DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 01/02/2026.
Claims 1, 8, 12, 19, and 23 have been amended and are hereby entered.
Claims 24-28 have been added.
Claims 1-28 are currently pending and have been examined.
This action is made Non-Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/02/2026 has been entered.
Claim Objection
Claims 1, 12, and 25 are objected to because of the following informalities:
Claims 1 and 12 interchangeably recites the limitations “transaction input device” and “input device.” Similarly, dependent claims 9, 11, 20, 22, and 25, recites “input device.” Is the “a transaction input device” recited in Claims 1 and Claim 12 different that the “input device” also recited in Claims 1, 9, 11, 12, 20, 22, and 25? It appears there is a typographical mistake since the specification only points to one input device, used for this interpretation. For compact examination purposes, Examiner interpreted the instances of the “input device,” recited in Claims 1, 9, 11, 12, 20, 22, and 25 as “transaction input device.” Appropriate correction is required.
Claim 25: lines 1-2 recites the limitation “wherein the instruction received from an input device is based on reducing a gap between the test portfolio.” “An input device” is previously recited in Claim 1: line 22. Is the input device recited in Claim 25: lines 1-2 different than an input device recited in Claim 1: line 22? It appears there is a typographical mistake since the specification only points to one input device. For compact examination purposes, Examiner interpreted the instance recited in Claim 25: lines 1-2 as “wherein the instruction received from the input device is based on reducing a gap between the test portfolio.” Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 24-25 and 27-28 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. For instance, in In re Hayes Microcomputer Products, the written description requirement was satisfied because the specification disclosed the specific type of microcomputer used in the claimed invention as well as the necessary steps for implementing the claimed function. The disclosure was in sufficient detail such that one skilled in the art would know how to program the microprocessor to perform the necessary steps described in the specification. In re Hayes Microcomputer Prods., Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, ___ (Fed. Cir. 1992). In the present applicant, claim 24-28 recites “when a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier is reduced using the visualization of the comparison of the test portfolio relative to the initial portfolio;” “the instruction received from an input device is based on reducing a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier to achieve a desired risk-return profile via the visualization of the comparison of the test portfolio relative to the initial portfolio;” “modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier;” and “determining a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier;” where theses limitation recite “reducing a gap,” which are not disclosed within the application’s specification and to show possession of the invention at the time of filing. While one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of “when a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier is reduced using the visualization of the comparison of the test portfolio relative to the initial portfolio;” “the instruction received from an input device is based on reducing a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier to achieve a desired risk-return profile via the visualization of the comparison of the test portfolio relative to the initial portfolio;” “modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier;” and “determining a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier.”
In the present applicant, claims 27-28 recites “determine a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generate the test portfolio based on the modified asset allocation table;” and “determining a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generating the test portfolio based on the modified asset allocation table;” where “determine a target asset allocation…. generate the test portfolio;” and “determining a target asset allocation….generating the test portfolio” is not supported in the specification as to how the applicant is “… determining and generating…” in order to show possession of the invention at the time of filing. While one skilled in the art could have devised a way to accomplish this aspect of the invention, Applicant’s original disclosure lacks sufficient detail to explain how Applicant envisioned achieving the goal of “determine a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generate the test portfolio based on the modified asset allocation table;” and “determining a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generating the test portfolio based on the modified asset allocation table.”
Simply stating or re-stating the claim limitation does not provide enough support to show possession. Since these important details about how the invention operates are not disclosed, it is not readily evident that Applicant has full possession of the invention at the time of filing (i.e., the original disclosure fails to provide adequate written description to support the claimed invention as a whole). Neither the specification nor the drawings disclose in detail the specific steps or algorithm needed to perform the operation. If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112a, for lack of written description must be made. For more information regarding the written description requirement, see MPEP §2161.01- §2163.07(b).
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio without significantly more.
Examiner has identified claim 1 as the claim that represents the claimed invention presented in independent claims 1 and 12.
Claim 1 is directed to a system, which is one of the statutory categories of invention; and Claim 12 is directed to a method, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a system comprising a computer having at least one processor and a non-transient storage medium storing computer readable instructions, that when executed by said at least one processor of the computer, cause the computer to perform operations comprising: providing a user interface screen for portfolio optimization, the user interface screen comprising: a first curve displayed on a graph of expected returns and risk, the first curve indicating an efficient frontier of a model portfolio, the first curve derived from historical asset allocation data comprising features describing a set of historical assets, the historical asset allocation data provided as a first input from a transaction input device; a data point overlaid on the graph indicating positioning of an initial portfolio relative to the efficient frontier, the initial portfolio retrieved based on a second input of asset allocation data describing a set of assets defined using the same features as the first input, the assets defined in an interactive asset allocation table for including proposed allocations for each of the assets and displaying efficiency metrics for each of the assets predicted, by a prediction engine, from the historical asset allocation data; and a second curve displayed on the graph indicating a modified efficient frontier of feasible adjustments based on a third input of predetermined limits of upper and lower bounds of the features for at least one asset in the asset allocation table; and responsive to receiving an instruction from an input device to modify at least one asset of the set of assets on the user interface screen indicative of generating a test portfolio: generate modifications to the asset allocation table displayed and predict, by the prediction engine, updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data; and update the user interface screen, in real-time, to generate a display of a test data point based on the updated efficiency metrics for the test portfolio overlaid on the graph with the initial portfolio thereby visualizing a comparison of the test portfolio relative to the initial portfolio, the efficient frontier and the modified efficient frontier on a single graph, wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen. These series of steps describe the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio (with the exception of the italicized and bolded terms above), which is mitigating risk associated with assets allocation by asset portfolio optimization; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing asset data using updated efficient asset metrics to determine expected returns of the assets in an optimized portfolio, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the abstract idea is the evaluation of historical asset allocation data and updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data to generate an optimized portfolio, which is a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Therefore, a concept performed in the human mind is a Mental Process. Moreover, the abstract idea is generation of efficient frontier curves using historical data, which includes the historical returns and volatilities/standard deviations of different assets that could be included in a portfolio; where, the portfolios are plotted on a graphs having a x-axis representing the portfolio risk (standard deviation) and y-axis representing the portfolio return. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Similar arguments can be extended to the other independent claim, claim 12, and hence; claim 12 is rejected on similar grounds as claim 1.
