AI is such a use of technology that enables machines to act with higher levels of intelligence and consist the human abilities such as decision making etc. In the era of digital transformation, the use of AI in the administration of IP is constantly increasing. AI tools automate decision making using programming rules and, in some cases, training data sets. For example, AI tools can derive credit score measurements from disparate data sets, and detect and recognise objects from image data. The potential for human error is one risk that can be addressed and mitigated by usage of AI. Human lawyers are relatively expensive, inefficient, and slow compared to AI for certain tasks, such as document review. A study conducted in 1985 asked experienced
attorneys and paralegals to use keyword searching in a database of approximately 40,000 documents (roughly 350,000 pages of text) to find the documents that were responsive to 51 document requests. The results were unimpressive, as the search teams only succeeded in identifying less than 20 percent of the relevant documents (far less than their 75 percent goal).4 AI, however, is able to produce much higher results. How AI can be helpful in the administration of IP ? The administration of Intellectual Property is a complex task as it involves huge data which requires intensive time for its arrangement. In addition high costs are also involved in analyzing the IP data. Artificial intelligence has the capability to identify patterns and these patterns are recorded for further analysis. The use of artificial intelligence empowered technologies allows the IP offices to reduce the uncertainty that may otherwise not exist. The core of artificial intelligence is to mimic the human intelligence. It is highly used in various sectors of the economy to simplify the complicated and repetitive tasks so that the human intelligence can be deviated to explore the further dimensions. It reduces the dependence on the manual work as it automates the processes of administration of IP and alert the IP officers in any incorrection or manipulation is identified. The collection of IP data took months or even years to manage and administrate but with the usage of AI empowered technologies, it can be done in minutes and/or few seconds with highest accuracy. This happens because AI collects data from various sources and turn it into sample of adequate sizes. The ability of data driven decision making of AI makes it highly efficient and effective. AI has truly the potential to bring the administration of IP offices to new heights of efficiency and productivity. IP administration can leverage artificial intelligence to sift through millions of complex documents. Technology is changing every day and is becoming much interdisciplinary. AI is a system which learn new conceptual relationships from already available IP data and imposes no bias onto how different IP should interact with each other. This helps to find hidden connections that can lead to explore new opportunities. In the corporate world finding key patents in a portfolio is a complicated task where AI can be much helpful for the evaluation of predefined parameters. One of the biggest advantage of using AI in the administration of IP is that it can record the history of searches, data trends etc. which can be used to predict the future pattern of IP and can take decision immediately with accuracy. Case studies: Use of AI in IP Australia: IP office in Australia is the first fully digital service delivery agency in Australian federal government. This has enables IP Australia to re-imagine the customer experience through the incorporation of AI based tools to support the customers. It has started using AI even to provide decision support to the examination process. The following initiatives are taken by the IP Australia to enable the use of AI. Patent Auto Classification: This tool aims to analyze the contents of a patent application in unstructured PDF documents and predict relevant technology groups which enable prioritization and allocation to different patent examiners. It use internally developed software to build hierarchy classification models to effectively classify the patents. It is intended to save staff time, streamline the tech sorting process and achieve comparable accuracy to current manual process of classification. In early 2018 it was launched as pilot project and it focuses on International Examination Search Systems (INTESS) in which international patent application cases are automatically allocated to the correct team’s work tray for examination. IP office of Australia uses trade mark search – Image Search (Live) to search for existing trademark images based on the given image. It uses the Trademark Vision Image Recognition Software for image search functions. This helps in following ways: 1. Whether the logo or symbol or image is already in use 2. Whether the image can be protected as trademark as per the law 3. Similar trademarks which are already registered and may conflict with the applied image Smart Assessment Toolkit: It is a collection of smart models designed for trademark examination and prediction of possible objections. It is completely internally developed software after trained by dataset of historic adverse reports from 2008 to 2018 to identify similar trademarks. This toolkit has following