IP Library Granted Patent US 12,379,902
Granted Patent B2
US 12,379,902 · App. 17/015,071 · Granted Aug 5, 2025

Event-based entity scoring in distributed systems

Inventor: Edward Hunter (Gaithersburg, MD)
Assignee: Digital Asset Capital, Inc.
G06F8/33G06F8/65G06F9/547G06F16/1858G06F16/23G06F16/9024G06F16/904G06F16/951G06F17/18G06N3/048G06N3/08G06N3/086H04L9/3263H04L67/133H04L9/50
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 12,379,902
App. No.
17/015,071
Filed
Sep 8, 2020
Granted
Aug 5, 2025
Kind
B2
Art Unit
2154
USPC
707/798
Abstract

A method includes obtaining a directed graph of a self-executing protocol, the directed graph including a set of vertices associated with mutually exclusive category labels, where the self-executing protocol identifies a first entity. The method may include obtaining a first graph portion template that includes a vertex template and an edge template. The vertex template is associated with a category of the mutually exclusive category labels. The method may include determining whether the first graph portion template matches a graph portion in the directed graph and an edge of the directed graph matching the edge template. The method may include determining an outcome score based on the graph portion template matching the graph portion, determining whether the outcome score satisfies an outcome score threshold, and storing a value indicating that the outcome score satisfies the outcome score threshold.

Claims (99)

1. A tangible, non-transitory, machine-readable medium storing instructions that, when executed by a computing system, effectuate operations comprising:

obtaining, with a computing system, program state of a self-executing protocol, wherein the program state encodes:

a set of conditional statements;

a set of entities, wherein the set of entities comprises a first entity;

a directed graph, the directed graph comprising:

a set of vertices, wherein each respective vertex of the set of vertices is associated with a respective category label of a set of mutually exclusive categories;

a set of directed edges connecting respective pairs of vertices among the set of vertices;

obtaining, with the computing system, an entity profile of the first entity, wherein:

the entity profile comprises a first graph portion template,

the first graph portion template comprises a first vertex template and an edge template,

the first vertex template is associated in memory with a first category label of the set of mutually exclusive category labels, and

the edge template specifies an edge direction to or from a vertex matching the first vertex template;

determining, with the computing system, whether the first graph portion template matches a graph portion in the directed graph based on a first vertex of the directed graph matching the first vertex template and a first directed edge of the directed graph matching the edge template;

determining, with the computing system, an outcome score based on the first graph portion template matching the graph portion in the directed graph;

determining, with the computing system, whether the outcome score satisfies an outcome score threshold; and

in response to the outcome score satisfying the outcome score threshold, storing, with the computing system, a value indicating that the outcome score satisfies the outcome score threshold.

2. The medium of claim 1 , wherein:

the set of vertices are a set of norm vertices;

the first vertex is a first norm vertex;

the set of entities include parties to the self-executing protocol;

the operations further comprising:

obtaining a plurality of self-executing protocols comprising a plurality of directed graphs, wherein each respective directed graph of the plurality of directed graphs is associated with a respective set of entities that comprises the first entity;

determining the first graph portion template based on the plurality of directed graphs, wherein a second norm vertex of the plurality of directed graphs matches the first norm vertex template of the first graph portion template, and wherein a condition of the second norm vertex is indicated to have been failed by the first entity based on an event message; and

determining an outcome determination parameter based on a number of times that the first graph portion template matches with a respective graph portion in the plurality of self-executing protocols, wherein determining the outcome score comprises determining the outcome score based on the outcome determination parameter.

3. The medium of claim 1 , the operations further comprising:

obtaining a plurality of self-executing protocols comprising a plurality of directed graphs, wherein each respective self-executing protocol of the plurality of self-executing protocols comprises a respective directed graph of the plurality of directed graphs;

determining the first graph portion template based on the plurality of self-executing protocols, wherein a second vertex of a second directed graph of the plurality of directed graphs matches the first vertex template, and wherein a third vertex of the plurality of directed graphs matches a second vertex template, and wherein a condition of the third vertex is indicated as having been satisfied based on an event message; and

determining an outcome determination parameter based on a number of times that the first graph portion template matches with a respective graph portion in the plurality of self-executing protocols, wherein determining the outcome score comprises determining the outcome score based on the outcome determination parameter.

