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Market Scenario of the Artificial Intelligence in Aviation Market:

The most recent Artificial Intelligence in Aviation Market Research study includes some significant activities of the current market size for the worldwide Artificial Intelligence in Aviation market. It presents a point by point analysis dependent on the exhaustive research of the market elements like market size, development situation, potential opportunities, and operation landscape and trend analysis. This report centers around the Artificial Intelligence in Aviation-business status, presents volume and worth, key market, product type, consumers, regions, and key players.

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The prominent players covered in this report: Boeing, Micron, NVIDIA, Amazon, Airbus, General Electric,Lockheed Martin, Thales, Garmin, Xilinx, and Intel.

The market is segmented into By Offering (Hardware, Software, Service) By Technology (Machine Learning, Context Awareness, NLP, Computer Vision), By Application (Virtual Assistants, Smart Maintenance).

Geographical segments are North America, Europe, Asia Pacific, Middle East & Africa, and South America.

A 360 degree outline of the competitive scenario of the Global Artificial Intelligence in Aviation Market is presented by Quince Market Insights. It has a massive data allied to the recent product and technological developments in the markets.

It has a wide-ranging analysis of the impact of these advancements on the markets future growth, wide-ranging analysis of these extensions on the markets future growth. The research report studies the market in a detailed manner by explaining the key facets of the market that are foreseeable to have a countable stimulus on its developing extrapolations over the forecast period.

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This is anticipated to drive the Global Artificial Intelligence in Aviation Market over the forecast period. This research report covers the market landscape and its progress prospects in the near future. After studying key companies, the report focuses on the new entrants contributing to the growth of the market. Most companies in the Global Artificial Intelligence in Aviation Market are currently adopting new technological trends in the market.

Finally, the researchers throw light on different ways to discover the strengths, weaknesses, opportunities, and threats affecting the growth of the Global Artificial Intelligence in Aviation Market. The feasibility of the new report is also measured in this research report.

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Artificial Intelligence in Aviation Market 2020 | What Is The Estimated Market Size In The Upcoming Years? - The Daily Chronicle

Artificial intelligence has become a general-purpose technology. Not confined to futuristic applications such as self-driving vehicles, it powers the apps we use daily, from navigation with Google Maps to check deposits from our mobile banking app. It even manages the spam filters in our inbox.

These are all-powerful, albeit functional roles. Whats perhaps more exciting is AIs growing potential in sourcing and producing new creations and ideas, from writing news articles to discovering new drugs in some cases, far quicker than teams of human scientists.

With every new iteration in software design, computing power, and ability to leverage large data sets, AIs potential as an initiator of ideas and concepts grow, and this raises questions around its rights to Intellectual Property (IP).

Professor of Law and Health Sciences at the University of Surrey, Dr. Ryan Abbotts work is focused on the meeting of law and technology in particular, the regulation of AI. While Abbott doesnt believe AI should be entitled to its own IP, he believes the time is right to discuss the ability of people to own IP generated autonomously by AI or risk losing out on the technologys full potential.

Right now, we have a system where AI and human activity are treated very differently, Abbott told TechHQ.

Drug discovery is a tangible example of how AI contributes to society. Technology is making the discovery of new drugs faster, cheaper, and more successful. Its been used this way for decades, helping to identify new drug targets or validate drug candidates, and to help design trials in ways that can potentially shorten drug development timeframes, bringing treatments to market faster. But the critical nature of patent protection in life sciences, drug development, in particular, is holding back these advances.

Thats because, when it comes to AI-generated content and ideas, AI tends to be seen by experts and lawmakers as a tool, and not the source of the creation or discovery. In the same way that a paintbrush doesnt get the credit for an oil painting and CAD software isnt credited for the designs of an architect, AI is perceived as a vehicle to an end product. The trouble is, current laws are not consistent and clear cut. In the UK, where a work lacks a traditional human author, the producer of the work is deemed the author. In the US, the inventor is the person who conceives the idea. In either case, neither human may know what the AI system will produce or discover.

While patent rules in life sciences highlight the legal constraints on AI in research and development, these same challenges affect everything from the development of components for cars to spacecraft. The problem will become increasingly apparent as AI continues to improve, and people do not.

The consensus among legal experts is that its not clear whether AI could carry out the understood rights and obligations of an IP owner. IP rights are restricted to natural persons and legal entities such as businesses. The European Union reportedly abandoned plans to introduce a third type of entity electronic personality in 2018 after pressure from 150 experts on AI, robotics, IP, and ethics.

