Category: Applied Innovation

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Applied Innovation Healthtech

Machine Learning Model Accelerates Antibody Therapy Development

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Applied Innovation Healthtech

Machine Learning Model Accelerates Antibody Therapy Development

Therapeutic antibodies

Therapeutic antibodies are currently a popular type of medication having great efficacy and few negative effects. These are biopharmaceuticals that are designed to elicit a biological response.

These medications make use of antibodies, which are key participants in our body’s immune system. Individual assaults on specific antigens are feasible by leveraging the specificity of each antibody, which detects just one antigen. It isn’t easy to create and optimize therapeutic antibodies. Once an antibody that binds to the proper antigen is found, it goes through a time-consuming and resource-intensive optimization procedure.

Recently, computational techniques for dealing with such challenges have begun to follow machine learning paradigms, notably deep learning in many cases. This paradigm change improves known domains like structure or binding prediction while also opening up new possibilities like language-based modeling of antibody repertoires or machine-learning-based synthesis of novel sequences.

Machine Learning Algorithm aid Antibody Therapy Development

Researchers have now created a machine learning algorithm to aid in the optimization phase of antibody therapy development.
A few thousand therapeutic candidates can be tested in a lab using automated techniques. Machine learning has the potential to boost the first set of antibodies to be tested by millions. The more candidates there are to pick from, the more likely one will fit all of the requirements for medication development.

AI-enabled antibody design platform

We have innovators that develop, more effective antibody therapeutics for patients by combining machine intelligence and synthetic biology to create safer. We critically explore recent advances in (deep) machine learning techniques to therapeutic antibody design, with implications for completely computational antibody creation, in this review. Our AI-enabled antibody design platform provides the necessary technology to rapidly and reliably develop these game-changing medicines.

In each cycle, our machine learning algorithms generate hundreds of variations that are created and evaluated in our lab utilizing the most advanced synthetic biology technology. The biophysical features and influence on disease activity of each mutation are measured using cell-based or other functional tests that reproduce in vivo disease processes.

This fresh data is utilized to upgrade the AI/ML models so that these models learn to manufacture antibodies that fit our design blueprint across numerous cycles. For additional information on this solution as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com.

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Applied Innovation Healthtech

Digital Surgery Platform that makes surgery smarter and safer

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Applied Innovation Healthtech

Digital Surgery Platform that makes surgery smarter and safer

COVID-19 has accelerated the implementation of digital technology and procedures in healthcare, a positive development for patients, clinicians, and health systems alike. One of many areas impacted is the surgical procedures which used to remain fragmented and where most of the innovation occurred in silos that frequently did not interact or link effectively enough with one another.

But medical treatments are now achieving health results that were previously unattainable due to the invasive nature of conventional surgery, without jeopardizing the patient’s recovery, thanks to improved, connected, more intuitive, and efficient care made possible by revolutionary digital instruments.

Digital Surgery

Robotics, virtual and augmented reality, and artificial intelligence (AI) all hold the potential of data-driven precision surgery, with the ultimate objective of enhancing patient outcomes, surgical performance, and the productivity and efficiency of surgeons and their teams.

The definition of digital surgery as the use of technology to improve preoperative planning, surgical performance, therapeutic assistance, or training in order to improve outcomes and decrease damage received unanimous approval.

Digital technology use is not restricted to the operating room; it currently plays a role in areas as diverse as preoperative planning, surgical risk prediction, and surgical performance assessment. Commercial potential and the promise of better results for surgeons and patients are driving the rapid adoption of these technologies.

Digital Surgical Platform

We offer a digital surgery platform that provides actionable insights to make surgery smarter and safer. The digital surgery platform analyses massive amounts of real-world data in and around the operating room (OR) using proprietary software and artificial intelligence (AI).

