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
Claims 1-20 are pending in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method claim 8 as the claim that represents the claimed invention for analysis and is similar to independent system claim 1 and product claim 15. Claim 8 recites the limitations of creating a customized (meeting customer’s finance and goals) portfolio by trained algorithm based on historical data. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Retrieve (from many customers) (i) historical financial data, (ii) historical value parameter data (=historical customer’s goals), and (iii) historical portfolio data; using historical data, training model to predict a customized portfolio; storing model in memory; receiving customer financial data and customer value parameter data; and predicting customized portfolio, – specifically, the claim recites: “retrieve… historical financial data, historical value parameters data, and historical portfolio data associated with a plurality of customers, wherein the historical value parameters comprises historical customer goal data; train a PC model relating the historical financial data to the historical portfolio data and the historical value parameters data, wherein: the PC model is configured to predict a customized portfolio based upon user financial data and user value parameters data… training a neural network using a supervised learning algorithm and the historical financial data to the historical portfolio data and the historical value parameters data as training data store the trained PC model… receive customer financial data and customer value parameter data associated with a customer, wherein the customer value parameter data comprises customer goal data associated with the customer; and predict a customized allocation portfolio for the customer… based upon the received customer financial data and customer value parameter data”, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “a system”, “a portfolio completion (PC) computing device”, “”a processor, “a memory device”, “a PC model”, “a neural network”, and “a supervised learning algorithm”, in claim 8; the additional technical element of “at least one non-transitory computer-readable storage medium”, and “computer-executable instructions” in claim 15, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 1 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a system, a portfolio completion (PC) computing device, and a processor; a storage unit such as a memory device, and at least one non-transitory computer-readable storage medium; and software module and algorithm such as a PC model, a neural network, a supervised learning algorithm, and computer-executable instructions.
The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1, 8, and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 8, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 8, and 15 and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of update the historical financial data to include the current customer financial data, update the historical portfolio data to include the customized allocation portfolio for the customer, and update the historical value parameters data to include the current customer value parameter data; and re-train the trained PC model using the updated historical financial data, the updated historical portfolio data, and the updated historical value parameters data, which further narrows the abstract idea. Note that the technical element “the trained PC model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 discloses the limitation of the at least one processor is further configured to: transmit the predicted customized allocation portfolio to at least one third party, which further narrows the abstract idea. Note that the technical element “the at least one processor” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 discloses the limitation of the at least one third party is at least one of a bank, a financial institution, a financial advisor, and a credit card company, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of the at least one processor is further configured to: maintain a dataset of financial advisors, the dataset including an identifier and personal data for each financial advisor, which further narrows the abstract idea. Note that the technical element “the at least one processor” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the at least one processor is further configured to: receive a request from customer to be matched with financial advisor, the request including one or more selections; filter the dataset according to one or more selections in request; and provide the filtered data to the customer, which further narrows the abstract idea. Note that the technical element “the at least one processor” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 discloses the limitation of the customized allocation portfolio comprises an investment strategy for one or more assets of the customer, which further narrows the abstract idea.
Dependent claim 9 discloses the limitation of updating the historical financial data to include the current customer financial data, update the historical portfolio data to include the customized allocation portfolio for the customer, and update the historical value parameters data to include the current customer value parameter data; and re-training the trained PC model using the updated historical financial data, the updated historical portfolio data, and the updated historical value parameters data, which further narrows the abstract idea. Note that the technical element “the trained PC model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 discloses the limitation of transmitting the predicted customized allocation portfolio to at least one third party, which further narrows the abstract idea.
Dependent claim 11 discloses the limitation of the at least one third party is at least one of a bank, a financial institution, a financial advisor, and a credit card company, which further narrows the abstract idea.
Dependent claim 12 discloses the limitation of maintaining a dataset of financial advisors, the dataset including an identifier and personal data for each financial advisor, which further narrows the abstract idea. Note that the technical element “a dataset” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of receiving a request from customer to be matched with financial advisor, the request including one or more selections; filtering the dataset according to one or more selections in request; and providing the filtered data to the customer, which further narrows the abstract idea.
Dependent claim 14 discloses the limitation of the customized allocation portfolio comprises an investment strategy for one or more assets of the customer, which further narrows the abstract idea.
