IP Library › Granted Patent US 12,456,002
Granted Patent B2
US 12,456,002 · App. 18/346,609 · Granted Oct 28, 2025

Linguistically-driven automated text formatting

Inventors: Julie A. Van Dyke (New Haven, CT); Michael Gorman (Edina, MN); Mark Lacek (Minneapolis, MN)
Assignee: Cascade Reading, Inc.
G06F40/103G06F40/183G06F40/205G06F40/295G06F40/30
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 12,456,002
App. No.
18/346,609
Granted
Oct 28, 2025
Kind
B2
Abstract

Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR/AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.

Claims (73)

1. A non-transitory computer-readable medium comprising instructions for cascaded text formatting, when executed by at least one processor of a computing device, cause the at least one processor to perform operations that:

receive a text portion to be cascaded;

invoke a trained model to process the text portion, the trained model to receive the text portion as input and to identify formatting changes to create cascaded text from the text portion,

wherein the formatting changes includes respective line breaks and respective horizontal displacements to be added among respective words of the text portion,

wherein the respective line breaks are added to the text portion based on grammatical units of the respective words, wherein the grammatical units comprise constituency, and wherein the respective line breaks establish a vertical position of the respective words among separate lines or continuous units, and

wherein the respective horizontal displacements are added to the text portion based on syntactic functions of the respective words, wherein the syntactic functions comprise grammatical dependencies, and wherein the respective horizontal displacements establish a horizontal position of the respective words among the separate lines or continuous units; and

cause output of the cascaded text in real-time based on the formatting changes.

2. The non-transitory computer-readable medium of claim 1 , wherein the trained model is executed at the computing device, and wherein the trained model outputs the cascaded text or a language model to generate the cascaded text.

3. The non-transitory computer-readable medium of claim 1 , wherein the trained model is operated at a networked-accessible service at a computer system located remote to the computing device, and wherein the networked-accessible service outputs the cascaded text or a language model to generate the cascaded text.

4. The non-transitory computer-readable medium of claim 1 , further comprising instructions that cause the at least one processor to perform operations that:

output the cascaded text on a user interface of the computing device, wherein the user interface provides at least one display modification to the cascaded text including: a split-screen, automatic scrolling, text coloring, text italics, text underlining, or text highlighting.

5. The non-transitory computer-readable medium of claim 1 , wherein the grammatical units of the respective words are based on constituency rules, and wherein the syntactic functions of the respective words are based on dependency rules.

6. The non-transitory computer-readable medium of claim 1 , wherein the trained model implements at least one of: a neural network, a machine learning classifier, a natural language processing (NLP) parser, a rule engine, or an inference engine.

7. The non-transitory computer-readable medium of claim 1 , wherein the trained model is trained from a plurality of cascaded text examples corresponding to the plurality of cascaded text examples.

8. A method for cascaded text formatting, performed by a computing device, comprising:

identifying a text portion to be cascaded;

processing the text portion with a trained model, the trained model configured to receive the text portion as input and to identify formatting changes to create cascaded text from the text portion,

wherein the formatting changes includes respective line breaks and respective horizontal displacements to be added among respective words of the text portion, wherein the respective line breaks are added to the text portion based on grammatical units of the respective words, wherein the grammatical units comprise constituency, and wherein the respective line breaks establish a vertical position of the respective words among separate lines or continuous units, and

wherein the respective horizontal displacements are added to the text portion based on syntactic functions of the respective words, wherein the syntactic functions comprise grammatical dependencies, and wherein the respective horizontal displacements establish a horizontal position of the respective words among the separate lines or continuous units; and

displaying the cascaded text in real-time based on the formatting changes.

9. The method of claim 8 , wherein the trained model is executed at the computing device, and wherein the trained model outputs the cascaded text or a language model to generate the cascaded text.

10. The method of claim 8 , wherein the trained model is operated at a networked-accessible service at a computer system located remote to the computing device, and wherein the networked-accessible service outputs the cascaded text or a language model to generate the cascaded text.

11. The method of claim 8 , further comprising:

outputting the cascaded text on a user interface of the computing device, wherein the user interface provides at least one display modification to the cascaded text including: a split-screen, automatic scrolling, text coloring, text italics, text underlining, or text highlighting.

12. The method of claim 8 , wherein the grammatical roles units of the respective words are based on constituency rules, and wherein the syntactic functions of the respective words are based on dependency rules.

13. The method of claim 8 , wherein the trained model implements at least one of: a neural network, a machine learning classifier, a natural language processing (NLP) parser, a rule engine, or an inference engine.

14. The method of claim 8 , wherein the trained model is trained from a plurality of cascaded text examples corresponding to the plurality of cascaded text examples.

