IP Library Granted Patent US 12,657,384
Granted Patent B2
US 12,657,384 · App. 18/209,890 · Granted Jun 16, 2026

Context-based decoder correction

Inventors: Amer Aref Hassan (Kirkland, WA); Mahendra D. Sekaran (Sammamish, WA)
Assignee: Microsoft Technology Licensing, LLC
G06F40/232G06F40/253H04L1/0059
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 12,657,384
App. No.
18/209,890
Granted
Jun 16, 2026
Kind
B2
Abstract

Disclosed in some examples are methods, systems, and machine-readable mediums for utilizing context information to create decoding feedback information to improve decoder accuracy and/or performance. In some examples, the context information is from layers of a network stack above the layers in which the decoders are present. The context information may be or be based upon information about previously received and decoded data and/or information about the sender to provide decoding feedback information to the decoder that is used either to correct a previous decoding error or to inform the decoder on which of a plurality of decoding choices is more likely to be correct. This may increase decoding performance by decreasing errors and in some examples, reducing the complexity of choices by eliminating certain decoding possibilities and thus increasing decoder efficiency.

Claims (37)

1 . A method of improving channel decoding performance using language checking or predictive text, the method comprising:

receiving, over a transmission medium, encoded text encoded with a channel code;

decoding, by a channel decoder, at least a portion of the encoded text to produce a decoded text portion;

running a language check on the decoded text portion to produce a corrected decoded text portion, or inputting the decoded text portion into a predictive text generator, the predictive text generator generating subsequent text to produce a predicted decoded next text portion, the language check comprising a spelling check or a grammar check;

eliminating at least one potential decoding possibility for a second portion of the encoded text by utilizing the predicted decoded next text portion or the corrected decoded text portion when decoding, by the decoder, the second portion of the encoded text to create a second decoded text portion; and

displaying the second decoded text portion of the encoded text on a display.

2 . The method of claim 1 , wherein eliminating at least one potential decoding comprises trimming a trellis by eliminating choices inconsistent with the encoded feedback information.

3 . The method of claim 1 , wherein spell checking the decoded text portion comprises utilizing context sensitive spell checking that scans a plurality of words to determine improper spelling.

4 . The method of claim 3 , wherein the context sensitive spell checking utilizes historical usage of a transmitting user.

5 . The method of claim 1 , wherein the predictive text generator utilizes historical word choices of a transmitting user.

6 . The method of claim 1 , wherein the decoder is a trellis decoder.

7 . The method of claim 1 , wherein the language check comprises determining a most probable word based upon a word frequency usage of a transmitting user.

8 . A computing device for improving channel decoding performance using language checking or predictive text, the computing device comprising:

a hardware processor;

a memory, storing instructions, which when executed by the hardware processor causes the computing device to perform operations comprising:

receiving, over a transmission medium, encoded text encoded with a channel code;

decoding, by a channel decoder, at least a portion of the encoded text to produce a decoded text portion;

running a language check on the decoded text portion to produce a corrected decoded text portion, or inputting the decoded text portion into a predictive text generator, the predictive text generator generating subsequent text to produce a predicted decoded next text portion, the language check comprising a spelling check or a grammar check;

eliminating at least one potential decoding possibility for a second portion of the encoded text by utilizing the predicted decoded next text portion or the corrected decoded text portion when decoding, by the decoder, the second portion of the encoded text to create a second decoded text portion; and

displaying the second decoded text portion of the encoded text on a display.

9 . The computing device of claim 8 , wherein the operations of eliminating at least one potential decoding comprises trimming a trellis by eliminating choices inconsistent with the encoded feedback information.

10 . The computing device of claim 8 , wherein the operations of spell checking the decoded text portion comprises utilizing context sensitive spell checking that scans a plurality of words to determine improper spelling.

11 . The computing device of claim 10 , wherein the context sensitive spell checking utilizes historical usage of a transmitting user.

12 . The computing device of claim 8 , wherein the predictive text generator utilizes historical word choices of a transmitting user.

13 . The computing device of claim 8 , wherein the decoder is a trellis decoder.

14 . The computing device of claim 8 , wherein the language check comprises determining a most probable word based upon a word frequency usage of a transmitting user.

