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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/25 has been entered.
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 8/28/25, Applicant, on 12/29/25, amended claims. Claims 1-3, 5-7, 10-12, 14-17, and 19-25 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1-3, 5-7, 10-12, 14-17, and 19-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 now recites “receiving, at a computing system, input categories from a user device; determining, by a second predictive algorithm different from the first predictive algorithm, an appropriate analytic calculator based on the input categories, wherein the appropriate analytic calculator comprises the first predictive algorithm”… “wherein each of the attributes fit within the input categories.” Examiner is unable to find support for “categories” or the “attributes fit within the input categories.” It is unclear what the support of the claim limitations may be. Applicant has not identified any particular passage, so it is unclear what may be alluded to here, so Examiner is not sure what to suggest. Applicant is welcome to explain the support, amend the claim, or remove the limitations. Examiner recommends removing the limitations with new matter issues. Examiner’s best guess is to use [0033-0040] as published and FIG. 2D, and interprets the claim, for purposes of applying prior art only, as reciting “receiving, at a computing system, input a type of financial transaction and a purpose of the financial transaction from a user device; determining, by a second predictive algorithm different from the first predictive algorithm, an appropriate analytic calculator based on the input type and purpose of the financial transaction, wherein the appropriate analytic calculator comprises the first predictive algorithm”… “wherein each of the attributes relate to the type and purpose of the financial transaction” [based on [0060] as published “ the analytic calculator 202 that is presented has specific variables related to that type of transaction (see FIG. 2B).”].
Independent claims 10 and 19 are rejected for the same reasons.
Dependent claims 2-3, 5-7, 11-12, 14-17, and 20-25 are rejected for the same reasons.
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 1-3, 5-7, 10-12, 14-17, and 19-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
“A method of forecasting commercial financial transactions, the method comprising:
training a first predictive algorithm on historical data from past financial transactions, industry data obtained from at least one third party and portfolio data from one or more users;
receiving, …, input categories from a user …;
determining, by a second predictive algorithm different from the first predictive algorithm, an appropriate analytic calculator based on the input categories, wherein the appropriate analytic calculator comprises the first predictive algorithm;
receiving, …, a series of independent variables that represent attributes of a first financial transaction for a predetermined location, wherein each of the attributes fit within the input categories;
scaling and normalizing the series of independent variables;
assembling the scaled and normalized series of independent variables;
applying weightings to the assembled scaled and normalized series of independent variables ;
predicting, by the first predictive algorithm, a first value for the first financial transaction for the predetermined location by entering the weighted, assembled, scaled, and normalized series of independent variables into the predictive algorithm;
predicting, by the first predictive algorithm, a second value for a second financial transaction for the predetermined location using the first predictive algorithm, wherein the second financial transaction is different from the first financial transaction;
comparing the first value for the first financial transaction to the second value for the second financial transaction;
coordinating displaying … the comparison between the first financial transaction and the second financial transaction.”
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (commercial or legal interactions –contracts or marketing or fundamental economic principles (determining financial values in future)) and/or “mathematical relationships” as here we training business prediction algorithm from past financial transactions, industry data, and portfolio data (e.g. leases/loans in [0078 as published), receiving input categories, determining by a “second predictive algorithm”, an appropriate analytic calculator with financial transactions (this appears to be FIG. 2 and [0033 as published “The analytic calculator of the present disclosure provides guidance to the user on modeling any given scenario and then presents preset options (e.g., an analytic calculator library (AC library)) based on algorithms and mathematical order of operations “ and [0074] as published); receiving variables that represent attributes of a first financial transaction fitting input categories (claim 3 says they can represent various things such as location, type of center, comparable assets), the variables are scaled and normalized (e.g. [0087] as published – scale for “excellent” can be a score of 5; score for “above average” can be a score of 4); [0101] as published – transforming the series of independent variables, such that the features are within a specific range (scaling)); weighting different variables (e.g. [0096] as published gives example of increasing or decreasing influence variable has on the prediction); weightings apply, to then predict a value for the financial transaction, are forecasting a first value for a financial transaction using first predictive algorithm and a second value ( [0104] as published a financial value). See also August 4, 2025 Kim Memo page 3, where “training, by the computer, the ANN based on the input data and a selected training algorithm… a backpropagation algorithm and a gradient descent algorithm” requires specific mathematical calculations by name, as here we have explicit “training a first predictive algorithm from past financial transactions, industry data and portfolio data… predicting by first predictive algorithm a first value by entering weighted, assembled, scaled, and normalized series of independent variables.” See also Updated July 2024 Subject Matter Eligibility Update, Example 47, claim 2; Example 48, claim 1 – series of mathematical calculations from a mixed speech signal, includes the “training”; here we use general training such as a “predictive algorithm” based on financial transactions in [0095-0096] as published. Accordingly, claim 1 is directed to an abstract idea because it is doing a series of mathematical calculations to make a financial prediction.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. In particular, the claim 1 recites additional elements that are:
A method of forecasting commercial financial transactions, comprising:
training a first predictive algorithm on historical data from past financial transactions, industry data obtained from at least one third party and portfolio data from one or more users [presumably, though not recited at this time, the computer is performing the “training” here; Examiner still suggests reciting each step be performed by a computer; though that in and of itself still will not overcome the 101 rejection, it is a recommended first step here];
receiving, at a computing system, input categories from a user device;
…
receiving, at the computing system, a series of independent variables that represent attributes of a specific financial transaction wherein each of the attributes fit within the input categories;
…
coordinating displaying a graphical user interface (GUI) comprising the comparison between the first financial transaction and the second financial transaction
Examiner notes the active steps right now are “by a predictive algorithm”, which is the abstract idea. As initial suggestion, Examiner suggests reciting that the computer positively perform each step, as opposed to having the computer just “receive” information and “display” it at end. Nonetheless, MPEP 2106.05f applies –the claim involves a computer [even once it is amended to perform each step], and the claim is considered “apply it [the abstract idea] on a computer”; merely uses a computer as a tool to perform an abstract idea; MPEP 2106.05h “field of use” for “training” and “GUI” display that is presumably “by a computer”.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. The claim is directed to an abstract idea.