benefits: 1. Checklist of the key issues that need to be considered 2. Reduce the need for legal advice 3. Simplifies the management of IP IP Australia has designed AI based helpdesk which is an interactive 24*7 tool to educate and assist unrepresented trade mark applicants through the initial stages of trademark application process. It uses publicly available word association models for searching goods and services classification. It works on the model of “Learn, search and apply” by exploring applied trademark to reduce complexity and common applicant errors. It has developed virtual assistant with name “Alex” to guide trademark applicants. Use of AI in Canada: Canadian Intellectual Property Office uses commercially available semantic search engines to assist in conducting searches for prior art and citations. This tool rely on machine learning algorithms to better detect linkages between citations, applications and the current state of the art. Patent examiners in IP Canada also uses Google’s algorithms, specifically within their ‘Translate’, ‘Scholar’ tools for machine translation and access to full text documents and claim forms from contributing international patent offices in real time. For data manipulation it uses ‘Vantage point texting mining tool’ for discovering knowledge in search results from patent database while providing methods to filter, refine, automate, import etc. In early 2018 CIPO was exploring use of blockchain technology to streamline its copyright registration process and attempt to encourage information sharing. Use of AI in China The State Administration of Industry and Commerce (SAIC) has launched Goods system for trademark classification where it allocates goods into similar groups so as to establish the Good Relation Dictionary. With the help of this dictionary the system automatically allocates newly supplied goods into the respective similar group. For goods supplied for the first time, a parent good would be designated. SAIC uses an automatic administrative region matching system to fix an administrative region so as to provide data support for future regional data analysis. SAIC has also developed an image search system providing relatively accurate and reliable results. This system can search backwards to give figurative elements and results would be input into the system after examiner’s confirmation. In this way system can achieve self innovation and self learning and search efficiency would be improved. Use of AI in European Patent Office The EPO has been active in developing business solutions using machine learning and AI for patent searches at various degrees of implementation: Automatic Search of prior art for incoming patent applications; and Automatic generation of queries. Through its DataScience team, the EPO is mainly developing its own AI systems based on open source software libraries that are fit for purpose. The EPO combines its DataScience team`s expertise with business understanding through its examiners and collection of data; i.e. historical saved search data and the EPO prior art corpus. The EPO has been active in identiying migration/penetration trends of specific technologies (Computer Implemented Invention) in other technology sectors. The EPO uses Patent Translate in the area of machine translation but is also developing its own machine learned translation. The Patent Translate tool is made available to the public in EPO patent databases.and used by specially trained patent examiners at the Swedish Patent and Registration Office and UKIPO. The EPO has developed a Patent Document Model (PDM) and its implementation in the Knowledge and Information Management Environment (KIME). Together they enable an enrichment oriented management of patent and other data for machine learning purposes. Automatic pre-classification of incoming patent applications for allocation to corresponding units in charge of search and examination; Automatic Classification of patent documents according to Cooperative Patent Classification (CPC) scheme; Automatic re-classification of patent documents according to changes in CPC scheme. EUIPO uses a commercial multilingual natural language tool called Babelscape for The EUIPO developed an image search system that is integrated in its trademark database called TMVision. The system is used by internal examiners and made available to the public via the EUIPO's website. Youtube use AI in finding infringement of Copyright Youtube has launched its conent ID copyright control system since 2007. Content ID uses audio and visual fingerprinting to detect copyright material when uploaded to youtube’s database. When copright violation is detected then it gives the copyright holder an option to either claim ownership and terminate the duplicate content or allow continuance of duplicate material and reap the ad revenue. The 98% of the copyright violations are found through content ID copyright control system and majority of the copyright owner earn revenue. In recent post, youtube declared that it has updated its content ID system to use smarter fingerprinting that can detect tricks like stretching a video’s aspect ratio, flipping the image horizontally or slowing down the audio. Now content ID can detect melodies as well. In fact, according to Cecile Frot-Coutaz, head of EMEA, YouTube’s “number one priority” is to protect