Dependent claims 2-11, 13-22, and 24-28, are directed to a system and a method, respectively, which recite the steps that describe the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio. Furthermore, dependent claims 2, 8, 13, 19, and 27-28 are directed to a system and a method, respectively, which recites a series of steps, e.g., determine, via communications of the instruction with a constraints engine whether the modifications satisfy predefined set of constraints defining permissive modifications to the set of assets; responsive to determining that at least one modification does not satisfy the predefined set of constraints, automatically adjust the modification in the instruction to satisfy the permissive modifications; and generate a notification on the user interface screen indicating adjusting the modification; further comprising a machine learning model on the computer, having been trained on the historical asset allocation data, and further configured to predict a target asset allocation weighting to modify the proposed allocation for each said asset in the asset allocation table and display the target asset allocation weighting to improve a fit between the data point representing the initial portfolio on the graph and a predetermined one of the first curve and the second curve displayed on the user interface screen; and wherein the instructions further cause operations comprising: determine a target asset allocation weighting for the at least one asset of the set of assets using a machine learning model to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generate the test portfolio based on the modified asset allocation table; and display, using the user interface screen the test data point based on the test portfolio. These series of steps describe the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio (with the exception of the italicized and bolded terms above), which is mitigating risk associated with assets allocation by asset portfolio optimization; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing asset data using updated efficient asset metrics to determine expected returns of the assets in an optimized portfolio, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the abstract idea is the evaluation of historical asset allocation data and updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data to generate an optimized portfolio, which is a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Therefore, a concept performed in the human mind is a Mental Process. Moreover, the abstract idea is generation of efficient frontier curves using historical data, which includes the historical returns and volatilities/standard deviations of different assets that could be included in a portfolio; where, the portfolios are plotted on a graphs having a x-axis representing the portfolio risk (standard deviation) and y-axis representing the portfolio return. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, input device, constraints engine, and machine learning model. Thus, claims 2-11 and 13-22 recite an abstract idea. The additional elements of a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, input device, constraints engine, and machine learning model are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, input device, constraints engine, and machine learning model do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 23 is directed to a method, which is one of the statutory categories of invention. (Step 1: YES).
Claim 23 is directed to a method, which recites a series of steps, e.g., accessing, by a computing device, and from a first transaction device, a first input of historical asset allocation data comprising features describing each of the assets and historical efficiency metrics; receiving, by the computing device, and from an input device on a user interface, a second input of proposed asset allocation data defined using the features as the first input; receiving, by the computing device, and from the input device, a third input of predetermined restrictions on asset allocations provided on the user interface, the restrictions comprising upper and lower bound ranges for at least one asset; and applying the first, second, and third input by the computing device, to a machine learning model to: generate and present a constraint efficient frontier curve on a screen of the user interface using the restrictions and the historical allocation data having the historical efficiency metrics; predict, based on the constraint efficient frontier curve, a set of hypothetical modification changes to allocation weighting from the proposed asset allocation data based on relative risk reward ratio retrieved from the constraint efficient frontier curve and within the predetermined restrictions to lessen a difference between the proposed asset allocation data and the constraint efficient frontier curve; and present the prediction indicative of the modification changes and the constraint efficient frontier curve on the screen of the user interface for interaction therewith, wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen. These series of steps describe the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio (with the exception of the italicized and bolded terms above), which is mitigating risk associated with assets allocation by asset portfolio optimization; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing asset data using updated efficient asset metrics to determine expected returns of the assets in an optimized portfolio, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the abstract idea is the evaluation of historical asset allocation data and updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data to generate an optimized portfolio, which is a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Therefore, a concept performed in the human mind is a Mental Process. Moreover, the abstract idea is generation of efficient frontier curves using historical data, which includes the historical returns and volatilities/standard deviations of different assets that could be included in a portfolio; where, the portfolios are plotted on a graphs having a x-axis representing the portfolio risk (standard deviation) and y-axis representing the portfolio return. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a computing device, first transaction device, input device, user interface, machine learning model, and screen of the user interface, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 23 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a computing device, first transaction device, input device, user interface, machine learning model, and screen of the user interface, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 23 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 23 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a computing device, first transaction device, input device, user interface, machine learning model, and screen of the user interface, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 23 is not patent eligible.
Dependent claims 2-11 and 13-22 have further defined the abstract idea that is present in their respective independent claims: Claims 1 and 12; and thus correspond to Certain Methods of Organizing Human Activity and/or Mental Processes and/or Mathematical Concepts, and are abstract in nature for the reason presented above. The dependent claims 2-11 and 13-22 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, dependent claims 2-11 and 13-22 are directed to an abstract idea without significantly more.
Thus, claims 1-23 are not patent-eligible.
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.
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 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, 3-12, and 14-28 are rejected under 35 U.S.C. 103 as being unpatentable over Bonissone (U.S. Patent Application Publication No. US 2005/0187848 A1; hereinafter “Bonissone”) in view of Ho (U.S. Patent Publication No. US 2023/0267542 A1; hereinafter “Ho”).
Regarding Claims 1 and 12:
Bonissone teaches:
A system comprising a computer having at least one processor and a non-transient storage medium storing computer readable instructions, that when executed by said at least one processor of the computer, cause the computer to perform operations comprising: (Bonissone, See, Para. 29, 30, 279, 280, 283, 291; Fig. 2; Abstract);
providing a user interface screen for portfolio optimization, the user interface screen comprising: (Bonissone, The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem; (See, Abstract); the efficient frontier is visually observed or inspected by a user, e.g., a portfolio manager. For example, FIG. 13 is a diagram showing an efficient frontier in a 3D View in accordance with one embodiment of the invention. A suitable visualization tool 612 might be used for this visual observation. (See, Para. 231, 232, 243, 267, 276,293,294; Fig. 12-13));
a first curve displayed on a graph of expected returns and risk, the first curve indicating an efficient frontier of a model portfolio, the first curve derived from historical asset allocation data comprising features describing a set of historical assets, the historical asset allocation data provided as a first input from a transaction input device; (Bonissone, the decision maker, for instance portfolio manager or investor, might want to find the portfolio that minimizes risk (measured by standard deviation, value at-risk, credit risk, etc.) and maximizes expected return (measured by expect profits, accounting income, yield, etc.). ( See, Abstract; Para. 234); the resulting efficient frontier is a curve in a two-dimensional space as shown in FIG. 1. Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. For example, these assets may be a collection of securities. (See, Para. 5-10; 151-152; Fig.1, 24-26); The average transaction (across all ten portfolios) involves selling 9% of the allocation in this asset class. The histogram of the suggested changes in this asset class across all portfolios is shown in FIG. 26. By looking at this histogram we might decide that we do not want to increase our holdings in this class and decide to drop portfolio 2 and 5 (which would require further buy transactions in this asset class.) We could continue this process for other asset classes, the order again reflecting our sense of priority for each asset class, until the subset of 10 portfolios is reduced to a final point. (See, Para. 261-266));