Speaking to Raconteur previously, Julie Barrett-Major, consulting attorney at AA Thornton and member of the Chartered Institute of Patent Attorneys International Liaison Committee, explained: With patent ownership come certain obligations and responsibilities or at least opportunities to exercise these. For example, to enforce the rights awarded, the owner can sue for infringement or at least indicate a willingness to do so to maintain exclusivity.

[] the patent must be renewed at regular intervals, and there are other actions that need to be taken to ensure the rewards are not diluted, such as updating the government registers of patents with details of changes in ownership details, informing of licensees and so forth.

Abbott argues that, ultimately, the limitations of current IP frameworks may force organizations to continue to use people, where a machine might be more efficient.

Last year, Siemens was unable to file for multiple patents on inventions they believed to be patentable because they could not identify a human inventor. The involved engineers stated that the machine did the inventive work. Abbott himself is carrying out a legal test case, filing patents for two inventions made autonomously by AI. Both have been rejected from the US, UK, German, and European patent offices on the basis they failed to disclose a human inventor. The rejections are under appeal, but the idea to help raise dialogue on the issue.

Most of the time today AI is just working as a tool and helping augment people, but machines are getting increasingly autonomous and sophisticated, and increasingly doing the sorts of things that used to make someone an inventor, Abbott said.

The current status quo means that law can get in the way of AI development in certain areas, but not others. That means AIs benefits are not evenly spread across industries. While parents are important to drug development, for example, they are less important when it comes to making software. This imbalance could lead to the emergence of shady IP practices in certain sectors when it comes to using AI. The workaround, says Abbott, is people simply not disclosing AIs role in creating something valuable, whether thats an article, video, or song. Someone can just list themselves as the author and no one is going to question that.

The issue of patents and intellectual property in the fields of academic research, for many of us, might not seem like its worth our consideration. But the broader legal concept Ryan looks to highlight that we should question current standards of AI accountability and ownership affect how AI is being used around us.

Across all areas of the law, we are seeing the phenomenon of artificial intelligence stepping into the shoes of people and doing the sorts of things that people used to do, said Abbott.

Ultimately, for AI to be used to its full potential, there must be an open discussion, public consultation, and debates on the current litigation surrounding AI. Thats now happening. The issue has had recent attention by the World Intellectual Property Organization (WIPO) while the UK Intellectual Property Office just announced a public request for comments on whether the IP system is fit for purpose in light of AI. The US has just completed a similar consultation.

These efforts are a solid start to getting a diverse range of input from stakeholders, said Abbott. In time, legislators should get involved.

Originally posted here:
Tackling the artificial intelligence IP conundrum - TechHQ

I. Introduction

Artificial Intelligence (AI) refers to an intelligence technology similar to human implemented by means of ordinary computer programs. With rapid development of artificial intelligence technology and continuous reflection of commercial values thereof, patent applications related to artificial intelligence technology have become a hot field in patent applications, and the number of applications is continuously rising, and scopes of application fields are also expanding.

This article aims attempts to provide some patent application strategies in the field of artificial intelligence based on latest examination standards in China, and summarize similarities and differences between examination standards in the field of artificial intelligence in China, Japan, Korea, US and Europe, for reference by patent applicants, and patent attorneys, etc.

II. Main laws involved and coping strategies

In China, as a patent application involving a computer program, the primary examination focus of a patent application in the field of artificial intelligence is whether the patent application is an eligible object protected by a patent, and another examination focus is the inventiveness as provided in Article 22, Paragraph 3 of the Chinese Patent Law.

Figure 1

Figure 1 shows the general examination process of a patent application in the field of artificial intelligence in China.

For a patent application in the field of artificial intelligence, it may be drafted as product claim or method claim, and the product claim may be drafted as an eligible subject, such as a system, a device, and a storage medium, etc.

Table 1 Forms of drafting of claims

Following description mainly focuses on analysis of latest examination standards of China and coping strategies regarding whether a patent application in the field of artificial intelligence belongs to an eligible object protected by a patent and whether it is in conformity with the provisions of inventiveness.

1. Examination standards and coping strategies regarding an eligible object protected by a patent

1.1 The latest examination standards on eligible object issues

It is provided in Article 25, Paragraph 1, Item (2) of the Chinese Patent Law that no patent right shall be granted for rules and methods for mental activities.

