Index of AI initiatives in IP offices

80 record(s) found.

Country / Territory InstitutionBusiness applicationDescription
AustraliaIP AustraliaExamination (trademark, patent)

IP Australia is developing a Trade Mark International Classification Services (TMICS) to assist users when searching the Madrid Goods and Service (MGS) database. Leveraging Natural Language Processing (NLP) models (sentence-transformers), TMICS helps the user find goods and services that not only match search terms entered but are also semantically related. This reduces the number of queries the user needs to perform in order to identify relevant goods and services, saving users time and effort, while also helping to improve the quality of trade mark applications when filing overseas.

Additionally, TMICS delivers business intelligence benefits to IP Australia through semantic comparison of the Australian Picklist with the MGS database to determine gaps in coverage.


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AustraliaIP AustraliaExamination (trademark, patent)

IP Australia completed an alpha minimum viable product of the Trade Mark Precedent Identification (TMPI) tool in July 2022. TMPI has been built to retrieve, rank, display and extract substantially identical text trade marks from the Australian trade marks register during examination.

It uses a combination of well-defined, automated business rules and Natural Language Processing (NLP) techniques including key word extraction, segmentation, lemmatisation, spelling correction and character replacement to ensure the automated search query includes relevant variations of the trade mark being searched. A pilot is planned to begin in the first quarter of the 22/23 financial year which will be used to gather feedback and further refine the accuracy of the tool.

The new search function intends to improve quality and consistency, when searching for substantially identical marks and increase the decision making capability of trade mark examiners, by providing them ready access to highly relevant information.


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AustraliaIP AustraliaImage search (trademark, design, patent)

IP Australia’s Trade Mark Image Search project was initiated in October 2021. The project is working to complete an assessment of the current image search capabilities and functionalities available across the organisation and provide recommendations on the future strategic direction and use of these technologies.

IP Australia uses Australian Trade Mark Search to allow users to search for existing trade mark images based on an uploaded image, which allows for comparison and relevance ranking of results. Australian Trade Mark Search uses the commercially available Clarivate Image Recognition software for our image search functionality. However, the project is looking to assess whether this functionality could be brought in-house, whether another service offering may be worth exploring or if enhancements are required within the current tool to meet the needs of the business.


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AustraliaIP AustraliaHelpdesk services

TM Embed is an AI-assisted, free, trade mark availability check being developed to embed in willing third-party business services and platforms (such as e-commerce platforms, domain registries, business accounting, legal services, etc.). The intent is to help small businesses as yet unfamiliar or not confident with intellectual property to consider whether a trade mark is appropriate for them. It is also intended to equip users with the necessary information to make an informed decision that is right for them. This is currently a pilot activity.


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AustraliaIP AustraliaPatent classification

IP Australia's Patent Auto Classification (PAC) service analyses the contents of a patent specification and predicts relevant technology groups enabling prioritisation and allocation to appropriate patent examiner sections.

The service is an internally-developed machine learning-based system, that performs technology sorting of patent applications using a sophisticated hierarchy classification model. The system allocates relevant International Patent Classification (IPC) marks based on the extracted abstract, descriptions and claim texts form the patent specifications. This in turn allows for the distribution of patent applications to the appropriate patent examination section, based on the top IPC mark returned from the model.

PAC replaced the manual process previously in place that required a patent examiner to perform a technology sorting function: to read a patent

specification, make a decision on the most appropriate IPC classification, and assign that application to the appropriate examination section for search and examination.


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AustraliaIP AustraliaExamination (trademark, patent)

IP Australia’s Family Member Analyser (FMA) tool provides patent examiners with direct links to family members and documents from their electronic dossiers (where available) during patent examination. Examiners will often consider observations made in Foreign Examination Reports (FERs) of closely related patent family members to improve examination quality and to avoid duplication of work where appropriate.

To assist with this process, FMA is used to retrieve and identify the most relevant family members suitable for examination purposes. Natural Language Processing (NLP) is used to perform a pairwise comparison between the claims of the family member and those of the queried application. A relevance score, which can be viewed by the user, is then assigned to each family member based on the comparison. FMA also facilitates a deep dive into FERs using the FER Feature Analyser (FFA) function that searches examination reports for novelty and inventive step objections and presents these specifically to examiners for their assessment.


