Method and system for a parallel distributed hyper-swarm for amplifying human intelligence

• US 10,817,158 B2
• Filed: 12/21/2018
• Issued: 10/27/2020
• Est. Priority Date: 03/26/2014
• Status: Active Grant

• Current Assignee: Unanimous A.I., Inc.
• Original Assignee: Unanimous A I Incorporated
• Inventors: Rosenberg, Louis B., Willcox, Gregg
• Primary Examiner(s): Levy, Amy M
• Application Number:
  US16/230,759
• Publication Number:
  US 20190121529A1
• Time in Patent Office: 676 Days
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Systems and methods for amplifying the intelligence of networked human populations, maximizing the accuracy of collaborative forecasts and group insights. This includes systems and methods for sending and presenting a forecasting query for a future event to a plurality of participants, each using a networked computing device, the forecasting query describing a future event to be collaboratively predicted by the population of human participants. An initial forecast response is collected from each participant and analyzed by a central server. A plurality of unique overlapping subsets of responses are determined by the server. Unique overlapping subsets of unique initial forecast responses are then displayed, at substantially the same time, on each computing device. A second forecast response is collected from each participant and a final forecast is determined.

Claims

1. A method for computer-moderated collaborative forecasting among a population of human participants using a plurality of networked computing devices, the method comprising:

providing a collaboration server running a collaboration application, the collaboration server in communication with the plurality of the networked computing devices, each computing device associated with one participant;

providing a local forecasting application on each networked computing device, the local forecasting application configured for displaying forecasting information to and collecting forecasting input from the one participant associated with that networked computing device; and

enabling through communication between the collaboration application running on the collaboration server and the local forecasting applications running on each of the plurality of networked computing devices, the following sequential steps;

send a forecasting query to the plurality of networked computing devices, the forecasting query describing a future event to be collaboratively predicted by the population of human participants;

present, at substantially the same time, a representation of the forecasting query to each participant on a display of the computing device associated with that participant;

collect an initial forecast response from each participant via a user interface on the computing device associated with that participant;

store each initial forecast response in a unique location in a data structure in a memory accessible by the collaboration server, wherein each initial forecast response is associated with the participant the response was collected from and is a member of a set of initial forecast responses;

identify, for each participant, a subset of the locations in the data structure of the set of initial forecast responses, wherein each subset of the locations in the data structure represents a unique subset of initial forecast responses and has a unique membership of the set of initial forecast responses and wherein at least one initial forecast response member of the subset of locations is also included in at least one other subset of locations;

display, at substantially the same time, the initial forecast responses associated with the identified subset of locations for participants to each participant on the computing device associated with that participant, thereby enabling each participant to consider initial forecasts responses provided by a different unique subset of human participants selected from the full population of human participants;

after displaying the unique subset of initial forecast responses to each member, collect an updated forecast response from each participant via the user interface on the computing device associated with that participant;

store a set of updated forecast responses from the population of human participants in a memory accessible by the collaboration server; and

compute a final collaborative forecast based at least in part upon the set of initial forecast responses and the set of updated forecast responses, the collaborative forecast providing an answer to the forecasting query.

2. The method of claim 1 wherein computing the final collaborative forecasting includes assessing the change, for participant, between the initial forecast response they provided and the final forecast response they provided.

3. The method of claim 1 further including the step of assigning a unique sub-population to each participant, wherein each sub-population is a unique subset of the full population of networked human participants that shares at least one participant with at least one other sub-population.

4. The method of claim 3 wherein the subset of initial forecast responses for each participant consists of the initial forecast responses of the sub-population assigned to that member.

5. The method of claim 1 wherein the steps of presenting the forecast query, collecting updated forecast responses, and storing the set of collected responses are repeated multiple times prior to the step of computing the final collaborative forecast, wherein a plurality of sets of updated forecast responses are stored over a time period, and wherein the final collaborative forecast is based at least in part upon the plurality of sets of updated forecasts collected forecast responses stored over the time period.