II-SDV 2015, 20 - 21 April, in Nice
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1) Current patent search systems use boolean and keyword searches dominated by metadata, but new systems use ranked retrieval and move away from metadata. 2) Evaluating search requires moving from anecdotes to statistical benchmarks like TAPAS using real queries against patent corpora. 3) To improve search, we need to go beyond proximity queries that approximate meaning to true semantic search connecting mentions to normalized entities, though coverage exists primarily in biomedical domains. 4) Future search systems will integrate technologies like natural language processing, topic modeling, knowledge bases, and visual search of images and diagrams to build a true understanding of applications and prior art.
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Recent progress in incorporating word order and semantics to the decades-old, tried-and-tested bag-of-words representation of text meaning has yielded promising results in computational text classification and analysis. This development, and the availability of a large number of legal rulings from the PTAB (Patent Trial and Appeal Board motivated us to revisit possibilities for practical, computational models of legal relevance -- starting with this narrow and approachable niche of jurisprudence. We present results from our analysis and experiments towards this goal using a corpus of approximately 8000 rulings from the PTAB. This work makes three important contributions towards the development of models for legal relevance semantics: (a) Using state-of-art Natural Language Processing (NLP) methods, we characterize the diversity and types of semantic relationships that are implicit in select judgements of legal relevance at the PTAB (b) We achieve new state-of-art results on practical information retrieval tasks using our customized semantic representations on this corpus (c) We outline promising avenues for future work in the area - including preliminary evidence from human-in-loop interaction, and new forms of text representation developed using input from over a hundred interviews with practitioners in the field. Using the PTAB data set for testing relevance in patent document retrieval, instead of traditional citations search, also shows a bigger gap between the needs of practitioners and the capabilities of current information retrieval and NLP technologies.Technological Route between Pioneerism and Improvement
The document presents a methodology for determining the technological route between pioneering inventions and incremental improvements using patent data. The methodology involves: (1) performing an initial patent search, (2) analyzing patent trends over time to calculate an "intellectual property density", (3) fitting the density data to logistic curves to identify major technological phases, and (4) using text clustering to identify new areas of innovation. As a case study, the methodology is applied to analyze the evolution of textile loom weft insertion technologies from 1932 to 2008. Three major technological phases were identified from the logistic curve fitting, each characterized by midpoint year and other metrics.
Crowdsourced query augmentation through the semantic discovery of domain spec...
Talk Abstract: Most work in semantic search has thus far focused upon either manually building language-specific taxonomies/ontologies or upon automatic techniques such as clustering or dimensionality reduction to discover latent semantic links within the content that is being searched. The former is very labor intensive and is hard to maintain, while the latter is prone to noise and may be hard for a human to understand or to interact with directly. We believe that the links between similar user’s queries represent a largely untapped source for discovering latent semantic relationships between search terms. The proposed system is capable of mining user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free.
II-SDV 2017: Applications of RNN (Recurrent Neural Networks) within Machine T...
Pierre Bernassau will present the state of the art in Artificial Intelligence and Recurrent Neural Networks applied to natural language and in particular in the Machine Translation domain. This disruptive technology does not shift previous practices based on rules and statistics, but it makes new fields possible. New fields for end-users that can apply MT technologies on new languages, text styles, documents, or messages with a ‘good-enough’ result. But also new fields in terms of good practices, where new projects, new workflow, new applications are addressed that expand de facto the MT market.
With the experience of several recent projects done by his consulting team, Pierre will explain the best practices to apply Neural Technologies within the NLP field.
Adding Open Data Value to 'Closed Data' Problems
Drawing on cutting edge examples from the University of Bristol and the City of Bristol, Simon will discuss innovative applications of data science that derive business value from open data through enriching and integrating with confidential 'closed data'. He also highlights recent technological advances that are enabling open data science on highly sensitive closed data.
Interleaving, Evaluation to Self-learning Search @904Labs
Presented at Open Source Connections Haystack Relevance Conference on 904Labs' "Interleaving: from Evaluation to Self-Learning". 904Labs is the first to commercialize "Online Learning to Rank" as a state-of-art for technical Self-learning Search Ranking that automatically takes into account your customers human behaviors for personalized search results.