This real-world evidence may be used by the care team in real-time during surgery and viewed by others outside the operating room via the platform’s dedicated telehealth link. Following a procedure, the platform provides insights that assist surgeons in benchmarking and improving their care, hospital administrators in making better use of surgical resources, medical device companies in developing better products, and insurance companies in understanding risk and developing more tailored policies.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

Visual Inspection System improving Production Process

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Applied Innovation Industry 4.0

Visual Inspection System improving Production Process

Manufacturing operations attempt to provide the greatest level of quality at all stages of the manufacturing or assembly process. This requires quality checks that need visual confirmation to verify that the pieces are in the correct places, have the correct form, color, or texture, and are free of flaws such as scratches, pinholes, foreign particles, and so on. Because of the volume of inspections and product variation, as well as the fact that flaws can appear anywhere on the product and be of any size, automating these visual quality checks is extremely challenging.

A visual inspection enables the production process to be improved. The vision inspection system has sensors and cameras and relies upon computer vision technology. A visual inspection machine compares two objects in order to provide a response or result. For performing the comparison, vision inspection systems contain all of the information required to categorize all of the items included in the inspection.

To decide which elements will pass the comparison and which will fail, the visual inspection machine incorporates photographs of past tests that were deemed successful. The cases of ideal elements, the elements to be classed, and those that will pass the tests are included in the system, both visually and by information from another class. Those elements that are included through images serve as a guide to be able to compare all the others, as stated earlier this is the visual comparison.

Visual comparison occurs when one piece is placed next to another, observed from multiple perspectives, and able to produce some form of link between them. The qualities to be compared are observed from various perspectives, first to make them correspond in their orientation and allow for a more accurate comparison, or it may also be accomplished by re-creating the photos in 3D format, superimposing one over the other, and describing the differences.

We have solutions for object recognition, fault detection, and process control. Our visual defect and dimensional sorting solutions for a variety of items deliver high productivity with excellent product handling and better inspection efficiency. These solutions are designed with the customer’s needs in mind and are highly customizable to the user. It is combined with cutting-edge in-house software that is dependable and accurate, resulting in the best final product.

Some key features of the solution are:

– Real-Time Monitoring
– Resident Database SQL
– Remote Control and Setting
– Vision Software Programmable by User
– Statistical Reports easily accessible through UI
– Machine Vision Software
– Accuracy up to ±10 microns
– Support for multiple Camera units
– Run as a Turn-Key system
– Simple Setup and Adjustments
– Short Setup Time for multiple codes

To learn more about this product and how you can use it to grow your business, increase productivity, improve quality, and create a better working environment for your employees, please contact us at open-innovator@quotients.com.

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Applied Innovation Healthtech

Federated Learning for Medical Research

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Applied Innovation Healthtech

Federated Learning for Medical Research

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) have emerged as the most popular and fascinating technologies in the intelligent healthcare industry.

The traditional healthcare system is centered on centralized agents providing raw data. As a result, this system still has significant risks and problems. When combined with AI, the system would consist of several agent collaborators capable of successfully connecting with their intended host.

Federated Learning, a novel distributed interactive AI paradigm, holds promise for smart healthcare since it allows several clients (such as hospitals) to engage in AI training while ensuring data privacy. FL’s noteworthy characteristic is that operates decentralized; it maintains communication based on a model in the selected system without exchanging raw data.

The combination of FL, AI, and XAI approaches has the potential to reduce the number of restrictions and issues in the healthcare system. As a consequence, the use of FL in smart healthcare might speed up medical research using AI while maintaining privacy.

The Federated Learning approach may be used to provide several enticing benefits in the development of smart healthcare. Local data, for example, are not necessary for training. To train other machine learning algorithms by mixing a large number of local datasets without transmitting data. During training, local Machine Learning (ML) models are trained on local heterogeneous datasets.

When opposed to traditional centralized learning, FL is also capable of delivering a good balance of precision and utility, as well as privacy enhancement. FL may also help to reduce communication costs, such as data latency and power transmission, connected with raw data transfer by avoiding the dumping of huge data quantities to the server.