15. An apparatus, comprising:

means for obtaining a text portion to be cascaded;

means for invoking an automated formatting system to process the text portion, the automated formatting system to receive the text portion as input and to identify formatting changes to create cascaded text from the text portion,

wherein the formatting changes includes respective line breaks and respective horizontal displacements to be added among respective words of the text portion, wherein the respective line breaks are added to the text portion based on grammatical units of the respective words, wherein the grammatical units comprise constituency, and wherein the respective line breaks establish a vertical position of the respective words among separate lines or continuous units, and

wherein the respective horizontal displacements are added to the text portion based on syntactic functions of the respective words, wherein the syntactic functions comprise grammatical dependencies, and wherein the respective horizontal displacements establish a horizontal position of the respective words among the separate lines or continuous units; and

means for outputting the cascaded text based on the formatting changes.

16. The apparatus of claim 15 , wherein the automated formatting system outputs the cascaded text or a language model to generate the cascaded text.

17. The apparatus of claim 15 , wherein the means for outputting the cascaded text provides at least one display modification to the cascaded text including: a split-screen, automatic scrolling, text coloring, text italics, text underlining, or text highlighting.

18. The apparatus of claim 15 , wherein the grammatical units of the respective words are based on constituency rules, and wherein the syntactic functions of the respective words are based on dependency rules.

19. The apparatus of claim 15 , wherein the automated formatting system implements at least one of: a neural network, a machine learning classifier, a natural language processing (NLP) parser, a rule engine, or an inference engine.

20. The apparatus of claim 15 , wherein the automated formatting system includes a trained model that is trained from a plurality of cascaded text examples corresponding to the plurality of cascaded text examples.

21. A method for cascaded text formatting of an input text, comprising:

receiving, by at least one processor of a computing device, the input text to be cascaded;

obtaining, by the at least one processor, dependency information for the input text;

determining, by the at least one processor, constituency information for the input text;

generating, by the at least one processor, a language model from the constituency information and the dependency information, wherein the language model is a data structure comprising: a parse tree representing dependencies among constituents of the input text, and encoded data representing relationships between the constituents and dependencies of the constituents of the input text;

applying, by the at least one processor, cascade rules to the constituents of the input text, using the language model, the cascade rules comprising instructions to:

determine a vertical arrangement of each constituent, wherein each constituent comprises a word or group of words that functions as an individual grammatical unit in a hierarchy of the parse tree of the generated language model, and wherein the vertical arrangement separates the constituents of the input text into respective lines or continuous units; and

determine a horizontal arrangement of each constituent, wherein one or more dependency relationships and a head of each constituent are determined from the encoded data of the language model, and wherein the horizontal arrangement provides a same horizontal displacement for the constituents of the input text that have a dependency relationship to a same head or a dependency relationship on each other;

generating output, by the at least one processor, wherein the output includes line breaks to implement the vertical arrangement and indentation to implement the horizontal arrangement within the input text; and

causing, by the at least one processor, a display of the output on a display device.

22. A non-transitory computer-readable medium including instructions for cascaded text formatting of an input text, which when executed by at least one processor of a computing device, cause the at least one processor to perform operations that:

receive the input text to be cascaded;

obtain dependency information for the input text;

determine constituency information for the input text;

generate a language model from the constituency information and the dependency information, wherein the language model is a data structure comprising: a parse tree representing dependencies among constituents of the input text, and encoded data representing relationships between the constituents and dependencies of the constituents of the input text;

apply cascade rules to the constituents of the input text, using the language model, the cascade rules comprising instructions to:

determine a vertical arrangement of each constituent, wherein each constituent comprises a word or group of words that functions as an individual grammatical unit in a hierarchy of the parse tree of the generated language model, and wherein the vertical arrangement separates the constituents of the input text into respective lines or continuous units; and

determine a horizontal arrangement of each constituent, wherein one or more dependency relationships and a head of each constituent are determined from the encoded data of the language model, and wherein the horizontal arrangement provides a same horizontal displacement for the constituents of the input text that have a dependency relationship to a same head or a dependency relationship on each other;

generate output, wherein the output includes line breaks to implement the vertical arrangement and indentation to implement the horizontal arrangement within the input text; and

cause a display of the output on a display device.