Content + Signals: The value of the entire data estate for machine learning
Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved.
In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.
Data science
The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.
Programming with Semantic Broad Data
Joint Keynote at Int. Conference on Knowledge Engineering and Semantic Web and Prague Computer Science Seminar, Prague, September 22, 2016
The challenges of Big Data are frequently explained by dealing with Volume, Velocity, Variety and Veracity. The large variety of data in organizations results from accessing different information systems with heterogeneous schemata or ontologies. In this talk I will present the research efforts that target the management of such broad data.
They include: (i) an integrated development environment for programming with broad data, (ii) a query language that allows for typing of query results, (iii) a typed lambda-calculus based on description logics, and (iv) efficient access to data repositories via schema indices.
Overview of Data Science and AI
The document provides an overview of data science, artificial intelligence, and machine learning. It discusses the differences between AI and machine learning, as well as what constitutes data science. Examples are given of applying data science in healthcare to study the impact of remote patient monitoring devices and identify high-risk patients. State-of-the-art machine learning techniques like neural networks, deep learning, and deep reinforcement learning are also overviewed. Finally, the document discusses how companies are using data science and AI and provides next steps for learning and applying these fields.
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Haystack 2018 - Algorithmic Extraction of Keywords Concepts and Vocabularies
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Towards Correlating Search on Google and Asking on Stack Overflow
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ANALYTICS OF PATENT CASE RULINGS: EMPIRICAL EVALUATION OF MODELS FOR LEGAL RE...
ANALYTICS OF PATENT CASE RULINGS: EMPIRICAL EVALUATION OF MODELS FOR LEGAL RE...
Technological Route between Pioneerism and Improvement
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Interleaving, Evaluation to Self-learning Search @904Labs
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Content + Signals: The value of the entire data estate for machine learning
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II-SDV 2015, 20 - 21 April, in Nice
This document discusses exploring patent citations and their benefits and challenges. It begins by noting that while citations provide valuable information, they can be complex and underused. It then provides details on what a patent citation is, where citation data comes from, and why citations are useful for finding related patents, analyzing patent portfolios, competitor monitoring, and identifying licensing opportunities. Finally, it acknowledges some pitfalls like data quality and coverage issues but emphasizes that citations are a valuable resource when used properly.
II-SDV 2015, 20 - 21 April, in Nice
This document discusses patent landscaping and describes a patent landscape analysis of the life sciences industry in Cambridge, UK. It defines patent landscaping as creating a collection of patent families to analyze and answer business questions. The analysis of Cambridge identified 23 patent filing life sciences companies, with AstraZeneca having the largest portfolio. Visualizations show the technology areas and timeline of patenting. The landscape provides insights into competitors, mergers and acquisitions, and opportunities for further analysis.
II-SDV 2015, 20 - 21 April, in Nice
Richard Resnick, CEO of II-SDV, discussed the challenges of keyword and biological sequence searching in life sciences. Integrating the two search types can provide more relevant results from broad patent authority coverage. However, keyword searching in life sciences is challenging due to issues like spelling variations and domain-specific terminology. Sequence searching also presents challenges regarding alignment and interpretation of results. Building reports from different search platforms is further challenging due to inconsistencies in output formats and a lack of cross-platform integration. An integrated life science search platform could provide a complete report for analysis by combining text and sequence search results into a single workfile.
II-SDV 2015, 20 - 21 April, in Nice
The document discusses using chemical structures as a common denominator to link scientific information. It describes publisher efforts to semantically enrich articles with chemical data through named entity recognition and structure generation. Case studies include Wiley's smart articles, which generate chemical structures from texts and allow structure searching, and a Springer demonstrator that indexed structures across 6 million documents to create a single search interface linking multiple data repositories. The chemical structure is an efficient way to retrieve and link different information sources.