its users from harmful content. In pursuit of that, the company invested in not only human specialists but the machine learning technology to support the effort. AI has contributed greatly to YouTube's ability to quickly identify objectionable content. Before using artificial intelligence, only 8% of videos containing "violent extremism" (banned on the platform) were flagged and removed before ten views had occurred; but after machine learning was used, more than half of the videos removed had fewer than ten views. On the contrary AI can be used in adverse way as well. AI algorithms can, allegedly subtly, tweak the audio in video submissions so that any copyrighted music present can evade detection by YouTube's AI bots after they are uploaded. Boffins at University of Maryland in America reckon their code successfully manipulated the audio in two songs – Stevie Wonder’s smash hit Signed, Sealed, Delivered that peaked at number 3 in 1970; and Kesha’s infectious track Tik Tok, which topped the chart in 2010 – so that after they were uploaded to YouTube, they avoided detection, and still sounded more or less the same as the originals. Challenges of using AI in the administration of IP Definition of AI: The artificial intelligence has a broad scope. Currently there is no universally accepted definition of AI. AI is beyond mere use of technology. There is still consensus yet to be made to identify what should be included and excluded in the definition of AI. Even a system which appears to behave intelligently but it does not have any kind of consciousness or decision making ability about what it is doing, still it is considered as AI by few countries but not recognized by others. For example an online chat system might appear to hold natural conversation but it has actually no sense of who it is or why it is communicating. On the other hand, there are technologies where chat robots behave intelligently and think as human does with a subjective mind. Dynamic: The artificial intelligence is continuously evolving. It is difficult to put AI into the boundaries of regulations. The dynamic nature of AI makes it vulnerable as technology is changing rapidly. Due to its continuous changing nature it is difficult for the under developed and developing countries to keep pace with it. Infrastructure: Without a proper and robust infrastructure it is not possible to incorporate Artificial Intelligence in the administration of intellectual property. The use of AI is still in very initial stage and no one can assure the magnitude of results it can provide. Few developed countries like IP Australia and USFTO have taken initiatives to launch AI in the administration at advanced stage. For building the future workforce who can manage the use of AI in the administration, the nations are required to significantly allocate more resources for scienece, technology, engineering etc. for instance UK government has taken initiative as it had planned to build over 1,000 government supported PhD researchers by 2025. Many countries have instituted dedicated public offices such as Ministry of AI (UAE), and Office of AI and AI Council (U.K.) while China and Japan have allowed existing ministries to take up AI implementation in their sectoral areas. Not just national governments, but even local city governments have become increasingly aware about the importance and potential of AI and have committed public investments. Opportunities of Using Blockchain Technology in IP Blockchain technology has become popular with the use of cryptocurrencies in the form of Bitcoin and its recognition by various countries. In its basic form it is an open ledger of information that can be used to record and tract transactions, which can be exchanged and verified on peer to peer network. This is cost and time effective technology in the context of IP industry and it offer immense possibilities for IP protection. Recording of IP records in decentralized ledger rather than traditional ledger would turn simple IP rights into Smart IP rights. The biggest advantage of using blockchain technology is that it can track the entitre life of the IP rights such as when trademark or patent or industrial design was applied, renewed, transferred or licensed. Another benefit is that it can play a significant role in respect of unregistered IP rights such as unregistered copyright or design. It can provide legal evidence of their conception, use, qualification requirements, country in which it first was recorded and it’s status. In the IP offices Blockchain technology can be used in a number of scenarios to reduce time spend on administrative tasks and maintenance of IP records. Initiatives taken by India The office of the Controller General of Patents, Designs and Trademarks (CGPDTM) issued a notice no. IPO/POD/EOI/2018/1 dated 02/08/2018 titled “ E-Request for Expression of Interest for Making use of Artificial Intelligence, Blockchain, Internet of Things (IoT) and other latest technologies in Patent Processing System of IP Office’’. This EOI is an endeavor to generate competition and receive an expression of interest from interested vendors by invitation of bids open to all the entities registered in India as well as abroad. National Institution for Transforming India (NITI) Aayog is exploring the use of blockchain and Artificial Intelligence in diverse