DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the Applicant Response filed on 12/24/2025.
Claims 1-20 are currently pending and have been examined.
This action is made Non-FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Claim 1 recites a process, which is a statutory category of invention (Step 1: YES). Claim 15 recites a product (apparatus), which is a statutory category of invention (Step 1: YES). Claim 18 recites a system, which is a statutory category of invention (Step 1: YES).
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a)). Yes.
The claims are analyzed to determine whether it is directed to a judicial exception. The following claims identify the limitations that recite additional elements in bold and the abstract idea without bold. Underlined claim limitations denote newly added claim limitations:
The claims are analyzed to determine whether it is directed to a judicial exception. Claim 1, 15 and 18 recite a method performed by a computing system, the method comprising: for a first Deep Reinforcement Learning (DRL) model in a plurality of DRL models, generating a first plurality of trading strategies based at least in part on one or more (i) technical features corresponding to individual securities in a set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; for a second DRL model in the plurality of DRL models, generating a second plurality of trading strategies based at least in part on one or more (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; determining a momentum score for each security in the set of securities; selecting a candidate subset of securities from the set of securities, wherein individual securities in the candidate subset of securities have a momentum score that satisfies a momentum threshold; for each security in the candidate subset of securities, (i) generating a first set of one or more trading signals for the security by applying the first plurality of trading strategies to data associated with the security, (ii) generating a second set of one or more trading signals for the security by applying the second plurality of trading strategies to data associated with the security, and (iii) generating an aggregated trading signal for the security based on the first set of one or more trading signals for the security and the second set of one or more trading signals for the security, wherein the aggregated trading signal indicates a signal strength; and adding one or more securities from the candidate subset of securities to a trading portfolio based on the aggregated trading signals for the securities in the subset of securities. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity, but for the recitation of generic computer components. Under human activity, the limitations are commercial interactions, specifically sales activities, as well as business relations. The claim limitations are also managing interactions between people, specifically following instructions. More specifically, under fundamental economic practice, the claims involve mitigating risk. Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: Yes. The claims recite an abstract idea).
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d)). No.
The above judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a system, computing system, tangible, non-transitory computer-readable media, and processors. The additional elements of a system, computing system, tangible, non-transitory computer-readable media, and processors, are just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The computer components are recited at such a high-level of generality (i.e. as a generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. (Step 2A-Prong 2: NO. The judicial exception is not integrated into a practical application).
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05). No.
The claims are next analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements of (a system, computing system, tangible, non-transitory computer-readable media, and processors) in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO; The claims do not provide significantly more, and are not patent eligible).
Claim 2 recites determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 3 recites The method of claim 2, wherein determining a portfolio weight for each security in the trading portfolio comprises determining the portfolio weight w for security j according to the equation wj=esj/Eni=1esi, where s1,…..sn, are strengths of the aggregated trading signals for each of security 1 through security n in the trading portfolio. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 4 recites wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security: adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 5 recites wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security: adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 6 recites wherein an aggregated trading signal for an individual security is a value between -1 and +1, wherein an aggregated trading signal having a positive value corresponds to a buy indication, wherein an aggregated trading signal having a negative value corresponds to a sell indication, wherein an aggregated trading signal having a value closer to +1 is a stronger buy indication than an aggregated trading signal having a positive value closer to 0, wherein an aggregated trading signal having a value closer to -1 is a stronger sell indication than an aggregated trading signal having a negative value closer to 0. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 7 recites monitoring changes in the aggregate trading signals for each security in the trading portfolio, and removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 8 recites wherein removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security comprises: for an individual security having a long position in the trading portfolio, removing the individual security from the trading portfolio when the aggregated trading signal changes from a positive value to a negative value. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 9 recites wherein removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security comprises: for an individual security having a short position in the trading portfolio, removing the individual security from the trading portfolio when the aggregated trading signal changes from a negative value to a positive value. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 10 recites generating the first plurality of trading strategies for the first DRL model comprises using a first DRL agent to learn the first plurality of trading strategies by using a first DRL algorithm to interact with an environment comprising the (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; generating the second plurality of trading strategies for the second DRL model comprises using a second DRL agent to learn the second plurality of trading strategies by using a second DRL algorithm to interact with the environment comprising the (i) technical features corresponding to individual securities in a set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; wherein the first DRL algorithm comprises one of (i) an Advantage Actor Critic (A2C) algorithm, (ii) a Deep Q-Networks (DQN) algorithm, or (iii) a Proximal Policy Optimization (PPO) algorithm; and wherein the second DRL algorithm comprises one of (i) an Advantage Actor Critic (A2C) algorithm, (ii) a Deep Q-Networks (DQN) algorithm, or (iii) a Proximal Policy Optimization (PPO) algorithm. