63 record(s) found.
| Country / Territory | Institution | Business application | Description |
|---|---|---|---|
| Australia | IP Australia | Patent classification | IP Australia's Patent Auto Classification (PAC) tool aims to analyze the contents of a patent application in unstructured PDF documents and predict relevant technology groups enabling prioritization and allocation to appropriate patent examiner sections. The tool uses internally developed software/machine learning technologies to build sophisticated hierarchy classification models to analyze the contents of each patent case in unstructured PDF documents. The predictive models have been trained using the IP Australia's specific patent data, and will be extended with larger patent datasets from USPTO and the European Patent Office (EPO). In early 2018 a pilot of the tool was undergoing final review and testing before being released to production. The tool is intended to save staff time, streamline the tech sorting process and achieve comparable accuracy to the current manual process. The focus of the pilot was on the International Examination Search System (INTESS) in which international patent application cases are automatically allocated to the correct team's work tray for examination. Opportunities to extend PAC to distribute national applications in the Patents Administration Management System (PAMS) and to other patent classifications and search functions were under investigation. Related links |
| Australia | IP Australia | Image search (trademark, design, patent) | IP Australia uses Australian Trade Mark Search - Image Search (Live) - to search for existing trademark images based on a given image. Australian Trade Mark Search uses the commercially available TrademarkVision Image Recognition software for image search functions. Related links |
| Australia | IP Australia | Examination (trademark, patent) | IP Australia's Smart Assessment Toolkit (under development in early 2018) is a collection of advanced models designed to support trade mark examination and predict objections. The Smart Assessment Toolkit uses a combination of natural language processors and internally developed software trained by a dataset of historic adverse reports from 2008 to 2016 to detect similar existing trademarks. Once trained, it provides high ranking results to the user. Related links |
| Australia | IP Australia | Helpdesk services | IP Australia's Trade Mark Assist (Beta) is an interactive 24/7 tool designed to educate and assist unrepresented trade mark applicants (in particular Small and Medium Enterprises) through the initial stages of the application process. Trade Mark Assist uses publicly available word association models for searching Goods and Services and classifications. The model is being trained by trade mark examiners on a regular basis, who are given a list of terms and rank result relevance. Related links |
| Austria | The Austrian Patent Office | Patent prior art search | In early 2018 the Austrian Patent office was trialing several commercial providers for application of AI to pre-search of patents. |
| Austria | The Austrian Patent Office | Patent classification | In early 2018 the Austrian Patent office was trialing several commercial providers for application of AI to pre-classification and classification of patents. |
| Brazil | National 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. |
| Canada | Canadian 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. |
| Canada | Canadian 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. |
| Canada | Canadian 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. |
| Canada | Canadian 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. |
| Chile | National 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. |
| China | State 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. |
| China | State 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. |
| China | State 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. |
| European Union | European 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. Related links |
| European Union | European 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. Related links |
| European Union | European 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. Related links |
| European Union | European 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. Related links |
| European Union | European 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. Related links |
| European Union | European 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. Related links |
| European Union | European Union Intellectual Property Office (EUIPO) | Machine translation | EUIPO uses a commercial multilingual natural language tool called Babelscape for internal examiners. |
| European Union | European Union Intellectual Property Office (EUIPO) | Image search (trademark, design, patent) | 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. |
| Finland | Finnish 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. |
| Finland | Finnish 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. |
| Germany | German Patent and Trade Mark Office (DPMA) | Digitization and process automation | Many trademark applications are classified fully automatically at the DPMA. |
| Germany | German 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. |
| Germany | German 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. |
| Japan | Japan Patent Office | Helpdesk services | In fiscal year 2016, the JPO conducted tests to validate the possibility of using AI to respond to questions from users (by phone, e-mail etc.). Using past records of responses and various manuals, the JPO validated (1) a support function for responding to questions (AI-based systems suggesting possible answers to the JPO staff responsible for responding to questions from users); and (2) an automatic answering function (AI-based systems to answer questions submitted by e-mails, chat messages, or verbal messages). The tests were limited to responding to questions from only certain business operations. The AI-based system achieved a top-5 accuracy rate of 80% (ie. the rate at which the correct answer is among the 5 most likely answers suggested by the system). Related links |
| Japan | Japan Patent Office | Digitization and process automation | In early 2018 the JPO was validating it systems to verify the possible uses of AI to digitize filing procedures. The JPO was testing the conversion of image data of past paper filings into text data by using an AI-based character recognition funtion (which includes converting handwritten documents into text data). Related links |
| Japan | Japan Patent Office | Trademark classification (goods & services) | In early 2018 the JPO was 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 was testing functions to (1) assign tentative similar-group codes to unclear designated goods and services in trademark applications; and (2) check whether or not the fundamentals of applicants' designated goods and/or services are changed after amendments have been made to their trademark applications. Related links |
| Japan | Japan Patent Office | Patent classification | In early 2018 the JPO was 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 was verifying a function to make suggestions for patent classifications (F-terms) and grounds for assigning these classifications. Related links |
| Japan | Japan Patent Office | Patent prior art search | In early 2018 the JPO was validating its systems to verify possible uses for AI to support the formulation of search terms and queries when conducting prior art searches. Using text data of examined patent documents and the retrieval history of search queries used in the examinations, the JPO was validating a function to suggest keywords and patent classifications that should be included in search queries. Related links |
| Japan | Japan Patent Office | Image search (trademark, design, patent) | In early 2018 the JPO was 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 was validating functions to (1) retrieve prior figurative trademarks by inputting image data of claimed figurative trademarks, which might be identical with, or similar to, the claimed trademarks; and (2) eliminate noise in search results based on the International Classification of the Figurative Elements of Marks or the Vienna Classification (so as to eliminate trademarks that are clearly dissimilar to claimed trademarks). Related links |
| Morocco | Moroccan 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. |
| Morocco | Moroccan 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. |
| Morocco | Moroccan 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. |
| Norway | Norwegian 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. |
| Philippines | Intellectual 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. |
| Philippines | Intellectual 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 Korea | Korean 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. Related links |
| Republic of Korea | Korean 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. Related links |
| Republic of Korea | Korean 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. Related links |
| Russian Federation | Federal 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. Related links |
| Russian Federation | Federal 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. Related links |
| Russian Federation | Federal 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. Related links |
| Serbia | Intellectual Property Office of the Republic of Serbia | Machine 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. |
| Serbia | Intellectual Property Office of the Republic of Serbia | Digitization 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. |
| Singapore | Intellectual 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. |
| Singapore | Intellectual 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. |
| Singapore | Intellectual 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. |
| Sweden | Swedish 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. |
| Switzerland | Swiss 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. Related links |
| United Kingdom | Intellectual 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. Related links |
| United Kingdom | Intellectual 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. Related links |
| United Kingdom | Intellectual 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. Related links |
| United States of America | United 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. Related links |
| United States of America | United 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. Related links |
| United States of America | United 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). Related links |
| Uruguay | National Directorate of Industrial Property | Helpdesk 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. |
| World | World 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. Related links |
| World | World 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. Related links |
| World | World 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. Related links |