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 October 27, 2025. Claims 1, 4, 10, 13, and 19 have been amended. Claims 1-20 are pending and examined.
Response to Amendments and Arguments
As to the rejection of Claims 1-20 under 35 U.S.C. § 101, Applicant’s arguments and amendments have been fully considered but are not persuasive. Applicant 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 (i.e. displays). 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”). As elaborated in the rejection below, using a processing unit is simply “apply it” using generic computer components rather than integrating the judicial exception into a practical application. The rejection is thereby maintained.
As to the rejection of claims 1-20 under 35 U.S.C. § 102, Applicant's arguments are moot given the new grounds of rejection of 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 1-20 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 of performing attribution analysis with respect to an investment portfolio. 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 for performing attribution analysis with respect to an investment portfolio, the method being implemented by at least one processor, the method comprising: identifying, by the at least one processor, a first set of attributes of each of a first investment portfolio and a second investment portfolio from among a plurality of investment portfolios; generating, by the at least one processor, a first graph that indicates respective relationships among a plurality of first decision points with respect to the first set of attributes that impact the first investment portfolio and a second graph that indicates respective relationships among a plurality of second decision points with respect to the first set of attributes that impact the second investment portfolio, wherein each of the first graph and the second graph further indicates a respective sequential order in which decisions are made ; and calculating, by the at least one processor based on the respective relationships among the plurality of decision points, a respective attribution result for each individual attribute from among the first set of attributes, in order to obtain a first set of attribution results that is associated with the first investment portfolio and a second set of attribution results that is associated with the second investment portfolio, wherein each of the generating and the calculating is performed by using parallel computing such that the generating of the first graph is performed in parallel with the generating of the second graph and the calculating of the first set of attribution results is performed in parallel with the calculating of the second set of attribution results.
(Step 2A prong 2) The additional elements are considered as follows:
“by at least one processor” This is merely “apply it” the processor is claimed at a high level of generality, they receive the information, perform the abstract idea, and output the results.
(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 performing attribution analysis with respect to an investment portfolio, over a generic computer network with generic computing elements, and generic hardware.
Analysis of dependent claims 2-9, 11-18, and 20, 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 (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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Phillips et al. (Publication No.: US 2015/0006433 A1) in view of Stubbs et al. (Publication No.: US 2015/0081592 A1).
As to Claim 1, Phillips teaches a method for performing attribution analysis with respect to an investment portfolio (see Fig. 5, and ¶[0063] – “certain components of attribution analysis, beginning with an initial portfolio 130 of assets”), the method being implemented by at least one processor, the method comprising:
identifying, by the at least one processor, a first set of attributes of each of a first investment portfolio and a second investment portfolio from among a plurality of investment portfolios (see Fig. 5, and ¶[0063] – “ In this case, additional modified versions of the portfolio (e.g., portfolio modifications 141 and 142) are obtained, and each is evaluated relative to one or more previous versions of the portfolio, e.g., in comparison to certain benchmark portfolios, as discussed in greater detail below.”);
generating, by the at least one processor, a first graph that indicates respective relationships among a plurality of first decision points with respect to the first set of attributes that impact the first investment portfolio (see Figure 1, and ¶[0016] – “In the former case, system 5 (and in particular server 10, either alone or in coordination with the user's device 21) functions as a tool, synthesizing and qualitatively transforming a great deal of data into a relatively few easy-to-understand metrics that can simplify a user's decision-making process. In the latter case, system 5 often can automatically allocate resources in a more efficient manner and, thereby, create significant value for the users 20 and, by extension, society as a whole.”); and
calculating, by the at least one processor based on the respective relationships among the plurality of decision points, a respective attribution result for each individual attribute from among the first set of attributes, in order to obtain a first set of attribution results that is associated with the first investment portfolio (see Fig. 6, and ¶[0064] – “FIG. 6 illustrates a process 170 for controlling resource management using attribution analysis according to certain representative embodiments of the present invention. Preferably, process 170 is executed by server 10, either alone or in combination with (e.g., by offloading some processing to) user device 21.”