II-SDV 2015, 20 - 21 April, in Nice
Humans create more information than can be understood by any one person. Semantic annotation can help organize and disambiguate this information by identifying relationships between concepts, such as different people with the same name. This process exploits linked open data to improve search and discoverability of knowledge. An example is the Deckard tool, which uses semantic annotation to dynamically manage content at a fine-grained level. This allows for better analytics across topics like athletes, teams, disciplines and venues. Semantic annotation provides a means to capture human knowledge at scale.
II-SDV 2015, 20 - 21 April, in Nice
The document discusses Questel's patent evaluation modules. It provides an overview of modules for scouting, licensing, ability to exclude competition, pruning patent portfolios, and licensing out patents. The modules allow users to benchmark their patent portfolio, compare it to competitors, identify opportunities and threats, understand pruning potential, find pruning candidates, determine comparative strengths, potential licensees, and high value patents. The evaluation modules drive analysis with extended normalization tools, technology clustering, and ability to share reports.
II-SDV 2015, 20 - 21 April, in Nice
Manish Sinha has over 10 years of experience in intellectual property solutions. He launched Patent iNSIGHT Pro in 2006 which gained over 400 users across 100 organizations globally. The PatSeer Global Patent Database crossed 500 users in 2014 with over 200 professional searchers. Sinha focuses on research and development and continuous product enhancements to solve greater challenges for IP professionals.
II-SDV 2015, 20 - 21 April, in Nice
RightsDirect provides content licensing and workflow solutions for publishers, rightsholders, and content users. They offer document delivery and licensing for over 600 million rights from publishers and authors. RightsDirect is the largest provider of pay-per-view scientific, technical, and medical content, delivering over 1.5 million documents annually to 35,000 companies worldwide. Their multinational copyright license provides consistent usage rights for sharing and reusing content across borders, complementing publisher subscriptions. The license covers millions of publications and fills gaps in rights that vary between individual publisher licenses.
II-SDV 2015, 20 - 21 April, in Nice
1. The document describes a semi-automatic patent curation workflow that uses various tools like ChemAxon ChemCurator and KNIME to extract chemistry-related information from patents.
2. The workflow involves retrieving patents, extracting structures from text, images, and attachments using ChemCurator or KNIME, and replacing chemistry in the documents with standardized IUPAC names.
3. The extracted information and IUPAC-enriched documents are then used for text mining and data extraction using Linguamatics I2E to retrieve additional information like bioactivity data, target genes, and claims.
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ODSC East 2017: Data Science Models For Good
Abstract: The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved
informatics_future.pdf
The document discusses the future of informatics as a fundamental science. It outlines how informatics studies information and information processing in natural systems, making it a new science with its own methods beyond traditional sciences. The University of Edinburgh helped establish the foundations and ethos that allowed informatics to mature as a discipline. Informatics has proven useful to other scientists and society by addressing problems that require an informatics approach due to their large scale. Momentum now comes from the critical mass and size the field has achieved in addressing issues only solvable through informatics.
Wc11 talk trawling_bibliome_3_r_alkema_25082021
This document discusses using text mining and artificial intelligence techniques to identify scientific literature related to the 3Rs (Replacement, Reduction, Refinement) of animal testing. It notes the challenges in manually searching for such literature due to a lack of standardized keywords. The proposed solution involves using a knowledge map and relationship mining of over 500,000 concepts and 200 million relations to connect papers to 3R-related words. A machine learning model is then trained on known 3R and non-3R papers to automatically classify new papers and help identify additional 3R-focused literature. The approach provides a proof of principle for an intelligent search engine to more efficiently find 3R methods papers.
Charleston Conference 2016
The document summarizes Anita de Waard's presentation on Elsevier's experiments with big and small data. It discusses Elsevier's work with text mining and knowledge graphs to extract information from over 14 million articles. It also describes Elsevier's Medical Graph which predicts the probability of over 2,000 medical conditions occurring based on analysis of clinical data from 6 million patients. Finally, it reviews Elsevier's various tools and services to help researchers preserve, process, share, comprehend, access, and discover research data and publications.