Step 2B in MPEP 2106.05 - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computing system, where “training” occurs to make a financial prediction; and displaying “a GUI” is treated as MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235); and MPEP 2106.05h (field of use)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent claim 10 is directed to an apparatus at step 1, which is a statutory category. Claim 10 recites similar limitations as claim 1 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. The additional limitations, of processor, memory including instructions causing a computer to perform functions, are all part of “apply it on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2b. The claim is not patent eligible.
Independent claim 19 is directed to an apparatus at step 1, which is a statutory category. Claim 19 recites similar limitations as claim 1 and claim 10 and is rejected for the same reasons at step 2a, prong one, 2a, prong 2, and step 2b. The additional limitations, of “computing system”, are part of “apply it on a computer” (MPEP 2106.05f) at step 2a, prong 2 and step 2b. The claim is not patent eligible.
Claim 2, 11, 20 narrow the abstract idea by giving various mathematical algorithms that are used.
Claims 3, 12, 21 narrow the abstract idea by stating descriptions of what variables represent (e.g. location, asset, lease, landlord, etc).
Claims 5, 14, 22 narrow the abstract idea by stating a mathematical operation of making a composite variable from combining other variables.
Claims 6, 15, 23 narrow the abstract idea by stating the mathematical operations are repeated for different transactions/portfolios.
Claims 7, 16, 24 narrow the abstract idea by stating net present value is predicted for a plurality of scenarios.
Claims 17, 25 narrows the abstract idea by stating that the score is based on a “comparable”/similar financial transaction.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-7, 10-12, 15-17, 19-21, and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Packes (US 2015/0317640) and Catalano (US 10,445,843) and Lyons (US 20110184884).
Concerning claim 1, Packes discloses:
A method of forecasting commercial financial transactions, the method (Packes –See par 18-20 - REP (“Real Estate Evaluating Platform”) may be utilized to predict pricing of real estate or predict value of buildings, neighborhoods, whole market, commercial spaces, or offices spaces), comprising:
training a first predictive algorithm on historical data from past financial transactions, industry data obtained from at least one third party… (Packes – see par 35 - reducing property value variation associated with time (e.g., inflation, housing market trends, etc.) in historical data may lead to better results when training and/or retraining a neural network to estimate property value based on differences in attribute values. Historical data may be obtained for a specified estimation time frame (e.g., the last year) for properties that have data regarding property values during the estimation time frame (e.g., properties that were sold during the last year and have a selling price, properties whose property values were evaluated during a previous preparation iteration, etc.). The obtained historical data may be sliced for each estimation time period (e.g., for each month during the last year)... Properties comparable (e.g., based on similarity of attribute values) to the best performing subset of the data set may be evaluated using the first set of neural networks to estimate property values for the time period (e.g., for the month) associated with the slice; With a good representation (e.g., based on the estimated missing sale values) in time, the prediction system using recurrent neuronal networks, for example, may be trained to predict the price evolution for the future.)
Packes does not appear to consider the next part of the limitation regarding portfolio data.
Lyons or Catalano discloses:
training a first predictive algorithm on historical data from past financial transactions, industry data obtained from at least one third party “and portfolio data” from one or more users (Applicant’s specification [0078] as published – Using the specific values of that scenario, the user can now calculate the financial provisions, along with other applicable provisions (as desired) against other portfolios of similar leases. With additional reference to FIG. 3, this may consist of a data set from the user’s own lease portfolio.
Lyons discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 16 - The predictive models are trained on historical data derived from a plurality of mortgage account profiles for a plurality of mortgages within the mortgage portfolio. See par 43 - The system 100 includes a source of historical data 118 that relates to a mortgage portfolio. A mortgage portfolio generally will include a multiplicity of mortgages held by a lender.
Catalano also discloses the limitations based on broadest reasonable interpretation in light of the specification – see Col. 12, lines 4-18 - compute the difference between a company's current relatively inelastic lease costs for a particular Site or even for that company's whole portfolio of Sites (remember, as defined here and in the system, a Site is something they already lease), and what the company's lease costs for a Comp, based on current market conditions might be, and extrapolate this difference several years into the future. A graphic showing this type of calculation is shown in FIG. 5. The invention performs this type of calculation, both on a per-Site basis versus an assortment of market Comps, and also on a real estate lease portfolio basis, and then presents the results to management in an easy to understand benchmark and graphical readout form; see par 78 – explanation of change in predicted value based on comparable property relative to the time of transaction).
