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 .
DETAILED ACTION
The following is a Final Office Action in response to communications received January 2, 2026. Claims 6 and 18 have been cancelled. Claims 1 and 13 have been amended. New claims 21 and 22 have been added. Claims 1-5, 7-17, and 19-22 are pending and examined.
Response to Amendments and Arguments
As to the rejection of Claims 1-5, 7-17, and 19-20 under 35 U.S.C. § 101, Applicant’s arguments and amendments have been fully considered but are not persuasive. Applicant first argues that the claims do not fall under “certain methods of organizing human activity” but rather to a “specific method of improving computer processing efficiency through strategic filtering and computation”. Examiner disagrees. The claims are directed toward the abstract idea of “determining recommendations for a financial portfolio”, and the claims are merely “apply it” where a computer is claimed at a high level of generality, where it receives information, performs the abstract idea, and outputs the results. Applicant also argues that the present claims as a whole integrate the judicial exception into a practical application of the exception to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Examiner disagrees. The claims in the instant application include an abstract idea, and when considered as a whole, the claims (independent and dependent) do not integrate the exception into a practical application, and merely add the words “apply it” to the “the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The additional elements do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. And simply relying on a computer to perform routine tasks or calculations more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, 134 S. Ct. at 2359 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept); Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (a computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”); cf. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258–59 (Fed. Cir. 2014) (finding a computer-implemented method patent eligible where the claims recite a specific manipulation of a general-purpose computer such that the claims do not rely on a “computer network operating in its normal, expected manner”). It is for these reasons that the present claims do not rise to the level of improvements to computer functionality or system operation as in Enfish, McRO, or NVIDIA. The rejection is thereby maintained.
As to the rejection of claims 1-5, 7-17, and 19-20 under 35 U.S.C. § 103, Applicant's arguments are moot given the new grounds of rejection for the claims as amended.
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 1-5, 7-17, and 19-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claims recite a system and method. For the purposes of this analysis, representative claim 1 is addressed.
(Step 2A, prong 1) Abstract ideas are in bold below, and represents certain methods of organizing human activity, as a system of determining recommendations for a financial portfolio. Determining recommendations for a financial portfolio is akin to certain methods of organizing human activity.
A system comprising: at least one processor; and a memory coupled to the at least one processor, wherein the memory stores: a parameter database including a set of parameters for a plurality of entities; and instructions for execution by the at least one processor; and wherein the instructions include, in response to receiving a request control signal including a scaling factor from a user device, performing a multi-stage filtering including, a first filtering stage that filters the set of actions by, obtaining a set of actions and a corresponding equivalence value for each action from the parameter database, the corresponding equivalence value including a delta value indicating a directional sensitivity of a corresponding one of the set of actions relative to an underlying entity; filtering the set of actions by, for each action, determining whether the delta value satisfies a known relationship with the scaling factor, and excluding actions from subsequent computational analysis that fail to satisfy the know relationship, and a second filtering stage performed on the filtered set of actions remaining after the first filtering stage, by, for each action of the filtered set of actions: obtaining a set of parameters from the parameter database; computing a recommendation factor based on the set of parameters; and adding the corresponding action to a recommendation list in response to the recommendation factor being less than a threshold; and electronically instructing the user device to render a graphical depiction of the recommendation list on the user device .
(Step 2A prong 2) The additional elements are considered as follows:
“at least one processor; and a memory coupled to the at least one processor, wherein the memory stores: a parameter database”. This is merely “apply it” this sever is claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results.
“electronically instructing the user device to render a graphical depiction of the recommendation list on the user device”. This is merely “apply it” this interface is claimed at a high level of generality, it receives and displays information. This is an extra solution activity, akin to transmitting and receiving information.
(Step 2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of determining recommendations for a financial portfolio, over a generic computer network with generic computing elements, and generic hardware. The additional elements do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment.
Analysis of dependent claims 2-5, 7-12, 21, and 22, recited additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more. For example, in Claim 2 “computing” and “displaying” are “apply it”. Claim 3 further narrows the limitation “the action scaling factor” and “the parameter” of Claim 2. Claim 4 “calculating” and “display” are “apply it”. Claim 5 “sorting” is “apply it". Claims 7 and 8 further narrow the “filtering” limitation of Claim 1. Claim 9 further narrows the limitations “the request control signal” and “obtaining the set of actions” of Claim 1. Claim 10 “determining” and “filtering” are “apply it”. Claim 11 further narrows the limitation “the request control signal” of Claim 10. Claim 12 narrows the limitation “the memory stores” and “determining the output” of Claims 1 and 10. Claim 21 “obtaining”, “computing”, “ordering”, “analyzing”, “detecting”, and “selectively further filtering” are “apply it”. Claim 22 “stores”, “periodically determining”, “automatically generating and transmitting” and “automatically generating” are “apply it”. Method Claims 13-17, 19, and 20 are similarly rejected.