MLforIR.pps
This document discusses machine learning and information retrieval. It introduces machine learning and describes some common applications like bioinformatics, robotics, and computer vision. It then discusses information retrieval, including traditional keyword search approaches and a new example-based approach. Several prototype systems are described that use this example-based approach for tasks like movie search, academic literature search, image retrieval, and protein search. The approach is statistically principled, computationally fast, and easily parallelized.
Introduction to machine_learning_us
1) Machine learning uses algorithms to identify patterns in large datasets and can help address challenges in healthcare like predicting hospital readmissions.
2) A study showed machine learning models could predict diabetes readmission risk 20% better than existing methods by exploring more patient-specific factors.
3) Examples of other healthcare applications include classifying medical images, assisting with diagnoses, and finding unsafe drug interactions.
From data lakes to actionable data (adventures in data curation)
From data lakes to actionable data (adventures in data curation)Novartis Institutes for BioMedical Research
The document discusses data curation from data lakes. It describes the data lake paradigm of collecting all data and making it searchable. It then discusses the importance of data curation and normalization to generate value from large and diverse datasets. Examples are provided showing how sample annotations can be normalized and structured to enable complex queries across multiple datasets. The document reflects on challenges around quantifying the value of data curation and need for curation as data volumes increase.Connected Data for Machine Learning | Paul Groth
Elsevier aims to construct knowledge graphs to help address challenges in research and medicine. Knowledge graphs link entities like people, concepts, and events to provide answers. Elsevier analyzes text and data to build knowledge graphs using techniques like information extraction, machine learning, and predictive modeling. Their knowledge graph integrates data from publications, clinical records, and other sources to power applications that help researchers, medical professionals, and patients. Knowledge graphs are a critical component for delivering value, especially as data volumes and needs accelerate.
RapidMiner, an entrance to explore MIMIC-III?
The presentation covers the use of Scalable Predictive Analysis in Critically Ill Patients using a Visual Open Data Analysis Platform (RapidMiner).
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-
complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. The presentation addresses the problem by focusing on an open, visual environment, suited to be applied by the medical community (RapidMiner). As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II / III database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, robust processes for automatic building, parameter optimization and evaluation of various
predictive models, under different feature selection schemes can be developed. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
Presentation at Laboratory for Computational Physiology (LCP)
Massachusetts Institute of Technology (MIT),
Building E25 room 101; December 8th 12-noon
Sven Van Poucke, MD, Anesthesiologist, Emergency Physician
Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium
Embi cri review-2012-final
This study evaluated the use of an interactive computer module to supplement a traditional paper informed consent form for pediatric endoscopy. Parents who received the supplemental electronic module were more likely to achieve informed consent compared to those who only received the paper form. The electronic module did not impact parent satisfaction, anxiety, or the number of questions asked of physicians. The results suggest that electronic tools can enhance traditional informed consent methods.
Martone grethe
The document discusses methodologies for sharing long-tail data and what has been learned. It notes that unique identifiers (PIDs) are important for identifying entities across contexts. Standards like MINI and common data elements (CDEs) help ensure data is findable, accessible, and reusable. The Neuroscience Information Framework (NIF) aggregates ontologies and searches over 200 data sources to organize information. What we have learned is that data should be in repositories, not personal servers; people are key to these efforts; and resources should be comprehensive and support each other to advance open data sharing.
Linked Data and Ontology Tutorial (for RD-Connect)
In this tutorial we explain the basics of a 'Linked Data and Ontology' approach for combining data, in particular for the study of rare diseases. The approach is motivated by a case study provided by health care researcher Ulrike Braisch. The main take home lesson is that with this approach the effort for data integration can be substantially lowered, i.e. lead to a shorter path to new treatments for (rare) diseases.
The presentation is based on a tutorial given at the RD-Connect/Neuromics/Euronomics plenary meeting in Heidelberg, Germany, February 26, 2014. It was made possible by RD-Connect, a European project to support Rare Disease research (http://www.rd-connect.eu).