a data point overlaid on the graph indicating positioning of an initial portfolio relative to the efficient frontier, the initial portfolio retrieved based on a second input of asset allocation data describing a set of assets defined using the same features as the first input, the assets defined in an interactive asset allocation table for including proposed allocations for each of the assets and displaying efficiency metrics for each of the assets predicted, [by a prediction engine], from the historical asset allocation data; and (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14));
a second curve displayed on the graph indicating a modified efficient frontier of feasible adjustments based on a third input of predetermined limits of upper and lower bounds of the features for at least one asset in the asset allocation table; and (Bonissone, initially, by using a common statistical transformation we can transform each pair (μi,1, ρi,1) into a confidence interval [ai,1,bi,1], where ai,1 and bi,1 are the lower and upper bounds of the confidence interval for a given confidence level α. The representation of a portfolio {overscore (X)}i in the performance space is now {overscore (Y)}1=[(ai,1, bi,1), . . . , (ai,n, bi,n)]. In contrast with the deterministic evaluation, each portfolio will now be represented in performance space Y by a hyper-rectangle instead of a single point.; in accordance with one embodiment of the invention, we project the Pareto Front on four projections: (B-yield, Risk1) 812, (B-yield, Risk2) 814, (Risk2, Risk1) 816, and (B-yield, DWM-Yield) 818. With reference to FIG. 15, the first two projections show tradeoffs between return and risk (using different metrics), while the third and fourth projections show relationship between risk metrics and between return metrics. (See, Para. 189, 192, 198-199, 249-258, 269; Fig. 1, 15, 30, 37, 38); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 5; Abstract));
responsive to receiving an instruction from an input device to modify at least one asset of the set of assets on the user interface screen indicative of generating a test portfolio: (Bonissone, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning (See, Para. 28, 29, 71, 84, 106-119, 232-233; Fig. 1-3));
generate modifications to the asset allocation table displayed and predict, [by the prediction engine], updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data; and (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14));
update the user interface screen, in real-time, to generate a display of a test data point based on the updated efficiency metrics for the test portfolio overlaid on the graph with the initial portfolio thereby visualizing a comparison of the test portfolio relative to the initial portfolio, the efficient frontier and the modified efficient frontier on a single graph. (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 1, 5, 15, 30, 37-38));
wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen. (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Bonissone does not specifically teaches that the assets predicted, [by a prediction engine], from the historical asset allocation data; and predict, [by the prediction engine], updated efficiency metrics for each of the assets.
However, Ho further teaches the following limitations:
the assets predicted, [by a prediction engine], from the historical asset allocation data; (Ho, the system further comprises a modelling processor arranged to model a predicted optimized portfolio by including the one or more financial products into the investment portfolio and optimizing the investment portfolio. (See, Para. 24-25, 45-46, 91; Abstract));
predict, [by the prediction engine], updated efficiency metrics for each of the assets. (Ho, the system further comprises a modelling processor arranged to model a predicted optimized portfolio by including the one or more financial products into the investment portfolio and optimizing the investment portfolio. (See, Para. 24-25, 45-46, 91; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Regarding Claims 3 and 14:
Bonissone teaches:
wherein the instruction to modify at least one asset comprises one of: modifying asset class weight of one asset relative to other assets held in a particular portfolio; and (Bonissone, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning (See, Para. 28, 29, 71, 84, 106-119, 232-233; Fig. 1-3));
adding a new asset with associated asset class weight, the instruction causing the computer to predict, using [the prediction engine], a new efficiency metric for the test portfolio including expected return and risk based on the historical asset allocation data. (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); at this point we could use additional metrics and constraints. For example, we could zoom into this new subset of 10 points and use other metrics, e.g., transaction cost (Delta), to select the lowest cost portfolio, as shown in the bar graph 818 in FIG. 22; alternatively, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (Para. 258-265; Fig. 1, 22-31)).
Bonissone does not specifically teaches that the computer to predict, using [the prediction engine], a new efficiency metric.
However, Ho further teaches the following limitations:
the computer to predict, using [the prediction engine], a new efficiency metric.(Ho, the system further comprises a modelling processor arranged to model a predicted optimized portfolio by including the one or more financial products into the investment portfolio and optimizing the investment portfolio. (See, Para. 24-25, 45-46, 91; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Regarding Claims 4 and 15:
Bonissone teaches:
wherein each asset in the asset allocation table is associated with an expected risk and an expected return defining a particular efficiency metric for an asset. (Bonissone, in FIG. 27, the non-dominated solutions might be generated by a solution set generation portion 912, as is described above. Then in step 920, the process imposes user-specified independent, or alternatively dependent, constraints on all relevant metrics, for example lower limits on return and upper limits on risk. As illustrated in FIG. 27, the application of the constraints in step 920 might be performed by an initial constraint portion 922. After step 920, the process passes to step 930….. the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. (See, Abstract; Para. 104, 28-30, 261-272, 275-276; Fig. 1, 14, 23, 27, 37-38; Abstract)).
Regarding Claims 5 and 16:
Bonissone teaches:
wherein receiving the instruction to modify at least one asset is received via an instruction to modify the asset on the asset allocation table or by selecting one or more data points on the graph of expected returns versus expected risk. (Bonissone, a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Regarding Claims 6 and 17:
Bonissone teaches:
wherein at least some of the data points in the first curve and the second curve providing the efficient frontier and the modified efficient frontier displayed on the graph are selectable such that the selection causes the initial portfolio to modify the proposed allocations to said selection and update the efficiency metric. (Bonissone, the decision maker, for instance portfolio manager or investor, might want to find the portfolio that minimizes risk (measured by standard deviation, value at-risk, credit risk, etc.) and maximizes expected return (measured by expect profits, accounting income, yield, etc.). ( See, Abstract; Para. 234); the resulting efficient frontier is a curve in a two-dimensional space as shown in FIG. 1. Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. For example, these assets may be a collection of securities. (See, Para. 5-10; 151-152; Fig.1, 24-26); The average transaction (across all ten portfolios) involves selling 9% of the allocation in this asset class. The histogram of the suggested changes in this asset class across all portfolios is shown in FIG. 26. By looking at this histogram we might decide that we do not want to increase our holdings in this class and decide to drop portfolio 2 and 5 (which would require further buy transactions in this asset class.) We could continue this process for other asset classes, the order again reflecting our sense of priority for each asset class, until the subset of 10 portfolios is reduced to a final point. (See, Para. 261-266); the final selection from this small subset can be achieved by applying additional metrics and constraints or by imposing preferences in the portfolio configuration space. For example, as discussed above, the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. After step 950, the process ends in step 960. (See, Para. 271)).
Regarding Claims 7 and 18:
Bonissone teaches:
wherein the user interface screen provided is configured to display a plurality of hypothetical asset allocation scenarios in both the asset allocation table and the graph to visualize a comparison to the efficient frontier and the modified efficient frontier. (Bonissone, the efficient frontier is visually observed or inspected by a user, e.g., a portfolio manager. For example, FIG. 13 is a diagram showing an efficient frontier in a 3D View in accordance with one embodiment of the invention. A suitable visualization tool 612 might be used for this visual observation. Based on this inspection, the user may well identify gaps in the efficient frontier. The user may be interested in points in the area of the gaps and wish to effect further sampling in the area of the gaps. Accordingly, the user may interactively place targets in those identified areas having gaps, in accordance with one embodiment of the invention. (See, Para. 29-30, 231-236; Fig. 1, 2, 9, 14-26, 28-31, 37-38; Abstract)).
