IP Library Granted Patent US 12,216,988
Granted Patent B2
US 12,216,988 · App. 18/313,544 · Granted Feb 4, 2025

Editing parameters

Inventors: Liam Roshan Dunan Emmart (Washington, DC); Jonathan Herr (Washington, DC); Daniel P. Broderick (Ashburn, VA); Daniel Edward Simonson (Syracuse, NY)
Assignee: BLACKBOILER, INC.
G06F40/166G06F40/40
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,216,988
App. No.
18/313,544
Granted
Feb 4, 2025
Kind
B2
Abstract

In some embodiments, a method is provided for updating an editing parameter for a model for automatically suggesting revisions to text data. The method may include displaying, on a graphical user interface (GUI) of a user device, one or more interactive input elements, wherein each of the one or more input elements is associated with an editing parameter for a model for automatically suggesting revisions to text data. The method may include receiving, via the GUI, an input from a selected input element of the one or more input elements, wherein the input comprises an indication of a value for a selected editing parameter associated with the selected input element. The method may include updating the selected editing parameter for the model based on the value. The method may include using the model with the updated selected editing parameter to apply an edit operation to an obtained text-under-analysis.

Claims (34)

1. A method for updating an editing parameter for a model for automatically suggesting revisions to text data, the method comprising:

displaying, on a graphical user interface (GUI) of a user device, one or more interactive input elements, wherein each of the one or more input elements is associated with an editing parameter for a model for automatically suggesting revisions to text data;

receiving, via the GUI, an input from a selected input element of the one or more input elements,

transmitting the input towards an application server, wherein the input comprises an indication of a value for a selected editing parameter associated with the selected input element;

obtaining a text under-analysis;

transmitting the text-under-analysis towards the application server; and

obtaining, from the application server, an edit operation to the obtained text-under-analysis, wherein the edit operation is determined by the model based on the selected editing parameter.

2. The method of claim 1 , wherein the selected editing parameter is a bucket size and the value comprises a first numeric value.

3. The method of claim 1 , wherein the edit operation is a point edit operation comprising one of an accept, reject, or revise edit operation.

4. The method of claim 2 , wherein the input further comprises an indication of a second value for a second selected editing parameter associated with a second selected input element, wherein the second selected editing parameter comprises a second numeric value corresponding to a weight of the bucket size editing parameter.

5. The method of claim 1 , wherein the selected editing parameter comprises a similarity score and the value is a minimum threshold similarity score.

6. The method of claim 1 , wherein the selected editing parameter comprises a presence threshold and the value indicates a minimum threshold presence score.

7. The method of claim 1 , wherein the selected editing parameter comprises a context threshold and the value indicates a minimum context score.

8. The method of claim 7 , wherein the input further comprises an indication of a second value for a second selected editing parameter from a second selected input element and wherein the second selected editing parameter comprises a second numeric value corresponding to a context window editing parameter.

9. The method of claim 1 , wherein the selected editing parameter comprises an alignment method associated with an identified cluster, and the value indicates one of: a before context, an after context, a before and after context, or a tail alignment method.

10. The method of claim 1 , wherein the selected editing parameter comprises a tail alignment method associated with an identified cluster and the value indicates one or more of a list, paragraph, section, or document tail alignment method.

11. A system comprising:

a processor; and

a non-transitory computer-readable medium coupled to the processor, wherein the processor is configured to:

display, on a graphical user interface (GUI) of a user device, one or more interactive input elements, wherein each of the one or more input elements is associated with an editing parameter for a model for automatically suggesting revisions to text data;

receive, via the GUI, an input from a selected input element of the one or more input elements,

transmit the input towards an application server, wherein the input comprises an indication of a value for a selected editing parameter associated with the selected input element;

obtain a text under-analysis;

transmit the text-under-analysis towards the application server; and

obtain, from the application server, an edit operation to the obtained text-under-analysis, wherein the edit operation is determined by the model based on the selected editing parameter.

12. The system of claim 11 , wherein the selected editing parameter is a bucket size and the value comprises a first numeric value.

13. The system of claim 12 , wherein the edit operation is a point edit operation comprising one of an accept, reject, or revise edit operation.

14. The system of claim 12 , wherein the input further comprises an indication of a second value for a second selected editing parameter associated with a second selected input element, wherein the second selected editing parameter comprises a second numeric value corresponding to a weight of the bucket size editing parameter.

15. The system of claim 11 , wherein the selected editing parameter comprises a similarity score and the value is a minimum threshold similarity score.