23. A system for cascaded text formatting of an input text, comprising:

at least one processor; and

memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:

receive the input text to be cascaded;

obtain dependency information for the input text;

determine constituency information for the input text;

generate a language model from the constituency information and the dependency information, wherein the language model is a data structure comprising: a parse tree representing dependencies among constituents of the input text, and encoded data representing relationships between the constituents and dependencies of the constituents of the input text;

apply cascade rules to the constituents of the input text, using the language model, the cascade rules comprising instructions to:

determine a vertical arrangement of each constituent, wherein each constituent comprises a word or group of words that functions as an individual grammatical unit in a hierarchy of the parse tree of the generated language model, and wherein the vertical arrangement separates the constituents of the input text into respective lines or continuous units; and

determine a horizontal arrangement of each constituent, wherein one or more dependency relationships and a head of each constituent are determined from the encoded data of the language model, and wherein the horizontal arrangement provides a same horizontal displacement for the constituents of the input text that have a dependency relationship to a same head or a dependency relationship on each other;

generate output, wherein the output includes line breaks to implement the vertical arrangement and indentation to implement the horizontal arrangement within the input text; and

cause a display of the output on a display device.

24. The method of claim 21 , wherein the horizontal arrangement provides an alignment of the respective lines that are linguistically related to each other, and wherein a dependency relationship determines the indentation for the respective lines that are linguistically dependent on each other.

25. The non-transitory computer-readable medium of claim 22 , wherein the horizontal arrangement provides an alignment of the respective lines that are linguistically related to each other, and wherein a dependency relationship determines the indentation for the respective lines that are linguistically dependent on each other.