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AustraliaIP AustraliaExamination (trademark, patent)

IP Australia’s Automated Preliminary Search tool (APST) executes an automated search query at the start of the search and examination process, looking for potential prior publication by the applicants or inventors named in a patent application. The tool searches IP Australia’s non-open to public inspection (OPI) database, as well as an external OPI data sources. The default query is based on the applicant and inventor names, as well as Cooperative Patent Classification (CPC) and International Patent Classification (IPC) symbols, and can be further refined by users. Natural Language Processing (NLP) is used to compare the potential citations as well as claims with the input application and provide a relevance ranking to the user.


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AustraliaIP AustraliaExamination (trademark, patent)

In Australia, the Commissioner of Patents may direct an applicant to request examination for a patent application. This process is known as a ‘direction’ and is used to manage the inventory of applications and examination requests within IP Australia.

IP Australia’s Outcome Based Directions service uses a machine learning model to identify applications that are ‘ready’ and ‘interested’ in pursuing examination, and issues directions to request examination in the order determined by the model. This offers an improvement to the process of the Commissioner of Patents issuing directions to applicants to request an examination of their patent application previously based entirely on chronological order.


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AustriaThe Austrian Patent OfficePatent classification

In early 2018 the Austrian Patent office was trialing several commercial providers for application of AI to pre-classification and classification of patents.

AustriaThe Austrian Patent OfficePatent prior art search

In early 2018 the Austrian Patent office was trialing several commercial providers for application of AI to pre-search of patents.

BrazilNational Institute of Industrial Property (INPI)Patent classification

In early 2018 INPI was undertaking an initiative to develop a neural network for internal automatic pre-classification of patent applications according to the International Patent Classification (IPC) and/or the Cooperative Patent Classification (CPC) for subsequent distribution of the applications among its technical divisions. INPI was considering Math Lab as the most adequate tool for this initiative.

CanadaCanadian Intellectual Property Office (CIPO)Copyright registration

In early 2018 CIPO was exploring the viability of using blockchain to streamline its copyright registration process and attempt to encourage information sharing by rights holders.

CanadaCanadian Intellectual Property Office (CIPO)Patent prior art search

CIPO's Patent Branch uses commercially available semantic AI search engines (Questel, STN, Clarivate Analytics) to assist in conducting searches for prior art and citations. These tools rely on machine learning algorithms to better detect linkages between citations, applications, and the current state of the art.

Patent examiners also make use of Google's algorithms, specifically within their "Translate", "Patent", and "Scholar" tools for machine translation and access to full-text documents and claims forms from contributing international patent offices in real time with citation metrics and related scholarly publications.

For data manipulation CIPO uses the Vantage Point text-mining tool for discovering knowledge in search results from patent and literature databases while providing methods to refine, automate, import, etc. the raw data produced.

CanadaCanadian Intellectual Property Office (CIPO)Data analysis

CIPO's Economic Research and Strategic Analysis Unit uses AI to help them conduct semantic searches and to collect, scrub, and analyze large datasets.

In the context of ongoing economic research, CIPO planned in early 2018 to explore the feasibility of machine learning to answer IP policy and research questions.

CanadaCanadian Intellectual Property Office (CIPO)Helpdesk services

In early 2018 CIPO was exploring the use of the IBM Watson suite of tools to conduct engagement with clients through social media outreach and analytics.

ChileNational Institute of Industrial Property (INAPI)Image search (trademark, design, patent)

In early 2018, INAPI and the Engineering School of the University of Chile were developing an image search system based on an algorithm developed by the Engineering School.

The system was trained with the image database of INAPI and was under evaluation with the trademark examiners.

ChinaState Administration for Industry and Commerce (SAIC)Trademark classification (goods & services)

SAIC's Standard Goods System allocates goods into similar groups so as to establish the Goods Relation Dictionary. With this dictionary, the system automatically allocates newly-supplied goods into the respective similar group. For goods supplied for the first time, a mother good would be designated.

ChinaState Administration for Industry and Commerce (SAIC)Image search (trademark, design, patent)

In early 2018 SAIC was developing 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 examiners' confirmation. In this way, the system can achieve self-innovation and self-learning and search efficiency would be improved.

ChinaState Administration for Industry and Commerce (SAIC)Data analysis

SAIC uses an Automatic Administrative Region Matching System to fix an administrative region so as to provide data support for future regional statistical analyses.