It is provided in the newly-amended Guidelines for Examination that if a claim contains a technical feature in addition to an algorithm feature or a commercial rule and a method feature, the claim as a whole is not a rule and method of an intellectual activity, and a possibility that it is granted a patent right shall not be excluded in accordance with Article 25, Paragraph 1, Item (2) of the Patent Law.

Moreover, it is provided in Rule 22, Paragraph 2 of the Implementing Regulations of the Chinese Patent Law that Invention as mentioned in the Patent Law means any new technical solution relating to a product, a process or an improvement thereof.

Correspondingly, it is provided in the newly-amended Guidelines for Examination that if steps involved in an algorithm in a claim reflect that they are closely related to the technical problem to be solved, for example, data processed by the algorithm are data having definite technical meanings in the technical field, execution of the algorithm is able to directly reflect a process of solving a technical problem by using natural laws, and produces a technical effect, then in general, the solution defined in this claim belongs to the technical solution provided in Article 2, Paragraph 2 of the Patent Law.

1.2 Application strategy for eligible object issues

Patent applications in the field of artificial intelligence may basically be divided into two types according to their application scopes: basic type patent applications and applied type patent applications. The so-called basic type patent application refers to that an algorithm involved in the patent application may be widely used in multiple particular fields, and the applied patent application refers to that an algorithm involved in the patent application is mainly combined with a particular field, and is an application in this field.

Taking two aspects into account, i.e. patent protection scope and conformity to examination requirements, ways of drafting the two types of patent applications are proposed for reference.

Table 2 Ways of drafting two types of patent applications

In addition, due to the development of Internet technology and big data technology, the artificial intelligence technology is also increasingly used in commercial and financial fields. In making an application for this type of patent, attention should be paid to combining a business rule, an algorithm feature and a technical feature in description.

Moreover, based on a stage of technological improvement, a patent application in the field of artificial intelligence may be divided into two stages: a training stage (learning stage) and an application stage. Following are corresponding ways of drafting.

Table 3 eligible subjects in two stages

2. Examination standard and coping strategy regarding inventiveness

2.1 Latest examination standards regarding inventiveness

It is provided in the newly-amended Guidelines for Examination that when examination regarding inventiveness is conducted on an application for patent for invention containing a technical feature and an algorithm feature, or a business rule and a method feature, the algorithm feature or the business rule and the method feature shall be taken into account together with the technical feature as a whole, when they functionally and mutually support the technical feature and have an interaction relationship between them and the technical feature.

2.2 Application strategy for examination on inventiveness

Based on the above examination standards, when an application for patent in the field of artificial intelligence is drafted, attention should be paid to combine an algorithm feature and a technical feature in describing the technical solution. Moreover, in describing a technical problem and a technical effect, emphasis should be placed on that the algorithm feature and the technical feature are specifically combined, and jointly solve the technical problem and produce a corresponding technical effect.

Furthermore, for some artificial intelligence patent applications not involved in improvement of a basic algorithm, their improvement points relative to existing technologies may mainly exist in application of an algorithm, such as a neural network, to a specific field, while the neural network itself is not changed much. For this type of patent applications, inventiveness may be considered mainly based on the following two aspects: first, whether the technical fields are similar; and second, a difficulty of applying the neural network to the technical field of the present application and whether a technical effect different from that in the original technical field is produced.

III. Comparison of examination standards of China, Japan, Korea, US and Europe

1. Comparison of examination standards of an eligible object protected by a patent

Comparisons of examination standards of an eligible object protected by a patent in China, Japan, Korea, US and Europe is as follows.

Table 4 Examination of an eligible object protected by a patent

in China, Japan, Korea, US and Europe

2. Comparison of examination standards of inventiveness

Comparison of examination standards of inventiveness in China, Japan, Korea, US and Europe is as follows.

Table 5 Examination of inventiveness in China, Japan, Korea, US and Europe

IV. Summary Patent applications in the field of artificial intelligence belong to patent applications involving computer programs, which need to meet the universal requirements on patent applications involving computer programs. Due to the specialty of the artificial intelligence technology, for patent applications in the field of artificial intelligence, the China National Intellectual Property Administration (CNIPA) has formulated new special examination regulations. Drafting of patent applications and the responses to examination opinions based on the latest examination standards are beneficial to applicants in obtaining patent rights of relevant technologies in China.

In addition, understanding of examination standards for patent applications in the field of artificial intelligence in major patent countries and regions in the world, namely China, Japan, Korea, US and Europe, is advantageous to global application strategy formulation and reasonable patent layout of applicants.