Packes, Lyons, and Catalano disclose:
receiving, at a computing system, input categories from a user device (Lyons discloses as best understood in light of the 112 issue – see par 174 - FIG. 9 shows four screen shots 910, 920, 930, 940 with equations and corresponding to the logic of the calculations for various metrics of interest. For example one of the screen-shots 940 shows a Standardized lifetime value calculation for a mortgage, an action based predictor model (action effect model) built as a component of the subject matter described herein and a lender model (model built by the lender) that are part of the network of models forming the decision model);
determining, by a second predictive algorithm different from the first predictive algorithm, an appropriate analytic calculator based on the input categories, wherein the appropriate analytic calculator comprises the first predictive algorithm (Lyons – See par 174 - FIG. 9 shows four screen shots 910, 920, 930, 940 with equations and corresponding to the logic of the calculations for various metrics of interest (e.g. “Foreclosure, Monthly Payment, Future Home Value, and/or NPV = Revenue – Loss – Restructuring Expense”). For example one of the screen-shots 940 shows a Standardized lifetime value calculation for a mortgage, an action based predictor model (action effect model) built as a component of the subject matter described herein and a lender model (model built by the lender) that are part of the network of models forming the decision model; see par 179-183 – Net Present Value of Profit; see par 177 - The implemented code of original monthly payment and the new monthly payment after loan modification are both based on this formula, with some adjustment. see table after par 178 – e.g. Monthly Interest Rate; New Monthly payment before and after modification; see par 222, FIG. 13 shows treatment mix scenarios and the values of some of the metrics of interest corresponding to a number of constrained and unconstrained optimization runs. FIG. 13 is a screenshot 1300 of a report depicting six optimization runs 1310, 1312, 1314, 1316, 1318, 1320. One of the optimization runs 1310 is an unconstrained optimization. The other five optimization runs 1312, 1314, 1316, 1318, 1320 are constrained. The columns associated with each of the optimization runs 1310, 1312, 1314, 1316, 1318, 1320 show details as to how the treatment mix is distributed for each of these optimization runs and what are the values of some of the key metrics for each of these optimization runs.);
receiving, at the computing system (Packes –See par 88 - The REP coordinator facilitates the operation of the REP via a computer system (e.g., one or more cloud computing systems); See par 89 – REP Coordinator includes a processor 801 that executes program instructions; see par 99 - Instructions for performing these processes may also be embodied as machine- or computer-readable code recorded on a machine- or computer-readable medium. In some embodiments, the computer-readable medium may be a non-transitory computer-readable medium. Examples of such a non-transitory computer-readable medium include, but are not limited to, a read only memory, … and a data storage device), a series of independent variables that represent attributes of a first financial transaction for a predetermined location (Packes – See par 18 - The REP may be utilized to predict the pricing of (e.g., urban) real estate, both at the time of the inquiry, and into the foreseeable future. Existing pricing schemes are geared to the horizontal modes of development in suburban and rural real estate markets and are inaccurate in multi-family and hi-rise development markets such as exist in cities all around the world. In some embodiments, the REP may be utilized to predict the value of individual apartment units for rental and sale, to predict the value of buildings, of neighborhoods, and/or of the whole market (e.g., as defined by any borough or boroughs with multifamily development). In some embodiments, the REP may be utilized to predict the value of commercial and office spaces (e.g., in vertical, or hi-rise, development structures). See par 31 - An attribute set selection may be obtained at step 105 of process 100. In one embodiment, real estate properties may have different attributes based on the unit type. For example, a condominium may have different attributes compared with a commercial unit (See Table after par 31 – e.g. “city, zip, state, neighborhood”)), wherein each of the attributes fit within the input categories (Lyons – see above - see par 174 - FIG. 9 shows four screen shots 910, 920, 930, 940 with equations and corresponding to the logic of the calculations for various metrics of interest. see par 179-183 – Net Present Value of Profit; see par 177 - The implemented code of original monthly payment and the new monthly payment after loan modification are both based on this formula, with some adjustment. see table after par 178 – e.g. Monthly Interest Rate; New Monthly payment before and after modification;
scaling and normalizing the series of independent variables ([0087] as published – scale for “excellent” can be a score of 5; score for “above average” can be a score of 4); [0101] as published – transforming the series of independent variables, such that the features are within a specific range (scaling)). Packes discloses the limitations based on broadest reasonable interpretation in light of the specification – See par 38 - In one implementation, attribute values may be normalized. For example, numerical values may be converted to a 0 to 1 interval, where 1 is equivalent to the biggest original value and 0 is equivalent to the smallest original value.);
assembling the scaled and normalized series of independent variables (Packes – See par 38 - In one implementation, attribute values may be normalized. For example, numerical values may be converted to a 0 to 1 interval, where 1 is equivalent to the biggest original value and 0 is equivalent to the smallest original value; see par 57, FIG. 4 - FIG. 4 shows a logic flow diagram illustrating a process 400 for estimating value (e.g., using a real estate value estimating (RVE) component) in accordance with some embodiments of the REP. FIG. 4 provides an example of how a set of neural networks may be used to estimate the value (e.g., property price, rental price, etc.) of a real estate property. In one implementation, the user may specify attribute values for any of the attributes discussed with regard to step 105 of process 100.);
applying weightings to the assembled scaled and normalized series of independent variables ([0096] as published states “a weight of one or more variables can be adjusted. In this way, the influence the one or more variables have on a prediction can be increased or decreased.” Packes discloses the limitations based on broadest reasonable interpretation in light of the specification – see par 32 - a grouping process may be employed to group multiple attributes (e.g., attributes with a low importance factor) to create a single new attribute with a higher importance factor or importance index value). Attributes with higher importance or weight may be used by the REP in priority over other attributes when training a particular type of neural network for a particular use. If, for example, the REP is to create a neural network using only 5 inputs, the REP may be operative to select or receive a selection of 5 inputs with the highest importance factor for that neural network type. Such a limitation of the number of inputs may be dictated, for example, by any suitable information provided by a user in an estimation process enabled by the REP);
Packes and Catalano disclose:
predicting, by the first predictive algorithm, a first value for the first financial transaction for the predetermined location by entering the weighted, assembled, scaled, and normalized series of independent variables into the first predictive algorithm (Packes – See par 33 - The goal of the training process may be to teach a neural network for pattern recognition. The best performances may be achieved with neural networks that may be specialized in the recognition of a limited number of patterns (e.g., a limited variation of the sale price). The unit localization may be one of, if not the, most important factors in sale price variation. By grouping and limiting the number of neighborhoods, the sale price for the units can be limited to a smaller range. For best training performances, the groups may be kept as small as possible. See par 61 – value of property may be estimated at step 417, 421; attribute values may be converted into numerical values and/or normalized prior to providing to neural network; see par 62 - When there are no more neural networks to utilize (e.g., as determined at step 413), then the overall result given by the neural networks in the selected set of neural networks may be calculated at step 425 of process 400. For example, the overall result may be displayed to the user. In one embodiment, the overall estimated property value may be calculated as the average of estimated property values from each of the neural networks in the selected set of neural networks (e.g., an average of each property value estimated at step 421)).
