DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/30/26 has been entered.
Status of Claims
This action is in reply to RCE, amendment and response filed on 3/30/26. Claims 1 and 16-18 were amended with non-substantive amendments. Claims 1-18 are pending and examined.
Response to Arguments
101: The Applicant’s amendments and arguments have been fully considered and are persuasive. The rejection is withdrawn.
103: The Applicant’s amendments and arguments have been fully considered but are not persuasive.
pp. 33-35, para. 49-55: Applicant argues that Browne does not teach “obtain a … simulation request datastructure structured as specifying … initial scenario parameter values for a set of scenario parameters, in which the initial scenario parameters include any of: … user profile data …”. The Examiner disagrees. Browne explicitly teaches simulation datastructure (see Brown FIG. 4, item 40 para. 64 “a parametric model is selected which defines a risk factor surface according to a plurality of parameters” that “the model” is interpreted as “datastructure” that stores scenario parameters such as those Browne discloses in para. 44 “provide an acceptable surface fit to the historical data in accordance with user preferences”). Furthermore, Browne specifically teaches scenario based simulation in para. 57 reciting “Although the changes in the calibration residuals could be analyzed and adjusted during the simulation process, preferably the calibration residuals are assumed to be constant in time for all generated scenarios”.
pp. 37-39, para. 56-59, the Applicant further argues that the secondary reference Penello does not teach concepts of scenario and retirement decision making. Penello specifically teaches concept of scenario in para. 50-51 “For each price scenario, the Monte Carlo module 124 simulates the price scenario to obtain simulated prices, marks the portfolio 108 to these simulated prices, and saves the resulting simulated portfolio value” and retirement decision making is not positively claimed (“obtain a … datastructure structured as specifying a set of initial user scenario parameters values for a set of scenario parameters, in which the initial user scenario parameters includes any of: user identifier, user goals, initial user’s retirement plan, user contributions to the user’s retirement plan, users retirement age, user profile data, user accounts, user expenses, user health profile,”) as such the BRI of the claim includes user risk preference for the user portfolio which Penello teaches in para. 56 “A function of the evaluation module is to determine if the portfolio 108 meets the user's 150 desired risk level and determine if changes to the portfolio 108 are necessary”.
As such, the rejection is maintained.
Claim Rejections - 35 USC § 103
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over US 20030018456 A1 (Browne) in view of US 20090006270 A1 (Penello).
As to claims 1, 16, 17 and 18,
Browne teaches,
obtain a system progression simulation request datastructure structured as specifying a set of initial user scenario parameters values for a set of scenario parameters (FIG. 4, item 40, ¶ 64 “a parametric model is selected which defines a risk factor surface according to a plurality of parameters”), in which the initial user scenario parameters includes any of: user identifier, user goals, initial user’s retirement plan, user contributions to the user’s retirement plan, users retirement age, user profile data (para. 44 “provide an acceptable surface fit to the historical data in accordance with user preferences”), user accounts, user expenses, user health profile,
determine for each respective user scenario parameter in the set of user scenario parameters, a set of parameter evaluation values for the respective user scenario parameter (FIG. 4, item 41, ¶ 64 “starting parameters”), in which each parameter evaluation value in the set of parameter evaluation values for the respective user scenario parameter is within an interval specified by a set of boundary values for the respective user scenario parameter (FIG. 4, item 42, ¶ 64 “A calibration residual which represents the difference between the source point value and the value indicated by the modeled surface is determined for at least some of the points used to define the starting surface parameters”, para. 57 “Although the changes in the calibration residuals could be analyzed and adjusted during the simulation process, preferably the calibration residuals are assumed to be constant in time for all generated scenarios”),
determine a set of scenario evaluation points (FIG. 4, item 41, ¶ 64 “determine the starting values of the surface parameters”), [in which each scenario evaluation point in the set of scenario evaluation points is structured as an intersection of parameter evaluation values from each of the determined sets of parameter evaluation values],
compute for each respective scenario evaluation point in the set of scenario evaluation points, a scenario result value (FIG. 4, item 43, ¶ 65 “the evolution of each of the parameters .beta..sub.0 . . . .beta..sub.n is simulated using a beta-evolution function”) for the respective scenario evaluation point via a system progression simulation (FIG. 4, item 43, ¶ 65 “simulated using a beta-evolution function”),
determine a set of surface descriptor datastructures (FIG. 4, item 44, ¶ 65 “a simulated risk factor surface”), in which each surface descriptor datastructure in the set of surface descriptor datastructures is structured as specifying a set of surface points defining a surface (FIG. 4, item 44, ¶ 65 “The sequences of simulated .beta..sub.0 . . . .beta..sub.n in values define a simulated risk factor surface for each time index of each simulation run”),