4. The medium of claim 1 , wherein the entity profile is a first entity profile, and wherein the operations further comprise:

determining a transaction score based on the directed graph, wherein the transaction score is associated with a transaction between the first entity and a second entity; and

updating an association between the first entity profile and a second entity profile based on the transaction score, wherein the second entity profile is associated with the second entity.

5. The medium of claim 1 , the operations further comprising:

determining whether the first entity has failed a conditional statement associated with a second vertex of the directed graph; and

in response to a determination that the first entity has failed the conditional statement, updating an entity score of an entity graph, wherein the entity score is associated with the first entity, and wherein the entity graph comprises a plurality of entity vertices, and wherein each respective entity vertex of the plurality of entity vertices is associated with a respective entity profile.

6. The medium of claim 5 , wherein the entity graph is stored on a distributed, tamper-evident ledger, and wherein updating the entity score comprises:

obtaining an encryption key associated with the first entity;

obtaining a previous entity score from the distributed, tamper-evident ledger based on the encryption key; and

updating the entity score based on the previous entity score.

7. The medium of claim 5 , wherein the entity graph is stored on a distributed, tamper-evident ledger, and wherein the operations further comprise:

determining whether the entity score satisfies an entity score threshold of a verification entity; and

in response to the entity score satisfying the entity score threshold, storing an indicator that the first entity satisfies the entity score threshold of the verification entity.

8. The medium of claim 5 , the operations further comprising:

determining whether the entity score satisfies an entity score threshold of a verification entity; and

sending a message to an application program interface, wherein the message indicates that the first entity satisfies the entity score threshold of the verification entity.

9. The medium of claim 5 , wherein the entity profile is a first entity profile, and wherein the operations further comprise:

determining a second entity score associated with the first entity, wherein the first entity profile does not comprise the second entity score;

obtaining a passkey value; and

in response to receiving the passkey value, sending a message comprising the second entity score.

10. The medium of claim 1 , wherein determining the outcome score comprises determining the outcome score using a neural network based on a feature set, wherein:

determining the feature set, wherein determining the feature set comprises determining whether the first graph portion template matches a graph portion in the directed graph; and

the neural network is trained on a plurality of directed graphs of a plurality of a self-executing protocols, wherein the first graph portion template matches a graph portion of a subset of the plurality of directed graphs.

11. The medium of claim 1 , wherein determining the outcome score comprises:

generating a set of embeddings based on a set of vertices of the directed graph, wherein each vertex of the set of vertices is associated with an embedding of the set of embeddings, and wherein each embedding comprises a vector;

determining a feature set based on the set of embeddings; and

determining the outcome score using a neural network based on the feature set.

12. The medium of claim 1 , wherein the entity profile is a first entity profile and the outcome score is a first outcome score, and wherein the operations further comprise:

obtaining a second entity profile, wherein the second entity profile is associated with a second entity, and wherein the second entity profile comprises the first graph portion template, and wherein a second outcome determination parameter is determined based on the first graph portion template;

determining a second outcome score associated with the second entity profile based on the second outcome determination parameter; and

selecting the first entity based on the first outcome score and the second outcome score.

13. The medium of claim 1 , the operations further comprising:

sampling the directed graph to determine a set of subgraphs;

determining a vector based on the set of subgraphs using a skip-gram model; and

determining the outcome score using a neural network based on the vector.

14. The medium of claim 1 , wherein the first graph portion template further comprises a second vertex template, wherein the second vertex template is associated with a second category label of the set of mutually exclusive category labels, and wherein the second category label is different from the first category label.

15. The medium of claim 1 , the operations further comprising:

updating the entity profile based a history of the first entity;

storing the entity profile on a centralized computing platform, wherein the entity profile is associated with an entity identifier; and

updating a value associated with the entity identifier, wherein the value is stored on a distributed, tamper-evident ledger operating on a distributed computing platform.

16. The medium of claim 1 , wherein the entity profile is a first entity profile, and wherein the operations further comprising:

obtaining a second entity profile;

determining whether a set of entity similarity criteria is satisfied based on the first entity profile and the second entity profile; and

storing value indicating that the first entity profile and the second entity profile satisfy the set of entity similarity criteria.

17. The medium of claim 1 , wherein the first graph portion template further comprises a second vertex template, wherein the second vertex template is not connected to the first vertex template in the first graph portion template by any edge templates.