An Improved Mining Of Biomedical Data From Web Documents Using Clustering
This document summarizes a research paper that proposes an improved method for mining biomedical data from web documents using clustering. Specifically, it develops an optimized k-means clustering algorithm to group similar biomedical documents together based on identifying relevant terms using the Unified Medical Language System (UMLS). The approach aims to more efficiently retrieve relevant biomedical documents for users. It compares the proposed method to the original k-means algorithm and finds it achieves an average F-measure of 99.06%, indicating more accurate clustering of biomedical web documents.
Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
Big Data & ML for Clinical Data
Big Data and machine learning are increasingly important in biomedical science and clinical practice. Big Data refers to large and complex datasets that are too large for traditional tools to handle. Machine learning involves algorithms that can recognize patterns in data without being explicitly programmed. Some challenges of working with big data and machine learning include issues with data volume, variety, and veracity. However, techniques like distributed analysis, standards, and validation can help address these challenges.
UCSF Informatics Day 2014 - Ida Sim, "Informatics Technologies: From a Data-C...
This document discusses moving from a data-centric to a hypothesis-centric view of clinical and translational research using electronic health records and other informatics technologies. It notes that most current research is observational rather than interventional, and outlines ways informatics could better support hypothesis testing through virtual, community-based, and point-of-care clinical trials by integrating risk calculators, structured note templates, surveys, and other tools directly into clinical workflows and patient portals. The presentation calls for further developing these informatics capabilities to facilitate more interventional research at lower cost.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
PLOS Visualization Project
The document describes a visualization of PLoS data created by mapping PLoS thesaurus terms to an existing map of all science created from Scopus data. It discusses mapping terms directly and indirectly, analyzing coverage by journal and year, and using the maps and underlying data to answer questions posed by PLoS about trends, relationships between fields, and how PLoS coverage compares to other databases. Visualizations are created in software like Pajek and Gephi and are meant to facilitate communication around science structure and metrics.
Nordic health data metadata
This document discusses using metadata and knowledge graphs to better organize health data and make it more findable. It explains how knowledge graphs work by connecting entities and their relationships, and how this can help match user search intent to the meaning of data. The document also discusses challenges in organizing diverse data sources and standards, and how semantic annotation and knowledge graphs can help integrate different data types and make them interoperable.
Peter Embi's 2011 AMIA CRI Year-in-Review
This document discusses Peter Embi's presentation on clinical research informatics. The presentation included summaries of 22 research papers on topics like data warehousing and knowledge discovery, researcher support and resources, and recruitment informatics. It also discussed ongoing efforts to integrate informatics approaches and resources to support clinical and translational research.
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Frankie Rybicki slide set for Deep Learning in Radiology / Medicine
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INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
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This document describes a metadata list of patent holders in various countries and regions compiled by Muchiu (Henry) Chang. The list is sorted geographically and includes patent holder names from Canada, China, Hong Kong, Macao, Taiwan, the Middle East, and Europe between 2009-2022. Key features include Chinese-English compatibility and use of open source intelligence. The list has previously been utilized by the Region of Peel in Ontario, Canada.
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Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed.
Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.
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It is relatively easy for a human to read a document and quickly figure out which concepts are important. However, this task is a difficult challenge for a machine. During the past few decades, there have been two main approaches for concept identification: Natural Language Processing and Machine Learning. During the early part of this century, Machine Learning made great strides as new techniques came into wider use (SVM’s, Topic Modeling, etc..). Sensing the competition, Natural Language Processing responded with deployment of new emerging techniques (sematic networks, finite state automata, etc..). Neither approach has completely solved the WHAT problem. Advances in Artificial Intelligence have the potential to significantly improve the situation. Where AI is making the most impact is as an enhancement to make Machine Learning and Natural Language Processing work better and, more importantly, work together. This presentation looks at some of this history and what might happen in the future when we blend the interpretation of language with pattern prediction.
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In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
This document discusses using AI tools to improve patent search and analysis. It provides metrics on how well an AI system called IPscreener can retrieve patent citations compared to examiners. The metrics show recall rates increase with longer input text and when users provide additional context. Machine translation negatively impacts performance, but the AI can help users navigate patents by selecting relevant text segments. The goal is for AI to boost innovation by improving how users search for and understand prior art.
