IP Library Granted Patent US 11,967,974
Granted Patent B2
US 11,967,974 · App. 18/501,987 · Granted Apr 23, 2024

System and method for data compression with protocol adaptation

Inventors: Joshua Cooper (Columbia, SC); Aliasghar Riahi (Orinda, CA)
Assignee: ATOMBEAM TECHNOLOGIES INC.
H03M7/3059G06N20/00H03M7/6005
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Quick Facts
Patent No.
US 11,967,974
App. No.
18/501,987
Filed
Nov 4, 2023
Granted
Apr 23, 2024
Kind
B2
Art Unit
2845
USPC
707/693
Abstract

A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

Claims (35)

1. A system for data compression with protocol adaptation, comprising:

a computing device comprising a processor and a memory;

a codebook generator comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:

receive a plurality of training data;

receive a plurality of protocol policy data;

use a subset of the training data and a subset of the protocol policy data as inputs to train a machine learning algorithm, wherein the machine learning algorithm is configured to produce a protocol appendix;

append the protocol appendix to a codebook; and

send the appended codebook to a decoder; and

the decoder comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:

receive the appended codebook from the codebook generator;

receive encoded data; and

decode the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data.

2. The system of claim 1 , wherein the codebook generator is further configured to generate the codebook based on analysis of a second training data.

3. The system of claim 1 , wherein the machine learning algorithm is selected from the group consisting of decision trees, random forest, k-Nearest Neighbors and support vector machines.

4. The system of claim 1 , wherein the machine learning algorithm is a deep learning algorithm.

5. The system of claim 4 , wherein the deep learning algorithm is a neural network.

6. The system of claim 1 , wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.

7. The system of claim 1 , wherein the protocol appendix is appended to the codebook in the form of bit extensions.

8. The system of claim 1 , wherein the protocol appendix is appended to the codebook in the form of a dimensional array.

9. A method for data compression with protocol adaptation, comprising the steps of:

receiving a plurality of training data;

receiving a plurality of protocol policy data;

using a subset of the training data and a subset of the protocol policy data as inputs to train a machine learning algorithm, wherein the machine learning algorithm is configured to produce a protocol appendix;

appending the protocol appendix to a codebook;

sending the appended codebook to a decoder;

receiving the appended codebook from a codebook generator;

receiving encoded data; and

decoding the encoded data using the appended codebook, wherein the decoded data is output as protocol formatted data.

10. The method of claim 9 , further comprising the step of generating the codebook based on analysis of a second training data.

11. The method of claim 9 , wherein the machine learning algorithm is selected from the group consisting of decision trees, random forest, k-Nearest Neighbors and support vector machines.

12. The method of claim 9 , wherein the machine learning algorithm is a deep learning algorithm.

13. The method of claim 12 , wherein the deep learning algorithm is a neural network.

14. The method of claim 9 , wherein the plurality of protocol policy data comprises at least data format and structure, message protocol standards, data transmission and encryption, data validation and sanitation rules, error handling and reporting, data versioning, data ownership and access control, data documentation, compliance and regulations, and monitoring and auditing processes.

15. The method of claim 9 , wherein the protocol appendix is appended to the codebook in the form of bit extensions.

16. The method of claim 9 , wherein the protocol appendix is appended to the codebook in the form of a dimensional array.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 31, 2023
From: COOPER, JOSHUA; RIAHI, ALIASGHAR
To: ATOMBEAM TECHNOLOGIES INC.
Reel/Frame 065987/0320 →
Continuity (18)
Continuation In Part 18190044 · Mar 24, 2023
Continuation In Part 17875201 · Jul 27, 2022
Continuation In Part 17727913 · Apr 25, 2022
Continuation 17514913 · Oct 29, 2021
Continuation 17458747 · Aug 27, 2021
Continuation 17404699 · Aug 17, 2021
Continuation In Part 17404699 · Aug 17, 2021
Continuation In Part 16923039 · Jul 7, 2020
Continuation In Part 16716098 · Dec 16, 2019
Continuation 16455655 · Jun 27, 2019
Continuation In Part 16455655 · Jun 27, 2019
Continuation In Part 16200466 · Nov 26, 2018
Continuation In Part 15975741 · May 9, 2018
Provisional Application 63388411 · Jul 12, 2022
Provisional Application 63027166 · May 19, 2020
Provisional Application 62926723 · Oct 28, 2019
Provisional Application 62578824 · Oct 30, 2017
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