Although Phillips substantially teaches the invention of Claim 1, it does not explicitly teach a second graph that indicates respective relationships among a plurality of second decision points with respect to the first set of attributes that impact the second investment portfolio, wherein the each of the first graph and the second graph further indicates an order a respective sequential order in which decisions are made; and a second set of attribution results that is associated with the second investment portfolio, wherein each of the generating and the calculating is performed by using parallel computing such that the generating of the first graph is performed in parallel with the generating of the second graph and the calculating of the first set of attribution results is performed in parallel with the calculating of the second set of attribution results. Stubbs does teach a second graph that indicates respective relationships among a plurality of second decision points with respect to the first set of attributes that impact the second investment portfolio, wherein the each of the first graph and the second graph further indicates an order a respective sequential order in which decisions are made; and a second set of attribution results that is associated with the second investment portfolio, wherein each of the generating and the calculating is performed by using parallel computing such that the generating of the first graph is performed in parallel with the generating of the second graph and the calculating of the first set of attribution results is performed in parallel with the calculating of the second set of attribution results (see Figures 9 & 10, ¶[0036] – “For each of the two risk model instances, WW and CRM, a set of backtests computing optimal portfolios were computed for nine different tracking errors (TE's) evenly spaced between a tracking error of 1.5% and 5.0%. In other words, in terms of Axioma's backtest product, a frontier backtest was performed for tracking errors of TE=1.50%, 1.94%, 2.38%, 2.81%, 3.25%, 3.69%, 4.13%, 4.56%, and 5.00%. For each risk model instance and each tracking error constraint, the set of optimal portfolios at each monthly rebalance was used to determine performance statistics for each tracking error and risk model. Of primary interest here is the realized annual return and the realized annual volatility of the active returns. When plotted on a graph with realized volatility on the horizontal axis and realized return on the vertical axis, the result is a realized efficient frontier. This graph indicates the relative risk/return tradeoff of each risk model instance. Note that even though the predicted tracking error is set to one of the nine values listed above, the realized tracking error for any backtest may be slightly less than or greater than the proscribed constraint depending on the realized portfolio returns.”, and Claim 3). 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 incorporate the features of Stubbs with those of Phillips 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. The motivation to combine is to be able to provide a more accurate attribution analysis with respect to an investment portfolio.
As to Claim 2, Phillips teaches the method of claim 1, wherein the calculating of the respective attribution result comprises calculating, for each individual attribute, a respective allocation attribution result that relates to a weight of an impact of the individual attribute as compared with a sum of impacts of attributes that act as sub-decision nodes on the graph, and a respective selection attribution result that relates to a comparison of returns for the sub-decision nodes with a return for the individual attribute (see at least Fig. 5, ¶[0071] – “ Any such comparison between the benchmark established as of the reference date (e.g., the base benchmark 132) and the neutral benchmark 134, e.g., with respect to the changes from the reference date to the evaluation date, provides a measure of the impact of the reference-date (e.g., initial) asset allocation. Any such comparison between the evaluation-date benchmark (e.g., benchmark 138) and the reference-date benchmark (e.g., the base benchmark 132), e.g., with respect to the changes from the reference date to the evaluation date, provides a measure of the impact of changing the asset allocation (as represented by the index weights) over time (as opposed to changes in specific assets), which is referred to herein as the Asset Allocation Component.”).
As to Claim 3, Phillips teaches the method of claim 1, wherein the calculating of the respective attribution result for each individual attribute is independently performable with respect to the calculating of the respective attribution results for other attributes (see at least Fig. 3, and ¶[0047] – “In certain embodiments, the predictor variables 60 are assumed to be independent of each other.”).
As to Claim 4, Phillips teaches the method of claim 1, further comprising displaying, on a user interface, each of the first set of attribution results and the second set of attribution results (see at least Fig. 4, and ¶[0033] – “In step 103, values pertaining to the predictor variables 60 within the selected set 60A are input. Similar to the preceding step, the present step preferably also involves providing a user interface on the user's device 21 through which the user 20 can specify, e.g.: (1) expected or estimated data values for the predictor variables 60, e.g., with respect to predetermined future point(s) in time (such as a time point(s) determined in step 102) and/or with respect to future point(s) in time that are specified by the user 20 through the provided user interface; (2) an expected or estimated amount of change that the user 20 expects there to be in the data values for the predictor variables 60 from their current values to any of the foregoing future points in time; (3) an indication of the user's level of confidence in such estimated future data values or future data value changes (e.g., specified as: (a) a direct indication of the user's level of confidence, such as on a scale of 1-5; (b) an amount of potential variation (plus or minus); (c) "maximum" and "minimum" expected values; or (d) a standard deviation, variance or other indication of expected variation); and/or (4) the type of distribution (e.g., normal, triangular or uniform) to be used with respect to the specified values.”).
As to Claim 5, Phillips teaches the method of claim 1, further comprising: obtaining, for each investment portfolio included in the plurality of investment portfolios, a respective set of attribution results that is associated with the corresponding investment portfolio; and combining the obtained sets of attribution results in order to determine an aggregate set of attribution results that is associated with the plurality of investment portfolios (see at least Fig. 4, and ¶[0052] – “In step 109, the simulation results generated in step 108 for the predictor variable 60 are transferred (or mapped) to the factor variables 70 (e.g., to obtain simulated values for the predictor variables 70) using the transfer function generated in step 105.”).
As to Claim 6, Phillips teaches the method of claim 5, further comprising displaying, on a user interface, the aggregate set of attribution results (see Fig. 4, and ¶[0033] – “The investment securities or financial instruments are assigned nodes on the graph; as non-limiting examples, the data entities may correspond to historical or current companies, sectors, products, securities, investments, loans, or components, aggregations, inputs, or outputs thereof.”).