Packes discloses that it can consider economic data including interest rates (See par 34) and training neural networks on mortgage rates (See par 121). Catalano discloses that landlords often wish to reduce mortgage costs by refinancing with more favorable long-term mortgage rates and terms from the debt market (Col. 11, lines 19-31).
Lyons discloses:
predicting, by the first predictive algorithm, a second value for a second financial transaction for the predetermined location using the first predictive algorithm, wherein the second financial transaction is different from the first financial transaction (Lyons – See FIG. 2, par 120 – decision model 200 is summation of analytical relationships being modeled, connecting lender actions and total portfolio; see par 124 – metric to optimize can be NPV (net present value); see par 126 – actions depicted can be Interest Reduction, Principal Reduction, and Term extension ; Refinance (with different treatments here depending on refinance terms)… or Change of Type of Loan (e.g. from ARM to conventional));
comparing the first value for the first financial transaction to the second value for the second financial transaction (Lyons – See par 153 - FIG. 5 shows a screen shot of a treatment editor 600 for deriving treatments in a case when only three actions are considered: Principal Reduction 610, Term Extension 620 and Interest Rate Reduction 630. As shown in FIG. 5, various combinations of parameters for treatments are determined. Each combination of different parameters (namely different values for principal reduction, interest reduction, and term extension) is given a unique ID 640; see par 211 – optimization runs corresponding to different optimization scenarios are completed; See par 222-2223 - Different runs with global constraints are compared in order to converge on the preferred situation that will be implemented as the optimized policy for the mortgage portfolio as shown in FIG. 14).
Packes discloses having a graphical user interface (See Par 30, FIG. 3, par 73, FIG. 6A) for estimating value of a property (See par 73, FIG. 6A). Catalano discloses having a graphical user interface for entering various lease expenses and calculating projected costs and expenses (See FIG. 12, col. 6, lines 23-29) where the system interfaces with the GUI (See col. 20, lines 19-27).
Lyons discloses a GUI with the comparison from the previous limitations:
coordinating displaying a graphical user interface (GUI) comprising the comparison between the first financial transaction and the second financial transaction (Lyons – See par 153 - FIG. 5 shows a screen shot of a treatment editor 600 for deriving treatments in a case when only three actions are considered: Principal Reduction 610, Term Extension 620 and Interest Rate Reduction 630; See also FIG. 9 – showing different analyses on transactions (e.g. NPV, Monthly Payment, etc) and FIG. 13 – showing different scenarios).
Packes, Catalano, and Lyons are analogous art as they are directed to analyzing financials related to real estate property (see Packes Abstract, par 18; Catalano Abstract; Lyons Abstract). 1) Packes discloses looking at historical data and comparable properties when estimating property values (See par 35, 78). Catalano improves upon Packes by disclosing analyzing a company’s whole portfolio of sites and what the company’s lease costs for a Comp, based on current market conditions might be, and extrapolate into the future (See col. 12, lines 4-18). One of ordinary skill in the art would be motivated to further include a portfolio of property sites to efficiently improve upon the estimated values of property by neural networks in Packes. 2) Packes discloses that it can consider economic data including interest rates (See par 34) and training neural networks on mortgage rates (See par 121). Catalano discloses that landlords often wish to reduce mortgage costs by refinancing with more favorable long-term mortgage rates and terms from the debt market (Col. 11, lines 19-31). Lyons improves upon Packes and Catalano by disclosing analyzing different treatments in changes to loans or refinancing terms (see par 120-126), evaluating different combinations of parameters for treatments and using logic of calculations for various metrics of interest including NPV (See par 153, 174-183, FIG. 5, FIG. 9, FIG. 13) and displaying different GUIs showing comparisons of treatments (See FIG. 2, FIG. 5, 13-15). One of ordinary skill in the art would be motivated to further include a portfolio of property sites with various loan modifications being altered and using logic of calculations for various metrics of interest including NPV to efficiently improve upon the estimated values of property by neural networks in Packes and the disclosure of using net present value and a desire to refinance mortgage costs in Catalano.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictions for real estate in Packes to further include analyzing portfolios of properties and consider refinancing mortgage costs as disclosed in Catalano, and to further evaluate different loan modifications and using logic of calculations for various metrics of interest including NPV as disclosed in Lyons, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning independent claim 10, Packes and Catalano and Lyons disclose:
A device for forecasting commercial financial transaction scores (Packes –See par 18-20 - REP (“Real Estate Evaluating Platform”) may be utilized to predict pricing of real estate or predict value of buildings, neighborhoods, whole market, commercial spaces, or offices spaces), comprising:
at least one processor (Packes – See par 88 - The REP coordinator facilitates the operation of the REP via a computer system (e.g., one or more cloud computing systems); See par 89 – REP Coordinator includes a processor 801 that executes program instructions); and
a memory, coupled to the at least one processor, the memory including instructions causing the at least one processor to: (Packes See par 89, FIG. 8 – REP Coordinator includes a processor 801 that executes program instructions; processor 801 is coupled to memory 820 and storage device 819; see par 99 - Instructions for performing these processes may also be embodied as machine- or computer-readable code recorded on a machine- or computer-readable medium. In some embodiments, the computer-readable medium may be a non-transitory computer-readable medium. Examples of such a non-transitory computer-readable medium include, but are not limited to, a read only memory, … and a data storage device).