We have solutions that use FL to link life science enterprises with world-class university academics and hospitals in order to exchange deep medical insights for drug discovery and development. The platform enables its partners to uncover siloed datasets while maintaining patient privacy and securing proprietary data by leveraging federated learning and cutting-edge collaborative AI technologies. This enables unprecedented cooperation to enhance patient outcomes by sharing high-value knowledge.

The platform has built a worldwide research network driven by federated learning, allowing data scientists to securely connect to decentralized, multi-party data sets and train AI models without the need for data pooling. When combined with fields of medicine specializing in diagnosis and treatment, scientists may use cutting-edge technology platforms to build potentially life-changing drugs for people all over the world.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

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Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

Incredible medical discoveries are revolutionizing our ability to treat and even cure patients; but, drug discovery and development is becoming more difficult and costly, leaving many patients without viable medicines.

Simultaneously, throughout the last decade, a revolution in machine learning has enabled answers to issues that were formerly deemed intractable. Machine learning approaches can currently caption photos, translate across languages, and identify voices at or above human performance levels.

One of the systems utilized in AI is neural networks, which may be used to identify chemical structures with medicinal significance. A neural network uses a large set of training data containing information about the chemical structure-biological activity relationship, which is preceded by successful neural network training and acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity.

The data is derived through experimental observations as well as from relevant quantum models. There were constraints in biological data a few years ago – while access to huge, rich data sets has spurred machine learning’s development, such data sets are still rare in biology, where data collection remains essentially artisanal. Recent advances in cell biology and bioengineering are now allowing us to change this by facilitating the generation of huge volumes of biological data. Besides, researchers have revealed that neural networks have a substantial capacity to create generalizations based on even very restricted training data.

Pipelines for drug discovery and development are lengthy, complicated, and dependent on a variety of factors. Machine learning (ML) techniques offer a collection of tools that can enhance discovery and decision-making for well-specified queries with a large amount of high-quality data. Opportunities to use ML arise at various phases of drug development.

Instead of depending on restricted “discovered” data, we have solutions that use contemporary biology technologies to build high-quality, huge data sets designed for machine learning, allowing us to unleash the full power of modern computational methodologies.

Our solutions are created by professional biologists and drug hunters who collaborate with cutting-edge technologists and machine learners. A group of life scientists and data scientists, software engineers, process engineers, bioengineers, translational scientists, and drug hunters are collaborating to answer problems that we would never have thought to ask on our own.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Healthtech Applied Innovation

AI solution for the screening of lung cancer and other respiratory disorders

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Healthtech Applied Innovation

AI solution for the screening of lung cancer and other respiratory disorders

Lung Cancer: 2nd most common cancer worldwide

Smoking is considered to be responsible for around 80% of lung cancer fatalities, and this figure is likely to be much higher in the case of small cell lung cancer (SCLC). By far the most significant risk factor for lung cancer is smoking.

Persons who smoke have a far higher chance of developing lung cancer than people who do not smoke. The longer you smoke and the more packs you smoke daily the higher your risk.

A risk factor is something that enhances a person’s likelihood of contracting an illness such as cancer. Risk factors for various malignancies vary. Some risk factors, such as smoking, can be modified. Others, such as a person’s age or family history, are unchangeable.

However, possessing a risk factor, or even several, does not guarantee that you will get the illness. Furthermore, some persons who have the condition may have little or no established risk factors.

Lung cancer is the 2nd most common cancer worldwide, according to WCRF. It is the most common cancer in men and the 2nd most common cancer in women. There were more than 2.2 million new cases of lung cancer in 2020.

Lung Cancer Screening

According to a study, the risk category for lung cancer has spread to a younger generation, whether smokers or nonsmokers. The WHO has identified air pollution as a critical contributor to the growing trend of young, nonsmokers being at risk for lung illnesses.

Some experts label air pollution a ‘hidden public health emergency’ and ‘the new tobacco’. It offers a new problem for doctors attempting to treat and prevent lung cancer: determining risk factors for the illness.

Simply put, how does one tackle the risk of lung cancer in a 25-year-old, non-smoking individual living a reasonably healthy lifestyle when a risk factor could be the simple act of breathing?