areas. The NITI is exploring a platform called ‘IndiaChain’ – a blockchain enabled infrastructure for Indian enterprises and government. Electronic Data Processing: The government of India is continuously focusing on enhancing the concept of digital India. Digitization and automation of application filing, improved Optical Character Reader (OCR) proof reading, increase in speed of information retrieval. Speed is the essence of AI that makes it so attractive that government is planning to replace traditional methods with AI. Patent Classification: The Office of the controller of Patent, Designs and Trademarks (CGPDTM) aims to classify the patent applications by understanding the natural language to automatically classify them; Software/machine learning capability to build sophisticated hierarchy of classification models to analyze the contents of each patent specification in unstructured PDF documents. Reengineering of IP procedures and reforms in IPO administration have been implemented which include, auto allocation of patent applications for examination across all patent offices to remove disparity in time of examination in similar field of technology, complete electronic processing of Patents and Trademarks applications through specialized modules, dynamic utilities for stage-wise real -time information of patent and trademark applications, e-mail communication by IP offices to stakeholders, online generation of certificates of grant of patent and registration of trademark and sending it to the applicant or his agent through e-mail, redesigning of IPO website for improved contents, real time IP information and ease of access and making it more interactive, informative and easy to navigate. Launching SMS alert service to stakeholders regarding examination reports and time bound actions to be taken Publishing periodic list of first examination reports issued in the Patent Office E-Journal Introduction of facility of e-verification of signature in addition to the present mode of digital signature Developing mobile app services for providing IP information and services to stakeholders. The tenders have been filed by the various domestic and foreign entities with detailed proposed plans. Undoubtedly the government has taken serious steps to incorporate AI based technology and identified opportunities to incorporate them. The process of developing AI based administration of IP is still in initial phase in India and only future will explain how much they are useful. But one thing is clear that it will bring transparency and efficiency in the administration. India has its own challenges which are unique to adopt and lead with such a nascent technology in the administration of IP. In implementation of AI in administration of IP India it has following serious challenges that need to be cope up with. Lack of data enabling ecosystems: In India still the administration of intellectual property is governed by traditional manual based approach. Although various efforts have been made during years but these are not upto the mark such as SMS alert, e-verification, mobile app, online helpdesk etc. The world has moved forward and started building powerful platforms for incorporating AI in the administration. In India the proper infrastructure is yet to be developed to give space to the Artificial Intelligence. It is the need of the hour to build strong ecosystem by consulting with each stakeholder. The IP director at Brose Group Dr. Beate Avenhaus said “IP departments and IP firms have to set up IP tools in a fully electronic format. By doing so, companies should digitalize their data fully and exchange documents not via e-mail with the IP department/advisor but via other cloud or portal based solutions.” Inadequate AI expertise: India’s capabilities in AI research are rather limited both in quantity and quality. We do not have the number of experts that are needed to build fundamentally powerful ecosystem for AI in the administration. The government has still not increased significantly its investment in building human resources to manage AI. Rather there is fear among the working workforce in the administration of IP of losing jobs and learning new skills. The employees are still resisting to learn new skills in AI. The government has not introduced any initiative to allow existing workforce to learn new technologies while working. The changes and challenges anticipated for the workforce will come from both the demand and supply side: demand for capabilities for jobs that don’t even exist today and diminished demand for some of the jobs that could be automated. Disappointingly Indian graduates are focused in routine IT development in larger number rather than innovating AI based technologies. As per the Global AI Talent Report 2018, which crawled LinkedIn for its analysis, India only has 386 of a total of 22,000 PhDeducated researchers worldwide, and is ranked 10th globally. The report also looks at leading AI conferences globally for presenters who could be considered influential experts in their respective field of AI. On this metric, India was ranked 13th globally, with just 44 top-notch presenters. While these two approaches have their limitations and inherent biases, anecdotal evidence based on discussions with top researchers reveals that serious research work in India is limited to less than 50 