Regarding Claims 8 and 19:
Bonissone teaches:
further comprising [a machine learning model on the computer], having been trained on the historical asset allocation data, and further configured to predict a target asset allocation weighting to modify the proposed allocation for each said asset in the asset allocation table and display the target asset allocation weighting to improve a fit between the data point representing the initial portfolio on the graph and a predetermined one of the first curve and the second curve displayed on the user interface screen. (Bonissone, a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem (a machine learning model on the computer), the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Bonissone does not specifically teaches that a machine learning model on the computer.
However, Ho further teaches the following limitation:
a machine learning model on the computer.(Ho, the diversification processor 206 may also proceed to use a modelling function or modelling processor to model the inclusion of the selected new asset within the existing investment portfolio held by a user and attempt to optimize such a portfolio to determine a diversification position that could then be compared to the optimized investment portfolio without the selected new asset. (See, Para. 24-25, 45-46, 91, 98-99; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Regarding Claims 9 and 20:
Bonissone teaches:
wherein the instructions when executed by said at least one processor further cause operations of the computer comprising: causing the user interface screen to display the asset allocation table configured to be editable to receive the third input from the input device, the asset allocation table displaying a table of features for each asset, the features comprising the upper bound and the lower bound of permissible increase or decrease in each asset with respect to other asset allocations. (Bonissone, initially, by using a common statistical transformation we can transform each pair (μi,1, ρi,1) into a confidence interval [ai,1,bi,1], where ai,1 and bi,1 are the lower and upper bounds of the confidence interval for a given confidence level α. The representation of a portfolio {overscore (X)}i in the performance space is now {overscore (Y)}1=[(ai,1, bi,1), . . . , (ai,n, bi,n)]. In contrast with the deterministic evaluation, each portfolio will now be represented in performance space Y by a hyper-rectangle instead of a single point.; in accordance with one embodiment of the invention, we project the Pareto Front on four projections: (B-yield, Risk1) 812, (B-yield, Risk2) 814, (Risk2, Risk1) 816, and (B-yield, DWM-Yield) 818. With reference to FIG. 15, the first two projections show tradeoffs between return and risk (using different metrics), while the third and fourth projections show relationship between risk metrics and between return metrics. (See, Para. 189, 192, 198-199, 249-258, 269; Fig. 1, 15, 30, 37, 38); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 5; Abstract); in FIG. 27, the non-dominated solutions might be generated by a solution set generation portion 912, as is described above. Then in step 920, the process imposes user-specified independent, or alternatively dependent, constraints on all relevant metrics, for example lower limits on return and upper limits on risk. As illustrated in FIG. 27, the application of the constraints in step 920 might be performed by an initial constraint portion 922. After step 920, the process passes to step 930….. the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. (See, Abstract; Para. 104, 28-30, 261-272, 275-276; Fig. 4, 23, 27)).
Regarding Claims 10 and 21:
Bonissone teaches:
wherein the instructions, when executed by the processor, further cause the computer to generate the user interface screen to visually differentiate on the first curve and the second curve, between various levels of expected risk and return such as to indicate one or more optimal points of each portfolio. (Bonissone, the resulting efficient frontier is a curve in a two-dimensional space as shown in FIG. 1. Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. For example, these assets may be a collection of securities. (See, Para. 5-10; 151-152; Fig.1, 24-26); In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 9, 15-22, 27-31; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); The heavy line 2802 indicates the Pareto optimal front—each point on it is non-dominated. Given the Pareto optimal front, a portfolio manager can choose a solution based on other criteria (e.g., cost of implementing the portfolio, risk measures, and other return measures); In accordance with the embodiment of the invention shown in FIG. 2, dominance filtering is a highly useful component of our approach to multi-objective portfolio optimization. To explain, given a set of M vectors to be partitioned into dominated and non-dominated subsets, and given N objectives, the worst-case computational complexity of the typical partitioning process is O(N M2). For large M and N>2, the time required to partition the set of M vectors grows rapidly. Also, since the PSEA described above is dependent on its ability to repeatedly and rapidly differentiate between the dominated and non-dominated solutions. (See, Para. 11, 205)).
Regarding Claims 11 and 22:
Bonissone teaches:
wherein in response to receiving an input from the input device for selecting points on the graph of the efficient frontier or the modified efficient frontier, (Bonissone, initially, by using a common statistical transformation we can transform each pair (μi,1, ρi,1) into a confidence interval [ai,1,bi,1], where ai,1 and bi,1 are the lower and upper bounds of the confidence interval for a given confidence level α. The representation of a portfolio {overscore (X)}i in the performance space is now {overscore (Y)}1=[(ai,1, bi,1), . . . , (ai,n, bi,n)]. In contrast with the deterministic evaluation, each portfolio will now be represented in performance space Y by a hyper-rectangle instead of a single point.; in accordance with one embodiment of the invention, we project the Pareto Front on four projections: (B-yield, Risk1) 812, (B-yield, Risk2) 814, (Risk2, Risk1) 816, and (B-yield, DWM-Yield) 818. With reference to FIG. 15, the first two projections show tradeoffs between return and risk (using different metrics), while the third and fourth projections show relationship between risk metrics and between return metrics. (See, Para. 189, 192, 198-199, 249-258, 269; Fig. 1, 15, 30, 37, 38); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 5; Abstract); in FIG. 27, the non-dominated solutions might be generated by a solution set generation portion 912, as is described above. Then in step 920, the process imposes user-specified independent, or alternatively dependent, constraints on all relevant metrics, for example lower limits on return and upper limits on risk. As illustrated in FIG. 27, the application of the constraints in step 920 might be performed by an initial constraint portion 922. After step 920, the process passes to step 930….. the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. (See, Abstract; Para. 104, 28-30, 261-272, 275-276; Fig. 4, 23, 27));
the instructions, when executed by the processor, further cause the computer to update the asset allocation table and associated proposed allocations to reflect the selected points and associated values reflective of optimal expected return and risk. (Bonissone, the resulting efficient frontier is a curve in a two-dimensional space as shown in FIG. 1. Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. (See, Para. 5-10; 151-152; Fig.1, 24-26); In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para. 261-271, Fig. 9, 15-22, 27-31; Abstract); The heavy line 2802 indicates the Pareto optimal front—each point on it is non-dominated. Given the Pareto optimal front, a portfolio manager can choose a solution based on other criteria (e.g., cost of implementing the portfolio, risk measures, and other return measures); In accordance with the embodiment of the invention shown in FIG. 2, dominance filtering is a highly useful component of our approach to multi-objective portfolio optimization. To explain, given a set of M vectors to be partitioned into dominated and non-dominated subsets, and given N objectives, the worst-case computational complexity of the typical partitioning process is O(N M2). For large M and N>2, the time required to partition the set of M vectors grows rapidly. Also, since the PSEA described above is dependent on its ability to repeatedly and rapidly differentiate between the dominated and non-dominated solutions. (See, Para. 11, 28-30, 205))..