18. The medium of claim 1 , wherein the directed graph is a first self-executing protocol directed graph, and wherein the operations further comprise:

determining a first transaction amount between the first entity and a second entity based on the first self-executing protocol directed graph;

determining a second transaction amount between the second entity and a third entity based on a second self-executing protocol directed graph;

updating a first association between the first entity and the second entity of an entity graph based on the first transaction amount;

updating a second association between the second entity and the third entity of the entity graph based on the second transaction amount; and

determining whether the first entity is associated with the third entity based on the first association, the first transaction amount, the second association, and the second transaction amount.

19. The medium of claim 18 , the operations further comprising:

determining whether the first entity has failed a conditional statement associated with the first vertex;

in response to a determination that the first entity has failed the conditional statement, updating an entity score is associated with the first entity; and

sending a message to the third entity in response to the updating of the entity score associated with the first entity.

20. A method comprising:

obtaining, with a computing system, program state of a self-executing protocol, wherein the program state encodes:

a set of conditional statements;

a set of entities, wherein the set of entities comprises a first entity;

a directed graph, the directed graph comprising:

a set of vertices, wherein each respective vertex of the set of vertices is associated with a respective category label of a set of mutually exclusive categories;

a set of directed edges connecting respective pairs of vertices among the set of vertices;

obtaining, with the computing system, an entity profile of the first entity, wherein:

the entity profile comprises a first graph portion template,

the first graph portion template comprises a first vertex template and an edge template,

the first vertex template is associated in memory with a first category label of the set of mutually exclusive category labels, and

the edge template specifies an edge direction to or from a vertex matching the first vertex template;

determining, with the computing system, whether the first graph portion template matches a graph portion in the directed graph based on a first vertex of the directed graph matching the first vertex template and a first directed edge of the directed graph matching the edge template;

determining, with the computing system, an outcome score based on the first graph portion template matching the graph portion in the directed graph;

determining, with the computing system, whether the outcome score satisfies an outcome score threshold; and

