US law allows only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product

Even as the debate around AI gaining sentience hasn’t subsided, artificial intelligence. US patent court struck down Stephen Thaler’s petition seeking a patent for the products developed by the AI system purportedly developed by him. CAFC, the US appeals court, in Thaler V. Vidal’s case ruled out the machine’s candidature to qualify as an inventor under the US Patent Act. The decision is in similar lines to the series of judgments delivered by courts from around the world for the same case. The legal tiff dates back to 2019 when he filed for a patent for the two products, an AI food container developed based on fractal geometry and a torch light. Instead of using his name in the patent document, he mentioned the neural network’s name, DABUS, claiming that the products are its brainchildren.

Back then US Patent and Trademark Office rejected his plea, reasoning that US laws allow only ‘natural persons’ to be registered as individuals; individuals who can take an oath swearing their paternity to the product, which machines or computer software are incapable of. This time the Court of Appeals, supporting the USPTO’s stand, was unambiguous in stating the obsolescence of “metaphysical matters” about “the nature of invention or rights, if any, of AI systems.” It stressed the repeated reference to the term “individuals” is very well in alignment with its interpretation. “While we do not decide whether an AI system can form beliefs, nothing in our record shows that one can, as reflected in the fact that Thaler submitted the requisite statements himself, purportedly on DABUS’ behalf,” CAFC explained. Cutting the game to chase, the judgment hinges on legal requirements which has little to do with the court’s understanding of AI’s capability.

Refusing to give up on his mission, he wants to contest the judgment in the US Supreme court. He says, “The court ignores the purpose of the Patent Act and the outcome that AI-generated inventions are now unpatentable, will have real negative social consequences.” This brings us to the question if artificial intelligence deserves patent rights and why. 

When Thaler says, negative consequences, he means the patent laws being misused under the cover of patent law. For example, AI has been responsible for inventions in the medical field which left unpatented could give leeway to idea hoggers, leaving businesses and funders discouraged to pursue research, preventing cutting-edge research to happen in the first place. Experts too are of the opinion that instead of rewriting the IP Acts completely, it would be a reasonable option to create a custom law including artificial intelligence, because, at the end of the day, AI learns by itself and makes decisions for itself. As artificial intelligence is becoming an omnipresent and inevitable part of day-to-day lives, it is highly imperative to consider if AI qualifies to be considered as an entity similar to a POSA (person with Ordinary Skills in Art). The lack of benchmarks to determine if AI’s invention is unique might be another challenge, given the AI’s ability to constantly improve itself.

This argument finds support in Australian Court’s initial assumptions that led to a favorable judgment only to be overruled later by the appeals court. The judges considered the possibility of AI setting its goal, freedom to choose options and pathways towards the goals, and the ability to trawl for data it requires. The court did spell out why AI’s autonomy is not a misnomer, as long as artificial neural networks can choose the algorithms and interact with other networks. If only this argument could be taken forward to establish AI as a legit entity – which would probably be Thaler’s next line of argument – the question of ‘persona’ could be put to rest.

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Even while acknowledging that AI application has business risks and some ethical issues

AI is having a long and highly dynamic intervention in almost every conceivable sphere of our life. It is hardly surprising then that it will also play an increasing role in the entrepreneurial domain, especially in business workflows and processes. Every organization seeking to achieve efficiency and effectiveness looks for appropriate business decisions. In the contemporary era, profitable business decisions to a great extent depend on high-tech applications, especially AI. But experience reveals that one cannot do it in a mechanical way. There is, for instance, a clear signal that approaching AI for short-term business capabilities seems to be paying more dividends than leveraging it as a research and development agency. So, the right kind of investment matters.

There is no doubt that in contemporary times there are a plethora of business strategies from which an organization can opt for ones according to its needs. Now we are well into the Information Society and the generation and maneuvering of data have become crucial for the success of any organization. But to repeat, the process of organizational change based on the adoption of new technology cannot be mechanical. Accumulation of mere data does not lead the organization anywhere as information overload is bad for the organization’s health. The AI can come to the rescue here with its up-end software to integrate the solution with the existing business architecture of a specific organization, without disturbing the business process and workforce. The best way to do it is to develop a comprehensive picture of the business process and workflows, rather than adopting what till now has been predominant, a sectoral approach in which an organization is perceived in terms of ‘slices.’ The AI-sourced ‘overall’ picture is being built up with as many components as product and/or service development, manufacturing, service operations, human resources, marketing and sales, supply chain management, risks, strategy, and so forth.