Regarding Claim 23:
Bonissone teaches:
A computer implemented method comprising: (Bonissone, See, Para. 28);
accessing, by a computing device, and from a first transaction device, a first input of historical asset allocation data comprising features describing each of the assets and historical efficiency metrics; (Bonissone, Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. For example, these assets may be a collection of securities. (See, Para. 5-10; 151-152; Fig.1, 24-26); The average transaction (across all ten portfolios) involves selling 9% of the allocation in this asset class. The histogram of the suggested changes in this asset class across all portfolios is shown in FIG. 26. By looking at this histogram we might decide that we do not want to increase our holdings in this class and decide to drop portfolio 2 and 5 (which would require further buy transactions in this asset class.) We could continue this process for other asset classes, the order again reflecting our sense of priority for each asset class, until the subset of 10 portfolios is reduced to a final point. (See, Para. 261-266); FIG. 7, an initial population 210 of cardinality n is created by randomly drawn solutions. In the context of portfolio risk optimization, the solutions constituting the initial population are randomly drawn from a solutions archive 218 which may be comprised of the initial points population retrieved from a database, and which was generated using the initial points generation algorithm described above. Typically, the size of such a population would be in range of 50 to 100 solutions, for example. After step 1, the process passes to step 2 (See, Para.28, 155; Fig. 7));
receiving, by the computing device, and from an input device on a user interface, a second input of proposed asset allocation data defined using the features as the first input; (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14));
receiving, by the computing device, and from the input device, a third input of predetermined restrictions on asset allocations provided on the user interface, the restrictions comprising upper and lower bound ranges for at least one asset; and (Bonissone, initially, by using a common statistical transformation we can transform each pair (μi,1, ρi,1) into a confidence interval [ai,1,bi,1], where ai,1 and bi,1 are the lower and upper bounds of the confidence interval for a given confidence level α. The representation of a portfolio {overscore (X)}i in the performance space is now {overscore (Y)}1=[(ai,1, bi,1), . . . , (ai,n, bi,n)]. In contrast with the deterministic evaluation, each portfolio will now be represented in performance space Y by a hyper-rectangle instead of a single point.; in accordance with one embodiment of the invention, we project the Pareto Front on four projections: (B-yield, Risk1) 812, (B-yield, Risk2) 814, (Risk2, Risk1) 816, and (B-yield, DWM-Yield) 818. With reference to FIG. 15, the first two projections show tradeoffs between return and risk (using different metrics), while the third and fourth projections show relationship between risk metrics and between return metrics. (See, Para. 189, 192, 198-199, 249-258, 269; Fig. 1, 15, 30, 37, 38); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 5; Abstract));
applying the first, second, and third input by the computing device, to [a machine learning model] to: generate and present a constraint efficient frontier curve on a screen of the user interface using the restrictions and the historical allocation data having the historical efficiency metrics; (Bonissone, a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem (machine learning model), the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14));
predict, based on the constraint efficient frontier curve, a set of hypothetical modification changes to allocation weighting from the proposed asset allocation data based on relative risk reward ratio retrieved from the constraint efficient frontier curve and within the predetermined restrictions to lessen a difference between the proposed asset allocation data and the constraint efficient frontier curve; and ((Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14));
present the prediction indicative of the modification changes and the constraint efficient frontier curve on the screen of the user interface for interaction therewith. (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 1, 5, 15, 30, 37-38)) ;
wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen. (Bonissone, instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Bonissone does not specifically teaches that a machine learning model.
However, Ho further teaches the following limitation:
a machine learning model.(Ho, the diversification processor 206 may also proceed to use a modelling function or modelling processor to model the inclusion of the selected new asset within the existing investment portfolio held by a user and attempt to optimize such a portfolio to determine a diversification position that could then be compared to the optimized investment portfolio without the selected new asset. (See, Para. 24-25, 45-46, 91, 98-99; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Regarding Claim 24:
Bonissone teaches:
wherein the instructions further cause operations comprising: when a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier is reduced using the visualization of the comparison of the test portfolio relative to the initial portfolio, providing an optimal asset allocation portfolio to generate at least one of a higher expected return or a lower risk. (Bonissone, the decision maker, for instance portfolio manager or investor, might want to find the portfolio that minimizes risk (measured by standard deviation, value at-risk, credit risk, etc.) and maximizes expected return (measured by expect profits, accounting income, yield, etc.). ( See, Abstract; Para. 234); instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Regarding Claim 25:
Bonissone teaches:
the instruction received from an input device is based on reducing a gap between the test portfolio and at least one of the efficient frontier and the modified efficient frontier to achieve a desired risk-return profile via the visualization of the comparison of the test portfolio relative to the initial portfolio. (Bonissone, the decision maker, for instance portfolio manager or investor, might want to find the portfolio that minimizes risk (measured by standard deviation, value at-risk, credit risk, etc.) and maximizes expected return (measured by expect profits, accounting income, yield, etc.). ( See, Abstract; Para. 234); instead of applying additional constraints, we could analyze the structure of the subset of 10 points in the portfolio configuration space (X) instead of the portfolio performance space (Y). In FIG. 23, we describe an example in which we visualize the asset allocation configuration of one of the selected ten portfolios. We could visualize the asset class allocation (2310) or individual securities in that asset class (2320). This will allow us to determine the overall transaction costs due to portfolio turnover and compare it with the other nine portfolios. (See, Para.1-8, 261-271, Fig. 1, 9, 15-22, 24-26; Abstract); a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Regarding Claim 26:
Bonissone teaches:
wherein the efficient frontier is a first efficient frontier and the modified efficient frontier is a first modified efficient frontier, the instructions further cause operations comprising: generate a second efficient frontier and a second modified efficient frontier based on the updated efficient metrics after the step of generating modifications to the asset allocation table; and wherein the step of updating the user interface screen, in real time, to generate a display of a test data point includes displaying the second efficient frontier and the second modified efficient frontier to visualize the effect of the modification of at least one asset of the set of assets on the portfolio and predict at least one of the risk or reward as comparted to an optimized portfolio shown in at least one of the second efficient frontier and the second modified efficient frontier.. (Bonissone, the decision maker, for instance portfolio manager or investor, might want to find the portfolio that minimizes risk (measured by standard deviation, value at-risk, credit risk, etc.) and maximizes expected return (measured by expect profits, accounting income, yield, etc.). ( See, Abstract; Para. 234); the resulting efficient frontier is a curve in a two-dimensional space as shown in FIG. 1. Each of these approaches uses variance as a sole risk measure. Each point on the efficient frontier is a portfolio consisting of a collection of assets. For example, these assets may be a collection of securities. (See, Para. 5-10; 151-152; Fig.1, 24-26); The average transaction (across all ten portfolios) involves selling 9% of the allocation in this asset class. The histogram of the suggested changes in this asset class across all portfolios is shown in FIG. 26. By looking at this histogram we might decide that we do not want to increase our holdings in this class and decide to drop portfolio 2 and 5 (which would require further buy transactions in this asset class.) We could continue this process for other asset classes, the order again reflecting our sense of priority for each asset class, until the subset of 10 portfolios is reduced to a final point. (See, Para. 261-266); the final selection from this small subset can be achieved by applying additional metrics and constraints or by imposing preferences in the portfolio configuration space. For example, as discussed above, the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. After step 950, the process ends in step 960. (See, Para. 271)).
