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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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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

From the smallest local business to the largest global players, I believe every organization must embrace the AI revolution, and identify how AI (artificial intelligence) will make the biggest difference to their business.

3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success

But before you can develop a robust AI strategy in which you work out how best to use AI to drive business success you first need to understand whats possible with AI. To put it another way, how are other companies using AI to drive success?

Broadly speaking, organizations are using AI in three main ways:

Creating more intelligent products

Offering a more intelligent service

Improving internal business processes

Lets briefly look at each area in turn.

Creating more intelligent products

Thanks to the Internet of Things, a whole host of everyday products are getting smarter. What started with smartphones has now grown to include smart TVs, smartwatches, smart speakers, and smart home thermostats plus a range of more eyebrow-raising "smart" products such as smart nappies, smart yoga mats, smart office chairs, and smart toilets.

Generally, these smart products are designed to make customers lives easier and remove those annoying bugbears from everyday life. For example, you can now get digital insoles that slip into your running shoes and gather data (using pressure sensors) about your running style. An accompanying app will give you real-time analysis of your running performance and technique, thereby helping you avoid injuries and become a better runner.

Offering a more intelligent service

Instead of the traditional approach of selling a product or service as a one-off transaction, more and more businesses are transitioning to a servitization model, in which the product or service is delivered as an ongoing subscription. Netflix is a prime example of this model in action. For a less obvious example, how about the Dollar Shave Club, which will deliver razor blades and grooming products to your door on a regular basis. Or Stich Fix, a personalized styling service that delivers clothes to your door based on your personal style, size, and budget.

Intelligent services like this are reliant on data and AI. Businesses like Netflix have access to a wealth of valuable customer data data that helps the company provide a more thoughtful service, based on what it knows the customer really wants (whether its movies, clothes, grooming products or whatever).

Improving internal business processes

In theory, AI could be worked into pretty much any aspect of a business: manufacturing, HR, marketing, sales, supply chain and logistics, customer services, quality control, IT, finance and more.

From automated machinery and vehicles to customer service chatbots and algorithms that detect customer fraud, AI solutions and technologies are being incorporated into all sorts of business functions in order to maximize efficiency, save money and improve business performance.

So, which area should you focus on products, services, or business processes?

Every business is different, and how you decide to use AI may differ wildly from even your closest competitor. For AI to truly add value in your business, it must be aligned with your companys key strategic goals which means you need to be clear on what it is you're trying to achieve before you can identify how AI can help you get there.

That said, its well worth considering all three areas: products, services and business processes. Sure, one of the areas is likely to be more of a priority than the others, and that priority will depend on your companys strategic goals. But you shouldnt ignore the potential of the other AI uses.

For example, a product-based business might be tempted to skip over the potential for intelligent services, while a service-based company could easily think smart products arent relevant to its business model. Both might think AI-driven business processes are beyond their capabilities at this point in time.

But the most successful, most talked-about companies on the planet are those that deploy AI across all three areas. Take Apple as an example. Apple built its reputation on making and selling iconic products like the iPad. Yet, nowadays, Apple services (including Apple Music and Apple TV) generate more revenue than iPad sales. The company has transitioned from purely a product company to a service provider, with its iconic products supporting intelligent services. And you can be certain that Apple uses AI and data to enhance its internal processes.

In this way, AI can throw up surprising additions and improvements to your business model or even lead you to an entirely new business model that you never previously considered. It can lead you from products to services, or vice versa. And it can throw up exciting opportunities to enhance the way you operate.

Thats why I recommend looking at products, services, and business processes when working out your AI priorities. You may ultimately decide that optimizing your internal processes (for example, automating your manufacturing) is several years away, and thats fine. The important thing is to consider all the AI opportunities, so that you can properly prioritize what you want to achieve and develop an AI strategy that works for your business.

AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

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3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success - Forbes

AI is changing the way customers interact with businesses. AI changes everything with how websites and bots will work along with many other tools and integrated systems. Businesses protect and manage digital assets and data of the company. There is a day-to-day struggle in businesses currently using artificial intelligence, which is made more difficult because of sequential technologies.

Many businesses are intrigued by the idea of turning to artificial intelligence for help in the sales process. AI is certainly capable of finding your best-qualified sales leads. AI can give you efficient issue resolution, and systems that feed actual data back in for future process and product improvements. However, most enterprises do not know where or how to get started with their new company AI.

