US 12,381,574 B2
System and method for distributed node-based data compaction
Joshua Cooper, Columbia, SC (US); Aliasghar Riahi, Orinda, CA (US); Mojgan Haddad, Orinda, CA (US); Razmin Riahi, Orinda, CA (US); Ryan Kourosh Riahi, Orinda, CA (US); and Charles Yeomans, Orinda, CA (US)
Assigned to ATOMBEAM TECHNOLOGIES INC, Moraga, CA (US)
Filed by AtomBeam Technologies Inc., Moraga, CA (US)
Filed on Dec. 5, 2023, as Appl. No. 18/530,147.
Application 18/530,147 is a continuation of application No. 18/303,399, filed on Apr. 19, 2023, granted, now 11,838,034.
Application 18/303,399 is a continuation in part of application No. 17/875,201, filed on Jul. 27, 2022, granted, now 11,700,013, issued on Jul. 11, 2023.
Application 17/875,201 is a continuation of application No. 17/514,913, filed on Oct. 29, 2021, granted, now 11,424,760, issued on Aug. 23, 2022.
Application 17/875,201 is a continuation of application No. 17/458,747, filed on Aug. 27, 2021, granted, now 11,422,978, issued on Aug. 23, 2022.
Application 17/514,913 is a continuation in part of application No. 17/404,699, filed on Aug. 17, 2021, granted, now 11,385,794, issued on Jul. 12, 2022.
Application 17/458,747 is a continuation in part of application No. 16/923,039, filed on Jul. 7, 2020, granted, now 11,232,076, issued on Jan. 25, 2022.
Application 16/923,039 is a continuation in part of application No. 16/716,098, filed on Dec. 16, 2019, granted, now 10,706,018, issued on Jul. 7, 2020.
Application 16/716,098 is a continuation of application No. 16/455,655, filed on Jun. 27, 2019, granted, now 10,509,771, issued on Dec. 17, 2019.
Application 17/404,699 is a continuation in part of application No. 16/455,655, filed on Jun. 27, 2019, granted, now 10,509,771, issued on Dec. 17, 2019.
Application 16/455,655 is a continuation in part of application No. 16/200,466, filed on Nov. 26, 2018, granted, now 10,476,519, issued on Nov. 12, 2019.
Application 16/200,466 is a continuation in part of application No. 15/975,741, filed on May 9, 2018, granted, now 10,303,391, issued on May 28, 2019.
Claims priority of provisional application 63/332,533, filed on Apr. 19, 2022.
Claims priority of provisional application 63/027,166, filed on May 19, 2020.
Claims priority of provisional application 62/926,723, filed on Oct. 28, 2019.
Claims priority of provisional application 62/578,824, filed on Oct. 30, 2017.
Prior Publication US 2024/0120940 A1, Apr. 11, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H03M 7/30 (2006.01); G06N 20/00 (2019.01); H04L 9/00 (2022.01)
CPC H03M 7/3059 (2013.01) [G06N 20/00 (2019.01); H03M 7/6005 (2013.01); H04L 9/50 (2022.05)] 18 Claims
OG exemplary drawing
 
1. A distributed node-based data compaction system, comprising:
a network compaction service, comprising a first plurality of programming instructions stored in a memory and operating on a processor of a computing device, wherein the first plurality of programming instructions, when operating on the processor, cause the processor to:
store a first reference codebook in the memory of each of a plurality of networked computing nodes, the first reference codebook pretrained by machine learning to determine sourceblocks and to associate codewords to each sourceblock;
store a first deconstruction algorithm in the memory of each of the plurality of networked computing nodes, wherein the first deconstruction algorithm, when operating on a processor of its respective computing node, causes the computing node to:
receive network data for a consensus pool maintained between the plurality of networked computing nodes;
deconstruct the network data for the consensus pool into a plurality of sourceblocks;
encode the first plurality of sourceblocks into a first codeword using the first reference codebook; and
send the first codeword to the consensus pool; and
store a first reconstruction algorithm in the memory of each of the plurality of networked computing nodes, wherein the first reconstruction algorithm, when operating on a processor of its respective computing node, causes the processor to:
receive the first codeword; and
reconstruct the network data by decoding the first plurality of sourceblocks using the first reference codebook; and
a compaction module, comprising a second plurality of programming instructions stored in a memory and operating on a processor of a computing device, wherein the second plurality of programming instructions, when operating on the processor, cause the processor to:
store a second reference codebook in the memory of each of the plurality of networked computing nodes, the second reference codebook pretrained by machine learning to determine sourceblocks and associated codewords of the consensus pool; and
store a second deconstruction algorithm in the memory of each of the plurality of networked computing nodes, wherein the first deconstruction algorithm, when operating on a processor of its respective computing node, causes the processor to:
deconstruct a data block into a second plurality of sourceblocks;
encode the second plurality of sourceblocks into a second codeword using the second reference codebook; and
append the second codeword to a compacted blockchain, the compacted blockchain comprising codewords for each data block of the consensus pool.