Patents by Inventor Michal Wroczynski
Michal Wroczynski has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11663403Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: GrantFiled: October 18, 2022Date of Patent: May 30, 2023Assignee: SAMURAI LABS SP. Z O.O.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Publication number: 20230058640Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: ApplicationFiled: October 18, 2022Publication date: February 23, 2023Applicant: Samurai Labs sp. z o.o.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Patent number: 11507745Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: GrantFiled: September 17, 2021Date of Patent: November 22, 2022Assignee: SAMURAI LABS SP. Z O.O.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Publication number: 20220004710Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: ApplicationFiled: September 17, 2021Publication date: January 6, 2022Applicant: Samurai Labs sp. z o.o.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Patent number: 11151318Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: GrantFiled: March 3, 2021Date of Patent: October 19, 2021Inventors: Michal Wroczynski, Gniewosz Leliwa
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Publication number: 20210248316Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: ApplicationFiled: March 3, 2021Publication date: August 12, 2021Applicant: Samurai Labs sp. z o.o.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Patent number: 10956670Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: GrantFiled: March 1, 2019Date of Patent: March 23, 2021Assignee: SAMURAI LABS SP. Z O.O.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Publication number: 20200267165Abstract: An intervention method and system for intervening in online bullying is described. in various embodiments, and an online violence detection system available online on communicatively coupled to multiple databases and the multiple system processors, wherein the online detection system is also communicatively coupled to multiple online communities, multiple data sources, and multiple other online systems and online applications. The method and system determine whether autonomous instant action is appropriate, or whether referring the interaction to a moderation dashboard is appropriate. A moderator dashboard is included in one embodiment.Type: ApplicationFiled: February 17, 2020Publication date: August 20, 2020Applicant: Fido Voice Sp. z o.o.Inventors: Gniewosz Leliwa, Michal Wroczynski, Grzegorz Rutkiewicz, Patrycja Tempska, Maria Dowgiallo
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Publication number: 20190272317Abstract: Embodiments include computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focuses on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods.Type: ApplicationFiled: March 1, 2019Publication date: September 5, 2019Applicant: Fido Voice sp. z o.o.Inventors: Michal Wroczynski, Gniewosz Leliwa
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Publication number: 20170017635Abstract: Embodiments of a system and method for natural language processing (NLP) utilize one or more extraction models, and an output of syntactic parser applied to a text to extract information from the text. In an embodiment, an extraction model defines one or more units or combinations of units within a grammar hierarchy (a word, a phase, a clause, or any combination of words, phrases and clauses) as an output of extraction process. An extraction model further comprises a set of rules where each rule sets one or more constraints on: a grammar structure output by extraction process; on the context of the output of extraction process; and on the relations between the output and the context.Type: ApplicationFiled: July 18, 2016Publication date: January 19, 2017Applicant: Fido Labs Inc.Inventors: GNIEWOSZ LELIWA, Michal Wroczynski
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Publication number: 20160062982Abstract: A natural language processing system is disclosed herein. Embodiments of the NLP system perform hand-written rule-based operations that do not rely on a trained corpus. Rules can be added or modified at any time to improve accuracy of the system, and to allow the same system to operate on unstructured plain text from many disparate contexts (e.g. articles as well as twitter contexts as well as medical articles) without harming accuracy for any one context. Embodiments also include a language decoder (LD) that generates information which is stored in a three-level framework (word, clause, phrase). The LD output is easily leveraged b various software applications to analyze large quantities of text from any source in a more sophisticated and flexible manner than previously possible. A query language (LDQL) for information extraction from NLP parsers' output is disclosed, with emphasis on on its embodiment implemented for LD.Type: ApplicationFiled: September 4, 2015Publication date: March 3, 2016Applicant: Fido Labs Inc.Inventors: Michal Wroczynski, Tomasz Krupa, Gniewosz Leliwa, Piotr Wiacek, Michal Stanczyk
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Publication number: 20140136188Abstract: A natural language processing system is disclosed herein. Embodiments of the NLP system perform hand-written rule-based operations that do not rely on a trained corpus. Rules can be added or modified at any time to improve accuracy of the system, and to allow the same system to operate on unstructured plain text from many disparate contexts (e.g. articles as well as twitter contexts as well as medical articles) without harming accuracy for any one context. Embodiments also include a language decoder (LD) that generates information which is stored in a three-level framework (word, clause, phrase). The LD output is easily leveraged by various software applications to analyze large quantities of text from any source in a more sophisticated and flexible manner than previously possible. A query language (LDQL) for information extraction from NLP parsers' output is disclosed, with emphasis on its embodiment implemented for LD.Type: ApplicationFiled: November 4, 2013Publication date: May 15, 2014Applicant: Fido Labs Inc.Inventors: Michal Wroczynski, Tomasz Krupa, Gniewosz Leliwa, Piotr Wiacek, Michal Stanczyk