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Patent application strategies in the field of artificial intelligence based on examination standards - Lexology

The world has evolved rapidly in the last few years and artificial intelligence (AI) has often been leading the change. The technology has been adopted by almost every industry with companies wanting to explore how AI can automate processes, increase efficiency, and improve business operations.

AI has certainly proved how it can be beneficial to us all, but a common misconception is that it is always objective and avoids bias, opinion, and ideologies. Based on this understanding, there has been a rise in recent years with companies utilising AI-based recruiting platforms in a bid to make the hiring process more efficient and devoid of human bias.

Yet, a Financial Times article quoted an employment barrister who doubted the progressive nature of AI tools and said that there is overwhelming evidence available that the machines are very often getting it wrong. A high-profile example of this being the case is when Amazon had to abandon its AI-recruiting tool in 2018 after the company realised it was favouring men for technical jobs.

However, AI has continued to advance at a rapid pace and its adoption by businesses has been further accelerated following COVID-19s arrival. With debates of whether AI can be relied upon to behave impartially still ongoing, how can the technology be governed so organisations continue to act ethically?

During a press conference in Brussels earlier this year, the European Commission said it was preparing to draft regulation for AI that will help prevent its misuse, but the governing body has set itself quite the challenge. The technology is developing constantly so after only a few weeks any regulation that is introduced may not go far enough. After a few months, it could become completely irrelevant.

Within the risk community however, there is no doubt that policies are needed as a study found that 80% of risk professionals are not confident with the AI governance in place. At the same time, there are also concerns from technology leaders who believe tighter regulations will stifle AI innovation and obstruct the potentially enormous advantages it can have on the world.

A certain level of creative and scientific freedom is required for companies to create innovative new technologies and although AI can be used for good, the increasing speed with which it is being developed and adopted across industries is a major consideration for governance. The ethical concerns need to be addressed.

Given the current and ongoing complexities that the global pandemic brings, as well as the looming Brexit deadline, we will likely have to wait for the EUs regulation to be finalised and put in place. In the meantime, businesses should begin to get their own houses in order if they havent already with their use of AI and governance, risk and compliance (GRC) processes to ensure they are not caught out when legislation does arrive.

By setting up a forward-looking risk management program around implementing and managing the use of AI, organisations can improve their ability in handling both existing and emerging risks by analysing past trends, predicting future scenarios, and proactively preparing for further risk. A governance framework should also be implemented around AI both within and outside the organisation to better overcome any unforeseen exposure to risk from evolving AI technologies and an ever-changing business landscape.

Unlike the financial services sector where internal controls and regulators require businesses to regularly validate and manage their own models, AI model controls are already being put in place, reflecting the abundant usage of AI within enterprises. It wont be long before regulators begin to demand proof points of there being the right controls in place, so organisations need to monitor where AI is being used for business decisions and ensure the technology operates with accuracy and is void of inherent biases and incomplete underlying datasets.

When an organisation is operating with such governance and a forward-looking risk management program towards its use of AI, it will certainly be better positioned once new regulation is eventually enforced.

Too often, businesses are operating with multiple information siloes created by different business units and teams in various geographic locations. This can lead to information blind spots and a recent Garter study found that poor data quality is responsible for an average loss of $15 million per year.

Now more than ever, businesses need to be conscious of avoiding unnecessary fines as the figures can be crippling. Hence, it is important that these restrictive siloes are removed in favour of a centralised information hub that everyone across the business can access. This way, senior management and risk professionals are always aware of their risks, including any introduced by AI, and can be confident that they have a clear vision of the bigger picture to be able to efficiently respond to threats.

Another reason for moving towards centralisation and complete visibility throughout the business is that it often gets touted that the reason AI fails to act impartially is that AI systems learn to make decisions based on training data that humans provide. If this data is incomplete or contains conscious or unconscious bias or reflects historical and social inequalities, so will the AI technology.

While an organisation may not always be responsible for creating AI bias in the first place, by having a good oversight and complete centralised information to hand at any time, it becomes a lot easier to see where there are blind spots that could damage a companys reputation.

Ultimately, it is down to organisations themselves to manage their GRC processes, have a clear oversight of the entire risk landscape and strongly protect their reputation. One of the outcomes of the pandemic is the increased laser focus on ethics and integrity, so it is critical that organisations hold these values at the core of their business model to prevent scrutiny from regulators, stakeholders and consumers. Until adequate regulation is introduced by the EU, companies essentially need to take AI governance into their own hands to mitigate any risk and to always perform with integrity.