Dependent claim 16 discloses the limitation of the instructions further cause the processor to: update the historical financial data to include the current customer financial data, update the historical portfolio data to include the customized allocation portfolio for the customer, and update the historical value parameters data to include the current customer value parameter data; and re-train the trained PC model using the updated historical financial data, the updated historical portfolio data, and the updated historical value parameters data, which further narrows the abstract idea. Note that the technical elements “the processor” and “the trained PC model”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 17 discloses the limitation of the instructions further cause the processor to: transmit the predicted customized allocation portfolio to at least one third party, which further narrows the abstract idea. Note that the technical element “the processor” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 18 discloses the limitation of wherein the at least one third party is at least one of a bank, a financial institution, a financial advisor, and a credit card company, which further narrows the abstract idea.
Dependent claim 19 discloses the limitation of the instructions further cause the processor to: maintain a dataset of financial advisors, the dataset including an identifier and personal data for each financial advisor, which further narrows the abstract idea. Note that the technical elements “the processor” and “a dataset”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Dependent claim 20 discloses the limitation of the instructions further cause the processor to: receive a request from customer to be matched with financial advisor, the request including one or more selections; filter the dataset according to one or more selections in request; and provide the filtered data to the customer, which further narrows the abstract idea. Note that the technical elements “the instructions”, “the processor”, and “the dataset”, are recited at a high level of generality. They do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Thus, the dependent claims 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, the dependent claims are directed to an abstract idea. Thus, the claims 1-20 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims -4, 7-11, and 14-18 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Marshall (20220122182) in view of Chatman (10410294).
Regarding claim 1, Marshall discloses
a portfolio completion (PC) computing device comprising at least one processor in communication with a memory device, the at least one processor configured to
(“[0038] The network interface 120 can provide an interface for the asset management system 100 to connect to a network 125... Through the network 125, the asset management system 100 can interact with a plurality of end user computers 130 and a plurality of financial databases... larger computer system such as a server or server network. The network interface 120 may take any suitable form for carrying out network interface functions”).
(“[0039] The processor 110 may comprise one or more processors such as general-purpose processors (e.g., a single-core or multi-core microprocessor), special-purpose processors (e.g., an application-specific integrated circuit or digital-signal processor)”).
(“[0040] The memory 115 may comprise one or more non-transitory computer-readable storage mediums, such as volatile storage mediums (e.g., random access memory, registers, and/or cache)”).
retrieve, from the memory device, historical financial data, historical value parameters data, and historical portfolio data associated with a plurality of customers
(“[0084] The asset management tool 500 may input certain parameters into the machine learning software to create the representative liability profile used for comparison. These inputs include historical market data and analytics 1405 to train and improve the machine learning software for rebalance decisions. Inputs also include representative and actual liability profiles 1400 of the end users of accounts stored within the asset management tool 500. Fund holding information 1410 for third party sources can also be inputted to train and improve the machine learning software for rebalancing decisions”).
(“[0041] The memory 115 may also store user input logic 145, which can take the form of a plurality of instructions configured for execution by processor 110 for receiving, processing, and delivering input data to the optimization logic 140 to aid in the calculation, timing, and determination of the flow of identified assets”).
wherein the historical value parameters comprises historical customer goal data
(“[0049] For the creation of a non-uniform liability profile 400, an account user 405 indicates one or more of payout amounts 410 and payout dates 415. After such information is inputted into the asset management system 100, the asset management tool can then calculate the initial investment amount 420, which can be automatically calculated for the account user 405. Alternatively, the account user 405 can indicate the investment amount 420 and a single payout date 415. In that instance, the calculated payout amount 410 can be automatically populated for the account user 405. This allows an account user 405 to approach the problem in a way best suited to the situation depending upon whether the account user 405 has a set investment or wants to know the required investment amount given the desired payout(s)”).
train a PC model relating the historical financial data to the historical portfolio data and the historical value parameters data, wherein the PC model is configured to predict a customized portfolio based upon user financial data and user value parameters data
(“[0059] Machine learning may be used to test and train the rebalance model used by the calculation engine 545”).
(“[0084] The asset management tool 500 may input certain parameters into the machine learning software to create the representative liability profile used for comparison. These inputs include historical market data and analytics 1405 to train and improve the machine learning software for rebalance decisions. Inputs also include representative and actual liability profiles 1400 of the end users of accounts stored within the asset management tool 500. Fund holding information 1410 for third party sources can also be inputted to train and improve the machine learning software for rebalancing decisions... The representative liability profile is compared to the actual liability profile of the end user of the account and the rebalance model may execute the redistribution of investment assets through the investment allocation optimizer 1440”).