As to Claim 7, Phillips teaches the method of claim 5, wherein the combining comprises: identifying a first path-dependent order and at least a second path-dependent order for combining the obtained sets of attribution results; and performing a first calculation based on the first path-dependent order and at least a second calculation based on each of the at least second path-dependent order, wherein the aggregate set of attribution results includes a first aggregate set of attribution results based on the first calculation and at least a second aggregate set of results based on the at least second calculation (see ¶[0067] – “In step 174, a modified portfolio (e.g., portfolio 136 in the first iteration of this step) is obtained. This modified portfolio is the result of changes to the previous version of the portfolio (e.g., initial portfolio 130 in the first iteration), including one or more purchases into and/or sales out of initial portfolio 130. Depending upon the particular embodiment and/or user-specified settings, the modified portfolio might, e.g., be the state of the portfolio: after a fixed period of time (e.g., three months, six months, one year, two years, three years or five years); after any asset purchase or sale; after any individual asset purchase or sale that exceeds a specified amount (e.g., at least 0.1%, 0.25%, 0.5%, 1%, 2% or 3% of the aggregate value of the portfolio), or any combination of purchases and/or sales that exceeds such specified amount; or at any time an oversight entity wants to evaluate management performance.”).
As to Claim 8, Phillips teaches the method of claim 7, further comprising displaying, on a user interface, each of the first aggregate set of attribution results and the at least second aggregate set of results (see at least Fig. 4, and ¶[0033] – “In step 103, values pertaining to the predictor variables 60 within the selected set 60A are input. Similar to the preceding step, the present step preferably also involves providing a user interface on the user's device 21 through which the user 20 can specify, e.g.: (1) expected or estimated data values for the predictor variables 60, e.g., with respect to predetermined future point(s) in time (such as a time point(s) determined in step 102) and/or with respect to future point(s) in time that are specified by the user 20 through the provided user interface; (2) an expected or estimated amount of change that the user 20 expects there to be in the data values for the predictor variables 60 from their current values to any of the foregoing future points in time; (3) an indication of the user's level of confidence in such estimated future data values or future data value changes (e.g., specified as: (a) a direct indication of the user's level of confidence, such as on a scale of 1-5; (b) an amount of potential variation (plus or minus); (c) "maximum" and "minimum" expected values; or (d) a standard deviation, variance or other indication of expected variation); and/or (4) the type of distribution (e.g., normal, triangular or uniform) to be used with respect to the specified values.”).
As to Claim 9, Phillips teaches the method of claim 1, wherein the first set of attributes includes at least one from among an asset class, a predetermined time period, a risk profile, a liquidity profile, and a geographic region (see at least Fig. 6, and ¶[0066] – “In step 172, a base benchmark 132 and a neutral benchmark 134 are identified. Both of such benchmarks 132 and 134 preferably are portfolios made up of the same assets and/or indexes, but typically with different weightings on such assets and/or indexes. In the preferred embodiments, this set of assets and/or indexes is sufficiently diverse to be able to closely replicate the performance (e.g., in terms of return and risk profiles) of all of the assets and/or asset classes that currently are included within the portfolio 130 and/or that potentially might be included within it in the future. The assets and/or indexes within the neutral benchmark 134 can be weighted in any of a variety of different ways, e.g., using equal weightings, fundamental indexing weighting, Fama-French three-factor weightings, or any other weighting approach. The weightings for base benchmark 132 preferably are selected such that base benchmark 132 tracks initial portfolio 130 with regard to a specified risk profile. More preferably, any of the techniques disclosed and/or claimed in the '219 patent are used for establishing the weightings on the assets and/or indexes within base benchmark 132.”).
Claim 10 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display (see Phillips at ¶[0080]), wherein the processor is configured to perform the method of Claim 1 and is rejected under the same reasoning as Claim 1.
Claim 11 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 2 and is rejected under the same reasoning as Claim 2.
Claim 12 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 3 and is rejected under the same reasoning as Claim 3.
Claim 13 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 4 and is rejected under the same reasoning as Claim 4.
Claim 14 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 5 and is rejected under the same reasoning as Claim 5.
Claim 15 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 6 and is rejected under the same reasoning as Claim 6.
Claim 16 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 7 and is rejected under the same reasoning as Claim 7.
Claim 17 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 8 and is rejected under the same reasoning as Claim 8.
Claim 18 is the computing apparatus comprising: a processor; a memory; a display; and a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to perform the method of Claim 9 and is rejected under the same reasoning as Claim 9.
Claim 19 is the non-transitory computer readable storage medium (see Phillips at ¶[0303]) storing instructions for performing attribution analysis with respect to an investment portfolio, the storage medium comprising executable code which, when executed by a processor, causes the processor to perform the method of Claim 1 and is rejected under the same reasoning as Claim 1.
Claim 20 is the non-transitory computer readable storage medium storing instructions for performing attribution analysis with respect to an investment portfolio, the storage medium comprising executable code which, when executed by a processor, causes the processor to perform the method of Claim 2 and is rejected under the same reasoning as Claim 2.
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)-273-3955. 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
6/15/2026
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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