The remaining limitations are similar to claim 1 above.
Claim 10 is rejected for the same reasons.
It would be obvious to combine Packes and Catalano and Lyons for the same reasons as claim 1.
Concerning independent claim 19, Packes and Catalano and Lyons disclose:
An apparatus for forecasting commercial financial transaction scores, the apparatus configured to (Packes –See par 18-20 - REP (“Real Estate Evaluating Platform”) may be utilized to predict pricing of real estate or predict value of buildings, neighborhoods, whole market, commercial spaces, or offices spaces; See par 88 - The REP coordinator facilitates the operation of the REP via a computer system (e.g., one or more cloud computing systems)), comprising:
receiving, at a computing system (Packes –See par 88 - The REP coordinator facilitates the operation of the REP via a computer system (e.g., one or more cloud computing systems); See par 89 – REP Coordinator includes a processor 801 that executes program instructions; see par 99 - Instructions for performing these processes may also be embodied as machine- or computer-readable code recorded on a machine- or computer-readable medium. In some embodiments, the computer-readable medium may be a non-transitory computer-readable medium. Examples of such a non-transitory computer-readable medium include, but are not limited to, a read only memory, … and a data storage device).
The remaining limitations are similar to claim 1 above.
Claim 19 is rejected for the same reasons.
It would be obvious to combine Packes and Catalano and Lyons for the same reasons as claim 1.
Concerning claims 2, 11, and 20, Packes discloses “the obtained training method parameters may include the number of neural networks (e.g., 10 neural networks to create initially, 5 best performing neural networks to select for further analysis and/or retraining, etc.) for the set of neural networks (e.g., as may be described below with respect to step 141).” Packes also discloses having a feedforward neural network to provide an estimated output (e.g. an estimated value for a real estate property) (See par 121). However, Packes and Catalano do not explicitly recite one of the alternative algorithms recited.
Lyons discloses:
The method of claim 1, wherein the predictive algorithm comprises at least one of ordinary least squares, random forests, decision trees (Lyons – see par 37-38, FIGS. 17-18 – strategy decision trees; See par 234 - A classification tree can be built to express exactly what the optimized actions for each account are. To this end, the decision keys will be used as predictors (independent variables in the classification tree) and the optimal treatment that is known at this point will be used as a categorical dependent (response) variable; see par 236 - FIG. 17 as a strategy tree 1700. The terminal nodes, such as node 1710, of this strategy tree 1700 are treatments corresponding to an optimized strategy. The split points, such as split point 1720, of this tree 1700 are decision keys (e.g., FICO Score, current LTV, etc). see par 237 - The tree in FIG. 17 is a real life example of a tree that corresponds to an optimized strategy defined by an optimization scenario with the following settings: [0238] Objective: maximize NPV of the portfolio [0239] Treatments: combination of principal reduction, interest reduction, and term extension).
It would be obvious to combine Packes and Catalano and Lyons for the same reasons as claim 1. Packes discloses “the obtained training method parameters may include the number of neural networks (e.g., 10 neural networks to create initially, 5 best performing neural networks to select for further analysis and/or retraining, etc.) for the set of neural networks (e.g., as may be described below with respect to step 141).” Packes also discloses having a feedforward neural network to provide an estimated output (e.g. an estimated value for a real estate property) (See par 121). Lyons improves upon Packes and Catalano by disclosing using decision trees with decision keys used as predictors in the tree (See par 17-18, 234-239, FIG. 17). One of ordinary skill in the art would be motivated to further include decision trees to efficiently improve upon the estimated values of property by neural networks in Packes.
Concerning claims 3 and 12 and 21, Packes discloses
The method of claim 1, wherein the series of independent variables comprises at least one of market, trade area, location, type of center, asset, lease, landlord, comparable assets, negotiator, and negotiation strategy (limitations in the alternative -Packes See par 31 - An attribute set selection may be obtained at step 105 of process 100. In one embodiment, real estate properties may have different attributes based on the unit type. For example, a condominium may have different attributes compared with a commercial unit (See Table after par 31 – e.g. “city, zip, state, neighborhood”) – disclosing alternative of “location”).
