US 11,748,439 B2
Computer-aided methods and systems for distributed cognition of digital content comprised of knowledge objects
Gary Kenneth Kooznetsoff, Castlegar (CA)
Assigned to Big Idea Lab, Inc., Castlegar (CA)
Filed by Big Idea Lab, Inc., Castlegar (CA)
Filed on May 4, 2021, as Appl. No. 17/308,040.
Claims priority of provisional application 63/019,827, filed on May 4, 2020.
Prior Publication US 2021/0342408 A1, Nov. 4, 2021
Int. Cl. G06F 16/958 (2019.01); G06F 16/906 (2019.01); G06F 16/908 (2019.01); G06N 5/02 (2023.01); G06N 5/022 (2023.01)
CPC G06F 16/958 (2019.01) [G06F 16/906 (2019.01); G06F 16/908 (2019.01); G06N 5/022 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for distributed cognition of digital content comprised of knowledge objects, the method comprising:
receiving, by a programmed computer, submissions from a plurality of community members with regard to a knowledge object, wherein each community member has a reputation value that is attributed to the community member, and each community member's submission regarding the knowledge object includes an evaluation value representing an evaluation of the knowledge object by the community member;
determining, by the programmed computer, a consensus evaluation of the knowledge object based on a calculated combination of the evaluation values in the submissions received and the reputation values of the respective community members who submitted the submissions; and
while submissions are being received from community members regarding the knowledge object, by the programmed computer:
iteratively updating the consensus evaluation of the knowledge object to produce an updated consensus evaluation, wherein the updated consensus evaluation is calculated based on submissions received from community members up to each iteration including submissions received since a previous updating of the consensus evaluation, the updated consensus evaluation being a calculated combination of the evaluation values in the submissions received and the reputation values of the respective community members who submitted the submissions; and
iteratively updating the reputation value for each community member who submitted the submissions to produce an updated reputation value, wherein the updated reputation value for each community member is calculated based on a determined contribution of the respective community member's submission to the updated consensus evaluation,
wherein the reputation value of a community member is enhanced to a degree that the evaluation value submitted by the community member causes the updated consensus evaluation to become closer to a final consensus evaluation of the knowledge object,
wherein the reputation value of a community member is diminished to a degree that the evaluation value submitted by the community member causes the updated consensus evaluation to be farther from the final consensus evaluation of the knowledge object, and
wherein updating the reputation value for a community member includes calculating a first mathematical distance between the evaluation value in the respective community member's submission and the consensus evaluation of the knowledge object.