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks.
More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project?
A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.
AI-SDV 2022: Copyright Clearance Center
Copyright Clearance Center
A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.
AI-SDV 2022: Lighthouse IP
Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.
AI-SDV 2022: New Product Introductions: CENTREDOC
CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
The everyday use of AI-driven algorithms for data search, analysis and synthesis comes with important time savings, but also reveals the need to understand and accept the limitations of the technology. Practical deployments on concrete topics are inevitable to assess and manage the challenges of neuronal network based AI. A workshop report.
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!
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【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
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三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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学位证书英文版，改成绩单，Offer、在读证明、学生卡、信封、证明信等全套材料，从防伪到印刷，从水印到钢印烫金，高精仿度跟学校原版100%相同.
b.真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认.
c.真实教育部国外学历学位认证，教育部存档，教育部留服网站100%可查.
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【业务选择办理准则】
1.如果您只是为了的应付父母亲戚朋友看下迪肯大学毕业证，那么办理一份学位即可
2.如果您是为了回国找工作，只是进私营企业或者外企，那么办理一份多大学历认证有时间限制吗《迪肯大学毕业证》微信176555708《DU学位证书购买》
毕业证丢失补办即可，因为私营企业或者外企是不能查询学位真假的！
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诚招代理：本公司诚聘当地合作代理人员，如果你有业余时间，有兴趣就请联系我们。
不能毕业如何获得(USYD毕业证)悉尼大学毕业证成绩单一比一原版制作
挂科购买☀【悉尼大学毕业证购买】【微信176555708】【USYD毕业证模板办理】加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造【悉尼大学大学毕业证】微信176555708【USYD学位证书购买】◆◆◆◆◆ — — — 归国服务中心 — — -◆◆◆◆◆
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一.毕业证、成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
国外毕业证、学位证、成绩单办理流程：
1、客户提供办理信息：姓名、生日、专业、学位、毕业时间等（如信息不确定可以咨询顾问：【微信176555708】我们有专业老师帮你查询）；
2、开始安排制作毕业证、成绩单电子图；
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专业服务，请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理，终生受用，一步到位，高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
◆招聘代理：本公司诚聘英国、加拿大、澳洲、新西兰、加拿大、法国、德国、新加坡各地代理人员，如果你有业余时间，有兴趣就请联系我们咨询【微信176555708】
没文凭怎么找工作。让您回国发展信心十足！
★、真实教育部学历学位认证；（一对一专业服务，可全程监控跟踪进度）
★、真实使馆认证，可以通过大使馆查询确认；（即教育部留服认证，不成功不收费）
★、毕业证、成绩单等材料，从防伪到印刷、水印到钢印烫金，高精仿度都是跟学校原版100%相同的；（敬请放心使用）
★、可以提供钢印、水印、烫金、激光防伪、凹凸版、最新版的毕业证、百分之百让您绝对满意、
★、印刷，DHL快递毕业证、成绩单7个工作日，真实大使馆教育部认证1个月。为了达到高水准高效率。
留学学历(UoA毕业证)奥克兰大学毕业证成绩单官方原版办理
制做办理奥克兰大学学历证书＜176555708微信＞【毕业证明信-推荐信做学费单＞【微信176555708】【制作UoA毕业证文凭认证奥克兰大学毕业证成绩单购买】【UoA毕业证书】{奥克兰大学文凭购买}】成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，＞【制作UoA毕业证文凭认证奥克兰大学毕业证成绩单购买】【UoA毕业证书】{奥克兰大学文凭购买}留信网认证。
奥克兰大学学历证书＜微信176555708（一对一服务包括毕业院长签字,专业课程,学位类型,专业或教育领域,以及毕业日期.不要忽视这些细节.这两份文件同样重要!毕业证成绩单文凭留信网学历认证！）[留学文凭学历认证(留信认证使馆认证)奥克兰大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理奥克兰大学奥克兰大学硕士毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。
可查真实(Monash毕业证)西澳大学毕业证成绩单退学买
办理假西澳大学毕业证【微信176555708】购买,办理Monash成绩单,Monash毕业证制作【微信176555708】【西澳大学毕业证】，Monash毕业证购买，Monash学位证，西澳大学学位证【Monash成绩单制作】【Monash毕业证文凭 Monash本科 澳洲学历认证原版制作【diploma certificate degree transcript 】【留信网认证，本科，硕士，海归，博士，排名，成绩单】代办国外（海外）澳洲、韩国、加拿大、新西兰等各大学毕业证。 ?我们对海外大学及学院的毕业证成绩单所使用的材料，尺寸大小，防伪结构（包括：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。 文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）都有原版本文凭对照。#一整套西澳大学文凭证件办理#—包含西澳大学西澳大学毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：西澳大学西澳大学毕业证成绩单毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理西澳大学西澳大学毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信176555708
Ready to Unlock the Power of Blockchain!
