US 12,093,311 B2
Generative AI systems and methods for economic analytics and forecasting
Brian McCarson, Chandler, AZ (US)
Filed by Brian McCarson, Chandler, AZ (US)
Filed on Jun. 27, 2023, as Appl. No. 18/342,461.
Application 18/342,461 is a continuation in part of application No. 17/840,390, filed on Jun. 14, 2022.
Application 17/840,390 is a continuation of application No. 16/797,640, filed on Feb. 21, 2020, granted, now 11,361,202, issued on Jun. 14, 2022.
Prior Publication US 2023/0342392 A1, Oct. 26, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/587 (2019.01); G06F 16/907 (2019.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/2133 (2023.01); G06F 18/2415 (2023.01); G06N 3/044 (2023.01); G06N 3/067 (2006.01); G06N 3/08 (2023.01); G06N 3/092 (2023.01); G06N 7/01 (2023.01); G06V 10/70 (2022.01); G06V 10/764 (2022.01); G06V 10/84 (2022.01); G06V 20/10 (2022.01); G06V 20/13 (2022.01); G06V 20/52 (2022.01); G08G 1/01 (2006.01); G08G 3/00 (2006.01); G08G 5/00 (2006.01); G06N 3/0464 (2023.01)
CPC G06F 16/587 (2019.01) [G06F 16/907 (2019.01); G06F 18/2133 (2023.01); G06F 18/217 (2023.01); G06F 18/24155 (2023.01); G06F 18/29 (2023.01); G06N 3/0675 (2013.01); G06N 3/08 (2013.01); G06N 3/092 (2023.01); G06N 7/01 (2023.01); G06V 10/70 (2022.01); G06V 10/764 (2022.01); G06V 10/84 (2022.01); G06V 20/13 (2022.01); G06V 20/188 (2022.01); G06V 20/52 (2022.01); G08G 1/0133 (2013.01); G08G 3/00 (2013.01); G08G 5/0004 (2013.01); G06N 3/044 (2023.01); G06N 3/0464 (2023.01); G06V 2201/10 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A generative artificial intelligence (AI) system for economic analytics and forecasting, comprising:
a plurality of data sources, wherein at least one of the data sources is sensor data derived from direct observation, by a sensor, of activity within an environment;
a context-aware AI database;
a probationary database;
an analytics engine, including a processor, communicatively coupled to the plurality of data sources, the context-aware AI database, and the probationary database;
wherein the analytics engine is configured to:
(a) generate a hypothesis object comprising independent variables, a dependent variable including a leading indicator of economic activity, a machine learning model trained from available data using a set of model parameters, and metadata associated therewith based on the data sources;
(b) train the machine learning model associated with the hypothesis object to produce experimental results;
(c) store the hypothesis object and the experimental results in the context-aware AI database in response to determining that the performance metric of the experimental results of the machine learning model is greater than or equal to a predetermined level;
(d) store the hypothesis object and the experimental results in the probationary database in response to determining that the performance metric of the experimental results of the machine learning model is less than the predetermined level; and
a publishing module configured to provide, to one or more subscribers, the leading indicator as computed by the trained machine learning model stored within the context-aware AI database while processing contemporaneous information received from the data sources;
wherein the analytics engine and context aware database interact to perform dynamic learning based on at least one of a determined context, a new set of parameters, a new set of data, and new set of experiments; an experimental result of the hypothesis object achieves a performance metric greater than or equal to the predetermined level; and the hypothesis object and new experimental result are thereafter transferred from the probationary database to the context-aware AI database.