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 11 recites wherein generating the first plurality of trading strategies for the first DRL model comprises using a first DRL agent to learn the first plurality of trading strategies by using a first DRL algorithm to interact with an environment comprising the (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data comprises: generating at least one DRL policy that maps a state to an action; wherein the state for the at least one DRL policy corresponds to a set of one or more (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; wherein the action for the at least one DRL policy corresponds to one of buying or selling a security; and wherein an individual trading strategy in the first plurality of trading strategies is based at least in part on the at least one DRL policy. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 12 recites wherein determining a momentum score for each security in the set of securities comprises, for an individual security in the set of securities: calculating a fast momentum score for the individual security based on a one month return of the security divided by a volatility of the security; calculating a z-score for the fast momentum score by determining a difference between a mean of momentums of all securities in the set of securities and the fast momentum score for the individual security, and dividing the difference by a standard deviation of momentums of all securities in the set of securities; calculating a slow momentum score for the individual security based on a six month return of the security divided by the volatility of the security; calculating a z-score for the slow momentum score for the individual security by determining a difference between a mean of momentums of all securities in the set of securities and the slow momentum score for the individual security, and dividing the difference by the standard deviation of momentums of all securities in the set of securities; and determining the momentum score for the individual security by determining a sum of the z-score for the fast momentum for the individual security and the z-score of the slow momentum for the individual security, and dividing the sum by two to produce the momentum score for the individual security. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra
Claim 13 recites wherein the volatility of the individual security is determined by computing an annualized standard deviation of daily returns of the individual security over a three year period. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 14 recites wherein selecting a candidate subset of securities from the set of securities, wherein individual securities in the candidate subset of securities have a momentum score that satisfies a momentum threshold comprises at least one of: selecting at least one security having a momentum score greater than a first threshold for inclusion in the candidate subset of securities as a candidate for a long position; or selecting at least one security having a momentum score less than a second threshold for inclusion in the candidate subset of securities as a candidate for a short position. These limitations are also part of the abstract idea identified in claim 1, and are similarly rejected under the same rationale as claim 1, supra.
Claim 16 recites wherein functions further comprise determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security, and wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security, at least one of: adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security; or adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security. These limitations are also part of the abstract idea identified in claim 15, and are similarly rejected under the same rationale as claim 15, supra.
Claim 17 recites wherein the functions further comprise: monitoring changes in the aggregate trading signals for each security in the trading portfolio, and removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security. These limitations are also part of the abstract idea identified in claim 15, and are similarly rejected under the same rationale as claim 15, supra.
Claim 19 recites wherein the program instructions, when executed by the one or more processors, cause the computing system to perform functions comprising determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security, and wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security, at least one of: adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security; or adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security. These limitations are also part of the abstract idea identified in claim 18, and the additional elements of computing system and processors are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 18 analysis above. Therefore, this claim is similarly rejected under the same rationale as claim 18, supra.
Claim 20 recites wherein the program instructions, when executed by the one or more processors, cause the computing system to perform functions comprising: monitoring changes in the aggregate trading signals for each security in the trading portfolio, and removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security. These limitations are also part of the abstract idea identified in claim 18, and the additional elements of computing system and processors are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 18 analysis above. Therefore, this claim is similarly rejected under the same rationale as claim 18, supra.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 5, 6, 10, 11, 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell/Goulding, et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023.
Regarding claims 1, 15 and 18, Yang discloses a method performed by a computing system (P. 1, "We propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy...We train a deep reinforcement agent"), the method comprising:
for a first Deep Reinforcement Learning (DRL) model in a plurality of DRL models, generating a first plurality of trading strategies based at least in part on one or more (i) technical features corresponding to individual securities in a set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data (Yang, “We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: "FIRST MODEL" - Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best feature of the three algorithms, thereby robustly adjusting to different market situations" (Abstract); Section 4.1.1., "We use a 181-dimensional vector consists of seven parts of information to represent the state space of multiple stocks trading environment [𝑏𝑡,𝒑𝑡,𝒉𝑡,𝑴𝑡,𝑹𝒕 ,𝑪𝒕 ,𝑿𝒕 ]; 𝑴𝑡, "Moving Average Convergence Divergence (MACD) is calculated using close price. MACD is one of the most commonly used momentum indicator that identifies moving averages"; Learned policies are mapped to actions over time, such as buy, sell, hold on each stock;
for a second DRL model in the plurality of DRL models, generating a second plurality of trading strategies based at least in part on one or more (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data ("SECOND MODEL" - Advantage Actor Critic (A2C)); Model DRL models: PPO (First DRL), A2C, DDPG, each a separate actor-critic agent)
Yang fails to disclose determining a momentum score for each security in the set of securities. However, Campbell discloses computing momentum scores of 12-month periods for an arithmetic average for slow momentum (or fast momentum, defined as SLOW or FAST throughout Campbell, et al), for assets like stocks and futures (Pg. 379, Note 3, “Annualized average monthly excess U.S. stock market returns following a negative average 12-month trailing return or negative 1-month trailing return, respectively, are 0.2% and 3.2% during the period 1927–07 to 2018-12”; See also Figure 1, over 12 month time frames; Pg. 382 as well).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the momentum score of Campbell. Doing so allows the user to understand stock price strength and whether to buy or not.