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.
Claims 1-5, 7-17, and 19-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeong et al. (Publication No.: KR 2017/0012702) in view of Papenbrock et al. (Publication No.: US 2014/0317019 A1) in view of the publication by Pavan Shah (Publication No.: US 2012/0041898 A1).
As to Claim 1, Jeong teaches a system comprising:
at least one processor; and
a memory coupled to the at least one processor,
wherein the memory stores:
a parameter database including a set of parameters for a plurality of entities; and
instructions for execution by the at least one processor; and
wherein the instructions include, in response to receiving a request control signal including a scaling factor from a user device (see ¶[0117] – “requesting by a customer terminal 110 an asset allocation service”),
obtaining a set of actions and a corresponding equivalence value for each action from the parameter database (see ¶[0120] – “presenting a model portfolio to the customer terminal such as a high profit, a middle profit, and a safe type, and receiving a selection input by a customer”);
filtering the set of actions
obtaining a set of parameters from the parameter database (see ¶[0121] – “considering analyzing a customer performance, a customer risk, investment efficiency, and a past performance of the diversified investment by comparing a customer portfolio with a model portfolio”);
computing a recommendation factor based on the set of parameters (see ¶[0121]); and
adding the corresponding action to a recommendation list in response to the recommendation factor being less than a threshold (see ¶[0132] – “providing comments on results of a comprehensive analysis, profit/risk, investment efficiency and the degree of diversified investment); and
electronically instructing the user device to render a graphical depiction of the recommendation list on the user device
Although Jeong substantially teaches the invention in Claim 1, it does not explicitly teach at least one processor; and a memory coupled to the at least one processor, wherein the memory stores: a parameter database including a set of parameters for a plurality of entities; and instructions for execution by the at least one processor, performing a multi-stage filtering; a first filtering stage that filters the set of actions; the corresponding equivalence value including a delta value indicating a directional sensitivity of a corresponding one of the set of actions relative to an underlying entity; and by, for each action, determining whether the delta value satisfies a known relationship with the scaling factor, and excluding actions from subsequent computational analysis that fail to satisfy the know relationship, and a second filtering stage performed on the filtered set of actions remaining after the first filtering stage. Papenbrock does teach at least one processor; and a memory coupled to the at least one processor, wherein the memory stores: a parameter database including a set of parameters for a plurality of entities; and instructions for execution by the at least one processor (see ¶[0034] – a system comprising a processor coupled to a memory; Figure 2A; and ¶[0066] – a processor operatively coupled to a tangible, non-transitory storage medium, wherein the non-transitory storage medium is adapted to store sets of time series of financial data for a predefined group of investment assets and to store information identifying assets in an investment portfolio). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to use a computer to implement the creation of a recommendation list as in Jeong as both the inventions are using these factors to improve the accuracy of matching parameters with asset allocation.
Shah teaches performing a multi-stage filtering; a first filtering stage that filters the set of actions; the corresponding equivalence value including a delta value indicating a directional sensitivity of a corresponding one of the set of actions relative to an underlying entity; and by, for each action, determining whether the delta value satisfies a known relationship with the scaling factor, and excluding actions from subsequent computational analysis that fail to satisfy the know relationship, and a second filtering stage performed on the filtered set of actions remaining after the first filtering stage (see ¶[0378]-¶[0382] – “Position processing in SPAN consists of processing each position within each combined commodity represented in the portfolio, for the purposes of: [0379] Scanning: scaling up the risk array(s) for the contract by the position quantity, and incrementing the overall risk array(s) by these scaled-up risk array(s) [0380] Delta calculation: scaling up the SPAN composite delta(s) for the contract by the position quantity, and incrementing the overall position delta(s) for the associated delta period by these scaled-up composite delta(s) [0381] Short option minimum calculation: determining the effect of the position on the quantity for determination of the short option minimum charge (also called the minimum commodity charge). [0382] Position value calculation: evaluating the current monetary value of each position, and incrementing the overall current monetary values for the combined commodity, broken out by whether the position is long or short and by whether the contract is valued futures-style or premium-style.”, ¶[0646] – “if any spreads can be formed, determining for each leg the delta consumed by the spread”, and ¶[1042] – “The engine 102 can subsequently provide or convey the extracted and filtered residual and volatility data from the residual and volatility module 402 to the standardization module or processor 404. The standardization module 404 can, in turn, standardize the extracted and filtered residual and volatility data by dividing each residual by a corresponding conditional standard deviation to simulate and approximate the noise associated with each residual. The standardized results (i.e., the standardized residuals) and the square of the standardized residuals can be analyzed according to the autocorrelation function (ACF) discussed above. The resulting correlated standardized residuals can be utilized by the standardization module 404 to calibrate and fit a Student-t copula by estimating and defining a correlation matrix and degrees of freedom (DoF). In the present example, the standardized residuals are correlated across different instruments but are uncorrelated with respect to an individual instrument. The correlation matrix and degrees of freedom resulting from the copula provide a quantifiable indication of how related each standardized residual is to the remaining standardized residuals.”). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to combine the corresponding equivalence of Shah with the use of a computer of Papenbrock to implement the creation of a recommendation list as in Jeong as all the inventions are using these factors to improve the accuracy of matching parameters with asset allocation.