16. The system of claim 11 , wherein the selected editing parameter comprises a presence threshold and the value indicates a minimum threshold presence score.

17. The system of claim 11 , wherein the selected editing parameter comprises a context threshold and the value indicates a minimum context score.

18. The system of claim 17 , wherein the input further comprises an indication of a second value for a second selected editing parameter from a second selected input element and wherein the second selected editing parameter comprises a second numeric value corresponding to a context window editing parameter.

19. The system of claim 11 , wherein the selected editing parameter comprises an alignment method associated with an identified cluster, and the value indicates one of: a before context, an after context, a before and after context, or a tail alignment method.

20. The system of claim 11 , wherein the selected editing parameter comprises a tail alignment method associated with an identified cluster and the value indicates one or more of a list, paragraph, section, or document tail alignment method.

Continuity (3)
Continuation 17562352 · Dec 27, 2021
Provisional Application 63133568 · Jan 4, 2021
Related Publication 20230359810A1 · Nov 9, 2023
References Cited (116)
US 5692206A · Shirley · 1997 [cited by applicant]
US 6253177B1 · Lewis · 2001 [cited by examiner]
US 6438543B1 · Kazi · 2002 [cited by applicant]
US 7080076B1 · Williamson · 2006 [cited by applicant]
US 7519607B2 · Anderson, IV · 2009 [cited by applicant]
US 7668865B2 · Mcdonald · 2010 [cited by applicant]
US 8037086B1 · Upstill · 2011 [cited by applicant]
US 8046372B1 · Thirumalai · 2011 [cited by applicant]
US 8196030B1 · Wang · 2012 [cited by applicant]
US 8442771B2 · Wang · 2013 [cited by applicant]
US 8788523B2 · Martin et al. · 2014 [cited by applicant]
US 8886648B1 · Procopio et al. · 2014 [cited by applicant]
US 9514103B2 · Kletter · 2016 [cited by applicant]
US 9672206B2 · Carus · 2017 [cited by applicant]
US 10102193B2 · Riediger · 2018 [cited by applicant]
US 10127212B1 · Kim · 2018 [cited by applicant]
US 10191893B2 · Riediger · 2019 [cited by applicant]
US 10216715B2 · Brockerick · 2019 [cited by applicant]
US 10489500B2 · Herr · 2019 [cited by applicant]
US 10515149B2 · Herr · 2019 [cited by applicant]
US 10713436B2 · Herr · 2020 [cited by applicant]
US 10755033B1 · Dass · 2020 [cited by applicant]
US 10824797B2 · Herr · 2020 [cited by applicant]
US 10970475B2 · Herr · 2021 [cited by applicant]
US 11681864B2 · Emmart · 2023 [cited by examiner]
US 20020002567A1 · Kanie · 2002 [cited by applicant]
US 20030023539A1 · Wilce · 2003 [cited by applicant]
US 20030069879A1 · Sloan · 2003 [cited by applicant]
US 20030074633A1 · Boulmakoul · 2003 [cited by applicant]
US 20040102958A1 · Anderson · 2004 [cited by applicant]
US 20050182736A1 · Castellanos · 2005 [cited by applicant]
US 20070073532A1 · Brockett et al. · 2007 [cited by applicant]
US 20070106494A1 · Detlef et al. · 2007 [cited by applicant]
US 20070192355A1 · Vasey · 2007 [cited by applicant]
US 20070192688A1 · Vasey · 2007 [cited by applicant]
US 20070300295A1 · Kwok · 2007 [cited by applicant]
US 20080103759A1 · Dolan et al. · 2008 [cited by applicant]
US 20090007267A1 · Hoffmann · 2009 [cited by applicant]
US 20090076792A1 · Lawson-Tancred · 2009 [cited by applicant]
US 20090099993A1 · Seuss · 2009 [cited by applicant]
US 20090138257A1 · Verma · 2009 [cited by applicant]
US 20090138793A1 · Verma · 2009 [cited by applicant]
US 20090216545A1 · Rajkumar et al. · 2009 [cited by applicant]
US 20090300064A1 · Dettinger · 2009 [cited by applicant]
US 20090300471A1 · Dettinger · 2009 [cited by applicant]
US 20100005386A1 · Verma · 2010 [cited by applicant]
US 20110055206A1 · Martin · 2011 [cited by applicant]
US 20120011433A1 · Skrenta et al. · 2012 [cited by applicant]
US 20130036348A1 · Hazard · 2013 [cited by applicant]
US 20130091422A1 · Potnis · 2013 [cited by applicant]
US 20130151235A1 · Och · 2013 [cited by applicant]
US 20140040270A1 · O'Sullivan · 2014 [cited by applicant]
US 20150347393A1 · Futrell · 2015 [cited by applicant]
US 20150379887A1 · Becker · 2015 [cited by applicant]
US 20160012061A1 · Sperling · 2016 [cited by applicant]