Lung cancer screening is a procedure that detects the existence of lung cancer in otherwise healthy persons who are at high risk of developing lung cancer. To detect lung cancer, doctors utilize a low-dose computerized tomography (LDCT) scan of the lungs. Lung cancer is more likely to be treated if it is diagnosed at an early stage.

Artificial Intelligence (AI) solution for Screening

At various degrees of detection difficulty, artificial intelligence (AI) can enhance the identification of pulmonary nodules on chest radiographs. When compared to the unaided interpretation of chest x-rays, AI-aided interpretation can dramatically enhance the identification of pulmonary nodules.

On imaging, most early lung malignancies appear as pulmonary nodules, which can be readily overlooked on chest radiographs. When compared to unaided interpretation, an AI system may enhance the diagnostic performance of radiologists with varying degrees of expertise in recognizing pulmonary nodules on chest radiographs.

We have an artificial intelligence (AI) solution for chest X-rays that have been tried, tested, and trusted to aid in the identification and reporting of missing nodules, emphasizing the need for opportunistic screening for detecting probable lung malignancies early.

Our solution is backed by research that has found that AI-based systems might better detect key discoveries on Chest X-Rays, such as cancerous nodules. This raises the prospect of opportunistic screening for lung cancer and other respiratory disorders becoming the norm.

These solutions may genuinely make a difference, supplementing physicians’ and radiologists’ efforts every time a Chest X-ray or Chest CT is performed.

To know more about such innovative solutions and evolving use cases in different domains along with collaboration and partnership opportunities please write us at open-innovator@quotients.com

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Applied Innovation Industry 4.0

Cobots making automation simpler for businesses worldwide

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Applied Innovation Industry 4.0

Cobots making automation simpler for businesses worldwide

With Industry 4.0, robots’ role in intelligent production is expanding. The major objective is to increase production profitability, which is partly dependent on the organization of safety and efficient human-robot interaction in a shared work environment.

Robotic technologies incorporated with intelligent manufacturing can help to increase production automation while maintaining safety compliance. Robots’ mobility and compactness enable them to enhance the manufacturing process and increase efficiency.

The capacity to move robots outside of boundaries and set them near humans was a significant breakthrough in robotics. Every year, the market for robotic solutions expands. This suggests that there is a considerable demand for the use of robots in intelligent manufacturing. We can already observe how the major players in automated manufacturing have taken the first steps toward Industry 4.0.


A collaborative robot (cobot) is a robot designed to operate alongside humans. These are robots that are low-cost, safe, easy to install, and can make automation simpler than ever, even for small and medium-sized businesses worldwide. Cobots are meant to operate alongside people, making automation easier than ever for organizations of all sizes. All of these advantages have made cobots game changers in a wide range of applications. Additionally, these are excellent productivity tools for practically any manufacturing since they assist everyone in the organization in meeting performance goals.


We have solutions that make robots accessible to everyone. Human-robot interface technology that can be operated by experts or novices, without the need for them to be fluent in any programming languages or proprietary APIs. To do this, it is designed and manufactured from the ground up to seamlessly combine hardware and software with built-in collaboration capabilities, plug-and-play application adaptability, and, most crucially, the ability to operate securely alongside people without the use of any safety barriers.

These robots are totally software-driven, with a sophisticated software platform available via an intuitive and responsive interface that can be used to teach, collaborate, automate, and connect with a manufacturing ecosystem.

To know more about this product and learn how you may expand your business, boost productivity, enhance quality, and create a better working environment for your staff with these solutions please write to us at open-innovator@quotients.com.

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Healthtech Applied Innovation

AI-based Voice Assistants for Healthcare Industry

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Healthtech Applied Innovation

AI-based Voice Assistants for Healthcare Industry

Voice AI can play a great role in the healthcare industry, transforming day-to-day operations for healthcare providers, and patients, and ensuring compliance. With the advanced algorithm of machine learning and natural language processing, they can help in preventing physician burnout, eliminating transcription delays, reducing documentation, and enabling healthcare providers to concentrate on patient care.