ChinaState Administration for Industry and Commerce (SAIC)Machine translation

At present, CNIPA has translated part of foreign patent data into Chinese by using machine translation technology, so that the examiners can search and browse foreign patent data in Chinese.

ChinaState Administration for Industry and Commerce (SAIC)Patent classification

For invention and utility model patents, CNIPA has developed IPC automatic classification system, which carries out batch pre classification for newly applied patents. The automatic classification system could give precise results in subclass level. For design patents, CNIPA has also developed LOC automatic classification system based on text information, which can give precise results in subclass level.

ChinaState Administration for Industry and Commerce (SAIC)Patent prior art search

In 2021, CNIPA launched the new intelligent search system and the intelligent semantic search technology was applied in the system. The intelligent search function mainly provides 2 search modes. The first one is the automatic search mode. When the examiner clicks on the "Semantic Search" button,the system will push similar documents, which are sorted by similarity.

The second search mode is semantic sort to Boolean search results. The examiners could do Boolean search and get the results in the first step, and then conduct the semantic sort based on the results. This may help the examiner to view the closest documents, and improve the efficiency of finding reference documents.

Czech RepublicIndustrial Property Office of the Czech Republic (IPO CZ)Examination (trademark, patent)

Starting from 2024, the Industrial Property Office of the Czech Republic plans to launch internal AI examination support tool, which will help patent examiners with the file distribution, classification of patent applications and prior art search.

Czech RepublicIndustrial Property Office of the Czech Republic (IPO CZ)Image search (trademark, design, patent)
Patent classification

In terms of the introduction of automated search and classification system, the Industrial Property Office of the Czech Republic has run the proof of concept. It confirmed that such a project is helpful and needed. The preparatory phase of this project has been finished and the development work will take place in next two years. The service should be ready for public use starting from 2024.

Czech RepublicIndustrial Property Office of the Czech Republic (IPO CZ)Helpdesk services

The Industrial Property Office of the Czech Republic is currently working on creation of automated IP helpdesk which will be nonstop available to the users. The idea is to start with the provision of IP related advice in general. Later the Office wishes to enlarge this service and provide also the information dedicated to procedures of different IP applications. In this context, the Office would like to take advantage of the cooperation with Czech universities and introduce a chatbot to improve the helpdesk service. For enhancing this service, the Office will also analyze the use of voice recognition.

European UnionEuropean Patent Office (EPO)Image search (trademark, design, patent)

The EPO has been active in developing business solutions using machine learning and AI for patent searches at various degrees of implementation including automatic figure and image search for patent drawings.


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European UnionEuropean Patent Office (EPO)Patent prior art search

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.

The EPO has generated its own reference data (gold standards) and system for measuring the performance of automated search tools.

The EPO also makes use of commercial products in the automatic annotation area through software providers in different projects.


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European UnionEuropean Patent Office (EPO)Patent classification

The EPO has developed business solutions using machine learning and AI for patent classification at various degrees of implementation: 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.


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European UnionEuropean Patent Office (EPO)Examination (trademark, patent)

The EPO has been active in developing business solutions using Machine Learning and AI to manage patent files at various degrees of implementation: Automatic annotation of patent literature; Automatic detection of problem/solution in patent document; Automatic detection of exclusion from patentability;

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.


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European UnionEuropean Patent Office (EPO)Machine translation

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.


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European UnionEuropean Patent Office (EPO)Data analysis

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.


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European UnionEuropean Union Intellectual Property Office (EUIPO)Machine translation

EUIPO is making use of machine translation for Case Law documents through eSearch Case Law. The Office provides automatic translations in the website for EUIPO decisions. This allows the user to grasp the main idea of the content of the decision.


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European UnionEuropean Union Intellectual Property Office (EUIPO)Image search (trademark, design, patent)

The EUIPO has developed an in-house image search system that is integrated in eSearch plus to search Trademarks and designs using images.


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European UnionEuropean Union Intellectual Property Office (EUIPO)Trademark classification (goods & services)

EUIPO is making use of AI based semantic search for Goods and Services in Easy Filing helping users to find the right protection for their trade marks.


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European UnionEuropean Union Intellectual Property Office (EUIPO)Helpdesk services

EUIPO has its first Chatbot included in Easy Filing helping users to ask trademark related questions using standard responses with a possibility to go back to an Human agent.