See also Fig. 14 Item 1400, 1405, and 1410
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store the trained PC model in the memory device
(“[0041] Memory 115 may store software programs or instructions that are executed by processor during operation of the system. For example, the memory 115 may include optimization logic 140, which can take the form of a plurality of instructions configured for execution by processor 110 for executing purchases and sells of identified assets in communication with the asset management system 100”).
receive customer financial data and customer value parameter data associated with a customer, wherein the customer value parameter data comprises customer goal data associated with the customer
(“[0041] The memory 115 may also store user input logic 145, which can take the form of a plurality of instructions configured for execution by processor 110 for receiving, processing, and delivering input data to the optimization logic 140 to aid in the calculation, timing, and determination of the flow of identified assets”).
(“[0056] A liability profile 535 is present within the asset management tool 500. The asset management tool 500 can create a custom liability profile 535 to the specification of the end user. As discussed above, certain inputs may be inputted into the asset management system 100 by the end user to be used by the asset management tool 500 to create the liability profile 535”).
(“[0049] For the creation of a non-uniform liability profile 400, an account user 405 indicates one or more of payout amounts 410 and payout dates 415. After such information is inputted into the asset management system 100, the asset management tool can then calculate the initial investment amount 420, which can be automatically calculated for the account user 405. Alternatively, the account user 405 can indicate the investment amount 420 and a single payout date 415. In that instance, the calculated payout amount 410 can be automatically populated for the account user 405. This allows an account user 405 to approach the problem in a way best suited to the situation depending upon whether the account user 405 has a set investment or wants to know the required investment amount given the desired payout(s)”).
predict a customized allocation portfolio for the customer using the trained PC model based upon the received customer financial data and customer value parameter data
(“[0084] The representative liability profile is compared to the actual liability profile of the end user of the account and the rebalance model may execute the redistribution of investment assets through the investment allocation optimizer 1440... Afterwards, the rebalance engine 1445 finishes up recommendations in the management of the actual liability profile, the results are outputted to the end user for viewing on the graphical user interface”).
(“[0056] Once created, the asset management tool 500 selects specific asset holdings that will make up the Individual Spending Account investment portfolio”).
See also Fig. 14 Item 1400, 1405, and 1410
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Marshall does not disclose, however, Chatman teaches
training the PC model comprises training a neural network using a supervised learning algorithm and the historical financial data to the historical portfolio data and the historical value parameters data as training data
(“C3 L35-45 (9) the plan may include a personal budget, a work budget, a financial plan, a retirement plan, a savings plan, a spending plan, and/or the like. In some implementations, the event may include a recurring time period (e.g., daily, weekly, monthly, and/or the like), a vacation, a business trip, a transaction, college, and/or the like”). and
(“C5 L6-28 (17) Additionally, or alternatively, the plan platform may train the machine learning model using a supervised training procedure that includes receiving input to the machine learning model from a subject matter expert, which may reduce an amount of time, an amount of processing resources, and/or the like to train the machine learning model of activity automatability… the plan platform may use one or more other model training techniques, such as a neural network technique… the plan platform may perform an artificial neural network processing technique… to perform pattern recognition with regard to patterns of the historical plan information. In this case, using the artificial neural network processing technique may improve an accuracy of the trained machine learning model generated by the plan platform by being more robust to noisy, imprecise, or incomplete data, and by enabling the plan platform to detect patterns and/or trends undetectable to human analysts or systems using less complex techniques”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Marshall to include training the PC model comprises training a neural network using a supervised learning algorithm and the historical financial data to the historical portfolio data and the historical value parameters data as training data as taught by Chatman to use software/algorithm that were modeled using supervised training and historical information to improve an accuracy of the trained machine learning model generated by the plan platform by being more robust to noisy, imprecise, or incomplete data, and by enabling the plan platform to detect patterns. See “C5 L6-28 (17) Additionally, or alternatively, the plan platform may train the machine learning model using a supervised training procedure that includes receiving input to the machine learning model from a subject matter expert, which may reduce an amount of time, an amount of processing resources, and/or the like to train the machine learning model of activity automatability relative to an unsupervised training procedure. In some implementations, the plan platform may use one or more other model training techniques, such as a neural network technique, a latent semantic indexing technique, and/or the like. For example, the plan platform may perform an artificial neural network processing technique (e.g., using a two-layer feedforward neural network architecture, a three-layer feedforward neural network architecture, and/or the like) to perform pattern recognition with regard to patterns of the historical plan information. In this case, using the artificial neural network processing technique may improve an accuracy of the trained machine learning model generated by the plan platform by being more robust to noisy, imprecise, or incomplete data, and by enabling the plan platform to detect patterns and/or trends undetectable to human analysts or systems using less complex techniques”.