in response to the outcome score satisfying the outcome score threshold, storing, with the computing system, a value indicating that the outcome score satisfies the outcome score threshold.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 24, 2020
From: HUNTER, EDWARD
To: DIGITAL ASSET CAPITAL, INC.
Reel/Frame 053869/0784 →
Continuity (16)
Continuation 16893318 · Jun 4, 2020
Continuation 16893290 · Jun 4, 2020
Continuation 16893299 · Jun 4, 2020
Continuation 16893295 · Jun 4, 2020
Provisional Application 63056984 · Jul 27, 2020
Provisional Application 63055783 · Jul 23, 2020
Provisional Application 63053217 · Jul 17, 2020
Provisional Application 63052329 · Jul 15, 2020
Provisional Application 63034255 · Jun 3, 2020
Provisional Application 63033063 · Jun 1, 2020
Provisional Application 63020808 · May 6, 2020
Provisional Application 62959377 · Jan 10, 2020
Provisional Application 62959481 · Jan 10, 2020
Provisional Application 62959418 · Jan 10, 2020
Provisional Application 62897240 · Sep 6, 2019
Related Publication 20210073289A1 · Mar 11, 2021
References Cited (201)
US 7512588B2 · Chou · 2009 [cited by applicant]
US 7634778B2 · Mosier · 2009 [cited by applicant]
US 8108241B2 · Shukoor · 2012 [cited by applicant]
US 8156134B2 · Sun · 2012 [cited by applicant]
US 8312049B2 · Chayes · 2012 [cited by applicant]
US 8387000B2 · Avadhanula · 2013 [cited by applicant]
US 8533182B1 · Charboneau · 2013 [cited by applicant]
US 8614703B1 · Fong · 2013 [cited by applicant]
US 8732685B2 · Moler · 2014 [cited by applicant]
US 9547728B2 · Bornhoevd · 2017 [cited by applicant]
US 9836183B1 · Love · 2017 [cited by applicant]
US 10169454B2 · Ait-Mokhtar · 2019 [cited by applicant]
US 10320891B2 · Agarwal · 2019 [cited by applicant]
US 10424399B2 · Clark · 2019 [cited by applicant]
US 10496752B1 · Crossley · 2019 [cited by applicant]
US 10509844B1 · Mcintyre · 2019 [cited by applicant]
US 10509863B1 · Arfa · 2019 [cited by applicant]
US 10515000B2 · Moretto · 2019 [cited by applicant]
US 10529137B1 · Black · 2020 [cited by applicant]
US 10558759B1 · Arfa · 2020 [cited by applicant]
US 10558933B2 · Bhowan · 2020 [cited by applicant]
US 10700852B2 · Xie · 2020 [cited by examiner]
US 10705939B2 · McChord · 2020 [cited by applicant]
US 10810193B1 · Subramanya et al. · 2020 [cited by applicant]
US 10810210B2 · Choudhury · 2020 [cited by applicant]
US 11106458B2 · Adams · 2021 [cited by examiner]
US 11265171B2 · Struttmann et al. · 2022 [cited by applicant]
US 11271717B2 · Shi · 2022 [cited by applicant]
US 20020087275A1 · Kim · 2002 [cited by applicant]
US 20020095276A1 · Rong · 2002 [cited by applicant]
US 20040019601A1 · Gates · 2004 [cited by applicant]
US 20040181361A1 · Ikeda · 2004 [cited by applicant]
US 20050036615A1 · Jakobsson · 2005 [cited by applicant]
US 20050038533A1 · Farrell · 2005 [cited by applicant]
US 20050065955A1 · Babikov · 2005 [cited by examiner]
US 20060045027A1 · Galou · 2006 [cited by applicant]
US 20060161560A1 · Khandelwal · 2006 [cited by applicant]
US 20060195747A1 · Pramanick · 2006 [cited by applicant]
US 20070239694A1 · Singh · 2007 [cited by applicant]
US 20080033777A1 · Shukoor · 2008 [cited by applicant]
US 20080052692A1 · Chockler · 2008 [cited by applicant]
US 20080079724A1 · Isard et al. · 2008 [cited by applicant]
US 20080098375A1 · Isard · 2008 [cited by applicant]
US 20080120292A1 · Sundaresan · 2008 [cited by applicant]
US 20080126450A1 · O'Neill · 2008 [cited by applicant]
US 20090083262A1 · Chang et al. · 2009 [cited by applicant]
US 20100153152A1 · Kind · 2010 [cited by applicant]
US 20100312545A1 · Sites · 2010 [cited by applicant]
US 20110137919A1 · Ryu · 2011 [cited by applicant]
US 20110153662A1 · Stanfill · 2011 [cited by applicant]
US 20110238409A1 · Larcheveque et al. · 2011 [cited by applicant]
US 20120054255A1 · Buxbaum et al. · 2012 [cited by applicant]
US 20120143808A1 · Karins · 2012 [cited by applicant]
US 20120253793A1 · Ghannam et al. · 2012 [cited by applicant]
US 20120303358A1 · Ducatel et al. · 2012 [cited by applicant]
US 20130055302A1 · De et al. · 2013 [cited by applicant]
US 20130138699A1 · Schacher · 2013 [cited by applicant]
US 20140006394A1 · Kritt et al. · 2014 [cited by applicant]
US 20140189651A1 · Gounares · 2014 [cited by applicant]
US 20140236965A1 · Yarmus · 2014 [cited by applicant]
