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AssigneeFmr LLC
InventorsAaron Gao, Samarjit Walia, Deepak Bhaskaran, Jiawen Dai, Xiao Zhang, Peng Sun, Christine Thompson, Niyu Jia, Songyang Li, Yongsheng Gao
Estimating the term…
The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using neural networks. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved. Portfolio weights of securities in the universe of securities are optimized to generate a set of tradeable transactions that maximize expected portfolio return. The set of tradeable transactions is executed.
A machine learning portfolio generating apparatus, comprising:
A machine learning portfolio generating processor-readable, non-transient medium, comprising processor-executable instructions structured as:
A machine learning portfolio generating processor-implemented system, comprising:
A machine learning portfolio generating processor-implemented process, comprising executing processor-executable instructions to:
Dependencies are inferred from the claim text.
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From the US filing history. Foreign family members aren’t listed.
Backward citations are indexed for grants only.
Asset management predictions or forecasting: asset management predictions or forecasting using artificial intelligence, machine learning or neural…
Updates performed during online database operations; commit processing: bulk updating operations
Query formulation: query languages
Of structured data, e.g. relational data: visual data mining; Browsing structured data
Term end not estimated
This application for letters patent disclosure document describes inventive aspects that include various novel innovations (hereinafter “disclosure”) and contains material that is subject to copyright, mask work, and/or other intellectual property protection. The respective owners of such intellectual property have no objection to the facsimile reproduction of the disclosure by anyone as it appears in published Patent Office file/records, but otherwise reserve all rights.
The present innovations generally address machine learning and database systems, and more particularly, include Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems.
However, in order to develop a reader's understanding of the innovations, disclosures have been compiled into a single description to illustrate and clarify how aspects of these innovations operate independently, interoperate as between individual innovations, and/or cooperate collectively. The application goes on to further describe the interrelations and synergies as between the various innovations; all of which is to further compliance with 35 U.S.C. § 112.
Appendices and/or drawings illustrating various, non-limiting, example, innovative aspects of the Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (hereinafter “MLPO”) disclosure, include:
FIGS. 1A-B show a datagraph illustrating data flow(s) for the MLPO;
FIGS. 2A-B show a logic flow illustrating embodiments of a machine learning simulated scenario processing (MLSSP) component for the MLPO;
Generally, the leading number of each citation number within the drawings indicates the figure in which that citation number is introduced and/or detailed. As such, a detailed discussion of citation number 101 would be found and/or introduced in FIG. 1. Citation number 201 is introduced in FIG. 2, etc. Any citations and/or reference numbers are not necessarily sequences but rather just example orders that may be rearranged and other orders are contemplated. Citation number suffixes may indicate that an earlier introduced item has been re-referenced in the context of a later figure and may indicate the same item, evolved/modified version of the earlier introduced item, etc., e.g., server 199 of FIG. 1 may be a similar server 299 of FIG. 2 in the same and/or new context.
The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (hereinafter “MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs, via MLPO components (e.g., MLSSP, DTET, ERC, PC, PSC, SPRV, BPRV, PRV, ARMC, etc. components), into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. The MLPO components, in various embodiments, implement advantageous features as set forth below.
The MLPO provides unconventional features (e.g., executing tradeable transactions to create an optimized portfolio based on expected returns simulated using machine learning techniques, a SQL database calculation engine) that were never before available in machine learning and database systems.