26. The system of claim 23 , wherein the horizontal arrangement provides an alignment of the respective lines that are linguistically related to each other, and wherein a dependency relationship determines the indentation for the respective lines that are linguistically dependent on each other.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 5, 2023
From: VAN DYKE, JULIE A.; GORMAN, MICHAEL; LACEK, MARK
To: CASCADE READING, INC.
Reel/Frame 064797/0637 →
Continuity (4)
Continuation 17453763 · Nov 5, 2021
Continuation 17233339 · Apr 16, 2021
Provisional Application 63173298 · Apr 9, 2021
Related Publication 20230351090A1 · Nov 2, 2023
References Cited (220)
US 4889422A · Pavlidis · 1989 [cited by applicant]
US 5778402A · Gipson · 1998 [cited by applicant]
US 5802533A · Walker · 1998 [cited by third party]
US 6088711A · Fein et al. · 2000 [cited by applicant]
US 6279017B1 · Walker · 2001 [cited by examiner]
US 6568939B1 · Edgar · 2003 [cited by applicant]
US 7036075B2 · Walker · 2006 [cited by applicant]
US 7069508B1 · Bever et al. · 2006 [cited by applicant]
US 7346489B1 · Bever et al. · 2008 [cited by applicant]
US 7743324B1 · Bever et al. · 2010 [cited by applicant]
US 7765471B2 · Walker · 2010 [cited by applicant]
US 7823061B2 · Chan · 2010 [cited by applicant]
US 7861163B2 · Walker · 2010 [cited by applicant]
US 8019590B1 · Kinder · 2011 [cited by applicant]
US 8190419B1 · Kinder · 2012 [cited by applicant]
US 8200487B2 · Peters et al. · 2012 [cited by applicant]
US 8209601B2 · Bever et al. · 2012 [cited by applicant]
US 8306356B1 · Bever et al. · 2012 [cited by applicant]
US 8332221B2 · Peters et al. · 2012 [cited by applicant]
US 8418057B2 · Knight et al. · 2013 [cited by applicant]
US 8442814B2 · Ceusters et al. · 2013 [cited by applicant]
US 8731905B1 · Tsang et al. · 2014 [cited by applicant]
US 8918718B2 · Burgess · 2014 [cited by applicant]
US 9026907B2 · Lum · 2015 [cited by applicant]
US 9069731B2 · Stern et al. · 2015 [cited by applicant]
US 9128906B2 · Peters et al. · 2015 [cited by applicant]
US 9292494B2 · Ceusters et al. · 2016 [cited by applicant]
US 9378201B2 · Kinder · 2016 [cited by applicant]
US 9390080B2 · Walker · 2016 [cited by applicant]
US 9430555B2 · Duncan et al. · 2016 [cited by applicant]
US 9478146B2 · Skinner et al. · 2016 [cited by applicant]
US 9529795B2 · Kondadadi et al. · 2016 [cited by applicant]
US 9672788B2 · Rosen et al. · 2017 [cited by applicant]
US 9916295B1 · Crawford · 2018 [cited by applicant]
US 9940317B2 · Walker · 2018 [cited by applicant]
US 9953026B2 · Kinder · 2018 [cited by applicant]
US 10062295B2 · Puvanachandran et al. · 2018 [cited by applicant]
US 10102182B2 · Lum · 2018 [cited by applicant]
US 10180936B2 · Mcclintock et al. · 2019 [cited by applicant]
US 10303742B2 · Joshi · 2019 [cited by applicant]
US 10311135B1 · Yang · 2019 [cited by applicant]
US 10515138B2 · Walker · 2019 [cited by third party]
US 10650089B1 · Walker · 2020 [cited by third party]
US 11170154B1 · Van Dyke et al. · 2021 [cited by applicant]
US 11200413B2 · Burdick et al. · 2021 [cited by applicant]
US 11734491B2 · Van Dyke et al. · 2023 [cited by applicant]
US 12086532B2 · Gorman et al. · 2024 [cited by applicant]
US 12254870B2 · Van Dyke et al. · 2025 [cited by applicant]
US 20030093275A1 · Polanyi et al. · 2003 [cited by applicant]
US 20060129922A1 · Walker · 2006 [cited by applicant]
US 20080222518A1 · Walker · 2008 [cited by applicant]
US 20100128042A1 · Confrey et al. · 2010 [cited by applicant]
US 20100332217A1 · Wintner et al. · 2010 [cited by applicant]
US 20120303371A1 · Labsky et al. · 2012 [cited by applicant]
US 20130035961A1 · Yegnanarayanan · 2013 [cited by applicant]
US 20130041685A1 · Yegnanarayanan · 2013 [cited by applicant]
US 20130124962A1 · Mitchell · 2013 [cited by examiner]
US 20130297347A1 · Cardoza et al. · 2013 [cited by applicant]
US 20150213634A1 · Karmarkar et al. · 2015 [cited by applicant]
US 20150262580A1 · Bisani et al. · 2015 [cited by applicant]
US 20150286618A1 · Walker · 2015 [cited by applicant]
US 20150348538A1 · Donaldson · 2015 [cited by applicant]
US 20160062982A1 · Wroczynski et al. · 2016 [cited by applicant]
US 20160111016A1 · Govindaraj et al. · 2016 [cited by applicant]
US 20160188558A1 · Shikawa et al. · 2016 [cited by applicant]
US 20170046311A1 · Walker · 2017 [cited by examiner]
US 20170097987A1 · Bao et al. · 2017 [cited by applicant]
US 20170097988A1 · Bao et al. · 2017 [cited by applicant]
US 20170358238A1 · Casutt · 2017 [cited by applicant]
US 20180096625A1 · Kochura et al. · 2018 [cited by applicant]
US 20180253280A1 · Kawahara et al. · 2018 [cited by applicant]