Concerning claims 6 and 15 and 23, Packes discloses:
The method of claim 1, wherein the method is repeated for a series of first commercial financial transactions (Packes – see par 19 - the REP may be utilized to predict the value of individual apartment units for rental and sale, to predict the value of buildings, of neighborhoods, and/or of the whole market (e.g., as defined by any borough or boroughs with multifamily development). See par 31 – attribute set includes estimated values of each unit in a multi-unit building; see par 66 - the appropriate set of neural networks may be selected based on the unit type (e.g., one set of neural networks may be used to predict the value for a condominium, another set of neural networks may be used to predict the value for a commercial unit, and another set of neural networks may be used to predict the value for a multi-unit building;
See also Lyons – see par 215-218 - FIG. 12 is a tornado diagram 1200 where each bar corresponds to a variable (e.g., X.sub.1 through X.sub.6) and represents the range of change of an objective's value V (e.g., NPV) resulting from that variable's variation in a specified domain between a minimum and a maximum value; entire process may then be repeated until the analyst and the decision maker are satisfied with the final model.)
It would be obvious to combine Packes and Catalano and Lyons for the same reasons as claim 1.
Concerning claims 7 and 16 and 24, Packes discloses looking at property price, rental price, future pricing, direction of the market (See par 84).
Catalano discloses:
The method of claim 1, wherein the predicting the first value comprises predicting net present value for the first financial transaction… (Catalano – see col 24, lines 58-65 – FIG. 25 shows how system can handle dynamic number of difference lease costs per proposal and system can total Net Present Value (NPV)).
Lyons discloses the entire limitation in addition to NPV:
The method of claim 1, wherein the method further comprises predicting net present value for the specific commercial financial transaction for a plurality of scenarios (Lyons – see par 204-205 - In selecting the settings for a given scenario of FIG. 10 (showing 15 different scenarios), the mortgage portfolio manager will consider various mixes of treatments, objectives and constraints as shown in FIG. 11. More specifically, FIG. 11 shows three screenshots 1110, 1120, 1130 that present selectable options to a user, such as a Mortgage Portfolio Manager. A screenshot is one presentation associated with a graphical user interface associated with the subject matter described herein. Screenshot 1110 presents four objective functions from which one is selected. In screenshot 1110, the objective function (NPV) of the optimization is chosen or selected as depicted by the highlighting).
It would be obvious to combine Packes and Catalano and Lyons for the same reasons as claim 1. In addition, Packes discloses looking at property price, rental price, future pricing, direction of the market (See par 84). Catalano improves upon Packes by disclosing including a “Net Present Value” calculation. Lyons improves upon Packes and Catalano by disclosing net present value calculation that includes scenarios. One of ordinary skill in the art would be motivated to further include net present value calculations comprising scenarios to efficiently improve upon the estimated values of property by neural networks and the future pricing (see par 84) in Packes and the disclosure of using net present value in Catalano.
Concerning claims 17 and 25, Packes discloses:
The method of claim 1, wherein the first value is based on comparable financial transactions (Packes – see par 35 - For each slice of the obtained historical data, a first set of neural networks may be generated (e.g., using the NNG component and process 100) using the slice as a data set, and the best performing subset (e.g., a strict or proper subset) of the data set may be determined (e.g., 10% of records for which the first set of neural networks gives the smallest output error (e.g., at step 141)). Properties comparable (e.g., based on similarity of attribute values) to the best performing subset of the data set may be evaluated using the first set of neural networks to estimate property values for the time period (e.g., for the month) associated with the slice).
Claims 5 and 14 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Packes (US 2015/0317640) and Catalano (US 10,445,843) and Lyons (US 20110184884), as applied above to claims 1-3, 6-7, 10-12, 15-17, 19-21, and 23-25, and further in view of Cozine US 2018/0225593.
Concerning claim 5 and 14 and 22, Packes discloses:
The method of claim 1, wherein:
the method further comprises:
generating a composite score of the first financial transaction by combining one or more of the series of independent variables, (Packes– see par 32 - a grouping process may be employed to group multiple attributes (e.g., attributes with a low importance factor) to create a single new attribute with a higher importance factor or importance index value; For example, by combining multiple low importance factor "sports" amenity attributes, such as three sports amenities for "swimming pool?", "gym?", and "track?" into a single high importance factor "combined sports amenities" attribute, the value of such a high importance factor grouped or combined attribute may be operative to reflect the properties of each low importance factor attribute of the group (e.g., if each of the three importance factor weight attribute's property was "yes", the value of the grouped attribute may be a 9 (e.g., the highest value), whereas if none of three low importance factor attribute's property was "yes", the value of the grouped attribute may be a 0 (e.g., the lowest value); See par 59 - the appropriate set of neural networks may be selected based on the type of value desired (e.g., one set of neural networks may be used to predict property prices, while another set of neural networks may be used to predict rental prices)).
Packes discloses repeating portions of the training process (See par 32), but does not appear to scale, normalize, weight the composite score as best understood.