US 20160055196A1 · Collins · 2016 [cited by applicant]
US 20160224524A1 · Kay · 2016 [cited by applicant]
US 20170039176A1 · Broderick et al. · 2017 [cited by applicant]
US 20170161260A1 · Broderick · 2017 [cited by applicant]
US 20170287090A1 · Hunn · 2017 [cited by applicant]
US 20170364495A1 · Srinivasan · 2017 [cited by applicant]
US 20180005186A1 · Hunn · 2018 [cited by applicant]
US 20180253409A1 · Carlson · 2018 [cited by applicant]
US 20180268506A1 · Wodetzki · 2018 [cited by applicant]
US 20180365201A1 · Hunn · 2018 [cited by applicant]
US 20180365216A1 · Kao · 2018 [cited by applicant]
US 20190065456A1 · Platow · 2019 [cited by applicant]
US 20200089743A1 · Herr et al. · 2020 [cited by applicant]
US 20200089752A1 · Herr et al. · 2020 [cited by applicant]
US 20200380541A1 · Laing · 2020 [cited by examiner]
CA 2901055A1 · 2017 [cited by applicant]
WO 0244932A2 · 2002 [cited by applicant]
Zhemin Zhu et al, A Monolingual Tree-based Translation Model for Sentence Simplification, Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp. 1353-1361, Beijing, Aug. 2010. [cited by applicant]
Bill Maccartney, Michel Galley, Christopher D. Manning, A Phrase-Based Alignment Model for Natural Language Inference, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 802-811,… [cited by applicant]
Ion Androutsopoulos and Prodromos Malakasiotis, A Survey of Paraphrasing and Textual Entailment Methods, Journal of Artificial Intelligence Research 38 (2010) 135-187 Submitted Dec. 2009; published May 2010. [cited by applicant]
Marie-Catherine De Marneffe, Trond Grenager, Bill Maccartney, Daniel Cer, Daniel Ramage, Chlo'e Kiddon, Christopher D. Manning, Aligning semantic graphs for textual inference and machine reading, American Association fo… [cited by applicant]
Bill Maccartney and Christopher D. Manning, An extended model of natural logic, Proceedings of the 8th International Conference on Computational Semantics, pp. 140-156, Tilburg, Jan. 2009. [cited by applicant]
Md Arafat Sultan, Steven Bethard and Tamara Sumner, Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence, Transactions of the Association for Computational Linguistics, 2 (2014) 2… [cited by applicant]
Rada Mihalcea, Courtney Corley, Carlo Strapparava, Corpus-based and Knowledge-based Measures of Text Semantic Similarity, American Association for Artificial Intelligence, p. 775-780, 2006. [cited by applicant]
Rohit J. Kate, A Dependency-based Word Subsequence Kernel, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 400-409, Honolulu, Oct. 2008. [cited by applicant]
Yangfeng Ji and Jacob Eisenstein, Discriminative Improvements to Distributional Sentence Similarity, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 891-896, Seattle, Washingt… [cited by applicant]
Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, Christopher D. Manning, Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection, Advances in Neural Information Processing Systems 2… [cited by applicant]
Michael Heilman and Noah A. Smith, Extracting Simplified Statements for Factual Question Generation, Mar. 29, 2010. [cited by applicant]
Amit Bronner and Christof Monz, User Edits Classification Using Document Revision Histories, Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 356-366, Avig… [cited by applicant]
Felix Hill, Kyunghyun Cho, Anna Korhonen, Learning Distributed Representations of Sentences from Unlabelled Data, CoRR, abs/1602.03483, 2016, available at: http://arxiv.org/abs/1602.03483. [cited by applicant]
Shashi Narayan, Claire Gardent. Hybrid Simplication using Deep Semantics and Machine Translation. the 52nd Annual Meeting of the Association for Computational Linguistics, Jun. 2014, Baltimore, United States. pp. 435-44… [cited by applicant]
Kristian Woodsend and Mirella Lapata, Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 40… [cited by applicant]
Bill Maccartney, Christopher D. Manning, Modeling Semantic Containment and Exclusion in Natural Language Inference, Proceeding COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics—vol… [cited by applicant]
Bill Maccartney, Christopher D. Manning, Modeling Semantic Containment and Exclusion in Natural Language Inference (a presentation), Aug. 2008. [cited by applicant]