US 20190019509A1 · Lee et al. · 2019 [cited by applicant]
US 20190087239A1 · Adibowo · 2019 [cited by applicant]
US 20190236085A1 · Galitsky · 2019 [cited by applicant]
US 20200097502A1 · Trim et al. · 2020 [cited by applicant]
US 20200125791A1 · Walker · 2020 [cited by examiner]
US 20200243076A1 · Kim · 2020 [cited by applicant]
US 20200286463A1 · Galitsky · 2020 [cited by applicant]
US 20210019364A1 · Govindjee et al. · 2021 [cited by applicant]
US 20210056263A1 · Xia et al. · 2021 [cited by applicant]
US 20210056952A1 · Van Der Ploeg et al. · 2021 [cited by applicant]
US 20210073302A1 · Srinivasaraghavan et al. · 2021 [cited by applicant]
US 20210173893A1 · Luo · 2021 [cited by applicant]
US 20210200965A1 · Yerli · 2021 [cited by applicant]
US 20210225358A1 · Monge et al. · 2021 [cited by applicant]
US 20210295829A1 · Noel et al. · 2021 [cited by applicant]
US 20210327415A1 · Park et al. · 2021 [cited by applicant]
US 20220051665A1 · Bade et al. · 2022 [cited by applicant]
US 20220059077A1 · Thomson · 2022 [cited by applicant]
US 20220068279A1 · Embar et al. · 2022 [cited by applicant]
US 20220115018A1 · Desserrey et al. · 2022 [cited by applicant]
US 20220335203A1 · Van Dyke et al. · 2022 [cited by applicant]
US 20230123328A1 · Gorman et al. · 2023 [cited by applicant]
US 20230315558A1 · Saxena et al. · 2023 [cited by applicant]
US 20240257802A1 · Van Dyke et al. · 2024 [cited by applicant]
AU 2022255037B2 · 2023 [cited by applicant]
CN 112686051A · 2021 [cited by applicant]
CN 115769219A · 2023 [cited by applicant]
CN 117561516A · 2024 [cited by applicant]
DE 212021000356U1 · 2023 [cited by applicant]
DE 112022002081T5 · 2024 [cited by applicant]
EP 0917698B1 · 2007 [cited by applicant]
IL 307467 · 2025 [cited by applicant]
IN 202317076513 · 2024 [cited by applicant]
JP H04348464A · 1992 [cited by applicant]
JP 2000516002A · 2000 [cited by applicant]
JP 2024511893A · 2024 [cited by applicant]
JP 7541422 · 2024 [cited by applicant]
JP 2024152832 · 2024 [cited by applicant]
KR 20150107178A · 2015 [cited by applicant]
KR 20160115566A · 2016 [cited by applicant]
KR 102754856 · 2025 [cited by applicant]
WO WO2017058298A1 · 2017 [cited by applicant]
WO WO2021207422A1 · 2021 [cited by applicant]
WO WO2022217087A1 · 2022 [cited by applicant]
WO WO2023059818A1 · 2023 [cited by applicant]
“Canadian Application Serial No. 3,214,349, Examiners Rule 86(2) Report mailed Nov. 2, 2023”, 6 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7038709, Voluntary Amendment Filed Nov. 9, 2023”, w english claims, 29 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Non Final Office Action mailed Dec. 4, 2023”, 74 pgs. [cited by applicant]
“Japanese Application Serial No. 2023-562214, Voluntary Amendment Filed Nov. 29, 2023”, w english claims, 11 pgs. [cited by applicant]
“U.S. Appl. No. 17 917,171, Third-Party Submission Under 37 CFR 1.290, mailed Nov. 13, 2023”, 6 pgs. [cited by applicant]
Milekic, Slavoljub, “Using gaze actions to interact with a display”, U.S. Pat. No. 7,561,143 B1, Application granted. Independent Claims 1, 21, 22, (Jul. 14, 2009), 22 pgs. [cited by applicant]
Rayner, Keith, “The Role of Eye Movements in Learning to Head and Reading Disability”, Remedial and Special Education, 6(6), [Online] Retrieved from the internet:https: doi.org 10.1177 074193258500600609, 8 pgs. [cited by applicant]
“U.S. Appl. No. 18/033,243, Examiner Interview Summary mailed Nov. 25, 2024”, 2 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Corrected Notice of Allowability mailed Apr. 3, 2024”, 2 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Examiner Interview Summary mailed Feb. 14, 2024”, 2 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Notice of Allowance mailed Mar. 25, 2024”, 7 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Response filed Feb. 29, 2024 to Non Final Office Action mailed Dec. 4, 2023”, 15 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Supplemental Amendment filed Mar. 8, 2024”, 10 pgs. [cited by applicant]
“Canadian Application Serial No. 3,214,349, Response filed Feb. 26, 2024 to Examiners Rule 86(2) Report mailed Nov. 2, 2023”, 11 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/045924, International Preliminary Report on Patentability mailed Apr. 18, 2024”, 6 pgs. [cited by applicant]
“Japanese Application Serial No. 2023-562214, Notification of Reasons for Rejection mailed May 14, 2024”, W/English Translation, 6 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7038709, Notice of Preliminary Rejection mailed Apr. 12, 2024”, w/ English Translation, 16 pgs. [cited by applicant]