Imagine a world where data flows freely, yet remains secure. A world where trust is built into the fabric of every transaction. This is the promise of blockchain, a revolutionary technology poised to reshape our digital landscape.
Toptal Tech is at the forefront of this innovation, connecting you with the brightest minds in blockchain development. Together, we can unlock the potential of this transformative technology, building a future of transparency, security, and endless possibilities.
假文凭国外(Adelaide毕业证)澳大利亚国立大学毕业证成绩单办理
购买澳大利亚国立大学毕业证>【微信176555708】办理澳大利亚国立大学毕业证成绩单【微信176555708】Adelaide毕业证成绩单Adelaide学历证书Adelaide文凭【Adelaide毕业套号文凭网认证澳大利亚国立大学毕业证成绩单】【哪里买澳大利亚国立大学毕业证文凭Adelaide成绩学校快递邮寄信封】【开版澳大利亚国立大学文凭】Adelaide留信认证本科硕士学历认证(诚招代理)微信：176555708办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理澳大利亚国立大学澳大利亚国立大学毕业证成绩单流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:176555708）欢迎咨询！
办理新西兰奥克兰大学毕业证学位证书范本原版一模一样
原版一模一样【微信：741003700 】【新西兰奥克兰大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Gen Z and the marketplaces - let's translate their needs
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
HijackLoader Evolution: Interactive Process Hollowing
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
国外证书(Lincoln毕业证)新西兰林肯大学毕业证成绩单不能毕业办理
退学办理新西兰林肯大学毕业证【微信176555708】学历认证怎么做：原版仿制新西兰林肯大学电子版成绩单毕业证认证【新西兰林肯大学毕业证成绩单】、新西兰林肯大学文凭证书成绩单复刻offer录取通知书、购买Lincoln圣力嘉学院本科毕业证、【新西兰林肯大学毕业证办理Lincoln毕业证书哪里买】、新西兰林肯大学 Offer在线办理Lincoln Offer新西兰林肯大学Bachloer Degree。1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信176555708我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：新西兰林肯大学新西兰林肯大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信176555708）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。
办理毕业证(UPenn毕业证)宾夕法尼亚大学毕业证成绩单快速办理
官方原版办理宾夕法尼亚大学毕业证【微信176555708】学历认证怎么做：原版仿制宾夕法尼亚大学电子版成绩单毕业证认证【宾夕法尼亚大学毕业证成绩单】、宾夕法尼亚大学文凭证书成绩单复刻offer录取通知书、购买UPenn圣力嘉学院本科毕业证、【宾夕法尼亚大学毕业证办理UPenn毕业证书哪里买】、宾夕法尼亚大学 Offer在线办理UPenn Offer宾夕法尼亚大学Bachloer Degree。（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
学历顾问：微信：176555708
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II-SDV 2015, 20 - 21 April, in Nice
- 1. Future Challenges in (automated) Patent Search Alexander G. Klenner-Bajaja, PhD. aklenner@epo.org
- 2. Why Search – European Patent Convention 2 Information Management`s Task: Support Search
- 3. Introduction – What do we want, where are we? 3
- 4. The current Search System A boolean search system, documents are returned as sets Search is dominated by meta-data search as well as keywords 4 Search Space boolean query
- 5. The current Search System A Lucene elastic search based system, documents are returned as ranked lists (pilot – fully available but no extensive training) Moving away from a meta-data dominated search...? 5 Search Space k Lucene query 1
- 6. Patent Gold Standards We have “manually” curated search reports for about 40 million simple patent families The relevant documents are mentioned in the search report as either –X(I,N),A,Y,... documents 6 median: 5 citations in search reports
- 7. Citation temporal distribution 50% of all citations are younger than 10 years (2005-now); 80% of all citations are younger than 20 years; only 5% of citations are older than 1974. 7
- 8. Setting up a benchmarking environment We need to move away from anecdotal evidence to statistically meaningful facts TAPAS 8 SEARCH INDEX Applications Method 1 Method 2 MAP:0.4 MAP:0.2 Patent Corpus 1 2 3 4 * Exploiting real queries