Historically, a personal assistant or secretary would handle activities including taking dictation, reading aloud text or email messages, searching up phone numbers, scheduling, making phone calls and reminding the user of upcoming appointments. Currently, popular virtual assistants including Amazon Alexa, Apple Siri, Google Assistant, and Cortana, carry out the user’s requests. This application software known as a virtual assistant can recognize natural language voice commands and performs tasks according to user requests.

Voice assistants can provide efficiencies that lessen effort and enhance care. Automation of repetitive operations, the removal of data stream obstacles, and a direct reduction in the time and effort required by care groups can be made possible by AI-powered voice assistants. Despite appearing in healthcare records and other databases, patient information is rarely captured throughout the patient journey. AI-powered patient insights with omnichannel voice insight, in this case, may also be used to generate value for the patient and the practitioner, whether it be through symptom checks, providing high-quality treatment, or enhancing outcomes.

One of the challenges for AI-powered voice assistants is the complex medical vocabulary, but there are solutions that are now offering better ergonomic data entry with good accuracy and robust medical vocabularies. Another challenge is that it is difficult to find the necessary datasets to train AI models for these kinds of jobs as the type of data utilized has a significant impact on AI. There could be restrictions on the tones, accents, and languages that these algorithms can comprehend in the early stages. But there are solutions available that do not need voice profile training, from any device, anywhere.

With easy-to-use features, these solutions can work with different clinical systems and maintain electronic health records, etc. The privacy issue is another factor that may be a cause of concern as it collects data, particularly biometric data like voice data. But the solutions are available that are HIPAA Compliant, GDPR Compliant and other with certifications that take care of these concerns. With easy-to-use features, these solutions can work with different clinical systems and maintain electronic health records, etc.

New tools and technologies are already starting to make waves across the healthcare system but the industry in general has been a slow adopter of these changes. Emerging technologies like genome sequencing, digital tools, and artificial intelligence (AI) hold great promise to transform the delivery of health services in the near future but healthcare needs to be digitized for these technologies to penetrate further. The digitization of healthcare procedures will lead to fresh perspectives and hasten the field’s research and innovation. AI-powered Voice recognition can help in this digitization of the healthcare industry while improving efficiency and bettering patient care.


To know more about these solutions please write to us at open-innovator@quotients.com


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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

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Fintech Applied Innovation

AI-Based Models aiding Financial Institutions with Credit Score Assessments

Artificial intelligence has been around for a while now, and technology is only getting more capable. From self-driving cars to personal digital assistants, AI is integrating into our daily lives in ever-more sophisticated ways. In this article, we’ll explore how artificial intelligence can help financial institutions to generate credit reports and make lending decisions.

Importance of Credit Score for FI

A credit score is the first thing lenders check when you apply for a loan or a credit card. This three-digit numeric is a summary of the entire credit history that determines a consumer’s creditworthiness, the higher the score, the better the borrower’s chances of getting a loan. It is prepared based on lenders’ data and consolidated in the Credit Information Report or CIR. Criteria such as payment history, credit utilization ratio, Credit history length, Credit mix, etc are factored in for this. 

Limitations of Traditional Credit Scores

Although credit scoring systems are being implemented and used by most banks nowadays, they have limitations and can’t be used to make accurate predictions. The score depends upon the quality of data that is used, if the data is erroneous with missing values or outliers the resultant scores may not be accurate. Along with the availability of high-quality data, the data should also be predictive so conclusions can be drawn from it and defaults can be predicted. Traditional credit scores due to the limitations discussed are getting outdated and even irrelevant in some cases. 

AI-based Credit Scoring

Artificial intelligence (AI) can play a role here and make precise predictions based on smart models. AI through its cutting-edge analytical technologies can hugely impact the financial sector and can offer excellent returns on their investment. AI-based credit scoring unlike focusing on the past performance of the borrower can be more sensitive to real-time indicators of a potential borrower like the existing level of earnings, employment prospects, and their potential ability to earn. These models can also give individualized credit score assessments based on real-time factors, giving access to finance to more people with income potential.

