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 Non-Final Office Action in response to communications received September 2, 2025. Claim 1 has been amended. Claim 10 has been cancelled. Claims 1-9 are pending and examined.
Response to Amendments and Arguments
As to the rejection of Claims 1-9 under 35 U.S.C. § 101, Applicant’s arguments and amendments have been fully considered but are not persuasive. In response to Applicant’s arguments that the claims are not directed to any abstract idea, Examiner disagrees. The present claims are directed to certain methods of organizing human activity. Examiner argues that the claims do not amount to significantly more because the limitations, in effect, 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”). The rejection is thereby maintained.
As to the rejection of claims 1-9 under 35 U.S.C. § 103, Applicant's arguments and amendments have been fully considered but are not persuasive.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, both Lane and Zhang pertain to simulations meant to optimize financial outcomes for various scenarios. The rejection is thereby maintained. The claims are rejected as amended as detailed below.
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 1-9 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 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 method of predicting outcomes and providing customizable user plans. Predicting outcomes and providing customizable user plans is akin to certain methods of organizing human activity.
A method of developing an optimized retirement plan, the method comprising the steps of:
receiving, at a platform server from a user, client information comprising income data and a client optimization goal;
creating a core data frame using the client information;
creating a baseline data frame using the core data frame, wherein the baseline data frame comprises the client information and real-time market data and wherein the baseline data frame is used to generate a baseline simulation;
creating a comprehensive data frame using the baseline simulation;
generating a set of simulations using the comprehensive data frame;
using the client optimization goal to evaluate simulation performance by comparing each simulation in the set of simulations to the baseline simulation in view of the client optimization goal;
identifying a simulation from the set of simulations as a high performing simulation;
creating an optimized solution by optimizing a set of high performing simulations that includes the high performing simulation using an optimization technique wherein the optimization technique comprises at least one of gradient descent particle swarm optimization, and evolutionary solution development;
developing an actionable retirement plan based on the optimized simulation; and
sending the actionable retirement plan to the user.
(Step 2A prong 2) The additional elements are considered as follows:
“at a platform server from a user” This is merely “apply it” the engine and interface are claimed at a high level of generality, they receive the information, perform the abstract idea, and output the results. 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 predicting outcomes and providing customizable user plans, over a generic computer network with generic computing elements, and generic hardware.
Analysis of dependent claims 2-5, and 7-9, 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.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
Claims 1-9 are rejected under 35 U.S.C. 103(a) as being unpatentable over Lane et al. (Publication No.: US 2023/0351515) in view of Sec. 14 of the America Invents Act further in view of the publication by Huaping Zhang titled “Optimization of risk control in financial markets based on particle swarm optimization algorithm” published April 2020 (hereinafter “Zhang”).
As to Claim 1, Lane teaches a method of developing an optimized retirement plan, the method comprising the steps of:
receiving, at a platform server from a user, client information comprising income data and a client optimization goal (see ¶[0135] – “user class” includes information “based on attributes of the user (e.g., geographic location, demographic group, relevant jurisdiction for the user, indicated risk profile for the user, etc.)”), ¶[0162] – “The example client engagement controller 602 includes an engagement definition circuit 604 that identifies a user and/or a user attribute 610, where information about the identified user is utilized to configure operations of a client engagement, for example using a selected engagement scheme 608 and/or an adjusted engagement scheme 608.”, and ¶[0219] – other client data used is “age, gender, health, and basic information such as an indication of tobacco use. In certain embodiments, the interface of FIG. 73 may include any other information that would assist in providing an informative initial illustration, such as credit ratings, income information, asset information, goals of the client (e.g., death benefit, charitable contribution targets, income generation, etc.)”);
creating a core data frame using the client information (see ¶[0135], ¶[0162], and ¶[0219]);
creating a baseline data frame using the core data frame, wherein the baseline data frame comprises the client information and real-time market data, and wherein the baseline data frame is used to generate a baseline simulation (see ¶[0207] – “an expert financial analyst associated with the platform and/or a carrier may set certain parameters, and an agent may have the ability to set other parameters (e.g., start date, retirement date, contribution plan, etc.). In certain embodiments, a client user or other user may additionally be able to adjust certain parameters, for example income over time or other parameters that the user may have a likelihood to impact as a part of the planning process. The assumptions available in FIG. 53 are a non-limiting example, and may be the type of parameters available for a middle capability user, such as an agent or IMO representative. However, the specific accessibility to different assumption and/or modeling parameters is a design choice that will depend to an extent on the process content of the platform, the configuration of the platform, and the user classes available on the platform.”, and ¶[0220] – “The utilization of rapid and high quality illustrations provides for a number of benefits that are not available in previously known systems, for example allowing clients to quickly understand the benefits of a program, to adjust goals of the program to understand what is achievable and what inputs (e.g., client contributions) achieve what results, to get real-time performance updates including a comparison of actual performance relative to planned performance.”);
creating a comprehensive data frame using the baseline simulation (see ¶[0207]);
