The latest Singapore National Population Health Survey has reported a concerning diabetes trend. From 2019-2020, 9.5% of the adults had diabetes, slightly dropping to 8.5% from 2021-2022. About 1 in 12 (8.5%) of residents aged 18 to 74 were diagnosed, with an age-standardised prevalence of 6.8% after accounting for population ageing. Among the diabetes patients, close to 1 in every 5 (18.8%) had undiagnosed diabetes, and 61.3% did not meet glucose control targets. Prediabetes is also prevalent, with 35% progressing to type 2 diabetes within eight years without lifestyle changes. Untreated Type 2 diabetes can lead to severe health issues. Tackling this challenge requires a holistic approach, focusing on awareness, early diagnosis, and lifestyle adjustments for diabetes and prediabetes. Recognising the need for innovation to address this, the technology owner develops a cost-effective and non-invasive AI-powered solution, Blood Glucose Evaluation And Monitoring (BGEM), that detects glucose dysregulation in individuals to monitor and evaluate diabetic risks. BGEM allows users to track their blood glucose levels regularly, identify any adverse trends and patterns, and adopt early intervention and lifestyle changes to prevent or delay the onset of diabetes. Clinically validated in 2022, with a research paper published in October 2023, the technology is open for licensing to senior care/home care providers, telehealth platforms, health wearables companies, and more. The BGEM technology is an end-to-end managed AI platform that leverages Photoplethysmography (PPG) enabled wearable sensors to monitor various heart rate variability (HRV) features associated with blood glucose fluctuation. The solution comprises the following features: Optimised and validated AI algorithm Mobile Demo App Including UI/UX design guideline User-friendly visualisations SaaS Scalability Security API Integration The BGEM technology offers a cost-effective, non-invasive approach to predicting an individual's diabetes risk. The applications include: Population Health Perspective: The technology leverages the high growth rate of smart wearables and hearables, presenting an opportunity to identify undiagnosed diabetes individuals within the population. Preventive Health Monitoring: With the ability to monitor blood glucose changes regularly at minimal cost, the technology empowers high-risk users to adopt a healthier lifestyle and, therefore, prevent or delay the onset of diabetes. Diabetes around the world in 2021: 537 million adults (20-79 years) are living with diabetes, 1 in 10. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is responsible for 6.7 million deaths in 2021 - 1 every 5 seconds. Diabetes caused at least USD 966 billion dollars in health expenditure – a 316% increase over the last 15 years. 541 million adults have Impaired Glucose Tolerance (IGT), which places them at high risk of type 2 diabetes. Overview of the wearable technology market: The market is projected to expand at a compound annual growth rate (CAGR) of approximately 12.5% between 2023 to 2030. Estimated to be worth USD 55.5 billion in 2022, with a projected revenue of USD 142.4 billion by 2030. Current blood glucose monitoring technologies either require finger pricking for blood extraction or the insertion of sensors into the skin and discomfort through wearing patches for extended periods. Instead, the technology uses external sensors and algorithms to detect and predict diabetes risk. No object needs to be inserted into the user's body or continuously worn throughout the day, resulting in minimal pain and discomfort. Additionally, the only equipment required for testing is the wearable device. No additional disposable equipment needles or test strips are needed, which makes blood glucose monitoring much more convenient and cost-effective than other "State-of-the-Art" solutions. The Unique Value Proposition of BGEM include: Market-ready: It is a market-ready non-invasive diabetes risk detection and prediction AI solution that leverages consumer-grade wearables to detect blood glucose dysregulation. Performance: Demonstrates outstanding prediction and detection capabilities. Cloud-based: Operates on a cloud-based platform for seamless integration. Third-party compatibility: Easily implemented with third-party devices and apps. Sustainability: Reduction in bio-medical waste such as needles, test strips etc User-friendly: Non-invasive, convenient and allows frequent measurement. Non-invasive measurement, blood glucose, diabetes mellitus, preventive healthcare, AI, ML, Wearables, PPG, Blood Glucose Monitoring, Diabetes Monitoring, Diabetes Evaluation, Non-Invasive Diabetes Monitoring, Diabetic Risk Assessment Infocomm, Artificial Intelligence, Healthcare, Diagnostics