generate for each respective surface descriptor datastructure in the set of surface descriptor datastructures (FIG. 5, ¶ 68 “define the simulated volatility surface”), [a scenario evaluation datastructure structured as specifying an evaluation function determined via interpolation on a set of surface points specified by the respective surface descriptor datastructure],
determine a first generated scenario evaluation datastructure matching the set of initial scenario parameters values (¶ 68 “the precalculated values are extracted synchronously across the various matrices and used to simulate the option price”),
determine a scenario result value for the set of initial scenario parameters values via an evaluation function specified by the first matching scenario evaluation datastructure (¶ 70 “the simulated option volatility along with the appropriate risk factor values (extracted from the corresponding simulated risk factor matrices) are applied to the option pricing model to produce a simulated option price for the particular option at issue. This process is repeated for each step of each simulation run and the results are stored in a simulated option price matrix”).
Browne does not explicitly teach,
[determine …], in which each scenario evaluation point in the set of scenario evaluation points is structured as an intersection of parameter evaluation values from each of the determined sets of parameter evaluation values,
[generate …], a scenario evaluation datastructure structured as specifying an evaluation function determined via interpolation on a set of surface points specified by the respective surface descriptor datastructure,
however, Penello teaches,
[determine …], in which each scenario evaluation point in the set of scenario evaluation points is structured as an intersection of parameter evaluation values from each of the determined sets of parameter evaluation values (¶ 56 “The output of the evaluation module 130 is a decision 1030 whether to leave the portfolio 108 as it is or to modify the portfolio 108. A function of the evaluation module is to determine if the portfolio 108 meets the user's 150 desired risk level and determine if changes to the portfolio 108 are necessary”),
[generate …], a scenario evaluation datastructure structured as specifying an evaluation function determined via interpolation on a set of surface points specified by the respective surface descriptor datastructure (¶ 64 “generating a correlation matrix”, para. 50-51 “For each price scenario, the Monte Carlo module 124 simulates the price scenario to obtain simulated prices, marks the portfolio 108 to these simulated prices, and saves the resulting simulated portfolio value”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine stress testing of simulated behavior of financial portfolio of Browne with performance risk management of Penello because performance risk management improves stress testing of simulated behavior of financial portfolio by “maintain[ing] worst case scenarios at or above acceptable thresholds, while minimizing as much as possible the negative impact on the best case scenario”, see Penello, ¶ 60.
with respect to claim 1,
Browne teaches,
a real-time system progression simulation interaction apparatus (claim 9 “a system”),
at least one memory (claim 9 “at least one data store”),
a component collection stored in the at least one memory; at least one processor disposed in communication with the at least one memory, the at least one processor executing processor-executable instructions from the component collection, the component collection storage structured with processor-executable instructions (see Browne, claim 9).
with respect to claim 16,
Browne teaches,
a real-time system progression simulation interaction processor-readable, non-transient medium, the medium storing a component collection, the component collection storage structured with processor-executable instructions (see Browne, claim 9).
with respect to claim 17,
Browne teaches,
a real-time system progression simulation interaction processor-implemented system, comprising: means to store a component collection; means to process processor-executable instructions from the component collection, the component collection storage structured with processor-executable instructions (see Browne, claim 9).
with respect to claim 18,
Browne teaches,
a real-time system progression simulation interaction processor-implemented process, including processing processor-executable instructions via at least one processor from a component collection stored in at least one memory, the component collection storage structured with processor-executable instructions (see Browne, claim 9).
As to claim 2, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
a set of boundary values for a scenario parameter comprises a minimum value and a maximum value (¶ 73 “range”).
As to claim 3, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
a set of boundary values for a scenario parameter is determined via a set of default values (¶ 73 “range”).
As to claim 4, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
a set of boundary values for a scenario parameter is determined via a calculation that utilizes an initial scenario parameters value corresponding to the scenario parameter (¶ 73 “range”).
