US 12,277,605 B2
System and method for the valuation and securitization of content
Georges Ayoub, Vancouver (CA); and Zeid Ayoub, Vancouver (CA)
Assigned to Sharematter Inc., Beaverton, OR (US)
Filed by Sharematter Inc., Beaverton, OR (US)
Filed on Aug. 16, 2023, as Appl. No. 18/450,975.
Application 18/450,975 is a continuation of application No. 18/105,409, filed on Feb. 3, 2023, granted, now 11,763,387.
Prior Publication US 2024/0265447 A1, Aug. 8, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/04 (2012.01); G06F 16/28 (2019.01); G06Q 10/04 (2023.01); G06Q 30/02 (2023.01)
CPC G06Q 40/04 (2013.01) [G06F 16/285 (2019.01); G06Q 10/04 (2013.01); G06Q 30/0278 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A system for transforming data associated with one or more content catalogues as an asset to generate a valuation for securitization and trading, the system comprising:
a securitization and trading system including a physical storage and one or more processors executing instructions to:
receive asset data from one or more sources, where the asset is an authenticate source or account information associated with the asset via an authentication module;
automatically value the asset using machine learning techniques based on collected data including historical performance metrics, usage statistics, and cash flow records;
adjust valuation parameters dynamically based on actual cash flows derived from previously valued and/or traded assets;
categorize asset components into analytical groups based on predefined characteristics such as age, utilization, or performance and apply predictive decay or growth rates to each group;
generate expected cash flow forecasts for the asset or its components based on adjusted values determined by the analysis module;
aggregate the cash flow forecasts and compute a comprehensive valuation for the asset;
solicit investment commitments;
allocate shares in the securitized asset based on commitment level, timing, and investor metrics; and
optimize allocation decisions using machine learning techniques.