- 9. Setting up a prototyping environment - KNIME 9 1 2 3 4 1 1 1 1 2 2 2 3 3 3 1
- 11. Graph Databases are valid tools - if we have a good starting document (seed) 11
- 12. Graph Databases are valid tools - if we have a good starting document (seed) 12
- 13. Graph Databases are valid tools - if we have a good starting document (seed) 13
- 14. Graph Databases are valid tools - if we have a good starting document (seed) 14 Again Meta-Data based!
- 15. But where do we start with an incoming patent application? 15 ? Patent Application
- 16. This has been implemented during the last 1-3 years, but Literature suggest that we are sealed with our parameter optimization strategies applying classic IR methods We ignore the huge NPL part of the citations The problem becomes worse every day (~3000 applications per week) 16
- 17. A searcher tries to work around “meaning” by: Proximity Queries simulate or approximate “meaning” Assumption: certain distances transport more meaning than others (e.g. 3w or p) . We want to ask “Give me all documents that are relevant with regards to treatment of migraine pain with Aspirin” But we actually ask “Migraine AND Pain AND Aspirin” or many variants of that. Classification is a very strong aid, representing a meaningful relation <belongs to> 17
- 18. What does search actually mean? Claim 1: A composition comprising a combination of paracetamol and aspirin for use in the treatment a migraine pain in a human subject. Claim 2: A composition according to claim 1 where the composition further comprises caffeine 18
- 19. A Knowledge Map of Claims 1 & 2 Claim 1: A method for treating migraine pain comprising administering to a human subject a composition comprising a combination of paracetamol and aspirin. Claim 2: A method according to claim 1 where the composition further comprises caffeine 19
- 20. What is the Δ of Prior Art and the Application? Δ 20
- 21. We use meta-data knowledge maps with simple relations already 21
- 22. Moving towards real knowledge maps Normalized Annotations are one step towards semantic search connecting mentions in patents with normalized entities Good coverage for biomedical domains Lack of good terminologies for everything else 22
- 24. Patents have multi-modal information content: Images Images – Chemical Formulas – Flow Diagrams – Circuits – Technical Drawings 24
- 26. Image Search 26 Search Space Query State of the art Image processing Filtering and Visualisation
- 27. Image Search using S&K prototype 27
- 28. Modelling Search – which direction do we go? 28 PA X Is modelling the Examiner the best choice?
- 29. Enrichment and Annotations Natural Language Processing Topic ModellingInformation Extraction Knowledge Bases Visualisation Techniques Workflow Management Information Retrieval Modelling the Search Process Knowledge Organisation Systems Technologies that can guide us 29
- 30. Future Search Ecosystem bringing together many technologies • Captured Domain Knowledge allows to merge and get relevant third party documents/results • „Machine“ Understanding of Application allows for „Auto-Query“ generation • IR System retrieves relevant documents from query • Enrichment allows „semantic“ search • Examiner is „Search Pilot“ 30
- 31. Thank you for your attention aklenner@epo.org 31