Frey, Aline, et al., “Perceptual span, visual span, and visual attention span: Three potential ways to quantify limits on visual processing during reading”, Visual Cognition, vol. 26, No. 6, (May 11, 2018), 412-429. [cited by applicant]
Gao, Wei, et al., “Visual-Syntactic Text Formatting: Developing EFL Learners' Reading Fluency Components”, Journal of Psycholinguistic Research, 51, (2022), 707-727. [cited by applicant]
Ozaki, Sachiko, et al., “The effects of digital scaffolding on adolescent English reading in Japan: An experimental study on visual-syntactic text formatting”, The JALT CALL Journal: Regular Papers, vol. 16, No. 3, (202… [cited by applicant]
Park, Youngmin, et al., “Scaffolding learning of language structures with visual-syntactic text formatting”, British Journal of Educational Technology, vol. 0 No. 2018, [Online]. Retrieved from the Internet: <URL: https… [cited by applicant]
Reichle, Erik, et al., “The E-Z Reader model of eye-movement control in reading: Comparisons to other models”, Behavioral and Brain Sciences, 26, (2003), 445-476. [cited by applicant]
Veldre, Aaron, et al., “Understanding the Visual Constraints on Lexical Processing: New Empirical and Simulation Results”, Journal of Experimental Psychology: General, vol. 152, No. 3. American Psychological Association… [cited by applicant]
“U.S. Appl. No. 18/033,243, Examiner Interview Summary mailed Sep. 5, 2024”, 2 pgs. [cited by applicant]
“U.S. Appl. No. 18/033,243, Response filed Sep. 16, 2024 to Non Final Office Action mailed Jun. 6, 2024”, 14 pgs. [cited by applicant]
“U.S. Appl. No. 18/033,243, Final Office Action mailed Sep. 26, 2024”, 9 pgs. [cited by applicant]
“U.S. Appl. No. 17/233,339, Examiner Interview Summary mailed Aug. 20, 2021”, 3 pgs. [cited by applicant]
“U.S. Appl. No. 17/233,339, Non Final Office Action mailed Jun. 24, 2021”, 11 pages. [cited by applicant]
“U.S. Appl. No. 17/233,339, Notice of Allowance mailed Sep. 17, 2021”, 5 pgs. [cited by applicant]
“U.S. Appl. No. 17/233,339, Response filed Sep. 3, 2021 to Non Final Office Action mailed Jun. 24, 2021”, 19 pages. [cited by applicant]
“U.S. Appl. No. 17/453,763, Corrected Notice of Allowability mailed Jul. 27, 2023”, 5 pgs. [cited by applicant]
“U.S. Appl. No. 17/453,763, Non Final Office Action mailed Dec. 19, 2022”, 7 pgs. [cited by applicant]
“U.S. Appl. No. 17/453,763, Notice of Allowance mailed Apr. 3, 2023”, 9 pgs. [cited by applicant]
“U.S. Appl. No. 17/453,763, Preliminary Amendment filed Feb. 23, 2022”, 9 pgs. [cited by applicant]
“U.S. Appl. No. 17/453,763, Response filed Mar. 20, 2023 to Non Final Office Action mailed Dec. 19, 2022”, 9 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171 Preliminary Amendment filed Oct. 5, 2022”, 6 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Examiner Interview Summary mailed Aug. 9, 2023”, 3 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Final Office Action mailed Jul. 13, 2023”, 92 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Non Final Office Action mailed Mar. 9, 2023”, 51 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Response filed Jun. 9, 2023 to Non Final Office Action mailed Mar. 9, 2023”, 10 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Response filed Oct. 10, 2023 to Final Office Action mailed Jul. 13, 2023”, 13 pgs. [cited by applicant]
“Canadian Application Serial No. 3,214,349, Voluntary Amendment Filed Oct. 5, 2023”, 14 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/026270, International Preliminary Report on Patentability mailed Oct. 20, 2022”, 9 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/026270, International Search Report mailed Aug. 2, 2021”, 3 pgs. [cited by applicant]
“International Application Serial No. PCT/US2021/026270, Written Opinion mailed Aug. 2, 2021”, 7 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/024070, International Preliminary Report on Patentability mailed Oct. 19, 2023”, 6 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/024070, International Search Report mailed Jul. 25, 2022”, 5 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/024070, Written Opinion mailed Jul. 25, 2022”, 4 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/045924, International Search Report mailed Jan. 26, 2023”, 3 pgs. [cited by applicant]
“International Application Serial No. PCT/US2022/045924, Written Opinion mailed Jan. 26, 2023”, 3 pgs. [cited by applicant]
“Software—Stanford Parser”, The Stanford Natural Language Processing Group, [Online]. Retrieved from the Internet: <URL: https://nlp.stanford.edu/software/lex-parser.shtml>, (Accessed May 30, 2023), 14 pgs. [cited by applicant]