Modified Yang also fails to disclose selecting a candidate subset of securities from the set of securities, wherein individual securities in the candidate subset of securities have a momentum score that satisfies a momentum threshold. However, Campbell discloses securities with nonnegative momentum scores (threshold of 0) are selected for long positions (candidate for buying), while negative scores trigger shorts (Campbell, Pg. 382, discussing momentum speed and market cycles; “We construct a simple framework. At date t, if the trailing 12-month excess return (arithmetic average monthly excess return), rt−12,t, is nonnegative, then SLOW goes long one unit in the subsequent month, otherwise, it goes short one unit… If the prior 1-month return, rt−1,t, is nonnegative then FAST goes long one unit in the subsequent month, otherwise, it goes short one unit…”)
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the momentum score threshold of Campbell. Doing so allows the user to understand long versus short positions for various assets being traded, based on SLOW versus FAST calculations.
Modified Yang discloses the Sharpe ratio for fast and slow momentum (See throughout Yang, “we ensemble the three agents together using the Sharpe ratio that measures the risk-adjusted return. The effectiveness of the ensemble strategy is verified by its higher Sharpe ratio than both the min-variance portfolio allocation strategy and the Dow Jones Industrial Average”), but fails to disclose for each security in the candidate subset of securities, generating a first set of one or more trading signals for the security by applying the first plurality of trading strategies to data associated with the security, generating a second set of one or more trading signals for the security by applying the second plurality of trading strategies to data associated with the security, and generating an aggregated trading signal for the security based on the first set of one or more trading signals for the security and the second set of one or more trading signals for the security, wherein the aggregated trading signal indicates a signal strength. However, Campbell discloses implicit subsets based on momentum direction, aligning with cycle identification (bull for positive, slow/fast momentum) and cycles are defined by signal agreement (Campbell, pg. 378, “The speed (or sensitivity to recent data) of the momentum signal balances the tension between reducing the impact of noise and reacting quickly to turning points”… pg. 379, Fig. 1, “A month ending at date t is classified as Bull if both the trailing 12-month return (arithmetic average monthly return), rt−12,t , is nonnegative and the trailing 1-month return, rt−1,t, is nonnegative. A month is classified as Correction if rt−12,t ≥ 0 but rt−1,t < 0; as Bear if rt−12,t < 0 and rt−1,t < 0; and as Rebound if rt−12,t < 0 but rt−1,t ≥ 0”; Section 2.3, pg. 386, Disclosing cycle frequencies and implication; “Bull months have been the most common with a relative monthly frequency of 48.3%, reflecting the average positive risk premium offered by the U.S. stock market. Bear months are relatively uncommon—approximately one-sixth of the time (16.7%)— whereas Correction and Rebound months amount to the remaining 35.0% of the months. In other words, about once every three months, on average, SLOW and FAST suggest a different position in the stock market” (Focusing exposure on upward-trend (Bull-state_ while reducing downward risk state (Bear); Pg. 379, “Figure 1 summarizes, over a recent 50-year period of the U.S. stock market, the conditional behavior of the average, volatility, and skewness of returns in months following four market cycles and the monthly relative frequency of such states.2 When both slow and fast momentum agree on the direction of trend, we call it a “Bull” or “Bear” state, depending on whether the agreement is to take a long or short position, respectively. These labels loosely map to phases of uptrend and downtrend: Bull states are followed by relatively high average returns with low volatility, and Bear states are followed by negative average returns with the highest relative volatility. When slow and fast momentum disagree, we call it a “Correction” state if slow momentum indicates a long position and a “Rebound” state if slow momentum indicates a short position”).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the aggregated trading signal of Campbell for signal strength. Doing so allows the trader or user to be a better position for trading, avoiding bear markets and capitalizing on bull markets.
Modified Yang discloses adding one or more securities from the candidate subset of securities to a trading portfolio based on the aggregated trading signals for the securities in the subset of securities (Campbell, Section 2.1, pg. 382-383; “Adding” the asset to the trading strategy/portfolio with a signed weight based on the signal; Pg. 390-393, Table 2 and Equations 11-13; Section 2.3, “Candidate” as market cycle filled with various different stocks, and different bull versus bear candidate states; Table 6, pg. 399 reinforcing signal rules; Section 4.1-4.2, pg. 390-392, “Aggregated” signals produce positive average positions but lower beta via scaling; Section 5, Proposition 5, Eqns 20-21, tying to signal returns slowing down after corrections and speeding up after rebounds).
Regarding claim 4, modified Yang discloses wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security: adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security (Campbell & Goulding, Fig. 4, Disclosing Rebound, Bull, Bear and Correction, based on Wslow and Wfast, and values between 1 and -1; Section 4, P. 390, Eq. 11, positive if net positive; Strength above a threshold strength, Section 4.1 (P. 390), Intermediate speed scales strength, threshold implicit at 0; Number of shares added based on portfolio weight, Table 2, p. 391, Average positions .18-.46, long, based on blended strength/weight).