As to Claim 2, Jeong teaches the system of claim 1 wherein the instructions include, for each action of the recommendation list:
computing an action scaling factor; and
displaying the action scaling factor in the recommendation list (see ¶[0099] – “considering that an analysis unit 230 provides information to a customer terminal that an asset allocation is required when the weighted value is less than 2 points, that an asset allocation needs to be considered when the weighted value is more than 2 points and less than 4 points, and that an asset allocation is currently well handled when the weighted value is more than 4 points).
As to Claim 3, Papenbrock teaches the system of claim 2 wherein:
the action scaling factor is based on at least one of: (i) a change value, (ii) a parameter of a corresponding entity, and (iii) an amount of the action, and
the parameter of the corresponding entity corresponds to time-series data associated with the corresponding entity (see claims 1 and 12 – “a processor-based system for portfolio management, the system comprising a first tangible, non-transitory storage medium adapted to store sets of time series of financial data for a predefined group of investment assets, wherein the one or more processors are further adapted to determine a function of at least one asset within the network model and to change a weighting of the at least one asset in a portfolio based on the function of the at least one asset within the network model”).
As to Claim 4, Jeong teaches the system of claim 2 wherein the instructions include, for each action of the recommendation list:
calculating a difference between the corresponding action scaling factor and the scaling factor; and
displaying the corresponding difference in the recommendation list (see ¶[0123] – “when comparing the profit/risk information of the customer portfolio with the model portfolio, in case of the risk level, the investment risk of the customer portfolio is 7.80% and the investment risk of the model portfolio is 5.14%, therefore, it means that the customer portfolio has higher investment risk than the model portfolio that meets the safety standard, and thus is at risk”; and Figure 5).
As to Claim 5, Jeong teaches the system of claim 4 wherein the instructions include: sorting the recommendation list based on the difference (see ¶[0145]; and Figure 15).
As to Claim 7, Shah teaches the system of claim 1 wherein:
filtering the set of actions includes removing a first action from the set of actions when the corresponding equivalence value is negative and the scaling factor is positive (see ¶[0437]-¶[0439], ¶[0517]-¶[0518] – “initializing the total long delta for the specific tier by taking the sum of all period deltas contained within the tier which are positive (i.e., net long ) and initializing the total short delta for the specific tier by taking the sum of all period deltas contained within the tier which are negative (i.e., net short), wherein if the marginable position is negative: if the option is a put, the number of short puts is equal to the absolute value of the product of the marginable position and the delta-scaling factor).
As to Claim 8, Shah teaches the system of claim 1 wherein: filtering the set of actions includes removing a first action from the set of actions when the corresponding equivalence value is positive and the scaling factor is negative (see ¶[0437]-¶[0439], ¶[0517]-¶[0518]).
As to Claim 9, Papenbrock teaches the system of claim 1 wherein: the request control signal includes an asset identifier; and obtaining the set of actions includes obtaining each action associated with the asset identifier (see claim 1 – “a second tangible non-transitory storage medium adapted to store information identifying assets in an investment portfolio”).
As to Claim 10, Shah teaches the system of claim 1 wherein the instructions include: determining an output to realize the scaling factor; and filtering the set of actions based on the output to realize the scaling factor (see ¶[0240] – “an indirectly calculated requirement is one that is derived from another requirement, at a different requirement level, by the application of a simple multiplicative scaling factor”).
As to Claim 11, Shah teaches the system of claim 10 wherein: the request control signal includes the output to realize the scaling factor (see ¶[0240]).
As to Claim 12, Jeong teaches the system of claim 10 wherein: the memory stores a user parameter database including account information of a user operating the user device, and determining the output includes:
obtaining the account information of the user; identifying the output based on the account information; and excluding account information including an association with an entity (see claim 1 – “setting a customer portfolio by selecting an analysis target holding account by a customer terminal”).