generating a set of simulations using the comprehensive data frame (see ¶[0207]);
using the client optimization goal to evaluate simulation performance by comparing each simulation in the set of simulations to the baseline simulation in view of the client optimization goal (This limitation has been interpreted as a strategy for reducing, avoiding, or deferring tax liability (“tax strategy”) pursuant to Section 14 of the Leahy-Smith America Invents Act. Accordingly, this claim limitation is being treated as being within the prior art and is insufficient to differentiate the invention of claim 1 from the prior art.);
identifying a tax liability (“tax strategy”) pursuant to Section 14 of the Leahy-Smith America Invents Act. Accordingly, this claim limitation is being treated as being within the prior art and is insufficient to differentiate the invention of claim 1 from the prior art.);
creating an optimized solution by optimizing a set of high performing simulations that includes the high performing simulation using an optimization technique 90, another example engagement predictor 10102 is schematically depicted. The engagement predictor 10102 may be configured to determine engagement predictions 10010 and determine platform configurations 10018 that may improve or optimize the engagement predictions 10010. The example engagement predictor 10102 may include a configuration management circuit 10103 that may be configured to provide suggestions for configurations and/or change a platform configuration to improve the engagement predictions. The engagement predictor 10102 may be configured to select one or more predefined platform configurations 10018 and/or modify elements of the predefined platform configurations 10018. The engagement predictor 10102 may be configured to process the engagement predictions 10010, user attributes 10012, and platform configurations 10018 and provide alternative configurations 10104. The alternative configurations 10104 may be one of the platform configurations 10018 that are previously generated and/or approved. The alternative configurations 10104 may be a new configuration and, in some cases, may require review and approval from an administrative user such as an agent for deployment. Alternative configurations 10104 may include changes in the configuration that include one or more of a change in the next event (e.g., alternating a goal-setting stage and an education stage), a change in illustration types, a change in communications (e.g., email, messages, etc.), a change in a graphical user interface, and/or the like.”);
developing an actionable retirement plan based on the optimized simulation (see ¶[0368] – “An example method 10700 may include the steps of identifying user-specific data of the wealth planning platform 10702, predicting, based on the user-specific data, a set of user-specific stress-test parameters 10704, generating a stress test scenario based on the stress-test parameters 10706, and simulating the stress test scenario to determine effects on the user-specific data 10708.”); and
sending the actionable retirement plan to the user (see Figure 144, items 10710 & 10712, and ¶ 368).
Although Lane substantially teaches the method of claim 10, it does not explicitly teach wherein the optimization technique comprises at least one of gradient descent, particle swarm optimization, and evolutionary solution development. Zhang does teach wherein the optimization technique comprises at least one of gradient descent, particle swarm optimization, and evolutionary solution development (see Abstract). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the optimization technique of Lane for the particle swarm optimization of Zhang. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As to Claim 2, Lane teaches the method of claim 1, further comprising the step of creating utility functions to improve database performance (see ¶[0162] – the user identification and/or attribute may include a user class, user profession, user demographic value, or any other parameter that may be related to the relevant workflow for the platform 102 to usefully configure the engagement of the user interface with the user. In certain embodiments, the user class, profession, age, time of thy that the user typically interacts with the platform, materials that the user may have already accessed (e.g., which can be determined based on user prior history, the inviting agent or agency for the user, etc.), the user's goals (e.g., important dates such as retirement date, investment horizon, etc.), the user's risk profile or category, or the like may be utilized to improve information presented on the user interface to be relevant to the user, to utilize metrics (e.g., rate of return, fees, investment types, etc.) that are relevant and/or important to the user, or the like.“).
As to Claim 3, Lane teaches the method of claim 1, wherein the comprehensive data frame comprises a set of scenario parameters comprising a plurality of possible values (see Figure 15 – item 394, and ¶[0070] – “The prospective client 30 can see the effects of modifications to the retirement plan by using entry options 394 to change their retirement age, monthly contributions to RRSPs, etc., and selecting an edit option 396. The prospective client 30 can also select a view results option to see an evaluation of their saving plan. The features shown in FIG. 15 provide additional context around the client's activities and what they need to do to achieve their goal. The entry options 394 includes an interactive slider tool that enables the client to dynamically modify their plans.”).
As to Claim 4, Lane teaches the method of claim 3, wherein each simulation of the set of simulations is evaluated year-by-year by executing functions using parameter values for each simulation (see ¶[0178]).
As to Claim 5, Lane teaches the method of claim 1, wherein the optimization goal comprises at least one of: minimized taxes paid, maximized total estate value after tax net of management fees, maximized ending ROTH balance, maximized total cash flow available during a client’s lifetime, a maximized account value, and adjusted total return (This limitation has been interpreted as a strategy for reducing, avoiding, or deferring tax liability (“tax strategy”) pursuant to Section 14 of the Leahy-Smith America Invents Act. Accordingly, this claim limitation is being treated as being within the prior art and is insufficient to differentiate the invention of claim 1 from the prior art.).
Claim 6 is rejected under the same reasoning as Claim 1.
Claim 7 is rejected under the same reasoning as Claim 2.
Claim 8 is rejected under the same reasoning as Claim 4.
Claim 9 is rejected under the same reasoning as Claim 5.
Conclusion
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)-272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IRENE KANG/
Examiner, Art Unit 3695
01/09/2026
/EDWARD CHANG/Primary Examiner, Art Unit 3696