Regarding Claim 27:
Bonissone teaches:
wherein the instructions further cause operations comprising: determine a target asset allocation weighting for the at least one asset of the set of assets using [a machine learning model] to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generate the test portfolio based on the modified asset allocation table; and display, using the user interface screen the test data point based on the test portfolio. (Bonissone, a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem (a machine learning model on the computer), the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. (See, Para. 28-30, 189, 192, 198-199, 249-258, 261-271, Fig. 1, 9, 15-22, 24-26, 30, 37, 38; Abstract); For example, a parallel coordinates approach might be used. Using parallel coordinates, we can visualize a portfolio as line segments that pass through a sequence of post (one for each corresponding metrics). The order of the posts indicates the relative priorities of the metrics in the tradeoff process. FIG. 14 shows an example of the parallel coordinates approach. (See, Para. 104-114, 244-250; Fig. 2, 14)).
Bonissone does not specifically teaches that a machine learning model.
However, Ho further teaches the following limitation:
a machine learning model on the computer.(Ho, the diversification processor 206 may also proceed to use a modelling function or modelling processor to model the inclusion of the selected new asset within the existing investment portfolio held by a user and attempt to optimize such a portfolio to determine a diversification position that could then be compared to the optimized investment portfolio without the selected new asset. (See, Para. 24-25, 45-46, 91, 98-99; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Regarding Claim 28:
Bonissone teaches:
further comprising: determining a target asset allocation weighting for the at least one asset of the set of assets using [a machine learning model] to modify the proposed allocation of the at least one asset of the set of assets in the asset allocation table to reduce the gap between the initial portfolio and at least one of the efficient frontier and the modified efficient frontier; generating the test portfolio based on the modified asset allocation table; and displaying, using the user interface screen, the test data point based on the test portfolio. (Bonissone, initially, by using a common statistical transformation we can transform each pair (μi,1, ρi,1) into a confidence interval [ai,1,bi,1], where ai,1 and bi,1 are the lower and upper bounds of the confidence interval for a given confidence level α. The representation of a portfolio {overscore (X)}i in the performance space is now {overscore (Y)}1=[(ai,1, bi,1), . . . , (ai,n, bi,n)]. In contrast with the deterministic evaluation, each portfolio will now be represented in performance space Y by a hyper-rectangle instead of a single point.; in accordance with one embodiment of the invention, we project the Pareto Front on four projections: (B-yield, Risk1) 812, (B-yield, Risk2) 814, (Risk2, Risk1) 816, and (B-yield, DWM-Yield) 818. With reference to FIG. 15, the first two projections show tradeoffs between return and risk (using different metrics), while the third and fourth projections show relationship between risk metrics and between return metrics. (See, Para. 189, 192, 198-199, 249-258, 269; Fig. 1, 15, 30, 37, 38); Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. (See, Para. 146-152; Fig. 5; Abstract); in FIG. 27, the non-dominated solutions might be generated by a solution set generation portion 912, as is described above. Then in step 920, the process imposes user-specified independent, or alternatively dependent, constraints on all relevant metrics, for example lower limits on return and upper limits on risk. As illustrated in FIG. 27, the application of the constraints in step 920 might be performed by an initial constraint portion 922. After step 920, the process passes to step 930….. the additional metrics or constraints might relate to a variety of parameters including asset allocation, commission rates, or transaction costs, for example. The imposition of these additional metrics might be illustratively performed by a subsequent constraint portion 950, for example. (See, Abstract; Para. 104, 28-30, 261-272, 275-276; Fig. 4, 23, 27)).
Bonissone does not specifically teaches that a machine learning model.
However, Ho further teaches the following limitation:
a machine learning model.(Ho, the diversification processor 206 may also proceed to use a modelling function or modelling processor to model the inclusion of the selected new asset within the existing investment portfolio held by a user and attempt to optimize such a portfolio to determine a diversification position that could then be compared to the optimized investment portfolio without the selected new asset. (See, Para. 24-25, 45-46, 91, 98-99; Abstract)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone with the features of Ho’s system because “there is provided a system for analysing a financial product comprising: an investor gateway arranged to receive an established portfolio of a user and obtaining a first performance characteristic of the established portfolio; a product gateway arranged to receive a financial product and obtaining a second performance characteristic of the financial product; and a diversification processor arranged to compare the first performance characteristic with the second performance characteristic to determine a diversity rating of the financial product with respect to the established portfolio, wherein the diversity rating is arranged to represent a rating of the diversification of the established portfolio if the portfolio was to include the financial product.” (Ho, Para. 26).
Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bonissone (U.S. Patent Application Publication No. US 2005/0187848 A1; hereinafter “Bonissone”), in view of Ho (U.S. Patent Publication No. US 2023/0267542 A1; hereinafter “Ho”), and further in view of Peters (U.S. Patent Publication No. US 2003/0088489 A1; hereinafter “Peters”).
Regarding Claims 2 and 13:
Bonissone teaches:
wherein the instructions further cause operations comprising: determine, via communications of the instruction with a constraints engine whether the modifications satisfy predefined set of constraints defining permissive modifications to the set of assets; (Bonissone, the invention provides a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning (See, Para. 28-29, 70, 247-261, 269-271, 287-288; Fig. 1, 27)).
responsive to determining that at least one modification does not satisfy the predefined set of constraints, automatically adjust the modification in the instruction to satisfy the permissive modifications; and (Bonissone, the first step is to define the independent constraints in the (Return, Risk) space, such as limits on the “minimal amount of return” (B-yield) and the maximum amount of risk (Risk1) that the decision maker is willing to accept. ….. the points satisfying the first set of constraints, but not the last one, are shown by the area 832. Further, the remaining points shown in FIG. 17 do not satisfy any constraints …… eliminate the points that do not satisfy all the previous constraints and zoom in the region with the most promising solutions. ….. impose the last independent constraint, by limiting the second yield metric DWM-yield to be greater or equal than 7.59%. As illustrated in FIG. 18, all the points satisfying this and all previous constraints are shown in the areas 840 (See, Para. 254-261; Fig. 17, 18; Abstract)).