In Singapore, more than 30,000kg of okara are generated from soya milk and tofu production. Due to the high amount of insoluble dietary fiber and a unique, poignant smell of okara, it is often discarded as a waste product. Despite okara's low palatability, it is rich in nutrients. Therefore, the technology owner has developed a cost-effective formulation to include okara in feed for abalone. The formulation can potentially be adapted and customised for other aquatic species. The technology owner is seeking potential partners to license and commercialise the technology. The technology allows for an alternative nutrient source for animal feed allowing for the sustainability of food supply and reduction of food waste. The formulation consists of a cost-effective plant-based functional ingredient, lowering the costs of feed for aquaculture farms. The nutritional composition can be tailored for different species to increase growth rates and survivability. Okara is used as a cost-effective feed for high-value abalone, a commonly cultured species of mollusc. Okara-based feed results in the beautiful purple colouration of the shell and increased growth and survivability of abalone. In comparison, the okara-based feed costs ~30% less than commercial feed used in the industry. There is potential for okara to be included in feed for other aquatic species such as shrimp and fish. The success of this method will valorise okara, transferring them into a nutrient-dense aquatic feed while promoting a more environmentally sustainable food production chain.       okara, aquatic feed Foods, Ingredients

Vision-based Artificial Intelligence (AI) models require substantial time to train, fine-tune and deploy in production. After production, this process is still required when performance degrades and re-training on a new dataset becomes necessary; this maintenance process exists throughout the model's lifetime to ensure optimal performance. Rather than embarking on the time-consuming and painful process of collecting/acquiring data to train and tune the AI model, many organisations have turned to the use of pre-trained models to accelerate the AI model development process. This technology consists of a suite of pre-trained models that are intended to detect food, human behaviours, facial features and count people. These AI models are operable on video footage and static images obtained from cameras. Models are tuned and trained on various use-cases and are accessible via API calls or embedded within software as a Software Development Kit (SDK) library. These models can be deployed as AI as a Service on Microservices platform providing customer data protection with blockchain technology. With customer protection enhanced with blockchain technology, AI Model performance can further be enhanced to meet customer requirement.   The technology consists of a suite of pre-trained AI models that provide high accuracy (over 80%) and can be further customised to improve accuracy and adapted to different use-case scenarios. Models can be integrated in the following ways:  Installed library package embedded within software on-device/on-premise HTTP-based Application Programming Interface (API) calls with video/image data to cloud-installed library package The following are the features for various AI models: Abnormal Behaviour Recognition Continuous monitoring and detection of abnormal human behaviours e.g. fighting, loitering Event Detection Recognises a variety of subjects and events e.g. sports day, graduation, wedding, festival, Christmas, from video footage Optimised for lightweight compute capability (Intel OpenVino) Food (Fresh and Packaged) Recognition Real-time detection of fresh and packaged foods Detects abnormal fresh food or defective packaged food Classifies food types e.g. lotus, spinach, cucumber, radish etc. Optimised for low compute capability Privacy-Preserving Person Recognition Privacy preserved people detection, counting and human activity recognition Images are blurred to preserve private information that can lead to personal identification (irreversible) Optimised for lightweight edge computing Free (Empty) Space Recognition Semantic segmentation to identify empty spaces Customisable for any free-space detection scenario High accuracy in night scenes Safety Monitoring Object detection with prohibited and allowed zones (e.g. person or forklift) Detects and identifies safety risks associated with safety distances Enables audible alarm systems of abnormal situations Wellbeing and Safety Detection Automatically detects and classifies nudity images from images  Enables alerts to be delivered to parent/caregiver's device Customisable to detect new categories of inappropriate content This technology offer comprises a suite of AI models for the following applications: Abnormal Behaviour Recognition Public areas or areas where social order needs to be maintained e.g. food & beverage, entertainment establishments Event Detection Automatic creation and/or organisation of media content i.e. photo classification Automated adjustment of device hardware parameters e.g. audio, colour, brightness when displaying specific types of content e.g. sports Food (Fresh and Packaged) Recognition Food stock level detection, food inventory management Automatic detection of fresh/packaged goods within a constrained area Privacy-Preserving Person Recognition Privacy protection of visual information, in high traffic areas, without deterioration of video quality Free (Empty) Space Recognition Vehicle position localisation on roads Navigation (free-space localisation) in partial/fully self-driving automotive vehicles Identification of free storage spaces in the logistics industry Safety Monitoring Automated compliance checks Workplace safety analysis and tracking Wellbeing and Safety Detection  Parental control in browsers, smartphones or other image storage devices e.g. Network Attached Storage (NAS), Solid State Drives (SSD) AI Models were rigorously tested in the fields of different scenarios. The microservice platform where AI Model ingest the visual data streams offers a secure customer data protection and privacy using blockchain technology. Making this Microservice platform capable of tracking customer’s data usage and thus ensure privacy when AI model operating on the platform are simultaneously improved using unique customer data captured on customer’s premise. Accelerate AI development - eliminate the need for dataset creation, annotation, tuning and testing Customisable AI models - fine-tuned to environment and condition Operational support to continuously improve AI accuracy from newly collected data   event detection, abnormal human behaviour recognition, safety monitoring, food package detection, food freshness, nudity detection, empty space Infocomm, Video/Image Analysis & Computer Vision, Video/Image Processing, Artificial Intelligence

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

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