US 20150067644A1 · Chakraborty · 2015 [cited by applicant]
US 20160012149A1 · Muchinsky · 2016 [cited by examiner]
US 20160105322A1 · Pullo · 2016 [cited by applicant]
US 20160203242A1 · Henrickson · 2016 [cited by applicant]
US 20160239753A1 · Loehlein et al. · 2016 [cited by applicant]
US 20160350662A1 · Jin · 2016 [cited by applicant]
US 20170154123A1 · Yurchenko · 2017 [cited by applicant]
US 20170161121A1 · Feng · 2017 [cited by applicant]
US 20170193390A1 · Weston et al. · 2017 [cited by applicant]
US 20170212781A1 · Dillenberger · 2017 [cited by applicant]
US 20170230791A1 · Jones · 2017 [cited by applicant]
US 20170270100A1 · Audhkhasi et al. · 2017 [cited by applicant]
US 20170329868A1 · Lindsley · 2017 [cited by applicant]
US 20170364534A1 · Zhang · 2017 [cited by applicant]
US 20180075030A1 · Gilder · 2018 [cited by applicant]
US 20180129957A1 · Saxena · 2018 [cited by applicant]
US 20180189294A1 · Anand · 2018 [cited by applicant]
US 20180205552A1 · Struttmann · 2018 [cited by examiner]
US 20180267958A1 · Danielyan et al. · 2018 [cited by applicant]
US 20180293486A1 · Bajic et al. · 2018 [cited by applicant]
US 20190050854A1 · Yang et al. · 2019 [cited by applicant]
US 20190095909A1 · Wright et al. · 2019 [cited by applicant]
US 20190116047A1 · Struttmann · 2019 [cited by applicant]
US 20190129893A1 · Baird, III · 2019 [cited by applicant]
US 20190147553A1 · Reber · 2019 [cited by applicant]
US 20190164087A1 · Ghibril · 2019 [cited by applicant]
US 20190164342A1 · Krs · 2019 [cited by applicant]
US 20190166162A1 · Anand · 2019 [cited by applicant]
US 20190180386A1 · Gandhi · 2019 [cited by applicant]
US 20190188285A1 · Scheau · 2019 [cited by applicant]
US 20190197357A1 · Anderson · 2019 [cited by applicant]
US 20190220496A1 · Ito · 2019 [cited by applicant]
US 20190222597A1 · Crabtree · 2019 [cited by applicant]
US 20190230092A1 · Patel · 2019 [cited by applicant]
US 20190303579A1 · Reddy et al. · 2019 [cited by applicant]
US 20190354582A1 · Schafer · 2019 [cited by applicant]
US 20200005117A1 · Yuan et al. · 2020 [cited by applicant]
US 20200069134A1 · Ebrahimi Afrouzi · 2020 [cited by applicant]
US 20200082016A1 · Lassoued et al. · 2020 [cited by applicant]
US 20200089769A1 · Crossley · 2020 [cited by applicant]
US 20200110619A1 · Rajaram · 2020 [cited by applicant]
US 20200110882A1 · Ripolles Mateu · 2020 [cited by applicant]
US 20200193286A1 · Byrnes · 2020 [cited by applicant]
US 20200210467A1 · Ravindran et al. · 2020 [cited by applicant]
US 20200249998A1 · Che · 2020 [cited by applicant]
US 20200334545A1 · Sinha · 2020 [cited by applicant]
US 20200401931A1 · Duan · 2020 [cited by applicant]
US 20210049700A1 · Nguyen · 2021 [cited by applicant]
US 20210383070A1 · Hunter · 2021 [cited by applicant]
US 20220027496A1 · Struttmann · 2022 [cited by applicant]
US 20220171984A1 · Takahashi · 2022 [cited by applicant]
JP 2004220227A · 2004 [cited by applicant]
KR 1020080021444A · 2008 [cited by applicant]
KR 1020090130854A · 2009 [cited by applicant]
KR 101565715B1 · 2015 [cited by applicant]
KR 1020180120570A · 2018 [cited by applicant]
KR 1020190092564A · 2019 [cited by applicant]
WO 2004104817A2 · 2004 [cited by applicant]
WO 2005022403A1 · 2005 [cited by applicant]
WO 2007002658A2 · 2007 [cited by applicant]
WO 2009014898A2 · 2009 [cited by applicant]
WO 2009081212 · 2009 [cited by applicant]
WO 2011115679A1 · 2011 [cited by applicant]
WO 2013040386A2 · 2013 [cited by applicant]
WO 2013044170A1 · 2013 [cited by applicant]
WO 2013112628A1 · 2013 [cited by applicant]
WO WO2015168251A1 · 2015 [cited by examiner]
WO 2017011601A1 · 2017 [cited by applicant]
WO 2017014744A1 · 2017 [cited by applicant]
WO 2017191525A2 · 2017 [cited by applicant]
WO 2017218986A1 · 2017 [cited by applicant]
WO 2018098037A1 · 2018 [cited by applicant]
WO 2019060468A1 · 2019 [cited by applicant]
WO 2020092900A2 · 2020 [cited by applicant]
WO WO2021046551A1 · 2021 [cited by examiner]
Christian Mayer et al., “ADWISE: Adaptive Window-Based Streaming Edge Partitioning for High-Speed Graph Processing”, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 685-695). [cited by examiner]
JungHyunKim et al., “PersonalizedPageRankinUncertainGraphswithMutuallyExclusiveEdges”, SIGIR'17, Aug. 7-11, 2017, Shinjuku, Tokyo, Japan, pp. 525-534. [cited by examiner]