Bonissone and Ho do not specifically teach generate a notification on the user interface screen indicating adjusting the modification.
However, Peters further teaches the following limitation:
generate a notification on the user interface screen indicating adjusting the modification. (Peters, At the time of acceptance by the client, the baseline strategy is captured in a database to be used in the portfolio maintenance module 254. Such dynamic linking of portfolio creation to portfolio measurement and maintenance is a clear improvement over prior art that requires a manual upload of portfolio data into portfolio maintenance and measurement systems. On a periodic basis as established by the advisor (i.e. quarterly or semi-annually), the system will notify the advisor of any deviations of the current portfolio from the baseline strategy, thereby prompting an action request. (See, Para.73; Fig. 2B)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Bonissone and Ho with the features of Peters’ system because the “automated investment advisory system …. incorporates an investment advisory method for users who desire an optimized investment portfolio. The method includes a number of steps, the first step involving the assessment of the user's risk profile. Once the system assesses the user's risk profile, the system maps the user's portfolio holdings into a set of asset classes. Based on a function of the mapped asset classes, the system returns an investment risk classification. Then, the system compares the user's investment risk classification with user's risk profile. Finally, the system recommends portfolio changes which correlate the user's investment risk and risk profile.” (Peters, Para. 20).
Response to Arguments
With respect to the 35 U.S.C. 112(a) rejection of claims 8, 19, and 23, the rejection has been withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 01/02/2026.
Applicant's arguments filed on 01/02/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-23 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “the claim amendments presented provide an improved user interface for a computing system. …… the claim amendments state the user interface screen displays "at least one selectable data point" such that selection thereof generates a visualization of details associated with the selected data point.…… Like in CoreWireless, the claim amendments presented herein are directed to an optimized approach of selectively displaying specific data on a user interface, thus improving upon conventional user interfaces.….. The claims of the present application are not directed to an abstract idea, law of nature, or natural phenomenon. Rather the claim amendments are focused on elements relating to an improved user interface and the selective display of details associated with at least one selectable data point. In CoreWireless, the Federal Circuit held that an improved user interface for electronic devices directed to a particular manner of summarizing and presenting information in electronic devices constitutes patent eligible subject matter. In line with the logic in CoreWireless, Applicant submits the present claims are directed to patent eligible subject matter. Furthermore, claims 1 and 12 have been amended to recite that updates to the user interface occur in "real-time". It is not possible for a human to perform "real-time" updates to a user interface. Only a computer can perform the claimed subject matter in "real-time". Thus, for this additional reason, claims 1 and 12 of the present application are not directed to an abstract idea.”
Examiner respectfully disagrees.
Under Step 2A: Prong I, Examiner respectfully notes that claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio; without significantly more. Unlike CoreWireless, the series of steps recited in claims 1, 12, and 23 describe the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio, which is mitigating risk associated with assets allocation by asset portfolio optimization; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the processing asset data using updated efficient asset metrics to determine expected returns of the assets in an optimized portfolio, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the abstract idea is the evaluation of historical asset allocation data and updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data to generate an optimized portfolio, which is a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Therefore, a concept performed in the human mind is a Mental Process. Moreover, the abstract idea is generation of efficient frontier curves using historical data, which includes the historical returns and volatilities/standard deviations of different assets that could be included in a portfolio; where, the portfolios are plotted on a graphs having a x-axis representing the portfolio risk (standard deviation) and y-axis representing the portfolio return. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. Furthermore, the system limitations, e.g., a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device (Claims 1 and 12) do not necessarily restrict the claim from reciting an abstract idea.
Moreover, as previously discussed, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case and as discussed in the Guidance on Patent Subject Matter Eligibility, it is determined that the additional limitations of technology do not necessarily restrict the claim from reciting an abstract idea. Furthermore, unlike CoreWireless, Examiner respectfully notes that the recited features in the limitations: “a system comprising a computer having at least one processor and a non-transient storage medium storing computer readable instructions, that when executed by said at least one processor of the computer, cause the computer to perform operations comprising: providing a user interface screen for portfolio optimization, the user interface screen comprising: a first curve displayed on a graph of expected returns and risk, the first curve indicating an efficient frontier of a model portfolio, the first curve derived from historical asset allocation data comprising features describing a set of historical assets, the historical asset allocation data provided as a first input from a transaction input device; a data point overlaid on the graph indicating positioning of an initial portfolio relative to the efficient frontier, the initial portfolio retrieved based on a second input of asset allocation data describing a set of assets defined using the same features as the first input, the assets defined in an interactive asset allocation table for including proposed allocations for each of the assets and displaying efficiency metrics for each of the assets predicted, by a prediction engine, from the historical asset allocation data; and a second curve displayed on the graph indicating a modified efficient frontier of feasible adjustments based on a third input of predetermined limits of upper and lower bounds of the features for at least one asset in the asset allocation table; and responsive to receiving an instruction from an input device to modify at least one asset of the set of assets on the user interface screen indicative of generating a test portfolio: generate modifications to the asset allocation table displayed and predict, by the prediction engine, updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data; and update the user interface screen, in real-time, to generate a display of a test data point based on the updated efficiency metrics for the test portfolio overlaid on the graph with the initial portfolio thereby visualizing a comparison of the test portfolio relative to the initial portfolio, the efficient frontier and the modified efficient frontier on a single graph, wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection. Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Applicant argues that “if the claimed subject matter of the present application is directed toward an abstract idea (denied), the idea is integrated into a practical application and, therefore, patent eligible. For example, the claim amendments presented herein are directed at an improvement in the functioning of a computer, namely, an improvement in user interfaces and the manner by which users can interact with a computer….The claim amendments presented herein reflect an improvement in the functioning of a user interface, namely by allowing users to selectively display and visualize details associated with at least one selected data point. This improves the manner by which users can interact with a computer and enables an improved manner by which users can visualize and comprehend displayed data….the systems and methods of the present application do not merely use a computer as a tool to passive present static information. Rather, the at least one selectable data point recited in the claims improve the ability of a user to dynamically interact with a user interface……. To address this challenge, the claim amendments presented herein provide for one or more selectable data points to allow users to interactively select the one or more selectable data points to display details associated therewith. This solution allows users to visualize details associated with a selected data point when necessary. ”
Examiner respectfully disagrees.