Modern organizations are invariably complex organizations. If the business process and workflows are ‘sliced’ and perceived in terms of separate compartments the outcome has a high chance of getting complicated and futile. AI algorithms are now facilitating organizational management by making available reliable structured reports based on real-time actionable data.  Various reports mention that AI adoption is highest within the product-or service-development and service-operations functions.

AI software has become so sophisticated that it is causing the end of the days of trial-and-error methods with extremely precise predictability. AI is at the same time giving rise to ‘smart’ communication with astonishing exactness for the intended users of the organization. With AI reaching such heights in organizational life and its business domain its predictive capacity is also combined with prescriptive capability, which in turn induces organizations to break the glass ceiling. According to a McKinsey’s global survey, published in a report titled The State of AI in 2020, the largest share of respondents reports revenue increases for inventory and parts optimization, pricing and promotion, customer-service analytics, and sales and demand forecasting. More than two-thirds of the respondents who report adopting each of those cases affirm that its adaptation has led to increased revenue. Over half of the respondents who report adopting each of that mention that the use of AI in those areas reduced costs. Such developments have special significance in the post-COVID 19 pandemic world and its transformation imperatives. According to an IBM report, negotiating the disastrous effects of the pandemic AI continues to enable organizations to address urgent and immediate business priorities—quickly and at scale. The report notes that AI improves a company’s cost base—augmenting human capability to motivate greater and more expansive efficiencies. It is also stated to help enhance or protect top-line revenue, experience, and engagement. The report’s six-year data series shows that COVID-19 has accelerated AI’s shift from experimental to widely adopted as a key lever of sustainable competitive advantage and profitability across businesses and industries around the world.

Even while acknowledging that AI application has its risks and some ethical issues, the fact remains that the right investment in AI goes on to benefit an organization.

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AI is used in every aspect of aviation, from facial recognition at borders to self-service check-in automation. On the other hand, current research using computer-aided security screening to support operators and deep learning approaches demonstrated promising outcomes.

AI systems utilize a variety of datasets. Machine learning is being used by technologists to analyze data for airport security and identify hazards more quickly than people. Passengers can keep items that previously required separate scanning in their checked bags as they move through security checkpoints.

The Open Platform Software Library was created through the OTAP project, which was developed with numerous aviation security sector partners, comprising algorithm developers, X-ray manufacturers, and software specialists (OPSL).

Pacific Northwest National Laboratory created a body-scanning system called the High-Definition Passenger Imaging System. To create an enhanced full-body machine that can more precisely detect threats, Sandia and PNNL collaborated in 2017. They added the scanner with OPSL.

The team is currently verifying the accuracy of the CT and AIT systems’ sensors utilizing bags, personal items, laptops, and simulated explosives while using programmed threat recognition software.

Eight airports have been selected by the Airport Authority of India (AAI) to test the potential of AI in baggage screening. One of them, Pune Airport, adopted the “Baggage AI” technology. The AI-powered device improves airport security measures.

An AI-based model called Baggage AI serves as the security x-ray machines’ threat detection system. From the x-ray images produced during the screening of luggage, our AI software can automatically recognize numerous objects and other dangers and notify personnel.

Biometrics is a noteworthy AI innovation. In the upcoming two years, major airports have chosen to implement biometric ID management. The main use of biometrics is facial recognition, which is already in use at several major airports to scan travelers as they pass through customs.

At TSA checkpoints, self-service kiosks, or boarding gates, passengers can utilize facial recognition scanners to identify themselves. The use of fingerprints, facial recognition, and retinal scanning may become mandatory verification techniques for airport security checks.

On the contrary, recent failures and emerging threats have made these cutting-edge AI-based airport security technologies urgently necessary. Technology that allows passengers to keep their shoes on, keep their belts on, and lower friction while traveling must be a good thing for air travelers. AI can detect undiscovered risks in addition to identifying existing ones. Cybersecurity relies heavily on AI, notably its machine learning methodology. Terrorist attacks might be foreseen and prevented as automated threat recognition systems powered by AI develop. A smoother journey for passengers is ensured by increased security in the airports.

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