Claim 13 is the method for using the system of Claim 1 and is rejected under the same reasoning as Claim 1.
Claim 14 is the method for using the system of Claim 2 and is rejected under the same reasoning as Claim 2.
Claim 15 is the method for using the system of Claim 3 and is rejected under the same reasoning as Claim 3.
Claim 16 is the method for using the system of Claim 4 and is rejected under the same reasoning as Claim 4.
Claim 17 is the method for using the system of Claim 5 and is rejected under the same reasoning as Claim 5.
Claim 19 is the method for using the system of Claim 7 and is rejected under the same reasoning as Claim 7.
Claim 20 is the method for using the system of Claim 8 and is rejected under the same reasoning as Claim 8.
As to Claim 21, Shah teaches the system of claim 1, wherein: the computing the recommendation factor includes: obtaining, from the parameter database for the corresponding action, the one or more parameters including one or more of a bid price, an ask price, a mark price, a contract multiplier, an open interest value, and a trading volume value, and computing a liquidity factor based on a normalized bid-ask spread relative to the mark price, adjusted based on the one or more parameters; and wherein the second filtering stage includes: computing the liquidity factor for each action in the filtered set of actions, ordering the computed liquidity factors in ascending sequence, analyzing a rate of change between successive liquidity factors in the sequence, detecting an inflection point where the rate of change increases by at least a set factor indicating a transition from liquid to illiquid actions, and selectively further filtering the corresponding action among the filtered set of actions based on the liquidity factor and the inflection point (see ¶[1030]-¶[1031] – “ Liquidity risk represents a risk associated with a portfolio 104 including a large number of illiquid CDS such as large, single name positions, which may be determined, based on, for example, open interest and volume data. The engine 102 may include a liquidity risk module or processor 212 configured or programmed to execute or implement the liquidity risk portion of the multi-factor risk analysis. Depending on the liquidity risk, the required margin requirement may be increased based on a net notional basis by name. For example, as shown in Table 2, each large, single name CDS within the portfolio 104 may be grouped or assigned to a bucket according to a spread and minimum threshold value.”).
As to Claim 22, Jeong teaches the system of claim 1, wherein: the memory further stores a user configuration specifying a target leverage range having an upper bound and a lower bound, and at least one preferred underlying entity associated with a user account; and the instructions further include: periodically determining whether a current portfolio leverage factor falls outside the target leverage range, in response to determining the current portfolio leverage factor falls outside the target leverage range performing one or more of: automatically generating and transmitting an alert to the user device indicating the current portfolio leverage factor has deviated from the target leverage range, and automatically generating the request control signal with the scaling factor corresponding to the target leverage range (“the asset management server 120 can provide expected return and investment risk by comparing and analyzing the customer portfolio and the model portfolio. 12 is a view showing an example of analyzing a prospect according to an expected return rate and an investment risk by comparing a customer portfolio according to an embodiment of the present invention with a model portfolio. In FIG. 12, when the customer selects the heavy-demanding model portfolio, the expected return of the customer portfolio is 5.88% and the investment risk is 9.87%, which is lower than the expected return of the model portfolio of 6.08% and the investment risk is higher than 5.5% . 13, the asset management server 120 transmits a prospective analysis result as to whether the asset allocation is necessary, the asset allocation should be considered, and whether the asset allocation is good, as shown in FIG. 13, ). That is, the asset management server 120 needs to allocate the asset when the weighted reflection value is less than 2, and the asset allocation must be considered when the value is less than 4 and less than 2. If the asset allocation is 4 or more, And provides the result of the analysis to the customer terminal. 13 is a diagram illustrating an example of providing a result of analyzing a perspective by comparing a customer portfolio and a model portfolio according to an embodiment of the present invention. As shown in FIG. 13, in the case of the expected return, the customer portfolio is 5.88%, the model portfolio is 6.08%, the customer portfolio is 9.87%, and the model portfolio is 5.5% And the model portfolio has a low level of 0.79. In case of diversified investment, the number of assets is 4, the average asset is 1, The diversified investment effect is low and is presented as a 'very bad' condition. Therefore, when the customer chooses the model portfolio as 'heavy-paying', the expected return is high, the expected risk is very high, investment efficiency is low, and the degree of diversification is high The results of the comprehensive analysis suggest that asset allocation is very necessary.”).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE S KANG whose telephone number is (571)270-3611. The examiner can normally be reached on Monday through Friday between M-F 10am-2pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Gart may be reached at (571)-273-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IRENE KANG/
Examiner, Art Unit 3695
5/27/2026
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
Prosecution Insights