Under Step 2A: Prong II, as previously discussed, Examiner respectfully notes that there is no improved technology in simply providing, displaying, presenting, describing, inputting, retrieving, allocating, defining, predicting, modifying, generating, testing, updating, comparing, visualizing, and outputting data (i.e., expected returns and risks, historical assets, historical asset allocation, transaction, portfolio, efficiency metrics, test data, and etc.). The disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites in Claim 1: “a system comprising a computer having at least one processor and a non-transient storage medium storing computer readable instructions, that when executed by said at least one processor of the computer, cause the computer to perform operations comprising: providing a user interface screen for portfolio optimization, the user interface screen comprising: a first curve displayed on a graph of expected returns and risk, the first curve indicating an efficient frontier of a model portfolio, the first curve derived from historical asset allocation data comprising features describing a set of historical assets, the historical asset allocation data provided as a first input from a transaction input device; a data point overlaid on the graph indicating positioning of an initial portfolio relative to the efficient frontier, the initial portfolio retrieved based on a second input of asset allocation data describing a set of assets defined using the same features as the first input, the assets defined in an interactive asset allocation table for including proposed allocations for each of the assets and displaying efficiency metrics for each of the assets predicted, by a prediction engine, from the historical asset allocation data; and a second curve displayed on the graph indicating a modified efficient frontier of feasible adjustments based on a third input of predetermined limits of upper and lower bounds of the features for at least one asset in the asset allocation table; and responsive to receiving an instruction from an input device to modify at least one asset of the set of assets on the user interface screen indicative of generating a test portfolio: generate modifications to the asset allocation table displayed and predict, by the prediction engine, updated efficiency metrics for each of the assets in the test portfolio based on the historical asset allocation data; and update the user interface screen, in real-time, to generate a display of a test data point based on the updated efficiency metrics for the test portfolio overlaid on the graph with the initial portfolio thereby visualizing a comparison of the test portfolio relative to the initial portfolio, the efficient frontier and the modified efficient frontier on a single graph, wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen.” The recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (i.e., expected returns and risks, historical assets, historical asset allocation, transaction, portfolio, efficiency metrics data, etc.), and no technical solution or improvement has been disclosed.
Furthermore, there is no technology/technical improvement as a result of implementing the abstract idea. As previously discussed, the recited limitations in the pending claims simply amount to the abstract idea of processing asset data and updated efficiency metrics for each of the assets to generate and display a comparison between an initial asset portfolio and an optimized asset portfolio. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction and/or mental processes) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive. Moreover, the claims recite steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, these arguments are not persuasive.
Moreover, as previously discussed, Examiner respectfully notes that the claimed invention is simply directed to presenting information on a user interface. Presenting information is abstract in nature and presenting new information does not result in technical improvements to the interface. Thus, presenting new information on a display does not integrate the abstract idea into a practical application. The automatically features simply amounts to mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017). Thus, the automation feature is not sufficient to show an improvement in computer-functionality or technology/technical improvements (see MPEP 2106.05(a)(1)). The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, these arguments are not persuasive.
Additionally, these steps are recited as being performed by a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device (Claim 1), which are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). The claims recite: a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device, (Claim 1), which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong 1, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device,” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. The claims merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, Claims 1 and 12 do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.
Applicant argues that “it is not necessary to proceed to this step of the patent eligibility test. The present claims are eligible under either prong of Step 2A. Regardless, by virtue of the claim amendments presented herein, the inventive concept of the present claims incorporate at least the concept of an improved user interface for selectively displaying details associated with one or more selected data points. Again, by virtue of the ruling in CoreWireless, this inventive concept is significantly more than an abstract idea.”
Examiner respectfully disagrees.
Under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device (Claim 1) are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment. Furthermore, as explained above with respect to Step 2A, Prong II, the additional elements: a computer, at least one processor, non-transient storage medium, user interface screen, transaction input device, prediction engine, and input device (Claim 1), are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-28.
With respect to the rejection of claims 1-23 under 35 U.S.C. 103, Applicant argues that “the claim amendments presented herein conveniently provide a substantial improvement in the manner by which users can interact with the claimed user interface to selectively display details associated with at least one selectable data point. In turn, this allows users to choose whether details associated with a selected data point should be viewable, resulting in a more interactive and effective way of visualizing and analyzing data. Neither Bonissone, Ho, nor Peters teach or suggest the features of the Applicant's claims, including a user interface comprising at least one selectable data point such that selection thereof generates a visualization of details associated with the selected data point on the user interface screen….. For these at least these reasons, Applicant respectfully requests withdrawal of the rejection under 35U.S.C.§103.”
Examiner respectfully disagrees.
With respect to Applicant's arguments regarding 35 U.S.C. 103 rejection of Claims 1- 23, Examiner respectfully notes, as discussed above in the 103 rejection, Bonissone in view of Ho teaches: “update the user interface screen, in real-time, to generate a display of a test data point based on the updated efficiency metrics for the test portfolio overlaid on the graph with the initial portfolio thereby visualizing a comparison of the test portfolio relative to the initial portfolio, the efficient frontier and the modified efficient frontier on a single graph” (See, Bonissone: Para. 1-8, 28-30, 104-114, 146-152, 244-250, 261-271, Fig. 1, 2, 5, 9, 14-22, 24-26, 30, 37-38; Abstract). Furthermore, Bonissone in view of Ho teaches “wherein, the user interface screen displays at least one selectable data point such that selection of the at least one selectable data point generates a visualization of details associated with the selected data point on the user interface screen” (See, Bonissone: Para.1-8, 28-30, 104-114, 244-250, 261-271, Fig. 1, 2, 9, 14-22, 24-26; Abstract). Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 103 rejection of claims 1-28.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Markov (U.S. Patent Application Publication No. US 2013/0275341-A1) “System and method for visualization of results of multi-criteria financial optimizations”
Wen (U.S. Patent No. US 8,321,262-B1) “Method and system for generating pricing recommendations”
Mun (U.S. Patent Application Publication No. US 2022/0068094-A1) “Project economics analysis tool”
Schlossberg (U.S. Patent Application Publication No. US 2014/0351166-A1) “Streamlined portfolio allocation method, apparatus, and computer-readable medium”
Walia (U.S. Patent Application Publication No. US 2022/0108401-A1) “Machine learning portfolio simulating and optimizing apparatuses, methods and systems”
Zhu (U.S. Patent Application Publication No. US 2021/0027379-A1) “Generative network based probabilistic portfolio management”
Damschroder (U.S. Patent Application Publication No. US 2003/0088492-A1) “Method and apparatus for creating and managing a visual representation of a portfolio and determining an efficient allocation”
Caputo (U.S. Patent Application Publication No. US 20220383410-A1) “Differential evolution algorithm to allocate resources”
Labe, Jr. (U.S. Patent Application Publication No. US 2002/0091605-A1) “Asset allocation optimizer”
Salter (U.S. Patent Application Publication No. US 2014/0164290-A1) “Database for risk data processing”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693