Regarding claim 2, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
Marshall further discloses
the at least one processor is further configured to: update the historical financial data to include the current customer financial data, update the historical portfolio data to include the customized allocation portfolio for the customer, and update the historical value parameters data to include the current customer value parameter data; and re-train the trained PC model using the updated historical financial data, the updated historical portfolio data, and the updated historical value parameters data
(“[0084] The representative liability profile is compared to the actual liability profile of the end user of the account and the rebalance model may execute the redistribution of investment assets through the investment allocation optimizer 1440... Afterwards, the rebalance engine 1445 finishes up recommendations in the management of the actual liability profile... The predicted results 1450 from the rebalance engine 1445 itself can also be used as an input to the machine learning software 1425 given that the specific sets of parameters used in the lasted iteration may improve the machine learning software 1425”).
(“[0061] The asset management tool 500 will leverage machine-learning techniques to improve the accuracy of scaling factors over time. The asset management tool 500 will utilize default risk estimates provided by third-party data providers and apply additional adjustments based on a trained predictive model to improve accuracy. Ex-ante predicted default rates for specific issuers, classifications, categorizations, and other attributes of debt securities will be compared against ex-post actual default rates to train the predictive model. Improvement in the model over time will result in a reduction in the range of outcomes and thus help to minimize the surplus investment necessary to ensure all Individual Spending Plan (ISP) distributions will occur with high confidence”).
Regarding claim 3, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
Marshall further discloses
wherein the at least one processor is further configured to: transmit the predicted customized allocation portfolio to at least one third party
(“[0100] The custodian interface 2810 allows interaction and data exchange with custodial entities, which may include banks or other similar institutions. The investment manager interface 2815 allows interaction and data exchange with advisory platforms”).
Regarding claim 4, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
Marshall further discloses
wherein the at least one third party is at least one of a bank, a financial institution, a financial advisor, and a credit card company
(“[0100] The custodian interface 2810 allows interaction and data exchange with custodial entities, which may include banks or other similar institutions. The investment manager interface 2815 allows interaction and data exchange with advisory platforms”).
Regarding claim 7, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
Marshall further discloses
wherein the customized allocation portfolio comprises an investment strategy for one or more assets of the customer
See Fig. 14, item 1440
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Claim 8 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 9 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 10 is rejected using the same rationale that was used for the rejection of claim 3.
Claim 11 is rejected using the same rationale that was used for the rejection of claim 10.
Claim 14 is rejected using the same rationale that was used for the rejection of claim 7.
Claim 15 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 16 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 17 is rejected using the same rationale that was used for the rejection of claim 3.
Claim 18 is rejected using the same rationale that was used for the rejection of claim 17.
Claims 5-6, 12-13, and 19-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Marshall in view of Chatman further in view of and Rymer (7483847).
Regarding claim 5, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
The combination of Marshall and Chatman do not disclose but Rymer does teaches
wherein the at least one processor is further configured to: maintain a dataset of financial advisors, the dataset including an identifier and personal data for each financial advisor
(C8, L51-65 “(28) FIG. 4 shows the process by which referral fees are automatically assessed for referred accounts and provides detail regarding the on-going fee sharing agreement. At step 400 the processor system 212 operates to analyze data in the database system 202, and to identify referred client of the advisor financial accounts. Once the referred financial accounts have been identified by the processor system 212, the processor system determines if the advisor customer has entered into an electronic funds transfer agreement, which allows for electronic transfer of funds out of the referred customers financial account to pay for advisor management fees 410. If the customer has authorized electronic transfer of funds then the automated method can proceed. If the customer has not authorized electronic transfer of funds then many of the steps below are still applicable for determining the amount of the on-going referral, but the referral fee would have to be processed on a special handling basis 415. Specifically, the referral fee for the account would have to be manually billed to the advisor, or electronically transferred from other fees which the advisor is due. In the normal course of operation, the broker dealer and the advisor will encourage advisor customers to authorize electronic transfer of funds to pay management fees, which reduces the cost of delivering services to the customers”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Marshall to include wherein the at least one processor is further configured to: maintain a dataset of financial advisors, the dataset including an identifier and personal data for each financial advisor as taught by Rymer to create a database of potential financial advisors with various parameters that identify/match customers’ goals and advisor effective services – see (C6, L8-18) Another part of the method herein is identifying an independent financial advisor 310, who is likely to be a suitable candidate for a referral program. Objective criteria has been identified to aid the process of selecting advisors for the referral program, where the goal is to select advisors who are most likely to provide effective service to investors.