Cozine discloses:
scaling and normalizing the composite score (the claim appears to only be supported by [0105] as published stating: “n an example, the series of independent variables may include, but is not limited to at least one composite variable of multiple variables.” Cozine discloses the limitations based on broadest reasonable interpretation in light of the specification – See par 51 - one or more servers of the automated modeling system can aggregate data feeds (e.g., the property records received via various data connections) from various data sources into main property attribute, transaction and location data structure 340. This aggregation can include a series of steps for combining different property records having different format into a single database schema including the normalizing of data from all providers into that schema. See par 59 – segmentation 375 involves grouping geographic data objects, relative to certain attributes (e.g. property type, price quartile, etc); See par 64 - Since it is impractical to value more that 80 million properties daily, IHI 387 may be used to time shift stored AVM valuations between valuation dates as the system continuously cycles through segments and refreshes valuations of all properties in every segment as the system cycles through them. see par 74 - In some embodiments, the statistics are normalized across all Census Tracts after weighting within Census Tracts, if required. Then, Euclidean distances between all possible pairs of Census Tracts in each county are computed for all counties computing the distances from the weighted and normalized Census statistics. The method can be used with numerical attributes in any number of dimensions from any source and some sources other than US Census Bureau statistics may be used in accordance with various embodiments. );
assembling the scaled and normalized composite score (Cozine – See par 64 - Since it is impractical to value more that 80 million properties daily, IHI 387 may be used to time shift stored AVM valuations between valuation dates as the system continuously cycles through segments and refreshes valuations of all properties in every segment as the system cycles through them. see par 65 - At any rate, in various embodiments, the clustering/segmentation process 375 always creates segments with a sufficient number of transactions to support a robust model; see par 74 - In some embodiments, the statistics are normalized across all Census Tracts after weighting within Census Tracts, if required.);
applying weightings to the scaled and normalized composite score (Cozine – see par 65 - create robust training sets for KARL 390. In some embodiments, KARL 390 is an AVM which produces valuation estimates and attribute weights. See par 66 - To improve the accuracy of comparable pricing within the automatic ES application 391, the values of comparables is time shifted if necessary using the IHI 387 index for the segment. In addition, KARL 390 produces attribute weightings that quantify the relative importance of property attributes within each segment which ES 391 uses to more accurately adjust comparable properties for attribute differences compared to the subject property.); and
applying weightings to the assembled, scaled, and normalized composite score (Cozine – see par 65 - create robust training sets for KARL 390. In some embodiments, KARL 390 is an AVM which produces valuation estimates and attribute weights. See par 66 - In addition, KARL 390 produces attribute weightings that quantify the relative importance of property attributes within each segment which ES 391 uses to more accurately adjust comparable properties for attribute differences compared to the subject property.); and
predicting the first value for the first financial transaction comprises predicting the first value for the first financial transaction by entering the weighted, assembled, scaled, and normalized series of independent variables and the weighted, assembled, scaled, and normalized composite score into the first predictive algorithm (Cozine – See FIG. 3 par 60 - ES 391 is a computer-executed algorithm using “Comparable Sales Methodologies” that infer the value of a subject property by referring to transaction values for nearly identical properties. See par 66 - To improve the accuracy of comparable pricing within the automatic ES application 391, the values of comparables is time shifted if necessary using the IHI 387 index for the segment. In addition, KARL 390 produces attribute weightings that quantify the relative importance of property attributes within each segment which ES 391 uses to more accurately adjust comparable properties for attribute differences compared to the subject property).
Packes, Catalano, Lyons, and Cozine are analogous art as they are directed to analyzing financials related to real estate property (see Packes Abstract, par 18; Catalano Abstract; See Cozine – predict price of property). Packes discloses repeating portions of the training process (See par 32), and combining multiple factors into a “grouped or combined attribute.” Cozine improves upon Packes, Catalano, and Lyons by disclosing aggregating and combining records and normalizing records (See par 51), pulling data from certain dates (a form of scaling), and weighting property attributes that quantity the relative importance within a “segment” or composite of data (see par 66). One of ordinary skill in the art would be motivated to further include scaling, normalizing, and weighting for segmented/composite data to efficiently improve upon the estimated values of property by neural networks in Packes.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the predictions for real estate in Packes to further include analyzing portfolios of properties as disclosed in Catalano, to further evaluate different loan modifications as disclosed in Lyons, and further include scaling, normalizing, and weightings for composite/segmented data as disclosed in Cozine, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Response to Arguments
Applicant's arguments filed 12/29/25 have been fully considered but they are not persuasive and/or are moot in view of the new rejections.
With regards to the 101 rejection, Applicant argues the claim is not directed to an abstract idea and identifies a number of limitations, including the predictions for financial transactions and the mathematical limitations for the first 7 limitations. Remarks, pages 8-11. In response, Examiner respectfully disagrees. The revised rejection addresses the new limitations. The claim is directed to an abstract idea as “Mathematical Concepts” and certain methods of organizing human activity (“fundamental economic principles or practices”) consistent with MPEP 2106.04(a)(2)(I)(A) Examples of Mathematical Concepts (see e.g. iv. iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344), MPEP 2106.04(a)(2)(I)(C) Mathematical Calculations (see e.g. (i-vi), and MPEP 2106.04(a)(2)(II)(A)(i) (e.g. iv. offer-based price optimization, OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359 ). The training [if a computer is amended to perform it], the receiving “by a computer”, and the output on a “Graphical user interface” in the limitations is considered “apply it on a computer” (MPEP 2106.05f) and “field of use” (MPEP 2106.05h) for mere display on a computer of a result of the financial/mathematical analysis.
Applicant argues that the claims only “involve” the abstract idea and are not “directed to an abstract idea” similar to Example 39. Remarks, pages 12-14. In response, Examiner respectfully disagrees. Examiner also notes that this is not similar to Abstract Idea Example 39, in the January 2019 Guidance, was an improvement with specific steps of how the “training” of a neural network occurred, along with a disclosure discussing the technical issues with the image analysis that was occurring. See MPEP 2106.04(a)(1)(vii) where details related to a two-stage training system are present. See also 2019 Revised Patent Subject Matter Eligibility Guidance, available at https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility-examination-guidance-date , slide 105 detailing how Example 39 is for improving facial detection; slide 108 Explaining what Applicant invented is to address false positives in expanded training set by performing iterative training algorithm, to provide a “robust face detection model that can detect faces in distorted images while limiting the number of false positives.” In contrast here, we only have “training a first predictive algorithm” in claim 1 [claim 10 and 19 require a computer to perform it], such that the predictive algorithm is for predicting values of financial transactions. We do not have a similar situation here and the arguments are not persuasive. See also MPEP 2106.04(d)(1) “If the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.”