Systems and data must connect to allow full use of capabilities as if all information were native to each. And also, edgeways to present information to end-users, though data is evolving on a constant basis. The environment requires specialized insight and know-how to ensure a smooth and continuous integration thats both relevant and current.

The intelligent experience is all about leveraging AI to derive predictive insights that can be embedded in the workflow. Companies seeking competitive advantage must find ways to make their business operations more intelligent.

AI functionality is poised to be a game-changer, exploring possibilities and opening up new roles and more business-central activities. However, its important to first understand how intelligent experience can help improve? It starts with a shift in focus.

Artificial intelligence is edging into business processes across organizations, however, when an organization interacts with the use of AI correctly, that shouldnt be a sign AI is running the experience behind the scenes.

AI has the power to make customers feel they are making their choices, but its the machine learning and the algorithms that are handling those decisions.

The most useful sense, when it comes to shifting in focus, is vision keeping track of the ability to give suggestions on how to improve.

Artificial Intelligence is going beyond the senses and going straight to the source the brain. The very reactive tactic, oftentimes, companies are late, identifying customers likely when its too late. This is because there is a major difference between predicting significant changes in the economy and a financial sign that becomes apparent only after a large shift has taken place.

Artificial Intelligence aims to heavily impact a number of industries worldwide shaping online customer experience models. The AI technology will take hold across many industries over the coming decade, and businesses firmly need to decide how AI will help them to optimize conversions.

Automating most internal processes, the operational effort involved in maintaining and controlling devices is reduced. However, simultaneously shifting focus, the marketplace, significantly allows configuration.

More cost-efficiency is rising from artificial intelligence, so customers can focus on increasing the quality and operations of their processes with just an increase in resources.

It is crucial to assess the landscape of the acquisition time period. This often is where perceptive relations start to form. Customers are going to be comparing their initial experience to the expectations entrepreneurs set during the sales process.

Processes of Artificial Intelligence are making significant progress in reducing several walks of life problems. It also provides automation of not-get interpretation and grasping, restructure the information.

With AI, as per the market, you can spur on processes, get value from data, and provide clients with a better experience. All those benefits can help drive sales and boost revenue.

The application of the AI system may now be defined in considerable detail. As of a rule, the cost of Artificial Intelligence requires intelligence on the work being done for proactive development. The development work is usually split into several feasibility studies and set business and project objectives.

However, if Artificial intelligence claims to be a plug-and-play canned legacy, you need to be highly suspicious. You need to have someone trained to take care of this system. (source: coseer.com.)

The sufficient algorithm performance is a key cost-effective factor, as often a high-quality algorithm requires a round of tuning sessions. To decide between various algorithmic approaches towards businesses, one needs to understand how exactly inculcation takes place under the hood, and what can be done to obtain competency.

If it is not clear up-front, one may end up in a situation of not-more-performing. AI is certainly exciting, but business owners cannot jump into it without first laying the foundation with basic analytics.

With so many possibilities for applying AI across an organization, in all likelihood, deploying an AI system must be effective. AI is often considered solely from a technology perspective and little wonder since its capabilities rely onand continually improve throughtechnical innovations.

Deploy with quick-witted positioned skills and a variety of tools to create AI algorithms that can be inserted into enterprise applications. Quick wins bring an added bonus. Meaning that getting the most out of AI is about validating AIs ability to spark value, keeping momentum and funding, and going for longer-term projects.

AI doesnt thrive in a vacuum. Businesses that generate value from AI deal with it as a major business transformation initiative that requires non-similar parts of the company to come together and work with probable expectations. AI is the future of business operations.

When contemplating an investment in AI, be sure you have pragmatic predictions and have a setup that will allow you to embed insights into the daily workflow of your organization. Through the power of AI, you can start blurring the lines between sales, service, and marketing.

The power of artificial intelligence needs a hard edge at business processes and the majority of resources. From there, your company can use AI in a way that actually helps your business grow and ultimately boost your bottom line.

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Adedeji Omotayo is a Digital marketer, PR expert, content writer; the CEO, founder, and president of EcoWebMedia, a full-service digital marketing company. Adedeji is passionate about technology, marketing, and at the same time work with both small and big companies on their internet marketing strategies.

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