Regarding claim 8, the combination of Marshall and Chatman, as shown in the rejection above, discloses the limitations of claim 1.
The combination of Marshall and Chatman do not disclose but Rymer does teaches
the at least one processor is further configured to: receive a request from customer to be matched with financial advisor, the request including one or more selections; filter the dataset according to one or more selections in request; and provide the filtered data to the customer
(C7, L28-33 “(21) Ideally, the referral process will be such that a broker dealer can refer a qualified investor to a single financial advisor, and this referral will result in a successful match between the customer and the advisor. In some cases, however, a customer may be referred to a second financial advisor so as to increase the investor's options in selecting a financial advisor”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Marshall to include the at least one processor is further configured to: receive a request from customer to be matched with financial advisor, the request including one or more selections; filter the dataset according to one or more selections in request; and provide the filtered data to the customer as taught by Rymer to create a database of potential financial advisors with various parameters that identify/match customers’ goals and advisor effective services – see (C6, L8-18) Another part of the method herein is identifying an independent financial advisor 310, who is likely to be a suitable candidate for a referral program. Objective criteria has been identified to aid the process of selecting advisors for the referral program, where the goal is to select advisors who are most likely to provide effective service to investors.
Claim 12 is rejected using the same rationale that was used for the rejection of claim 5.
Claim 13 is rejected using the same rationale that was used for the rejection of claim 6.
Claim 19 is rejected using the same rationale that was used for the rejection of claim 5.
Claim 20 is rejected using the same rationale that was used for the rejection of claim 6.
Response to Arguments
Applicant's arguments filed 11/21/25 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“35 U.S.C. § 101… Claim 1, as amended, recites… (reciting amended claim 1)… claim 1 do not fall under the category of "certain methods of organizing human activity." Particularly, they do not fall under the sub-grouping of "fundamental economic principles or practices." Applicant respectfully notes that Claim 1 does not recite "managing risk." The output of claim 1 is "a customized allocation portfolio," which is based on financial data and value parameter data,”
the examiner respectfully disagrees. The primary purpose of customizing customer’s portfolio is to manage the risk of the customer not achieving her investment goal. The claim is clear that the algorithm used to predict a customized portfolio is based in part on “historical customer goal data”. See claim 1. It is clear that the claims recites a fundamental economic practice, directed specifically to mitigating risk.
In response to applicant's argument that:
“the limitations of "train a PC model relating the historical financial data to the historical portfolio data and the historical value parameters data, wherein: the PC model is configured to predict a customized portfolio based upon user financial data and user value parameters data; and training the PC model comprises training a neural network using a supervised learning algorithm and the historical financial data to the historical portfolio data and the historical value parameters data as training data; store the trained PC model in the memory device" do not cover fundamental economic practices. Instead, they cover a technically complex method of training a neural network using a supervised learning algorithm. This model is then used to predict a customized allocation portfolio. Applicant fails to see how the training of a neural network is a fundamental economic practice,”
the examiner respectfully disagrees. The technical elements “a PC model”, “a neural network” and “a supervised learning algorithm”, are recited at such a high level of generality that they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The applicant is not improving machine learning technology. As stated above: “these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality.” See Claim Rejections - 35 USC § 101 above.
In response to applicant's argument that:
“the limitation of "train a PC model relating the historical financial data to the historical portfolio data and the historical value parameters data, wherein: the PC model is configured to predict a customized portfolio based upon user financial data and user value parameters data; and training the PC model comprises training a neural network using a supervised learning algorithm and the historical financial data to the historical portfolio data and the historical value parameters data as training data" represents "a specific means or method that improves the relevant technology under McRO. This is the case at least because the neural network, trained using specific training data, and using a supervised algorithm represents a specific, limited means of improving the functioning of a computer by allowing it to create personalized portfolios on demand, without user intervention,”
the examiner respectfully disagrees. The claims state that particular relevant data (e.g. “historical portfolio data”) is used to train/refine self-learning customized portfolio model; this is similar to saying electricity is used to run the electric fan. The examiner notes that the applicant is not improving the machine learning technology/device. Rather the applicant is using machine learning in a business process – i.e., when data is available and received, the software updates its equations/algorithms. The machine learning appears to be recited more in a descriptive way of how the machine learning are trained/created, as opposed to how machine learning are performing actions in the body of the claim. Thus, these elements are recited as almost insignificant extra solution activity. They are operating as any generic computer would operate, recited at a high level of generality and are considered tools for performing the abstract idea.