15 . A machine-readable medium, storing instructions for improving decoding performance using language checking or predictive text, the instructions, which when executed, cause a machine to perform operations comprising:

receiving, over a transmission medium, encoded text encoded with a channel code;

decoding, by a channel decoder, at least a portion of the encoded text to produce a decoded text portion;

running a language check on the decoded text portion to produce a corrected decoded text portion, or inputting the decoded text portion into a predictive text generator, the predictive text generator generating subsequent text to produce a predicted decoded next text portion, the language check comprising a spelling check or a grammar check;

eliminating at least one potential decoding possibility for a second portion of the encoded text by utilizing the predicted decoded next text portion or the corrected decoded text portion when decoding, by the decoder, the second portion of the encoded text to create a second decoded text portion; and

displaying the second decoded text portion of the encoded text on a display.

16 . The machine-readable medium of claim 15 , wherein the operations of eliminating at least one potential decoding comprises trimming a trellis by eliminating choices inconsistent with the encoded feedback information.

17 . The machine-readable medium of claim 15 , wherein the operations of spell checking the decoded text portion comprises utilizing context sensitive spell checking that scans a plurality of words to determine improper spelling.

18 . The machine-readable medium of claim 17 , wherein the context sensitive spell checking utilizes historical usage of a transmitting user.

19 . The machine-readable medium of claim 15 , wherein the predictive text generator utilizes historical word choices of a transmitting user.