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European UnionEuropean Union Intellectual Property Office (EUIPO)Examination (trademark, patent)

EUIPO has created an algorithm assess a given pair of goods and/or services and provide a prediction as to the outcome of the comparison based on the historical data together with finding the closest semantically relevant matches. The tool is only available for examiners at the moment

European UnionEuropean Union Intellectual Property Office (EUIPO)Trademark classification (goods & services)

EUIPO has developed AI based tools to extract relevant information from letters and make decisions based on this information. EUIPO has applied this technique to analyse Classification, formalities and AG deficiencies in trademark applications and to analyse the deficiency rate and grounds in Design applications.

FinlandFinnish Patent and Registration Office (PRH)Patent prior art search

A patent search system by Finnish startup IPRally Technologies Ltd. was deployed for all patent examiners at the Finnish Patent and Registration Office in March 2020. We chose the system after comparing it to other AI based patent search systems that could be installed on our own servers, such as IPScreener and Teqmine. The search system by Teqmine Analytics Ltd. was installed on our servers previously (from late 2017 until early 2020), but it was never deployed for all examiners. In addition, we have previously tested other systems, such as InnovationQ Plus.

IPRally's system builds a graph from an input text that typically includes the claims and description of a patent application, where the dependencies between concepts are modeled by the structure of the graph. This graph is input into a Graph Neural Network (GNN), which produces a high-dimensional vector that can be compared to vectors produced from prior art patent documents. The system is trained using a large amount of search report data for patent publications. The output format from the system was customized so that it can be easily transferred into our existing search tools. An examiner can thus analyze whether the prior art publications represented by the closest vectors, i.e. top ranked documents, are actually relevant prior art.

The system performs better than the systems we have previously tested. In real world testing, a prior art document that was eventually used by an examiner to deny novelty or inventive step in the first office action was found among the top 20 ranked documents in more than 40 per cent of the cases. At least in some cases, using the system may thus allow for a faster search or for a better quality search.

The system can also be used to find possible classifications for a patent application by determining the most common classification symbols for the top ranked documents.

FinlandFinnish Patent and Registration Office (PRH)Patent classification

A patent search system by Finnish startup IPRally Technologies Ltd. was deployed for all patent examiners at the Finnish Patent and Registration Office in March 2020. We chose the system after comparing it to other AI based patent search systems that could be installed on our own servers, such as IPScreener and Teqmine. The search system by Teqmine Analytics Ltd. was installed on our servers previously (from late 2017 until early 2020), but it was never deployed for all examiners. In addition, we have previously tested other systems, such as InnovationQ Plus.

IPRally's system builds a graph from an input text that typically includes the claims and description of a patent application, where the dependencies between concepts are modeled by the structure of the graph. This graph is input into a Graph Neural Network (GNN), which produces a high-dimensional vector that can be compared to vectors produced from prior art patent documents. The system is trained using a large amount of search report data for patent publications. The output format from the system was customized so that it can be easily transferred into our existing search tools. An examiner can thus analyze whether the prior art publications represented by the closest vectors, i.e. top ranked documents, are actually relevant prior art.

The system performs better than the systems we have previously tested. In real world testing, a prior art document that was eventually used by an examiner to deny novelty or inventive step in the first office action was found among the top 20 ranked documents in more than 40 per cent of the cases. At least in some cases, using the system may thus allow for a faster search or for a better quality search.

The system can also be used to find possible classifications for a patent application by determining the most common classification symbols for the top ranked documents.

GermanyGerman Patent and Trade Mark Office (DPMA)Digitization and process automation

Many trademark applications are classified fully automatically at the DPMA.

GermanyGerman Patent and Trade Mark Office (DPMA)Patent prior art search

In 2016 the DPMA initiated a project for the implementation of a central service for the prior art search in various data sources of the DPMA (e.g. electronic file, specialist databases, etc.). The central service uses algorithms to improve textual similarity search.

GermanyGerman Patent and Trade Mark Office (DPMA)Patent classification

In 2011 a tool for automated International Patent Classification (IPC) was introduced at the DPMA as part of the Electronic Patent and Utility Model Management System (DPMApatente/gebrauchsmuster). This classifier, based on heuristic algorithms, provides a preliminary assignment of an IPC class to incoming patent applications and thus helps to route them to the appropriate patent examiners. The main disadvantage of this black-box tool is that it is not flexible, cannot be parameterized and is not suitable to cover diverse use cases at the DPMA.