Regarding claim 5, modified Yang discloses wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security: adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security (Campbell & Goulding, “Short” positions in downtrends, abstract; “The agreement of SLOW and FAST to go long (short) is more likely to indicate the market is in the midst of an uptrend (downtrend). We find this intuition is not only supported by our model, but also consistent with the returns behavior of both the U.S. and international stock markets after each of the four different phases, or market cycles, defined by the up or down directions of each of the slow and fast momentum strategies”; Adding as a short position, Section 2.1, p. 382, w=-1 for negative returns; negative value, Eq. 11, pg. 390, negative if net negative blend)
Regarding claim 6, modified Yang discloses wherein an aggregated trading signal for an individual security is a value between −1 and +1, wherein an aggregated trading signal having a positive value corresponds to a buy indication, wherein an aggregated trading signal having a negative value corresponds to a sell indication, wherein an aggregated trading signal having a value closer to +1 is a stronger buy indication than an aggregated trading signal having a positive value closer to 0, wherein an aggregated trading signal having a value closer to −1 is a stronger sell indication than an aggregated trading signal having a negative value closer to 0 (Campbell/Goulding, p. 3, “intermediate-speed momentum portfolios, blending slow and fast momentum strategies; Eq. 11; See Figure 4, showing relativity between -1 and 1; Slow/Fast boundaries between -1 and 1; “The “Market” column reflects a buy-and-hold portfolio and the “SLOW” column reflects a 12-month TS momentum portfolio. Indeed, both portfolios exhibit similar performance in average returns (approximately 6%) and Sharpe ratios (approximately 0.40)”; Table 1, negative vs positive statistics and buy or sell; Table 3, revealing market states and when to buy or sell – “The long-only market strategy (Market) and TS momentum strategies of various speeds are the same as those described in Table 2. Market states, st , are defined as follows. Bull: wSLOW,t = wFAST,t = +1; Correction: wSLOW,t = +1, wFAST,t = −1; Bear: wSLOW,t = wFAST,t = −1; and Rebound: wSLOW,t = −1, wFAST,t = +1”).
Regarding claim 10, modified Yang discloses generating the first plurality of trading strategies for the first DRL model comprises using a first DRL agent to learn the first plurality of trading strategies by using a first DRL algorithm to interact with an environment comprising the (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data; generating the second plurality of trading strategies for the second DRL model comprises using a second DRL agent to learn the second plurality of trading strategies by using a second DRL algorithm to interact with the environment comprising the (i) technical features corresponding to individual securities in a set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data (Yang, Fig. 1, disclosing trading agents, with ensemble strategies)
wherein the first DRL algorithm comprises one of (i) an Advantage Actor Critic (A2C) algorithm, (ii) a Deep Q-Networks (DQN) algorithm, or (iii) a Proximal Policy Optimization (PPO) algorithm; and wherein the second DRL algorithm comprises one of (i) an Advantage Actor Critic (A2C) algorithm, (ii) a Deep Q-Networks (DQN) algorithm, or (iii) a Proximal Policy Optimization (PPO) algorithm (See Yang, Fig. 1, Different ensemble strategies including PPO, A2C and DDPG).
Regarding claim 11, modified Yang discloses wherein generating the first plurality of trading strategies for the first DRL model comprises using a first DRL agent to learn the first plurality of trading strategies by using a first DRL algorithm to interact with an environment comprising the (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data (Yang, Fig. 1, disclosing trading agents, with ensemble strategies) comprises:
generating at least one DRL policy that maps a state to an action (Yang, generated policy based on action to a state – Fig. 1, State: observations, with reward as profit or loss; Also, Yang disclosing “state” spaces throughout);
wherein the state for the at least one DRL policy corresponds to a set of one or more (i) technical features corresponding to individual securities in the set of securities, (ii) fundamental features of individual securities in the set of securities, and (iii) macroeconomic data (Section 3.1, States include vectors with stock prices, which is economic data, a D equals number of stocks; “The policy is a probability distribution that is essentially a strategy for a given state, namely the likelihood to take an allowed action”; Fig. 2, “The state transition of a stock trading process is shown in Figure 2. At each state, one of three possible actions is taken on stock 𝑑 (𝑑 = 1, ..., 𝐷) in the portfolio”
wherein the action for the at least one DRL policy corresponds to one of buying or selling a security (Fig. 2, buy or sell; Selling 𝒌 [𝑑] ∈ [1, 𝒉[𝑑]] shares results in 𝒉𝒕+1 [𝑑] = 𝒉𝒕 [𝑑] − 𝒌 [𝑑], where 𝒌 [𝑑] ∈ Z+ and 𝑑 = 1, ..., 𝐷. Holding, 𝒉𝒕+1 [𝑑] = 𝒉𝒕 [𝑑]. Buying 𝒌 [𝑑] shares results in 𝒉𝒕+1 [𝑑] = 𝒉𝒕 [𝑑] + 𝒌 [𝑑]); Also, Section 4.1 describing environment for multiple stocks) and
wherein an individual trading strategy in the first plurality of trading strategies is based at least in part on the at least one DRL policy (“the best portfolio allocation strategy can be obtained by either maximizing the return for a given risk ratio or minimizing the risk for a pre-specified return”; “Another approach for stock trading is to model it as a Markov Decision Process (MDP) and use dynamic programming to derive the optimal strategy [4, 5, 34, 35]”; Fig. 1, Reinforcement learning-based stock trading strategy; “we propose a novel ensemble strategy that combines three deep reinforcement learning algorithms and finds the optimal trading strategy in a complex and dynamic stock market. The three actor-critic algorithms [24] are Proximal Policy Optimization (PPO) [28, 37], Advantage Actor Critic (A2C) [32, 48], and Deep Deterministic Policy Gradient (DDPG) [28, 29, 44]”; Table 1, showing winning DRL policy over different time periods).
