Discover new technologies by our partners

Bots are supposed to understand what humans want, guiding them to their desired outcome. However open-text chatbots tend to do poorly because they are unable to detect intents (what the customers are actually asking) accurately. Our technology puts intelligence into our clients' chat or voice bots, to know what their end-users want.  Our proprietary rules-based models predict user intentions by (1) automatically analysing human-bot conversations and (2) using this to train bots faster, better and in a more cost-efficient manner.  Using our platform takes 20x less human intervention. Consequently, bots become smarter much faster than if clients relied on manual training, typically reducing errors by 68% in as little as 3 weeks. This brings the current  average chatbot accuracy rates from 30-40% to 90+% within several training iterations. The platform also provides tools to help clients identify customer trends and their emotions towards the bot. We are looking for corporate clients and bot developers who want to differentiate themselves in the marketplace by offering chatbots that actually meet their customer metrics using our proprietary training platform.

Plastic is a very versatile material as it is lightweight, cheap, and durable. However, plastics take hundreds of years to decompose in nature, leading to the problem of plastic pollution. Furthermore, most commercially available biodegradable plastics are unable to be used as complete replacements for single-use plastics as they have weaker mechanical properties such as strength and temperature resistance. Therefore, a new biodegradable alternative is necessary to mitigate the serious environmental pollution problem brought about by conventional single-use plastics. This technology is a renewable, bio-based, and degradable resin that meets both international industrial and home composting standards. Produced through a propietary compounding process, the material can meet the standards for food contact grade. The polymer also has excellent fluidity, toughness and good moulding performance that meet the production requirements in injection moulding, extrusion, thermoforming, and other moulding processes. It does not require strict conditions to degrade and will achieve 100% self-degradation when placed in a natural environment together with ordinary waste, leaving no harmful substances. This material aims to be a viable replacement to replace conventional plastics in disposable tableware, food packaging, and sanitary ware.

In conventional electrolysis, water molecules first dissociate into intermediate ions, namely, negative hydroxyl ions (OH-), positive hydrogen ions (H+) and positive hydronium ions (H3O+), before further decomposition into oxygen and hydrogen. This process of generating hydrogen is inefficient due to the high probability of hydroxyl ions recombining with either hydrogen ions or hydronium ions to form back water molecules. The technology described herein is related to a hybridised process which enhances hydrogen production rate of a conventional electrolysis system through combining the hybridised process with photocatalytic decomposition reaction. By combining the hybridised process with photo-catalytic decomposition, the probability of intermediate radical/ion recombination is reduced. This results in an increase in hydrogen and oxygen production of up to 25%. A prototype has been developed to demonstrate the feasibility and effectiveness of producing hydrogen and oxygen based on the hybrid photocatalyst-electrolysis method. With a strong knowledge in optimisation of the operating parameters in a hybrid photocatalyst-electrolysis reaction, the technology owner would like to seek partnership from the industry to commercialise the technology.   

A tactile sensing glove embedded with multiple tactile sensors can measure each sensing point’s applied pressure and its corresponding position. Its pressure sensing layer is constructed with multiple rows and columns of piezoresistive sensing points. The piezoresistive sensor’s electrical resistance decreases when a pressure is applied on it. The tactile sensing glove can be put on a robotic gripper or prosthetic device to receive haptic feedback during manipulation tasks.

This technology offer is a small, secure, low power and cost-effective hardware module with a dedicated convolutional neural network (CNN) accelerator. The hardware accelerator supports many pre-trained artificial intelligence (AI) models, allowing edge processing at the IoT sensor node itself. As such, real time AI inference can be done without the need for high bandwidth communications to the cloud. Simple API calls through one of many communication interfaces makes adding AI capability to IoT nodes an easy task.

The production of shrimp roe is dependent on the age, weight, and size of the adult females. There is a concurrent specific nutritional requirement prior to roe development. Consequently, roe production is random and inconsistent within any shrimp batches. Traditional methods to increase spawning frequency and egg production involve the unsustainable method of eyestalk ablation whereby one eyestalk of the shrimps is removed to stimulate yolk formation (vitellogenesis). In addition to the anatomical stress on the shrimps, eyestalk ablation can only be performed on shrimp with a minimum weight of 70 - 90 g which requires 12 months to grow out. The proprietary epigenetic activated fermentation (EpAF) technology stimulates vitellogenesis naturally in female shrimps within 4 - 5 months, reaching 30 - 35 g. This greatly increases the quality and quantity of shrimp egg production. In addition, the taste of shrimp eggs can be altered subtly to suit the taste of the target market. The technology provider is looking for joint venture partners or licensing the technology to interested parties from the aquaculture and/or food industry.

Artificial Intelligence (AI) is increasingly deployed in a wide range of industries, including healthcare, retail and manufacturing. The global market for AI is expected to grow from US$117 million in 2019 to US$296 million by 2024, at a CAGR of 20.5%. However, the difficulty of understanding AI is a significant barrier. To address this, there has been growing interest and demand for explainable AI, that allows AI decisions to be both understandable and interpretable by humans. 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.

Some challenges faced during the fabrication of the proton exchange membrane in fuel cells include high costs incurred from multi-step metal ink production, potential overloading of metal and restricted substrate choices. The technology comprises of proprietary platinum-based catalyst ink formulations that allows for a simplified process to fabricate proton exchange membrane fuel cells. During membrane production, metal particles can be grown directly on the substrate and cured with low temperature cold plasma (<70°C). This technology can minimize the platinum loading over a wide range of substrates, thereby reducing the cost of producing fuel cell membranes.

A common issue with materials used in medical applications are their poor surface properties that results in a variety of problems including poor comfort and risk of infection. While the use of biocompatible materials seems to address the problems related to the surface properties, these materials exhibit a certain degree of bio-inertness that limits their use and performance. This technology aims to resolve such problems associated with materials in medical devices by means of a simple and scalable coating. The technology is a patented, bio-inspired coating that can create high hydrophilic surface on a variety of material surfaces. This coating offers full control over wetting, lubricity and fouling to the surface it is applied on. Along with its tunability and self-repairing mechanism, it can increase the comfort, safety and performance of medical devices and industrial applications.