researchers, concentrated mostly at institutes like IITs, IIITs and IISc. High resource cost and less awareness: Adoption of AI in India has remained rather limited, less than 1% corporates are using AI for administration of Intellectual property and startup ecosystem in AI is virtually non existent. There is still difficulty in access of structured and intelligent data, high cost and low availability of computing infrastructure. Unclear privacy, ethics and security policy: In order to adopt AI successfully, the government needs to ensure adequate privacy, security and IP related concerns and balancing of ethical considerations. AI should not be abused to harm the privacy and security of other IP’s. There are serious concerns and possible threats of cyberattacks and creating mischief. There should be an upper limit to keep checks and balances in using artificial intelligence. Additionally we need to ensure that use of AI should not result in issues of bias, privacy, accountability and transparency. While many express concerns about data privacy, Frank Chen, partner at the venture capital firm Andreessen Horowitz, suggests that some people may be prepared to give up some of their privacy in order to get the services they want. Eleonore Pauwels, Research Fellow of United Nations University (UNU), describes AI as a “glorified data optimization process.” She sees the challenge of developing fair and accurate AI as tied not only to algorithmic design, but also to the datasets used to train it. More data collection involves the highest risk of damaging individuals, which will then damage corporations which will in turn force countries to adopt stricter laws. It is a common perception that data driven decision making and algorithms are just and fair. However this is not always true, existing data may have biases which may not get reinforced overtime. These challenges are not exhaustive but they can be addressed with collaborative approach in a concerted manner by relevant stakeholders with government playing a leading role. Recommendations: Research: India has started building its research work in bringing AI based technologies in its operations. As per the report of world economic forum India has produced 2.6 million science and technology graduates which is second only to China and more than 4 times the graduates produced by the USA, thus India has requisite talented manpower to drive Artificial Intelligence in its operations. Although the research work in India is constantly increasing from past few years but this needs to be rise rapidly and this can happen when government will start investing and allocating significant amount from GDP. One of the serious initiative was taken by Government of Karnataka in setting up a centre of Excellence for Data Science and Artificial Intelligence with NASSCOM. Other state governments should also take like efforts to bring adequate research and development. The Detailed Project Report of Inter-Ministerial National Mission on Interdisciplinary Cyber Physical Systems (IM-ICPS) has suggested the following four-tier framework for promoting research focused on all aspects of technology life-cycle: research, technology deployment, translation and management: a) ICON (International Centres of New Knowledge): focusing on creation of new knowledge through basic research, b) CROSS (Centre for Research On Sub-Systems): focusing on developing and integrating core technologies developed at ICON layer and any other sources c) CASTLE (Center for Advanced Studies, Translational research and Leadership): focusing on development and deployment of application based research and d) CETIT (Centre of Excellence in Technology Innovation and Transfer): focusing on commercialisation of technologies developed Training : Reskilling of workforce is the core of successful use of AI in the administration of IP. In India it is commonly seen that research work in universities is limited to theoretical or laboratory scale. : Large scale experimental test-beds are difficult to construct, maintain and operate, solely by academic institutions. The existing employees should be encouraged to learn new skills and AI based technology such as blockchain, cloud computing, virtual assistant etc. Some people are afraid that AI will take over their jobs, but Belinda Gascoyne from IBM thinks differently: “At IBM we like to see AI as an Augmented Intelligence. It is a tool that helps humans to do their job faster, better and more efficiently rather than replacing their jobs.” Therefore, using AI for managing IP can be very helpful when it comes to simple tasks like patent searching, keyword classification and machine translation. Gascoyne continues “AI is here and now it is not a fiction for the future. The possible areas of training are as follows: a) Sensory AI (Computer Vision, IoT etc.), b) Physical AI (Robotics, Industrial Automation etc.), c) Cognitive AI (NLP, worker training etc.), d) General AI, e) High precision learning from small data sets, f) Research on new algorithms (e.g. advance cryptography, security), data sets etc., and g) Explainable AI Common Platform for learning: There is a need of hour to set up AI database to access and find the relevant information. The database can serve as the centre for all information, primarily managed by the government and serve as a single source of all