Claim(s) 2 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, as applied to claim 1 above, further in view of Li, in “An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning” from ACM International Conference, November 2021.
Regarding claim 2, modified Yang fails to disclose determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security. However, Li discloses system outputs positions or weights per asset, normalized to form the portfolio (Abstract, Section 3, p. 2-3) where portfolio weights are determined by softmax on per-asset trading signals, making weights proportional to exponentiated signal values (Section 3.2, p.3, “portfolio weight was decided by an exogenous softmax function based on the trading signal of each asset”).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the portfolio weights and proportional strength of the aggregated trading signal of Li. Doing so enables a more accurate trading algorithm and ensures greater profits and lower costs.
Regarding claim 16, modified Yang fails to disclose wherein functions further comprise determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security. However, Li discloses system outputs positions or weights per asset, normalized to form the portfolio (Abstract, Section 3, p. 2-3) where portfolio weights are determined by softmax on per-asset trading signals, making weights proportional to exponentiated signal values (Section 3.2, p.3, “portfolio weight was decided by an exogenous softmax function based on the trading signal of each asset”).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the portfolio weights and proportional strength of the aggregated trading signal of Li. Doing so enables a more accurate trading algorithm and ensures greater profits and lower costs.
Modified Yang discloses wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security, at least one of: adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security; or adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security (Campbell & Goulding, Fig. 4, Disclosing Rebound, Bull, Bear and Correction, based on Wslow and Wfast, and values between 1 and -1; Section 4, P. 390, Eq. 11, positive if net positive; Strength above a threshold strength, Section 4.1 (P. 390), Intermediate speed scales strength, threshold implicit at 0; Number of shares added based on portfolio weight, Table 2, p. 391, Average positions .18-.46, long, based on blended strength/weight & Campbell & Goulding, “Short” positions in downtrends, abstract; “The agreement of SLOW and FAST to go long (short) is more likely to indicate the market is in the midst of an uptrend (downtrend). We find this intuition is not only supported by our model, but also consistent with the returns behavior of both the U.S. and international stock markets after each of the four different phases, or market cycles, defined by the up or down directions of each of the slow and fast momentum strategies”; Adding as a short position, Section 2.1, p. 382, w=-1 for negative returns; negative value, Eq. 11, pg. 390, negative if net negative blend).
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, and Li, in “An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning” from ACM International Conference, November 2021, as applied to claim 1 above, further in view of Almahdi in “An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown,” from Expert Systems with Applications, 2017.
Regarding claim 3, modified Yang fails to disclose wherein determining a portfolio weight for each security in the trading portfolio comprises determining the portfolio weight w for security j according to the equation
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, where s1, . . . , sn, are strengths of the aggregated trading signals for each of security l through security n in the trading portfolio. However, Almahdi teaches an RRL algorithm to optimize the Calmar ratio instead of Sharpe ratio to generate the trading signals or positions of each asset, then the portfolio weight is decided by an exogenous softmax function based on the trading signal of each asset (Pg. 270 (bottom) to 271 (top), disclosing softmax function with assigned weights to each asset, where n is the number of assets in portfolio.
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the softmax function of Ahlmadi. Doing so increases the efficiency of the model, and maps actionable probabilities between 0 and 1 for different possible outcomes of buy, sell or hold, making the model more predictable.
Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, and Li, in “An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning” from ACM International Conference, November 2021, as applied to claim 16 above, further in view of Greenwood US 20020156722.
Regarding claim 17, Modified Yang fails to disclose the tangible, non-transitory computer-readable media of claim 16, wherein the functions further comprise: monitoring changes in the aggregate trading signals for each security in the trading portfolio, and removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security. However, Greenwood discloses an automated securities trading system with a trading engine (4) and automation engine (3) that monitors changes in signals for securities in the portfolio (investment advice information, Para 36, Claim 1, Para. 6, examining trending signals) with the system being able to make decisions based off of signals (Para. 53-54), and the system issuing a sell order (or closing a position) when a stop rule or condition is met.
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the automated system of modification and monitoring or Greenwood. Doing so ensures that the system loses less money and makes more profitable decisions in real-time.
Claim(s) 14 rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell/Goulding, et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, as applied to claim 1 above, further in view of Bhagwat US 20230153903.
Regarding claim 14, modified Yang fails to disclose wherein selecting a candidate subset of securities from the set of securities, wherein individual securities in the candidate subset of securities have a momentum score that satisfies a momentum threshold comprises at least one of: selecting at least one security having a momentum score greater than a first threshold for inclusion in the candidate subset of securities as a candidate for a long position; or selecting at least one security having a momentum score less than a second threshold for inclusion in the candidate subset of securities as a candidate for a short position. However Bhagwat discloses a method to compute a discount-score of a security’s stock price with a scoring range for making decisions between -1 and 1 (Fig. 2), with scores at different ranges suggesting stock is trading at a premium or at a discount.