Therefore, a new automated patent classification tool was developed as part of the new Patent Search System. After evaluation of different technologies a methodology based on neural networks with "distributed word representations" was chosen. As a first step an automated categorization at IPC sub class level was analyzed with the quality measures "Top Prediction" and "Three Guesses". Experiments with different training sets consisting of selected publications of German patent applications, granted patents and utility models from 2010 - 2015 were carried out.

The classifier implementation includes a pipeline mechanism with data preparation, training and evaluation. At each step parameters can be configured and partial results can be viewed thus providing flexibility and transparency of the whole classification process. The classifier can scale up regarding the IPC classification space and shows an acceptable performance of the training process. It is very fast at online classification of unknown texts.

The range of possible applications for the new classifier at the DPMA includes: Automated pre-classification of incoming patent applications (to improve the distribution of patent applications among the patent examiners); Interactive classification (to assist patent examiners by providing several predictions at a given IPC level); Re-classification (to support the introduction of new versions of the IPC); Continuous quality improvement of IPCs of prior art patent documents. A web-service built upon the classifier will provide on-the-fly IPC prediction for a given patent document part like the abstract, claims or description.

JapanJapan Patent OfficeExamination (trademark, patent)

The JPO is considering possible uses for AI to implement examination management tasks such as appropriate distribution of applications effectively and efficiently.


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JapanJapan Patent OfficeTrademark classification (goods & services)

The JPO is validating its systems to verify possible uses for AI to assign trademark classifications of designated goods and services. Using reference materials, such as the Examination Guidelines for Similar Goods and Services (which include many examples of specific goods and/or services and their appropriate similar-group codes), the JPO is testing functions to assign tentative similar-group codes to unclear designated goods and services in trademark applications.


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JapanJapan Patent OfficeImage search (trademark, design, patent)

The JPO is validating its systems to verify possible uses for AI to conduct prior searches of figurative trademarks. Using past results in prior searches of figurative trademarks, the JPO is validating functions to retrieve prior figurative trademarks by inputting image data of claimed figurative trademarks, which might be identical with, or similar to, the claimed trademarks.

The JPO is also validating its systems to verify possible uses for AI to conduct prior design searches.


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JapanJapan Patent OfficePatent prior art search

The JPO is validating its systems to verify possible uses for AI to support conducting prior art searches. (1)Using text data of examined patent documents and the retrieval history of search queries used in the examinations, the JPO is validating a function to suggest keywords and patent classifications that should be included in search queries. (2) Using image data of already filed documents, the JPO is validating functions to retrieve (i) images similar to designated patent image and (ii) images which are of specific types (like flowchart or circuit diagram). (3) Using past results in prior searches of patent, the JPO is validating a function to sort retrieved documents so that examiners can check the most related document first.


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JapanJapan Patent OfficePatent classification

The JPO is validating its systems to verify possible uses for AI to assign patent classifications. Using text data of already filed documents to which patent classifications were assigned, the JPO is verifying a function to assign patent classifications (FI/F-terms)to foreign patent document.


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MoroccoMoroccan Industrial and Commercial Property Office (OMPIC)Data analysis

OMPIC uses its Qlikview system to manage big data from its various databases, irrespective of where it is stored, and create a statistical database for reporting and quality control. The solution generates information on the fly, compresses data and stores it thereby ensuring its availability for immediate searching by multiple users without being limited by predefined paths in the hierarchy or preconfigured dashboards. This solution responds well to the needs of OMPIC and its clients. Reliable and easy to use, it has automated the creation of different dashboards presented in graphical or tabular form. The tool is used to generate a statistical barometer for industrial property for the general public and is available at www.barometreompic.ma.

MoroccoMoroccan Industrial and Commercial Property Office (OMPIC)Digitization and process automation

OMPIC employs an AI-assisted Optical Character Recognition (OCR) technology by ABBYY to convert images of documents into machine-encoded text. The technology obtains information from PDF files and introduces them into OMPIC databases following a well-defined structure (template). Check rules are then applied to ensure accuracy and incorrect data are passed for video-coding. OCR technology reduces processing delays in extracting data managed by OMPIC and reduces the cost of manual entries of more than 1 million documents. The positive experience also extended to the processing of patent documents.