Federico Matteo Benčić et al., “Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph”, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Jul. 2018, pp. 1569-1570. [cited by examiner]
International Preliminary Report on Patentability in related international application PCT/US2020/049776 mailed Mar. 17, 2022, pp. 1-6. [cited by applicant]
International Preliminary Report on Patentability in related international application PCT/US2020/049777 mailed Mar. 17, 2022, pp. 1-6. [cited by applicant]
International Preliminary Report on Patentability in related international application PCT/US2020/049755 mailed Mar. 17, 2022, pp. 1-5. [cited by applicant]
International Preliminary Report on Patentability in related international application PCT/US2020/049757 mailed Mar. 17, 2022, pp. 1-6. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,069 dated May 12, 2022, pp. 1-26. [cited by applicant]
Phetsouvanh et al., “EGRET: Extortion Graph Exploration Techniques in the Bitcoin Network,” 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Nov. 2018, pp. 244-251. [cited by applicant]
Masood et al., “An Overview of Distributed Ledger Technology and its Applications,” International Journal of Computer Sciences and Engineering, vol. 6, Issue 10, Oct. 2018, pp. 422-427. [cited by applicant]
Notice of Allowance in related U.S. Appl. No. 16/893,290 dated Aug. 11, 2021. [cited by applicant]
Notice of Allowance in related U.S. Appl. No. 17/337,239 dated Sep. 9, 2021. [cited by applicant]
International Search Report and Written Opinion in related international application PCT/US2021/035516. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 16/893,299 issued Oct. 21, 2020 (106 pages). [cited by applicant]
Gordon, Thomas F., Guido Governatori, and Antonino Rotolo. “Rules and norms: Requirements for rule interchange languages in the legal domain.” International Workshop on Rules and Rule Markup Languages for the Semantic W… [cited by applicant]
Shen, Yelong, et al. “M-walk: Learning to walk over graphs using monte carlo tree search.” Advances in Neural Information Processing Systems. 2018. (Year: 2018). [cited by applicant]
Chan, Wren, and Aspen Olmsted. “Ethereum transaction graph analysis.” 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2017. (Year: 2017). [cited by applicant]
Zinkevich, Martin, et al. “Regret minimization in games with incomplete information.” Advances in neural information processing systems. 2008. (Year: 2008). [cited by applicant]
Silver, David, et al. “Mastering chess and shogi by self-play with a general reinforcement learning algorithm.” arXiv preprint arXiv: 1712.01815 (2017). (Year: 2017). [cited by applicant]
Final Office Action in related U.S. Appl. No. 16/893,290 issued Nov. 13, 2020 (28 pages). [cited by applicant]
Allowance issued in related U.S. Appl. No. 16/893,318 dated Nov. 20, 2020 (20 pages). [cited by applicant]
International Search Report and Written Opinion in related international application PCT/US2020/049776 dated Dec. 3, 2020 (10 pages). [cited by applicant]
Julien M. Hendrickx, Graphs and Networks for the Analysis of Autonomous Agent Systems, Feb. 29, 2008. [cited by applicant]
International Search Report and Written Opinion in related international application PCT/US2020/049777 dated Dec. 8, 2020 (10 pages). [cited by applicant]
International Search Report and Written Opinion in related international application PCT/US2020/049755 dated Dec. 15, 2020 (9 pages). [cited by applicant]
International Search Report and Written Opinion in related international application PCT/US2020/049757 dated Dec. 15, 2020 (10 pages). [cited by applicant]
Notice of Allowance in related U.S. Appl. No. 16/893,299 dated Feb. 11, 2021. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 16/893,290 dated Mar. 24, 2021. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,028 dated Jun. 30, 2022, pp. 1-47. [cited by applicant]
D.R. Bull et al., “The optimisation of multiplier-free directed graphs: an approach using genetic algorithms,” 1994 IEEE International Symposium on Circuits and Systems (ISCAS), Jun. 1994, pp. 197-200. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,038 dated Jul. 11, 2022, pp. 1-46. [cited by applicant]