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the threshold for long-term decisions for Bhagwat. Doing so ensures that the company or user makes more profit and trims losses as necessary.
Claim(s) 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell/Goulding, et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, as applied to claims 1 and 18 above, further in view of Greenwood US 20020156722.
Regarding claim 7 and 20, modified Yang fails to disclose monitoring changes in the aggregate trading signals for each security in the trading portfolio, and removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security. However, Greenwood discloses an automated securities trading system with a trading engine (4) and automation engine (3) that monitors changes in signals for securities in the portfolio (investment advice information, Para 36, Claim 1, Para. 6, examining trending signals) with the system being able to make decisions based off of signals (Para. 53-54), and the system issuing a sell order (or closing a position) when a stop rule or condition is met.
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the automated system of modification and monitoring or Greenwood. Doing so ensures that the system loses less money and makes more profitable decisions in real-time.
Claim(s) 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell/Goulding, et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, and Greenwood US 20020156722, as applied to claim 7 above, further in view of Hernandez US 20060149649.
Regarding claim 8, modified Yang fails to disclose wherein removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security comprises: for an individual security having a long position in the trading portfolio, removing the individual security from the trading portfolio when the aggregated trading signal changes from a positive value to a negative value. Hernandez discloses
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the position and portfolio change of Hernandez. Doing so ensures the portfolio minimizes losses and takes advantages of gains.
Regarding claim 9, modified Yang discloses wherein removing an individual security from the trading portfolio based on a change in the aggregate trading signal corresponding to the individual security comprises: for an individual security having a short position in the trading portfolio, removing the individual security from the trading portfolio when the aggregated trading signal changes (Greenwood, disclosing short positions are closed when price or indicator behavior crosses a specified level indicating an unfavorable condition for remining short), but fails to disclose from a negative value to a positive value. However, Hernandez teaches that when a signal change causes the security to move from “good” to “bad”, the portfolio is downweighted from portfolio (cease to hold).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the portfolio action on a short position. Doing so ensures the portfolio minimizes losses and secure profits as a short covering position.
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Yang, et al., in “Deep reinforcement learning for automated stock trading: an ensemble strategy,” from ACM Digital Library, October 2020, in view of Campbell et al., in “Momentum Turning Points,” from Journal of Financial Economics, 2023, as applied to claim 18 above, further in view of Li, in “An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning” from ACM International Conference, November 2021.
Regarding claim 19, modified Yang fails to disclose wherein the program instructions, when executed by the one or more processors, cause the computing system to perform functions comprising determining a portfolio weight for each security in the trading portfolio, wherein for an individual security in the trading portfolio, the portfolio weight for the individual security is proportional to a strength of the aggregated trading signal corresponding to the individual security. However, Li discloses system outputs positions or weights per asset, normalized to form the portfolio (Abstract, Section 3, p. 2-3) where portfolio weights are determined by softmax on per-asset trading signals, making weights proportional to exponentiated signal values (Section 3.2, p.3, “portfolio weight was decided by an exogenous softmax function based on the trading signal of each asset”).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Yang with the portfolio weights and proportional strength of the aggregated trading signal of Li. Doing so enables a more accurate trading algorithm and ensures greater profits and lower costs.
Modified Yang also discloses wherein adding one or more securities from the candidate subset of securities to the trading portfolio based on the aggregated trading signals for the securities in the subset of securities comprises, for an individual security, at least one of:
adding the individual security to the trading portfolio as a long position when (i) the value of the aggregated trading signal for the individual security a positive value, (ii) the strength of the aggregated trading signal for the individual security is above a threshold strength, and (iii) a number of shares of the individual security added to the trading portfolio is based on the portfolio weight of the individual security (Campbell & Goulding, Fig. 4, Disclosing Rebound, Bull, Bear and Correction, based on Wslow and Wfast, and values between 1 and -1; Section 4, P. 390, Eq. 11, positive if net positive; Strength above a threshold strength, Section 4.1 (P. 390), Intermediate speed scales strength, threshold implicit at 0; Number of shares added based on portfolio weight, Table 2, p. 391, Average positions .18-.46, long, based on blended strength/weight), or
adding the individual security to the trading portfolio as a short position when (i) the value of the aggregated trading signal for the individual security is a negative value, (ii) the strength of the aggregated trading signal for the individual security is below a threshold strength, and (iii) a number of shares shorted in the trading portfolio is based on the portfolio weight of the individual security (Campbell & Goulding, “Short” positions in downtrends, abstract; “The agreement of SLOW and FAST to go long (short) is more likely to indicate the market is in the midst of an uptrend (downtrend). We find this intuition is not only supported by our model, but also consistent with the returns behavior of both the U.S. and international stock markets after each of the four different phases, or market cycles, defined by the up or down directions of each of the slow and fast momentum strategies”; Adding as a short position, Section 2.1, p. 382, w=-1 for negative returns; negative value, Eq. 11, pg. 390, negative if net negative blend).