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How do we govern artificial intelligence and act ethically? - Open Access Government

The perfect blend of AI collaboration needs workers to focus on the tasks where they excel.

Faced with the business impacts of one of the largest health crises to date, restaurants of all sizes are in a pivotal moment in time where every decisionshort term and long termcounts. For their businesses to survive, restaurant owners have had to act fast by rethinking operations and introducing pandemic-related initiatives.

Watching the worlds largest chains all the way down to the local mom-and-pops become innovators in such extreme times has shown the industrys tenacity and survival instinct, even when all odds are stacked against their favor. None of these initiatives would be possible without technology as the driving factor.

Why AI is on the Menu This Season

A recent Dragontail Systems survey found that 70 percent of respondents would be more comfortable with delivery if they were able to monitor their orders preparation from start to finish. Consumers want to be at the forefront of their meals creationthey dont want to cook it, but they do want to know it was prepared in a safe environment and delivered hot and fresh to their door.

Aside from AIs role on the back-end helping with preparation time estimation and driver scheduling, the technology is now being used in cameras, for example, which share real-time images with consumers so that they can be sure their orders are handled with care. Amid the pandemic, this means making sure that gloves and masks are used during the preparation process and that workspaces are properly sanitized.

It is clear that AI is already radically altering how work gets done in and out of the kitchen. Fearmongers often tout AIs ability to automate processes and make better decisions in faster time compared to humans, but restaurants that deploy it mainly to displace employees will see only short-term productivity gains.

The perfect blend of AI collaboration needs workers to focus on the tasks where they excel, like customer service, so that the human element of the experience is never lost, only augmented.

AI on the Back-End

Ask any store or shift manager how they feel about workforce scheduling, and almost none will say its their favorite part of the job. Its a Catch-22: even when its done, its never perfect. However, when AI is in charge, everything looks different.

Parameters such as roles in the restaurants, peak days and hours, special events such as a Presidential debate, overtime, seniority, skills, days-off and more can be easily tracked. Managers are not only saving time in handing off this daunting task, but also allowing the best decisions to be made for optimal restaurant efficiency.

Another aspect is order prioritizationby nature, most kitchens and restaurants prepare meals based on FIFO (first-in-first-out). When using AI that enhances kitchen prioritization, for example, cooks are informed when to cook an order, ensuring that there are actually drivers available to deliver it to the customer in a timely manner.

Delivery management then allows drivers to make more deliveries per hour just by following the systems decisions, which improve and optimize the dispatching functionality.

The Birth of the Pandemic Intelligent Kitchen/Store

With the pandemic, our awareness of sanitation and cleanliness went dramatically up and the demand for solutions came with it. AI cameras give customers exactly thata real-time, never-before-seen view inside the kitchen to monitor how their order is being prepped, managed, and delivered.

Another aspect where AI comes in handy is avoiding dine-in and doing more take-out and drive-thru. When a customer is making an order online and picking the order up in their car, an AI camera can detect the car plate number in addition to the customer location (phone GPS) when entering the drive-thru area to provide a faster service with a runner from the restaurant.

In addition, the new concept of contactless menus where the whole menu is online with a quick scan of a QR code is another element building popularity during the pandemic. The benefits go beyond minimizing contact with physical menus; when a restaurant implements a smart online menu, they can collect data and offer personalized suggestions based on customers favorite foods, food/drink combos, weather-based food recommendations, upsell, cross-sell personalized etc.all powered by AI.

Restaurants can no Longer Afford Aversion to Technology

Challenges associated with technology, including implementation and a long-roadmap, are fading awaymost technology providers are offering Plug & Play products or services, and most of them are working on a SaaS model. This means theres no commitment, they are easy to use, and integrate seamlessly with the POS.

Restaurants dont have to make a big investment to reap the benefits technology bringstaking little steps that slowly improve restaurant operations and customer experience can still lead to increased growth and higher profit margins, especially during the pandemic when money is tight.

Technology enhances the experience, giving consumers a reason to keep ordering from their favorite places at a time when the stakes have never been so high, and the competition has never been as fierce. The pandemic is far from over but the changes we are seeing will be here for a lifetime. Thats why it is so important to leverage technology and AI now in order to see improvements in customer satisfaction and restaurant efficiency in the long term.

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Why Artificial Intelligence Should Be on the Menu this Season - FSR magazine