Hua He and Jimmy Lin, Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement, Proceedings of NAACL-HLT 2016, pp. 937-948, San Diego, California, Jun. 12-17, 2016. [cited by applicant]
Dr. Radim Rehurek, scalability of semantic analysis in natural language processing, PH.D Thesis, May 2011. [cited by applicant]
Richard Socher Brody Huval Christopher D. Manning Andrew Y. Ng, Semantic Compositionality through Recursive Matrix-Vector Spaces, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Process… [cited by applicant]
Ilya Sutskever, Oriol Vinyals, Quoc V. Le, Sequence to Sequence Learning with Neural Networks, Advances in neural information processing systems, pp. 3104-3112, 2014. [cited by applicant]
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler, Skip-Thought Vectors, arXiv:1506.06726, Jun. 2015. [cited by applicant]
Radim Rehurek and Petr Sojka, Software Framework for Topic Modeling with Large Corpora, In Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks. Valletta, Malta: University of Malta, 2010. s. 46-50, 5 s. … [cited by applicant]
Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, Christopher Potts, A Fast Unified Model for Parsing and Sentence Understanding, arXiv:1603.06021, Mar. 2016. [cited by applicant]
Xiang Zhang, Yann Lecun, Text Understanding from Scratch, arXiv:1502.01710, Apr. 2016. [cited by applicant]
Furong Huang, Animashree Anandkumar, Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition, arXiv:1606.03153, Jun. 2016. [cited by applicant]
Chambers, N. and Jurafsky, D., “Unsupervised learning of narrative event chains,” Proceedings of ACL-08, Columbus, Ohio, Jun. 2008, pp. 789-797. [cited by applicant]
Chambers, N. and Jurafsky, D., “Unsupervised learning of narrative schemas and their participants,” Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Suntec, Singapore, Aug. 2-7, 2009, p… [cited by applicant]
Chen, D. and Manning, C. D., “A fast and accurate dependency parser using neural networks,” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, Oct. 25-29, 2014, pp. 740-… [cited by applicant]
Justeson, J. S. and Katz, S. M., “Technical terminology: some linguistic properties and an algorithm for identification in text,” 1995, Natural Language Engineering 1(1):9-27. [cited by applicant]
Honnibal, M. and Johnson, M., “An improved non-monotonic transition system for dependency parsing,” Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, Sep. 17-21, 2… [cited by applicant]
Petrov, S., et al., “Learning accurate, compact, and interpretable tree annotation,” Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, Australia, J… [cited by applicant]
Simonson, D. E., “Investigations of the properties of narrative schemas,” a dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University, Washington, DC, Nov. 17, 2017, 259 … [cited by applicant]
Aswani, N., and Gaizauskas, R., “A hybrid approach to align sentences and words in English-Hindi parallel corpora,” Jun. 2005, Proceedings of the ACL Workshop on Building and Using Parallel Texts, Association for Comput… [cited by applicant]
Manning, C. D., and Schütze, H., Foundations of statistical natural language processing, 1999, The MIT Press, Cambridge, MA, 44 pages. [cited by applicant]
International Search Report and Written Opinion issued for PCT/US2019/023854 dated Jun. 27, 2019, 17 pages. [cited by applicant]
Office Action in related Canadian application No. 3,076,629 dated May 13, 2020, 4 pages. [cited by applicant]
Office Action for Canadian Patent Application No. 3,076,629 dated Oct. 26, 2021, 7 pages. [cited by applicant]
Communication pursuant to Article 94(3) in European application No. 19 716 654.9 mailed Feb. 4, 2021, 9 pages. [cited by applicant]
Office Action in Canadian application No. 3,076,629 mailed Apr. 9, 2021, 6 pages. [cited by applicant]
Office Action in Canadian application No. 3,076,629 dated Nov. 25, 2020, 4 pages. [cited by applicant]
International Search Report and Written Opinion for PCT/US2019/057906 mailed Jan. 30, 2020, 13 pages. [cited by applicant]
International Search Report and Written Opinion for PCT/US21/65194 mailed Mar. 24, 2022, 11 pages. [cited by applicant]
International Preliminary Report and Written Opinion for corresponding application No. PCT/US2021/065194 mailed Jul. 4, 2023, 5 pages. [cited by applicant]