Response to Arguments
Applicant's arguments filed 12/24/2025 have been fully considered but they are not persuasive.
Applicant argues that the currently recited claims do not recite an abstract idea. Examiner disagrees. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity, but for the recitation of generic computer components. Under human activity, the limitations are commercial interactions, specifically sales activities, as well as business relations. The claim limitations are also managing interactions between people, specifically following instructions. Also, the claims are a fundamental economic practice. More specifically, under fundamental economic practice, the claims involve mitigating risk. Applicant own specification discusses momentum strategies that come with “inherent risks” (Specification, para. 10 & 82). Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea.
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
The term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. First, not all methods of organizing human activity are abstract ideas (e.g., "a defined set of steps for combining particular ingredients to create a drug formulation" is not a certain "method of organizing human activity"), In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1160-61, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP 2106.04(a)(3). Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.
The courts have used the phrases "fundamental economic practices" or "fundamental economic principles" to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks. The term "fundamental" is not used in the sense of necessarily being "old" or "well-known." See, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept); In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016) (describing a new set of rules for conducting a wagering game as a "fundamental economic practice"); In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential) (claims to a new method of allocating returns to different investors in an investment fund was a fundamental economic concept). However, being old or well-known may indicate that the practice is fundamental. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 219-20, 110 USPQ2d 1981-82 (2014) (describing the concept of intermediated settlement, like the risk hedging in Bilski, to be a "‘fundamental economic practice long prevalent in our system of commerce’" and also as "a building block of the modern economy") (citation omitted); Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ2d 1001, 1010 (2010) (claims to the concept of hedging are a "fundamental economic practice long prevalent in our system of commerce and taught in any introductory finance class.") (citation omitted); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1313, 120 USPQ2d 1353, 1356 (2016) ("The category of abstract ideas embraces ‘fundamental economic practice[s] long prevalent in our system of commerce,’ … including ‘longstanding commercial practice[s]’").
An example of a case identifying a claim as reciting a fundamental economic practice is Bilski v. Kappos, 561 U.S. 593, 609, 95 USPQ2d 1001, 1009 (2010). The fundamental economic practice at issue was hedging or protecting against risk. The applicant in Bilski claimed "a series of steps instructing how to hedge risk," i.e., how to protect against risk. 561 U.S. at 599, 95 USPQ2d at 1005. The method allowed energy suppliers and consumers to minimize the risks resulting from fluctuations in market demand for energy. The Supreme Court determined that hedging is "fundamental economic practice" and therefore is an "unpatentable abstract idea." 561 U.S. at 611-12, 95 USPQ2d at 1010.
Another example of a case identifying a claim as reciting a fundamental economic practice is Bancorp Services., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Cir. 2012). The fundamental economic practice at issue in Bancorp pertained to insurance. The patentee in Bancorp claimed methods and systems for managing a life insurance policy on behalf of a policy holder, which comprised steps including generating a life insurance policy including a stable value protected investment with an initial value based on a value of underlying securities, calculating surrender value protected investment credits for the life insurance policy; determining an investment value and a value of the underlying securities for the current day; and calculating a policy value and a policy unit value for the current day. 687 F.3d at 1270-71, 103 USPQ2d at 1427. The court described the claims as an "attempt to patent the use of the abstract idea of [managing a stable value protected life insurance policy] and then instruct the use of well-known [calculations] to help establish some of the inputs into the equation." 687 F.3d at 1278, 103 USPQ2d at 1433 (alterations in original) (citing Bilski).
"Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. An example of a claim reciting a commercial or legal interaction, where the interaction is an agreement in the form of contracts, is found in buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014). The agreement at issue in buySAFE was a transaction performance guaranty, which is a contractual relationship. 765 F.3d at 1355, 112 USPQ2d at 1096. The patentee claimed a method in which a computer operated by the provider of a safe transaction service receives a request for a performance guarantee for an online commercial transaction, the computer processes the request by underwriting the requesting party in order to provide the transaction guarantee service, and the computer offers, via a computer network, a transaction guaranty that binds to the transaction upon the closing of the transaction. 765 F.3d at 1351-52, 112 USPQ2d at 1094. The Federal Circuit described the claims as directed to an abstract idea because they were "squarely about creating a contractual relationship--a ‘transaction performance guaranty’." 765 F.3d at 1355, 112 USPQ2d at 1096.
An example of a claim reciting a commercial or legal interaction in the form of a legal obligation is found in Fort Properties, Inc. v. American Master Lease, LLC, 671 F.3d 1317, 101 USPQ2d 1785 (Fed Cir. 2012). The patentee claimed a method of "aggregating real property into a real estate portfolio, dividing the interests in the portfolio into a number of deedshares, and subjecting those shares to a master agreement." 671 F.3d at 1322, 101 USPQ2d at 1788. The legal obligation at issue was the tax-free exchanges of real estate. The Federal Circuit concluded that the real estate investment tool designed to enable tax-free exchanges was an abstract concept. 671 F.3d at 1323, 101 USPQ2d at 1789.