MoroccoMoroccan Industrial and Commercial Property Office (OMPIC)Patent prior art search

Since 2011, OMPIC uses Orbite Intelligence, a commercially available AI-powered patent analytics tool to searche global patent applications by technical domain or keywords. The map-based tool was introduced to meet the needs of the Moroccan Technology and Innovation Support Center network for searching the state of the art and the precedence of patents.

NorwayNorwegian Industrial Property Office (NIPO)Image search (trademark, design, patent)

NIPO uses a commercially available tool (Acsepto for trademark version 10, by Sword-Group) for trademark image search. The search results (hit list) are prioritized based on AI-assisted search on image property coding The AI technology used is commercially available trained algorithms for coding and trained search algorithms for coding of images.

NorwayNorwegian Industrial Property Office (NIPO)Image search (trademark, design, patent)

Distributed Ledger Technology / Blockchain, utilizing Ethereum II, for registration and maintenance of licenses and pledges to any IP-right. A pending project with conclusion in 2023. Goal to create a distributed register handling all registration and maintenance of licenses and pledges, such as mortgages, in order to increase availability to and transparency towards valuable data sets, create possibility of 100% self-services, and to allow businesses to create value added services.

PhilippinesIntellectual Property Office of the Philippines (IPOPHL)Patent prior art search

IPOPHL uses a third-party search engine called DTSearch for its patent search operations. Similar to all other search engines, the system has the capability to perform incremental index, fuzzy search, and other functions. Although the said system is a low-end of AI, it is more powerful than the traditional database search.

PhilippinesIntellectual Property Office of the Philippines (IPOPHL)Digitization and process automation

IPOPHL uses COGNOS, a commercially available Business Intelligence Software, to support the management reporting requirements of the Office. In using this system, IPOPHL undertakes an ETL (extract-transfer-and load) process from the IPAS database into COGNOS readable packages.

Republic of KoreaKorean Intellectual Property Office (KIPO)Helpdesk services

In early 2018 KIPO planned to develop and refine a pilot model of an AI patent customer service system based on text and voice recognition over the next three years.


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Republic of KoreaKorean Intellectual Property Office (KIPO)Patent prior art search

In April 2017, KIPO established an AI agreement with the Korean Electronics and Telecommunications Research Institute (ETRI). Together they are working to build a patent knowledge base for AI learning and cooperate on research for applying their developed AI system in IP administration.

A pilot model for intelligent patent search is under development with plans to be finalized by 2019. This model enhances the quality of prior art search by moving away from keyword search to a search system based on syntax and semantics.


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Republic of KoreaKorean Intellectual Property Office (KIPO)Machine translation

KIPO established a database using Patent Publication Data on the IPC Section H for machine learning. The database will be compiled with 100,000 terminological entries of patent technology and one million pieces of patent language analysis and drawing tagging information.


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Russian FederationFederal Service for Intellectual Property (Rospatent) / Federal Institute of Industrial Property (FIPS)Patent prior art search

Initiatives on the implementation of AI for search were launched in 2017. In 2018 it was put into operation for the examination of applications for inventions and utility models. Patent documents similarity search function implemented in the PatSearch system is being performed through a set of AI methods and techniques in combination with the best world information search practices. Currently the similarity search function operates in the database of Russian patent documents. A neural network trained on Russian patent documents on the criteria that take into account the complexity of the examination procedure and experts' experience is used for the establishment of the Russian distributional thesaurus.


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Russian FederationFederal Service for Intellectual Property (Rospatent) / Federal Institute of Industrial Property (FIPS)Trademark classification (goods & services)

Currently a new search engine for marks, GI and AOs is being developed and is going to be put into operation in the summer 2020. The new system uses neural networks for image similarity search as well as for intelligent word recognition on the mark (in respect of semantic similarity of terms). Moreover, the system functions include recognition of information on the mark (image) for indexing, namely the intelligent word recognition on marks and its automated classification under the Vienna classification. At this stage technical solutions for image search are being tested.


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Russian FederationFederal Service for Intellectual Property (Rospatent) / Federal Institute of Industrial Property (FIPS)Machine translation

Initiatives on the implementation of machine translation for patent documents were launched in 2015 within the frameworks of PatSearch system development. Translation is performed using the hybrid machine translation system developed by the Russian company PROMPT. The system comprises methods of thorough linguistic analysis. The neural network has been created through machine learning methods using parallel texts of patent documents in both Russian and English. Besides the translation of patent documents, the system is also capable of translating CPC into Russian.