“Universal Dependency Relations”, UD version 2, [Online]. Retrieved from the Internet: <URL: https://universaldependencies.org/u/dep/index.html>, (Accessed May 30, 2023), 3 pgs. [cited by applicant]
Arlot, Sylvain, et al., “A survey of cross-validation procedures for model selection”, Statistics Surveys, vol. 4, (2010), 40-79. [cited by applicant]
Arsi, Hiba, et al., “Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis”, Procedia Computer Science, 83, (2016), 1064-1069. [cited by applicant]
Beckman, Mary E, “The Parsing of Prosody”, Language and Cognitive Processes, 11:1-2, (1996), 17-67. [cited by applicant]
Bone, Daniel, et al., “Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion”, Journal of Child Psychology and Psychiatry, vol. 57, Issue … [cited by applicant]
Breen, Mara, et al., “Acoustic correlates of information structure”, Language and Cognitive Processes, 25:7, [Online]. Retrieved from the Internet: <URL: http://dx.doi.org/10.1080/01690965.2010.504378>, (2010), 1044-109… [cited by applicant]
Chomsky, Carol, “Reading, Writing and Phonology”, Harvard Educational Review, 40(2), (1970), 287-309. [cited by applicant]
Cooper, William, et al., “Acoustical aspects of contrastive stress in question-answer contexts”, J. Acoust. Soc. Am., 77(6), (1985), 2142-2156. [cited by applicant]
Cooper, William, et al., “Syntax and Speech”, Cognitive Science Series, vol. 3. Harvard University Press, [Online]. Retrieved from the Internet: <URL: https://www.hup.harvard.edu/catalog.php?isbn=9780674283947>, (1980),… [cited by applicant]
Craven, Timothy C., “Graphic display of larger sentence dependency structures”, Journal of the American Society for Information Science 42, No. 5, (1991), 323-331. [cited by applicant]
Eady, Stephen, et al., “Speech intonation and focus location in matched statements and questions”, J. Acoust. Soc. Am., 80(2), (1986), 402-415. [cited by applicant]
Fougeron, Cecile, et al., “Articulatory strengthening at edges of prosodic domains”, J. Acoust. Soc. Am., vol. 101, No. 6, (1997), 3728-3740. [cited by applicant]
Fry, D B, “Duration and Intensity as Physical Correlates of Linguistic Stress”, J. Acoust. Soc. Am., 27(4), (Jul. 1955), 765-768. [cited by applicant]
Guyon, Isabelle, et al., “Gene Selection for Cancer Classification using Support Vector Machines”, Machine Learning, vol. 46, Issue 1-3, [Online]. Retrieved from the Internet: <URL: https://doi.org/10.1023/A:10124873027… [cited by applicant]
Hoeks, John, et al., “Processing Coordinated Structures in Context: The Effect of Topic-Structure on Ambiguity Resolution”, Journal of Memory and Language, 46, (2002), 99-119. [cited by applicant]
Huang, C, et al., “An assessment of support vector machines for land cover classification”, International Journal of Remote Sensing, vol. 23, Issue 4, (2002), 725-749. [cited by applicant]
Jackendoff, Ray, “”, X Syntax: A study of Phrase Structure, 248 pages, Cambridge, Mass. MIT Press, (1977), 132 pgs. [cited by applicant]
James, Gareth, et al., “Chapter 5 Resampling Methods”, An Introduction to Statistical Learning: with Applications in R, Springer Texts in Statistics, (2013), 175-201. [cited by applicant]
Kaan, Edith, et al., “L2 Prediction during complex sentence processing”, J Cult Cogn Sci., 3, [Online]. Retrieved from the Internet: <URL: https://doi.org/10.1007/s41809-019-00038-0>, (2019), 203-216. [cited by applicant]
Kerkhofs, Roel, et al., “Discourse, Syntax, and Prosody: The Brain Reveals an Immediate Interaction”, Journal of Cognitive Neuroscience, 19:9. Massachusetts Institute of Technology, (2007), 1421-1434. [cited by applicant]
Klatt, Dennis, “Vowel lengthening is syntactically determined in a connected discourse”, Journal of Phonetics, 3, (1975), 129-140. [cited by applicant]
Kochanski, G, et al., “Loudness predicts prominence: Fundamental frequency lends little”, J. Acoust. Soc. Am., 118(2), (Aug. 2005), 1038-1054. [cited by applicant]
Lehiste, Iise, “Rhythmic units and syntactic units in production and perception”, J. Acoustic. Soc. Am., 54(5), (1973), 1228-1234. [cited by applicant]
Lehiste, Iise, et al., “Role of duration in disambiguating syntactically ambiguous sentences”, J. Acoust. Soc. Am., 60(5), (Nov. 1976), 1199-1202. [cited by applicant]
Lehiste, Ilse, “Influence of fundamental frequency pattern on the perception of duration”, Journal of Phonetics, 4, (1976), 113-117. [cited by applicant]
Lieberman, Philip, “Some Acoustic Correlates of Word Stress in American English”, J. Acoust. Soc. Am.,32(4), (Apr. 1960), 451-454. [cited by applicant]