Applicant argues that the claims only “involve” the abstract idea and are not similar to Example 47, claim 2. Remarks, pages 12-14. In response, Examiner respectfully disagrees. Applicant’s claims are similar to Example 47, claim 2. Applicant’s claims recite explicit mathematical calculations throughout the claim in a similar manner.
Applicant argues with respect to step 2a, prong 2 that there is an improvement in computer functionality since the claim now is similar to Enfish, Finjan, and Core Wireless because the claim recites “training a first predictive algorithm” and predicts using “predictive algorithms.” Remarks, pages 15-16. In response, Examiner respectfully disagrees. First, this is moot in view of the new rejection. Second, this is not persuasive as this is just displaying the result of the abstract idea of financial analysis using math “on a computer display”. MPEP 2106 summarizes Core Wireless decision as “An improved user interface for electronic devices that displays an application summary of unlaunched applications, where the particular data in the summary is selectable by a user to launch the respective application. Core Wireless Licensing S.A.R.L., v. LG Electronics, Inc., 880 F.3d 1356. By displaying only a limited list of common functions and data from which to choose, the invention spared users from time-consuming operations of navigating to, opening up, and then navigating within, each separate application. Id. The invention thus increased the efficiency with which users could navigate through various views and windows. Id. The claims here are not similar to Core Wireless.
Enfish was eligible because, as stated in MPEP 2106.05(a) “In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification’s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691.” There is nothing similar here in this claim.
Finjan was eligible at step 2A for “virus scan that generates a security profile identifying both hostile and potentially hostile operations.” Finjan also states that a behavior-based virus scan constitutes an improvement in computer functionality because, “in contrast to traditional “code-matching” systems, which simply look for the presence of known viruses, “behavior-based” scans can analyze a downloadable’s code and determine whether it performs potentially dangerous or unwanted operations” and the profile approach enables more flexible and nuanced virus filtering. See Slip opinion at pages 6-7. There is nothing similar here in this claim.
Applicants then argue it is a practical application, improving a graphical user interface, similar to Data Engine Techs. v. Google. Remarks, page 16-17. In response, Examiner respectfully disagrees. MPEP 2106.05(a)(I) summarizes Data Engine Techs as showing an improvement in computer functionality because “Specific interface and implementation for navigating complex three-dimensional spreadsheets using techniques unique to computers.” There is nothing similar here in this claim, so the arguments are not persuasive. Here, the financial predictions are just output and displayed.
Applicant argues the claims improve another technology similar to McRO, Diehr, and Thales decisions, because here there is a “specific” improvement in artificial intelligence. Remarks, page 17-19. In response, Examiner respectfully disagrees. First, eligibility based on 101 is not simply whether any “specific” limitations are recited in the claim – it needs to be a particular solution to “improve a computer or other technology.” Rather, McRo, as explained in MPEP 2106.05(a)(II)(“Improvements to Any Other Technology of Technical Field”), had a specific way to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters. As stated in MPEP 2106.05(a)(I)(“Improvement to Computer Functionality”),
in computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016).” See MPEP 2106.05(a)(I). See also MPEP 2106.04(a) “ if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” Here there are no technical details on the training; it is just “training” for predicting financial values. MPEP 2106.05(a)(II) stated Diehr was eligible because “Particular computerized method of operating a rubber molding press, e.g., a modification of conventional rubber-molding processes to utilize a thermocouple inside the mold to constantly monitor the temperature and thus reduce under- and over-curing problems common in the art.” There is nothing similar here. MPEP 2106.05(a)(II) stated Thales eligible because “vii. Particular configuration of inertial sensors and a particular method of using the raw data from the sensors.” There is nothing similar here – there is not even a first sensor for physical measurements.
Applicants then argue that the claim is eligible for “changes in the physical realm that transform the claim from merely claiming a result to instead describing a method of achieving it, as contemplated by the Federal Circuit in SAP America.” Remarks, page 19. In response, Examiner respectfully disagrees. It appears Applicant is referring to MPEP 2106.05(b)(II) where it states “additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. See, e.g., Versata Development Group v. SAP America, 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015) (explaining that in order for a machine to add significantly more, it must "play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly").” Examiner is unable to find anything in the claim similar to this argument of physical implementation? as best understood. Perhaps Applicant’s argument here is eligibility by “transformation” of data; but that is also unpersuasive. MPEP 21065.05(c) states “For data, mere "manipulation of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011).
Applicant argue that all the claim limitations result in “significantly more”. Remarks pages 19-21. In response, Examiner respectfully disagrees. Many limitations Applicant points to are part of the “directed to an abstract idea”; other limitations are part of “apply it [abstract idea] on a computer” (MPEP 2106.05f) as discussed in the revised rejection.
Applicant then appears to argue “conventional” evidence is needed to have a 101 rejection here for many of the limitations, including much of the mathematical operations. Remarks, pages 21-23. Examiner respectfully disagrees. With regards to step 2B, only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f) or “field of use” (MPEP 2106.05h), no evidence is needed.
Applicant’s arguments with respect to 103 are moot in view of the revised rejection.
Conclusion
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/IVAN R GOLDBERG/Primary Examiner, Art Unit 3619