With these scoring models, banks also can get unique insights into their customers’ financial behavior and leading to better customer segmentation in terms of associated credit risk. Also after the disbursement of the loan these customers can be monitored and red flags can be raised as soon as a behavior is deviant from standard practices. With the help of AI, it is also possible to speed up lending decisions and processing of loans leading to better customer service and productivity of employees.

Credit scores are determined by algorithms neural network-based scoring. A neural network is a machine-like system that can learn from data and make decisions based on those data sets. These types of scoring can detect small variations in data sets and make more accurate predictions and can help solve the problem of credit scoring. As these models can learn from data without requiring any rules-based algorithms they can better assess the credit risk. With these advantages, financial institutions are relying on machine learning to process big data and produce better insights.

Alternatives to Credit Score

Data points produced by a large number of digital transactions can offer valuable insight into how people manage their financial commitments. There are endeavors going on for developing alternatives to create credit score models based on AI methods to evaluate a person’s creditworthiness, particularly for those who do not have a formal credit repayment history.

In the various financial institutions, these solutions are being implemented at different levels and improving how they operate. Please contact us at open-innovator@quotients.Com if you would like additional information or explore this and other rapidly evolving solutions in a variety of fields.

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Applied Innovation Fintech

How is Artificial Intelligence Impacting Corporate Banking

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Applied Innovation Fintech

How is Artificial Intelligence Impacting Corporate Banking

With FinTech posing a challenge to the traditional banks and disrupting their core financial services, it has pushed the banks to innovate to remain relevant. There is an increased reliance on Artificial Intelligence (AI) by the banks to meet the competition posed by the FinTech players.

The impact of AI in retail banking can already be seen in retail banking, like customer service chatbots, credit scoring, and the use of data analytics in the segregation of customers and pitching of customized products. AI has also the potential to be leveraged in corporate banking and is being recognized now. Some of the application of AI in corporate banking is being discussed here.

Detect Money Laundering


AI can equip banks with technologically intelligent weapons to help detect money laundering. AI can be used to monitor and scan customer profiles and finding the origin of funds and identify high-risk individuals. Transactions can also be monitored to raise alerts in case of aberration from the regular transaction. Machine learning models can also be used to detect changes in customer behavior and the nature of their transactions. ML can also reduce the number of false alerts effectively compared to traditional anti money laundering devices.

Eliminate Discrimination in Lending


Apart from transaction data and data provided to the bank, AI can also analyze large amounts of external data related to customers like professional, institutional, political, and social like the information in media, social networks, and the public through natural language processing. Financial institutions can thus eliminate discrimination in lending, and to make credit decisions. Algorithms can assist in making the right credit decisions and improve customer relations.

Cross-Selling and Offering Tailored Services


AI-based solutions can equip Relationship Managers (RMs) to provide appropriate and timely advice to their clients by scanning their profiles and transaction history and generating the products best suited for them. This can help banks in cross-selling and offering tailored services By using predictive analytics and algorithms to analyze client behaviors, it can generate inputs in between the conversations and help close the deal.

Reducing Turnaround Time


Through Robotic process automation– using software robots — labor-intensive and repetitive tasks can be drastically reduced and turnaround time for various services and productivity of employees can be improved. It can play an important role in automatic report generation, account opening, customer onboarding, loan processing, and a wide range of back-office processes. Thus, by automating manual business procedures AI allows banks to stay competitive in an ever-evolving market.

Big Data for Predictive Analytics


Banks generate huge amounts of data through interactions and transactions. This can be analyzed by helping banks to comprehend all those client interactions and predict future behavior and providing them with valuable insights.

There are various solution is being deployed at various levels in the banking industry and having a positive impact on their functioning. To know the details and discuss more on this as well as other evolving solutions in multiple domains please write to us at open-innovator@quotients.com.


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