Miller, Justin, et al., “A Longitudinal Study of the Development of Reading Prosody as a Dimension of Oral Reading Fluency in Early Elementary School Children”, Read Res Q., 43(4), pp. 336-354, (2008), 29 pgs. [cited by applicant]
Pierrehumbert, Janet, “The Phonology and Phonetics of English Intonation (Thesis)”, MIT, (1980), 401 pgs. [cited by applicant]
Price, Patti, et al., “The Use of Prosody in Syntactic Disambiguation”, (1991), 372-377. [cited by applicant]
Street, Lynn, “Acoustic determinants of phrase boundary perception”, J. Acoust. Soc. Am., 64(6), (1978), 1582-1592. [cited by applicant]
Tate, Tamara, et al., “Visual-Syntactic Text Format: Improving Adolescent Literacy”, Scientific Studies of Reading, 23(4), [Online]. Retrieved from the Internet: <URL: https://escholarship.org/uc/item/4vw2g0m6>, (2019),… [cited by applicant]
Turk, Alice, et al., “Multiple targets of phrase-final lengthening in American English words”, Journal of Phonetics 35, (2007), 445-472. [cited by applicant]
Turk, Alice, et al., “The domain of accentual lengthening in American English”, Journal of Phonetics, 25, (1997), 25-41. [cited by applicant]
Vapnik, Vladimir, “An Overview of Statistical Learning Theory”, IEEE Transactions on Neural Networks, vol. 10, No. 5, (Sep. 1999), 988-999. [cited by applicant]
Walker, Randall, et al., “Live Ink®: Brain-Based Text Formatting Raises Standardized Reading Test Scores”, National Educational Computing Conference, (2005), 35 pgs. [cited by applicant]
Walker, Randall C, et al., “Visual-syntactic text formatting: Theoretical basis and empirical evidence for impact on human reading”, In IEEE International Professional Communication Conference, (2007), 1-14. [cited by applicant]
Walker, Stan, et al., “Visual-Syntactic Text Formatting: A New Method to Enhance Online Reading”, Reading Online, ISSN, [Online] Retrieved from the internet: <http://www.liveink.com/VSTF_ReadingOnline_IRA_2005_Walker.pd… [cited by applicant]
Warschauer, Mark, et al., “Transforming Digital Reading with Visual-Syntactic Text Formatting”, ALT CALL Journal ISSN vol. 7 No. 3, (Jun. 2011), 255-269. [cited by applicant]
Watson, Duane, et al., “The Role of Syntactic Obligatoriness in the Production of Intonational Boundaries.”, Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 32, No. 5, [Online]. Retrieved from … [cited by applicant]
Wightman, Colin, et al., “Segmental durations in the vicinity of prosodic phrase boundaries”, J. Acoust. Soc. Am., 91(3), (Mar. 1992), 1707-1717. [cited by applicant]
“U.S. Appl. No. 18/033,243, Non Final Office Action mailed Jun. 6, 2024”, 14 pgs. [cited by applicant]
“Israel Application Serial No. 307467, Office Action mailed Jun. 3, 2024”, 7 pgs. [cited by applicant]
“Chinese Application Serial No. 202280034092.8, Voluntary Amendment Filed May 28, 2024”, w english claims, 14 pgs. [cited by applicant]
“Japanese Application Serial No. 2023-562214, Response filed Jun. 26, 2024 to Notification of Reasons for Rejection mailed May 14, 2024”, w english claims, 11 pgs. [cited by applicant]
“U.S. Appl. No. 17/917,171, Notice of Allowance mailed Jul. 29, 2024”, 5 pgs. [cited by applicant]
“Korean Application Serial No. 10-2023-7038709, Response filed Aug. 1, 2024 to Notice of Preliminary Rejection mailed Apr. 12, 2024”, w english claims, 46 pgs. [cited by applicant]
“U.S. Appl. No. 18/033,243, Response filed Dec. 11, 2024 to Final Office Action mailed Sep. 26, 2024”, 13 pgs. [cited by applicant]
“U.S. Appl. No. 18/033,243, Notice of Allowance mailed Jan. 16, 2025”, 7 pgs. [cited by applicant]
Adhiguna Kuncoro, Miguel Ballesteros , Lingpeng Kong, Chris Dyer, Graham Neubig, Noah A. Smith. (2017). “What Do Recurrent Neural Network Grammars Learn About Syntax?” arXiv:1611.05774v2 [cs.CL] Jan. 10, 2017 Author: Ad… [cited by third party]
Title: Remote Access to Natural Language Parsing and Enhanced Text Presentation Technology for Improved Reading. Author: US Government, Small Business Innovation Research (SBIR) and Small Business Technology Transfer (S… [cited by third party]
Title: Noam Chomsky, Syntactic Structures, p. 15, 36, 37, (© 1957, The Mouton Co. Publishers, The Hague, Netherlands. ISBN 90 279 3385 5). Author: Noam Chomsky Date: 1957. [cited by third party]
Tamara P. Tate, Penelope Collins, Ying Xu, Joanna C. Yau, Jenell Krishnan, Yenda Prado, George Farkas &Mark Warschauer (2019) Visual-Syntactic Text Format: Improving Adolescent Literacy, Scientific Studies of Reading, 2… [cited by third party]
The Routledge Handbook of Corpora and English Language Teaching and Learning. Edited By Reka R. Jablonkal, Eniko Csomay. Chapter 32, Section 4: Visual Syntactic Text Formatting and Syntactic Processing. Authors: Xiaofe … [cited by third party]