20 . The machine-readable medium of claim 15 , wherein the decoder is a trellis decoder.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 18, 2023
From: HASSAN, AMER AREF; SEKARAN, MAHENDRA D.
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 064295/0742 →
Continuity (1)
Related Publication 20240419895A1 · Dec 19, 2024
References Cited (72)
US 5436918A · Kato · 1995 [cited by applicant]
US 5864805A · Chen · 1999 [cited by applicant]
US 6035007A · Khayrallah · 2000 [cited by applicant]
US 6185526B1 · Kato · 2001 [cited by applicant]
US 8484022B1 · Vanhoucke · 2013 [cited by examiner]
US 9711148B1 · Sharifi et al. · 2017 [cited by applicant]
US 10236031B1 · Gurijala · 2019 [cited by applicant]
US 11743001B2 · Jiang · 2023 [cited by applicant]
US 20040205383A1 · Sawaguchi · 2004 [cited by applicant]
US 20070083374A1 · Bates et al. · 2007 [cited by applicant]
US 20080059144A1 · Earhart · 2008 [cited by applicant]
US 20090106695A1 · Perry et al. · 2009 [cited by applicant]
US 20110202876A1 · Badger et al. · 2011 [cited by applicant]
US 20130028336A1 · Limberg · 2013 [cited by applicant]
US 20130289977A1 · Tanaka · 2013 [cited by applicant]
US 20140278418A1 · Chen · 2014 [cited by applicant]
US 20150006164A1 · Lu · 2015 [cited by applicant]
US 20150149174A1 · Gollan · 2015 [cited by applicant]
US 20170214413A1 · Wang · 2017 [cited by applicant]
US 20170244979A1 · Kumar · 2017 [cited by applicant]
US 20180013868A1 · Lin · 2018 [cited by applicant]
US 20190042881A1 · Lopatka · 2019 [cited by applicant]
US 20190066713A1 · Mesgarani et al. · 2019 [cited by applicant]
US 20190221205A1 · Czyryba · 2019 [cited by applicant]
US 20200279570A1 · Nakayama · 2020 [cited by applicant]
US 20210044775A1 · Lee · 2021 [cited by applicant]
US 20210295149A1 · Muraoka · 2021 [cited by applicant]
US 20210397780A1 · Pang · 2021 [cited by examiner]
US 20220165249A1 · Wu · 2022 [cited by applicant]
US 20220231700A1 · Zhang · 2022 [cited by applicant]
US 20220321149A1 · Dodgson · 2022 [cited by applicant]
US 20220383853A1 · Xu · 2022 [cited by applicant]
US 20220417659A1 · Narayanan · 2022 [cited by applicant]
US 20230086832A1 · Tadesse · 2023 [cited by applicant]
US 20230119685A1 · Kim · 2023 [cited by applicant]
US 20230198663A1 · Stoica · 2023 [cited by applicant]
US 20230232026A1 · Kim · 2023 [cited by applicant]
US 20240127812A1 · Inbavaluthi · 2024 [cited by applicant]
US 20240303178A1 · Paquin · 2024 [cited by applicant]
US 20240366157A1 · Moses · 2024 [cited by examiner]
US 20240420702A1 · Hassan · 2024 [cited by applicant]
US 20240420707A1 · Hassan · 2024 [cited by applicant]
US 20240422328A1 · Hassan · 2024 [cited by applicant]
CN 113271110A · 2021 [cited by applicant]
Akmal Akmalkhodzhaev, “Joint decoding of turbo code and AMR-WB vocoder in 3GPP LTE system”, 6th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)—IEEE, Oct. 6, 2014, pp.… [cited by applicant]
International Search Report and Written Opinion received for PCT Application No. PCT/US2024/031329, Sep. 4, 2024, 17 pages. [cited by applicant]
Wankhede, et al., “Enhancing Biometric Speaker Recognition Through MFCC Feature Extraction and Polar Codes for Remote Application”, IEEE Access, vol. 11, Nov. 14, 2023, pp. 133921-133930. [cited by applicant]
Zhao, et al., “Convolutional Neural Networks to Enhance Coded Speech”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, Issue 4, Apr. 2019, pp. 663-678. [cited by applicant]
Notice of Allowance mailed on Feb. 26, 2025, in U.S. Appl. No. 18/209,902, 08 pages. [cited by applicant]
Notice of Allowance mailed on Jul. 8, 2025, in U.S. Appl. No. 18/209,920, 10 pages. [cited by applicant]
Notice of Allowance mailed on Jul. 10, 2025, in U.S. Appl. No. 18/209,909, 05 pages. [cited by applicant]
Non-Final Office Action mailed on May 15, 2025, in U.S. Appl. No. 18/209,909 12 Pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Application No. PCT/US2024/031328, Sep. 16, 2024, 15 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Application No. PCT/US2024/031337, Sep. 16, 2024, 16 pages. [cited by applicant]
Kurka, et al., “Successive Refinement of Images with Deep Joint Source-Channel Coding”, IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Jul. 2, 2019, pp. 1-5. [cited by applicant]
Lee, et al., “Deep Learning-Constructed Joint Transmission-Recognition for Internet of Things”, IEEE Access, vol. 7, Jun. 5, 2019, pp. 76547-76561. [cited by applicant]
Li, et al., “Content-assisted file decoding for nonvolatile memories”, Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)—IEEE, Nov. 4, 2012, pp. 937-941. [cited by applicant]
Liang, et al., “An Iterative BP-CNN Architecture for Channel Decoding”, IEEE Journal of Selected Topics in Signal Processing, vol. 12, Issue 1, Jan. 15, 2018, pp. 144-159. [cited by applicant]
Luo, et al., “Error control coding combined with content recognition”, 8th International Conference on Wireless Communications & Signal Processing (WCSP)—IEEE, Oct. 13, 2016, pp. 1-5. [cited by applicant]
Strinati, et al., “6G networks: Beyond Shannon towards semantic and goal-oriented communications”, Computer Networks, vol. 190, May 8, 2021, pp. 1-17. [cited by applicant]
Sun, et al., “Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance”, IEEE 96th Vehicular Technology Conference (VTC2022-Fall), Sep. 26, 2022, pp. 1-7. [cited by applicant]
Non-Final Office Action mailed on Oct. 11, 2024, in U.S. Appl. No. 18/209,902, 12 pages. [cited by applicant]
International Search Report and Written Opinion received for PCT Application No. PCT/US2024/031340, Sep. 18, 2024, 17 pages. [cited by applicant]
Yao, et al., “Semantic Coding for Text Transmission: An Iterative Design”, IEEE Transactions on Cognitive Communications and Networking, vol. 8, Issue 4, Jul. 20, 2022, pp. 1594-1603. [cited by applicant]
Levow, Gina-Anne, “Characterizing and recognizing spoken corrections in human-computer dialogue”, The 17th International Conference on Computational Linguistics, vol. 1, 1998, pp. 736-742. [cited by applicant]
Non-Final Office Action mailed on Mar. 27, 2025, in U.S. Appl. No. 18/209,920, 34 pages. [cited by applicant]
International Preliminary Report on Patentability (Chapter I) received for PCT Application No. PCT/US2024/031340, mailed on Dec. 26, 2025, 12 pages. [cited by applicant]
International Preliminary Report on Patentability (Chapter I) received for PCT Application No. PCT/US2024/031328, mailed on Dec. 26, 2025, 10 Pages. [cited by applicant]
International Preliminary Report on Patentability (Chapter I) received for PCT Application No. PCT/US2024/031329, Mailed on Dec. 26, 2025, 11 Pages. [cited by applicant]
International Preliminary Report on Patentability (Chapter I) received for PCT Application No. PCT/US2024/031337, mailed on Dec. 26, 2025, 11 pages. [cited by applicant]
Non-Final Office Action mailed on Oct. 16, 2025, in U.S. Appl. No. 18/209,920, 22 Pages. [cited by applicant]
Notice of Allowance mailed on Apr. 23, 2026, in U.S. Appl. No. 18/209,920, 09 pages. [cited by applicant]