In response to applicant's argument that:
“the Office has failed to consider claim 1 as a whole in its Step 2A, prong 2 analysis,”
the examiner respectfully disagrees. In examining the application “as a whole”, the inquiry is “whether the claims are directed to an improvement to computer functionality versus being directed to an abstract idea”. Here, there is no improvement to any computer/technological functionality. In TLI Communication LLC. v. AV Automotive, L.L.C., the court made clear that claims directed to “the use of conventional or generic technology in a nascent but well-known environment, without any claim that the invention reflects an inventive solution to any problem” are not patent eligible. See Claim Rejections - 35 USC § 101 above.
In response to applicant's argument that:
“"significantly more"… claim 1 recites limitations amounting to an inventive concept and to significantly more than the abstract idea to which claim 1 is allegedly drawn,”
the examiner respectfully disagrees. The examiner notes that the applicant has a tendency to cite procedural ideas (e.g. using relevant data to train an algorithm) as technological advancements. They are not the same thing. Procedural ideas are not technological advancements. They are ideas for carrying out business activities. As stated above, the technical elements are recited at such a high level of generality that they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.
In response to applicant's argument that:
“35 U.S.C. § 102(a)(2)… Marshall does not disclose receiving historical value parameters data and using historical value parameters data to train a PC model. For example, for value parameters data, the Office maps to "liability profiles" of Marshall. However, the liability profiles of Marshall do not disclose the "value parameters data" of the present application. In the present application, "data associated with a customer's goals, risk tolerance, behaviors, and desires" is given as examples of "value parameters data." Specification, Paragraph [0041]. However, Marshall described "liability profile" as "simply the calculated/directed payouts from an Individual Spending Account (ISA)." Marshall, paragraph [0009]. Applicant respectfully asserts, therefore, that Marshall does not describe receiving or using for training value parameters data,”
the examiner respectfully disagrees. Marshall clearly teaches customer's goals and desires. See “[0049] the asset management tool can then calculate the initial investment amount 420, which can be automatically calculated for the account user 405. Alternatively, the account user 405 can indicate the investment amount 420 and a single payout date 415. In that instance, the calculated payout amount 410 can be automatically populated for the account user 405. This allows an account user 405 to approach the problem in a way best suited to the situation depending upon whether the account user 405 has a set investment or wants to know the required investment amount given the desired payout.”
In response to applicant's argument that:
“Marshall does not teach, suggest or motivate "retrieve, from the memory device, historical financial data, historical value parameters data, and historical portfolio data associated with a plurality of customers, wherein the historical value parameters comprises historical customer goal data" or receive customer financial data and customer value parameter data associated with a customer, wherein the customer value parameter data comprises customer goal data associated with the customer" ( emphasis added). This is the case at least because any historical value parameters or value parameter data allegedly disclosed by Marshall does not include customer goal data (emphasis original’s),”
the examiner respectfully disagrees. For the purpose of being responsive, the examiner will repeat – at the risk of being unnecessarily repetitive – Marshall clearly teaches customer's goals and desires. See “[0049] the asset management tool can then calculate the initial investment amount 420, which can be automatically calculated for the account user 405. Alternatively, the account user 405 can indicate the investment amount 420 and a single payout date 415. In that instance, the calculated payout amount 410 can be automatically populated for the account user 405. This allows an account user 405 to approach the problem in a way best suited to the situation depending upon whether the account user 405 has a set investment or wants to know the required investment amount given the desired payout.”
In response to applicant's argument that:
“The Applicant respectfully asserts that Rymer does not teach, suggest, or motivate "retrieve, from the memory device, historical financial data, historical value parameters data, and historical portfolio data associated with a plurality of customers, wherein the historical value parameters comprises historical customer goal data" or receive customer financial data and customer value parameter data associated with a customer, wherein the customer value parameter data comprises customer goal data associated with the customer",”
The argument is not found persuasive because the applicant is arguing against the references individually. Rymer is considered in view of Marshall. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413,208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091,231 USPQ 375 (Fed. Cir. 1986).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MARK H GAW/Examiner, Art Unit 3693