As to claim 5, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
a set of parameter evaluation values for a scenario parameter comprises equally spaced points along the interval associated with the scenario parameter (¶ 61 “The time-varying sequence”).
As to claim 6, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
the system progression simulation is structured as computing a scenario result value for a scenario evaluation point via a calculation that utilizes parameter evaluation values specified by the scenario evaluation point (¶ 65 “simulation run”).
As to claim 7, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
scenario result values for the set of scenario evaluation points are computed in parallel (¶ 21 “introducing parallel shifts to the volatility surface”).
As to claim 8, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
a surface point specified by a surface descriptor datastructure comprises parameter evaluation values of a corresponding scenario evaluation point and a computed scenario result value for the corresponding scenario evaluation point (¶ 61 “generate a corresponding historical volatility surface having respective surface parameter value …”).
As to claim 9, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
the surface is a quadratic surface (FIG. 1, ¶ 6 “the implied volatility surface”).
As to claim 10, combination of Browne and Penello teach all the limitations of claim 1.
Browne does not explicitly teach,
a scenario evaluation datastructure comprises a coefficients datastructure structured as specifying coefficients of a bilinear function.
however, Penello teaches,
a scenario evaluation datastructure comprises a coefficients datastructure structured as specifying coefficients of a bilinear function (¶ 47 “correlation coefficients”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine stress testing of simulated behavior of financial portfolio of Browne with performance risk management of Penello since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable because the cited references are analogous art in the field of financial asset management and because performance risk management improves stress testing of simulated behavior of financial portfolio by “maintain[ing] worst case scenarios at or above acceptable thresholds, while minimizing as much as possible the negative impact on the best case scenario”, see Penello, ¶ 60.
As to claim 11, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
the component collection storage is further structured with processor-executable instructions (see Browne, claim 9 “at least one data store”),
generate a system progression simulation response datastructure structured as specifying a result value determined via a calculation that utilizes the scenario result value for the set of initial scenario parameters values (¶ 55 “simulate changes in option price volatility by evolving the values of the beta surface parameters during simulation and applying the simulated .beta. values to the surface parameterization function”).
As to claim 12, combination of Browne and Penello teach all the limitations of claims 1 and 11.
Browne also teaches,
the system progression simulation request datastructure is structured as specifying a goal value (¶ 62 “appropriate values”), and in which the system progression simulation response datastructure is structured as specifying a result score determined via a calculation that utilizes the scenario result value for the set of initial scenario parameters values and the goal value (¶ 62).
As to claim 13, combination of Browne and Penello teach all the limitations of claim 1.
Browne also teaches,
the component collection storage is further structured with processor-executable instructions (see Browne, claim 9 “at least one data store”),
obtain a system progression simulation update request datastructure structured as specifying a set of updated scenario parameters values for the set of scenario parameters (FIG. 4, item 40, ¶ 64),
determine a second generated scenario evaluation datastructure matching the set of updated scenario parameters values (FIG. 4, item 41, ¶ 64),
determine a scenario result value for the set of updated scenario parameters values via an evaluation function specified by the second matching scenario evaluation datastructure (FIG. 4, item 42, ¶ 64).
As to claim 14, combination of Browne and Penello teach all the limitations of claims 1 and 13.
Browne also teaches,
the component collection storage is further structured with processor-executable instructions (see Browne, claim 9 “at least one data store”),
generate a system progression simulation update response datastructure structured as specifying a result value determined via a calculation that utilizes the scenario result value for the set of updated scenario parameters values (¶ 71 “simulate changes in the surface parameter values over time”).
As to claim 15, combination of Browne and Penello teach all the limitations of claims 1 and 13.
Browne also teaches,
the first generated scenario evaluation datastructure and the second generated scenario evaluation datastructure are identical (¶ 44 “equal”).
Conclusion
Reference made of record, not relied upon, pertinent to Applicant’s disclosure, includes KR 20120136204 A (Kim) disclosing a simulation game method for investment techniques.
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BROCK E TURK whose telephone number is (571)272-5626. The examiner can normally be reached Monday-Friday 9AM-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Calvin Hewitt II can be reached at 571-272-6709. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BROCK E TURK/Examiner, Art Unit 3692
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692
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