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SerbiaIntellectual Property Office of the Republic of SerbiaDigitization and process automation

IPO-RS uses WIPO Patent OCR Proofreading Platform. The WIPO OCR platform has potential capabilities to use machine learning for improved OCR proofreading. Due to limited local language resources in ABBY OCR (inadequate dictionary and grammar rules), machine training still adds minor value to OCR proofreading quality. The major problem which degrades OCR correctness in their experience was the presence of multiple scripts used in documents (Serbian Cyrillic, Serbian Latin, English, Chemical and Mathematical formulas).

In early 2018, IPO-RS was planning to take advantage of machine learning during manual OCR proofreading (provided by WIPO), in order to enhance dictionaries and design specific processing rules for patent documents in the Serbian language.

SerbiaIntellectual Property Office of the Republic of SerbiaMachine translation

In the scope of EPO Patent machine translation project, IPO-RS provided corpora of full-text patent specification document pairs (Serbian/English) for specific machine translation learning purposes. In early 2018, the specific machine translation tool for Serbian language in the Espacenet database had not produced satisfactory results.

SingaporeIntellectual Property Office of Singapore (IPOS)Examination (trademark, patent)

In early 2018 IPOS was exploring the feasibility of implementing a patents auto checker that uses Natural Language Processing (NLP) and other machine learning technologies to perform formalities check automatically.

SingaporeIntellectual Property Office of Singapore (IPOS)Image search (trademark, design, patent)

IPOS has implemented a commercial AI-powered image-based search solution on both our e-services web portal and our mobile app (IPOS GO). The solution enables the public and examiners to efficiently search for both visually similar trademarks as well as the conceptually similar trademarks.

SingaporeIntellectual Property Office of Singapore (IPOS)Patent classification

From 2019, IPOS was exploring the feasibility of implementing a patents auto classification tool that uses Natural Language Processing to understand patent documents and aid the examiners in classifying incoming patent applications.

SpainSpanish Patent and Trademark Office (OEPM)Digitization and process automation

In recent years, a process of discovery, analysis and implementation of possible robotizable processes, applying RPA technology, has been carried out continuously at the OEPM. All departments of the OEPM are involved in this process. Three phases should be highlighted:

- Pilot project 1: Implementation of the process of sending the original copy to WIPO, using proprietary "on-premise" technology: the risk that the proposed technology could lead to a situation of "technological captivity" was observed. In addition, the licensing model required an initial investment in licenses and infrastructure, which did not justify the return on investment.

- Pilot project 2: Implementation of a process for sending original copy to WIPO, using pay-per-use technology, installed in the cloud: The process is currently in production, and is executed on a daily basis. The technology tested opens the door to the robotization of additional processes. For the time being, the OEPM lacks the necessary experience to carry it out autonomously, so it will have to rely on expert suppliers. The licensing model applicable to this second pilot seems to ensure the maintainability of the project.

- Next steps: Detection and analysis of other processes to robotize with the technology used in the second pilot used in the second pilot.

SpainSpanish Patent and Trademark Office (OEPM)Image search (trademark, design, patent)

The OEPM is currently in a process of continuous analysis of state-of-the-art tools and services, which may be useful in different areas of the OEPM. In this context, contacts are being made with cloud service providers of recognized prestige to study the possibility of applying their capabilities, either directly or through specific training and development, to the needs of the OEPM in relation to image comparison. This functionality would be of direct application in the field of Detection of Priorities of Distinctive Signs.

SwedenSwedish Patent and Registration Office (PRV)Machine translation

Patent examiners at PRV use the machine translation services provided by the European Patent Office in EpoQueNet and Espacenet.

SwitzerlandSwiss Federal Institute of Intellectual Property (IPI)Digitization and process automation

The IPI uses classical rule based AI (Bosch SI Visual Rules and Camunda BPM) for process automation (eg. applications for rulings / decisions with fees or deadlines). According to IPI, rule based process automatism has the highest potential in reduction of repetitive administrative work. Basically, all decisions triggered by fees or deadlines, including document creation, are possibly automatized and the automatism works reliable. The IPI has central real time monitoring and strictly uses Business Process Modeling Notation (BPMN) processes for automatism.