resources. This can also serve as a forum for sharing various discussions related to research collaboration, and finding relevant cooperation. Data sharing: To incentivize a larger supply of AI training data sets and services, it is necessary to ensure availability of data, an audit trail mechanism to curb reselling of the same data, and ways to address security and privacy concerns. One solution is to have a centralised, trusted party to host the data on behalf of IP offices and decides the rules of sharing data among them. A more effective way to address these concerns is a decentralised data marketplace that is based on blockchain technology. The exchange platform should have the following features for data providers to share data: a) Traceability, b) Access Controls, c) Compliance with local and international regulations, and d) Robust price discovery mechanism for data Partnerships and collaboration: For success of any new development, partnership and collaborative efforts of stakeholders are necessary. In France, the PRAIRIE (Paris Artificial Research Institute) is a collaboration of industry and academia supported and led by the french government to create an institution which becomes an international benchmark in AI. The five year objective of this institute is to bring together AI scientific and industrial leaders and it a world leader in AI. Spreading Awareness: Steve Gong, Head of Intellectual Property Management Software at Google said “Technical transformation is only an aspect of digital transformation, but a big part of it is also cultural change. You must be able to see yourself in a new light. It is not just about automating something that is already done – it is about reinventing processes. That is really hard.” In fact, the Google Manager admits that the legal industry is very slow as far as digital transformation is concerned. This is because a lot of lawyer’s work depends on certain circumstances that cannot be measured or verified easily. However, he is convinced that they will still find a way for digital transformation. For Steve Gong, a tool is only valuable if specified business goals can be quantified and measured. This measurement is what makes technology in IP a challenge. The lack of awareness needs to be tackled carefully. The protection of this right with its multiple facets in a fast-changing technological environment will not just depend on State enforcement but by also making the citizens aware of their rights and how they can protect them. People often unknowingly give consent to sharing their data which they would not have ordinarily done had they known the purpose their data were being put to. New Legislations: A study by EY and NASCCOM found that by 2022, around 46% of the workforce will be engaged in entirely new jobs that do not exist today, or will be deployed in jobs that have radically changed skillsets. If some countries decide to wait for a few years to establish an AI strategy and put in place the foundations for developing the AI ecosystem, it seems unlikely that they would be able to attain and match up to the current momentum in the rapidly changing socio-economic environment. Therefore, the need of the hour is to develop a policy framework that will help set up a vibrant AI ecosystem in India. Dealing with Privacy issues: Sometimes AI collects data in non transparent way which can results in collection of data without consent, biasness, risk of discrimination etc. These are some of the issues which are required to be addressed in proper way. A committee under the leadership of Justice Srikrishna was formed on data protection law which suggested 7 core principle values of data protection and privacy such as informed consent, data controller accountability, data minimization, technology agnosticism, holistic application, deterrent penalties and structured enforcement. Adhering to international standards can result in preserving private data. European Union’s General Data Protection Regulation (GDPR) guidelines, which have been enforced in May 2018, encourage design of less-privacy invasive systems. French laws give a right to explanation for administrative algorithmic decisions, making it much more comprehensive than GDPR on administrative decisions. India’s privacy protection regime will have to be continually updated to reflect understanding of new risks and their impact. CONCLUSION Most of the international IP offices have reported that they are satisfied with the use of AI empowered technologies in their administration. The continuous experiments and development of in house AI enables systems have proved that use of AI in IP offices is reliable and accurate. The extent to which AI based applications are used is still limited to a largely predictable and patterned tasks which are of repetitive nature with a notable exception of US IP offices which as established its own in house advance analytical program to enhance an understanding of polices, processes and work flows. Although it is difficult to use AI applications for administration of more sophisticated tasks. Therefore human intervention is necessary to check and balance the administration and make it upto standards. Artificial intelligence is not to replace human intelligence rather it is used to complement with human intelligence.
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