Corinna Vehlow et al., “Visualizing edge-edge relations in graphs,” 2013 IEEE Pacific Visualization Symposium (PacificVis), Mar. 2013, pp. 1-8. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,065 dated Jul. 13, 2022, pp. 1-64. [cited by applicant]
John T. Rickard et al., “Hypercube Graph Representations and Fuzzy Measures of Graph Properties,” IEEE Transactions on Fuzzy Systems (vol. 15, Issue: 6, Dec. 2007), pp. 1278-1293. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,042 dated Jul. 22, 2022, pp. 1-59. [cited by applicant]
Ali Shahaab et al., “Applicability and Appropriateness of Distributed Ledgers Consensus Protocols in Public and Private Sectors: A Systematic Review,” IEEE access, vol. 7, Mar. 2019, pp. 1-15. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,073 dated Aug. 5, 2022, pp. 1-65. [cited by applicant]
Wei Wang et al., “A Novel Subgraph Querying Method on Directed Weighted Graphs,” 2018 14th International Conference on Computational Intelligence and Security (CIS) 2018, pp. 150-154. [cited by applicant]
Ren Liu et al., “Decentralized state estimation and remedial control action for minimum wind curtailment using distributed computing platform,” 2016 IEEE Industry Applications Society Annual Meeting, Oct. 2016, pp. 1-9. [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,074 dated Aug. 18, 2022, pp. 1-67. [cited by applicant]
Pavelexner et al., “Quantum graphs with vertices of a preferred orientation,” Physics Letters A, vol. 382, Issue 5, Feb. 6, 2018, pp. 283-287. [cited by applicant]
Moussa Amrani et al., “SAR-Oriented Visual Saliency Model and Directed Acyclic Graph Suport Vector Metric Based Target Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v… [cited by applicant]
Non-Final Office Action in related U.S. Appl. No. 17/015,069 dated Sep. 2, 2022, pp. 1-32. [cited by applicant]
Notice of Allowance in related U.S. Appl. No. 17/121,915 dated Jul. 27, 2022, pp. 1-10. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,042 issued on Dec. 16, 2023, pp. 1 to 110. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,028 issued on Dec. 2, 2023, pp. 1 to 44. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,065 issued on Dec. 9, 2023, pp. 1 to 41. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,073 issued on Dec. 9, 2023, pp. 1 to 86. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,074 issued on Dec. 14, 2023, pp. 1 to 40. [cited by applicant]
Final Office Action for related U.S. Appl. No. 17/015,038 issued on Dec. 9, 2023, pp. 1 to 64. [cited by applicant]
International Preliminary Report on Patentability for related International Patent Application PCT/US2021/035526 Issued on Dec. 15, 2023, pp. 1 to 8. [cited by applicant]
Non-Final Office Action for related U.S. Appl. No. 17/015,069 issued on Mar. 14, 2023, 52 pages. [cited by applicant]
Achille Souili et al., “Natural Language Processing (NLP)—A solution for knowledge extraction from patent unstructured data”, World Conference: TRIZ Future, TF 2011-2014, Procedia Engineering 131 (2015) 635-643. [cited by applicant]
Extended European Search Report for EP application No. 20861163.8 dated Aug. 25, 2023. [cited by applicant]
Extended European Search Report for EP application No. 20861551.8 dated Aug. 28, 2023. [cited by applicant]
Extended European Search Report for EP application No. 20861278.8 rec'd Aug. 29, 2023. [cited by applicant]
Office Action for CA application No. 3150253 dated Nov. 14, 2023. [cited by applicant]
Office Action for CA application No. 3150262 dated Nov. 21, 2023. [cited by applicant]
Office Action for CA application No. 3150320 dated Nov. 22, 2023. [cited by applicant]
Office Action for CA application No. 3150324 dated Dec. 5, 2023. [cited by applicant]
Office Action for CA application No. 3150262 dated Aug. 26, 2024. [cited by applicant]
Office Action for CA application No. 3150320 rec'd Aug. 26, 2024. [cited by applicant]
US Notice of Allowance for U.S. Appl. No. 17/015,028 dated Aug. 22, 2024. [cited by applicant]
US Notice of Allowance for U.S. Appl. No. 17/015,065 dated Oct. 23, 2024. [cited by applicant]
Christian Cachin et al: “The Transaction Graph for Modeling Blockchain Semantics”, IACR, International Association for Cryptologic Research, vol. 20171110:151455, Nov. 3, 2017 (Nov. 3, 2017), pp. 1-27, XP061034744, Retr… [cited by applicant]
US Notice of Allowance for U.S. Appl. No. 17/015,028 dated Jan. 10, 2025. [cited by applicant]