In early 2018 the IPI was about to launch self-learning AI for document classification using ABBYY Smart Classifier. The IPI continuously trains the self-learning AI with manually classified documents. The IPI automatically analyses the quality of results from the self-learning AI and then decides if manual confirmation is needed. The manual confirmation is then used to enhance the training set for the AI.

In early 2018 the IPI had plans to launch self-learning AI for information extraction using ABBY InfoExtractor for advanced corporate search.


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United KingdomIntellectual Property Office (UKIPO)Machine translation

UKIPO patent examiners are trained to use the European Patent Office's Patent Translate tool, and may use publicly available machine translation tools where appropriate.


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United KingdomIntellectual Property Office (UKIPO)Patent prior art search

The UKIPO trialed Derwent Innovation, a commercially available patent search tool. The tool comprises a semantic/smart search functionality that allows large amounts of plain text (e.g. claims, description) to be used as a search input. The search tool also has the ability to search non-patent literature alongside patent documents. Further features include the ability to manually set weightings of individual search terms in order to rank results in an answer set.


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United KingdomIntellectual Property Office (UKIPO)Patent classification

In early 2018, the UKIPO had conducted small-scale trials of commercially available automated tools, both for allocating patent applications to examining groups based on areas of expertise, and for applying classifications to applications. The existing tools could not match the 80 per cent manual success rate achieved by human allocators, but could be used to aid the allocators by suggesting possible destinations for the application which alone might speed up the allocation process.


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United States of AmericaUnited States Patent and Trademark Office (USPTO)Examination (trademark, patent)

The USPTO has a program combining AI with big data and machine learning for application in several fields including: the provision of the most useful and relevant information to determine patentability by an examiner; textual analysis of patent applications and subsequent office actions to analyze the entire patent prosecution history; and improving the application programming interfaces to provide better access to the public to USPTO data.

The program is developed n-house using open source technology (Java and Python) customized by the USPTO per application per system.

The USPTO's AI program includes improvements for Trademarks Operations in the following areas: 1) developing a quality review smart form with analytics; 2) ingesting office actions on the big data reservoir with advanced analytics including usage and descriptive statistics; and 3) determining the efficacy of deep machine learning for image searching for Trademarks.


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United States of AmericaUnited States Patent and Trademark Office (USPTO)Patent prior art search

In early 2018 the USPTO was delivering a proof of concept, Sigma, using machine learning/AI algorithms to search whole documents against a corpus of documents. For this version of Sigma, patent applications were searched against granted patents and pre-grant publications (US only).


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United States of AmericaUnited States Patent and Trademark Office (USPTO)Patent classification

In early 2018 the USPTO was researching deep machine learning Quality Chat Bots to provide ready access to "concept questioning" (instead of keyword) to the USPTO Manual Patent of Examination of Procedures (MPEP) and other claim analytics and classification analytics using algorithms and claim language to better understand trending of claim language and classification.


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United States of AmericaUnited States Patent and Trademark Office (USPTO)Patent prior art search

United States Patent and Trademark Office (USPTO) recently added the new artificial intelligence (AI)-based “Similarity Search” feature to examiners’ Patents End-to-End (PE2E) search suite. This new tool will support the USPTO’s goal to grant more robust and reliable patents by further assisting patent examiners as they search for prior art during the examination process.

UruguayNational Directorate of Industrial PropertyHelpdesk services

The National Directorate uses a notifications system that it developed in-house and that is connected with its online filing system. In early 2018, the National Directorate was developing a more sophisticated algorithm for notifications with the intention of identifying when a particular user was no longer using the system or had not used it for a while. In such cases, the algorythm would trigger additional notifications.

The algorythm would send emails, test notifications, feedback polls and requests for updating of personal information to ensure frequent communication with users and catching of data changes before deadlines.

WorldWorld Intellectual Property Organization (WIPO)Patent classification

IPCCAT helps patent filers and examiners in IPOs to automatically categorize patent applications into technical units according to their International Patent Classification (IPC) class, subclass or main group.


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WorldWorld Intellectual Property Organization (WIPO)Image search (trademark, design, patent)

Image search within the Global Brand Database allows trademark owners to identify visually-similar trademarks, as well as other brand-information records from among the millions of images in the collection.


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WorldWorld Intellectual Property Organization (WIPO)Machine translation

WIPO Translate is a world-leading instant translation tool, specially designed for patent documents. It's available through the PATENTSCOPE database and can also be integrated within IPO systems upon request.


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