An example of a claim reciting business relations is found in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108. The Federal Circuit described the claims as directed to the abstract idea of "processing an application for financing a loan" and found "no meaningful distinction between this type of financial industry practice" and the concept of intermediated settlement in Alice or the hedging concept in Bilski. 859 F.3d at 1054, 123 USPQ2d at 1108.
An example of a claim reciting managing personal behavior is Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). The patentee in this case claimed methods comprising storing user-selected pre-set limits on spending in a database, and when one of the limits is reached, communicating a notification to the user via a device. 792 F.3d. at 1367, 115 USPQ2d at 1639-40. The Federal Circuit determined that the claims were directed to the abstract idea of “tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)”, which “is not meaningfully different from the ideas found to be abstract in other cases before the Supreme Court and our court involving methods of organizing human activity.” 792 F.3d. at 1367-68, 115 USPQ2d at 1640.
An example of a claim reciting following rules or instructions is In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1161, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). The patentee claimed a method of playing a dice game including placing wagers on whether certain die faces will appear face up. 911 F.3d at 1160; 129 USPQ2d at 1011. The Federal Circuit determined that the claims were directed to the abstract idea of “rules for playing games”, which the court characterized as a certain method of organizing human activity. 911 F.3d at 1160-61; 129 USPQ2d at 1011.
Applicant also argues that the claims integrate any alleged abstract idea into a practical application. Examiner disagrees. In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. In contrast, the current claims are not directed to an improvement to computer functionality and instead merely recite the computer elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
In DDR Holdings LLC v. Hotels.com, LP, the claims were found eligible as they reflected improvements to the functioning of a computer, i.e. a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage. In contrast, the current claims do not contain limitations reflective of an improvement to computer functionality and instead merely recite the computer elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
In Finjan, the claims to a “behavior-based virus scan” were found to provide greater computer security and were thus directed to a patent-eligible improvement in computer functionality. In contrast, the current claims do not contain limitations reflective of an improvement to computer functionality and instead merely recite the computer elements at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
Ex Parte Smith was eligible because they added a timer to transactions in order to handicap electronic transactions and be fair with physical transactions. Simply changing trading decisions is not an improvement to computer functionality, such as specific UI improvements and improved data management, or a solution to a technical problem. Technical details must be in the claim and the specification. For a trading method to be patent-eligible under USPTO Section 101, merely "improving computational resources" is not enough to overcome the abstract idea exception established in Alice Corp. v. CLS Bank International. The claim must specify a concrete, technological improvement to the computer's functionality, rather than simply implementing a conventional trading practice on a generic computer.
The currently recited claims recite how a typical machine learning model (deep reinforcement learning) works, using specific attributes and parameters. However, the claims do not describe any particular improvement in the manner of computer functions. Although a machine learning model is used for the purposes of determining trading strategies, such uses is both generic and conventional. The object of the claims is to determine trading strategies, not to produce technology enabling a machine learning model to operate. The claims call for generic use of such a machine learning model in the manner such models conventionally operate. Simply reciting a particular technological module or piece of equipment in a claim does not confer eligibility. The MPEP notes this distinction.
The MPEP notes this distinction (For example, in MPEP 2106.05(f)(I), it states: Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743). In the instant application, the currently recited claims use machine learning as generic data processing.
Applicant also argues the Ex Parte Desjardins decision. Examiner notes that the Ex Parte Desjardins decision (as well as the Director Squires’ memo) stressed the specification of application 16/319040, in that the specification was curing a deficiency in the way that normal AI functions, and their specific way of training the AI had an improvement in the AI; the decision had nothing to do with the data itself. In the currently recited claims, the “invention” is in the data itself, and it is using AI at a “high level” which amounts to “apply it,” where there is no curing any technical problem with the way AI functions, and is just using a different set of data. Thus, the application (and currently recited claims) are more like Recentive, than Ex Parte Desjardins.
Applicant's argument that the rejection lacks Berkheimer evidence is not persuasive. Such evidence is only required to support a conclusion that an additional element is well-understood, routine, conventional activity. Here, the rejection does not assert well-understood, routine, conventional activity and instead identifies the additional elements drawn to the database as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. MPEP 2106.05(f). Because the evaluation in Step 2B is not a weighing test, it is not important how the elements are characterized or how many considerations apply from the list of considerations set forth in MPEP 2106.05. It is important to evaluate the significance of the additional elements relative to the invention, and to keep in mind the ultimate question of whether the additional elements encompass an inventive concept.
Lastly, the claims do not provide an inventive concept. As discussed above, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer. Even when viewed as whole, nothing in the claim adds significantly more (i.e. inventive concept) to the abstract idea. The currently recited claims solve aggregating trading signals using deep reinforcement learning, which is not a significant improvement to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dridi et al., in “Bullish/Bearish Strategies of Trading: a Nonlinear Equilibrium,” from the Journal of Financial and Quantitative Analysis, 2004, discloses similarities to claim 9, referencing the need to remove securities from a portfolio based on signals surrounding a bullish or bearish market.
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/BRANDON M DUCK/Examiner, Art Unit 3693