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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/17/2026 has been entered.
Response to Amendment
Applicant’s “Amendment” filed on 02/17/2026 has been considered.
Claims 1 and 11 are amended. Claims 1-7, 9, 11-17, and 19 remain pending in this application and an action on the merits follow.
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.
Claims 1-7, 9, 11-17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2020/0104935 to Schmitt et al., in view of U.S. Patent No. 11,663,668 to Bloom, in view of U.S. Patent Application Publication No. 2021/0034700 to Torres et al., in view of U.S. Patent Application Publication No. 2023/0130840 to Olesen et al., and further in view of U.S. Patent Application Publication No. 2009/0132348 to Bria et al.
With regard to claims 1 and 11, Schmitt discloses an apparatus for projecting a pecuniary strength metric, wherein the apparatus comprises:
at least a processor (paragraph 47, a data processing apparatus); and
a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to (paragraphs 26 and 47, The computing system 100 also includes a health trigger module 108 stored in memory of the computing system 100):
receive a pecuniary datum comprising a pecuniary assessment associated with a plurality of pecuniary categories from a user (Fig. 2, paragraphs 6, 8, and 32, receives user information relating to demographic profile, financial health, physical health, psychosocial health, financial planning maturity and/or financial preparedness or planning readiness of a user. Then, information can be obtained about the customer via a survey in at least the following high level areas: financial health, physical health, social and psycho-social health.), wherein the plurality of pecuniary categories comprises user finances, user lifestyle, and user physical health (paragraph 6, 8, and 32, information can be obtained about the customer via a survey in at least the following high level areas: financial health, physical health, social and psycho-social health. the survey questions include questions on demographics, holistic health, financial wellbeing, physical wellbeing, psychosocial wellbeing and financial preparedness of the user. The physical health information can include user information relating to at least one of user lifestyle conditions. Examiner notes that the plurality of pecuniary survey categories/areas comprises financial wellbeing, use lifestyle conditions, and physical wellbeing, which is considered as “the plurality of pecuniary categories comprises user finances, user lifestyle, and user physical health”), wherein the pecuniary assessment comprises a plurality of documents of the user (paragraph 6);
generate a plurality of pecuniary scores as a function of the pecuniary datum including the data collection processed at least a portion of the plurality of documents, wherein each pecuniary score of the plurality of pecuniary scores is associated with one pecuniary category of the plurality of pecuniary categories (Fig. 2, Fig. 4 and Fig. 6, paragraph 6, 32-33 and 37-38, claim 1, Next, key documents can be obtained for the user, such as bank account documents. Then, information can be obtained about the customer via a survey in at least the following high level areas: financial health, physical health, social and psycho-social health. Based on the data collected, a score can be generated and a comprehensive retirement plan can be devised. The responses to the survey questions can each be assigned a numerical value, which can then be weighted according to an algorithm and used to generate either a number. Examiner notes that a numerical value is associated with at least one of a plurality of survey areas/categories, wherein the plurality of survey areas/categories comprises financial health, physical health, user lifestyle conditions, social and psycho-social health, which is considered as “generate a plurality of pecuniary scores as a function of the pecuniary datum, wherein each pecuniary score of the plurality of pecuniary scores is associated with one pecuniary category of the plurality of pecuniary categories”. categorizing, by the computing device, the user into a career level classification based on demographic profile, the career level classification represented by a second number or vector. Examiner notes that a first number or vector based on a weighted sum of each numerical value multiplied by a weighting factor and a second number or vector based on the career level classification, can also be considered as, “generate a plurality of pecuniary scores as a function of the pecuniary datum, wherein each pecuniary score of the plurality of pecuniary scores is associated with one pecuniary category of the plurality of pecuniary categories”), wherein generating the plurality of pecuniary scores further comprises normalizing by a threshold value wherein the threshold value further comprises an average of each category weight within each pecuniary category of the plurality of pecuniary categories (paragraphs 34-37, For example, whole numbers having a range 0-100 can be used for numerical values (and in some cases weighting and normalization operations may be needed to correct biases in later calculating the overall WHealth score). The financial state FS is calculated to be a weighted average for a given career stage. Examiner notes that the calculated financial state is a weighted average for a given career stage, which is considered as “an average of each category weight within each pecuniary category of the plurality of pecuniary categories”),
wherein the plurality of pecuniary scores further comprises: at least a historical pecuniary score (paragraphs 39-41, the computing device calculates, based on the future projected financial state and the future projected health cost, a score indicating likelihood of achieving financial wellness in retirement. Examiner notes that an originally computed score indicating likelihood of achieving financial wellness in retirement can be considered as “at least a historical pecuniary score”); an associated time score for each pecuniary score of the plurality of pecuniary scores (Fig. 4, paragraph 43, The personal progress meter 412 can display a graph (e.g., a line graph) showing the progress of the Wealth Health score over time. Examiner notes that the a graph (e.g., a line graph) showing the progress of the Wealth Health score over time (monthly, weekly) is considered as “an associated time score for each pecuniary score of the plurality of pecuniary scores”), wherein the plurality of pecuniary scores is displayed as a pecuniary curve (Fig. 4, paragraph 43, The personal progress meter 412 can display a graph (e.g., a line graph) showing the progress of the Wealth Health score over time. Examiner notes that a graph showing the progress of the Wealth Health score over time is considered as “the plurality of pecuniary scores is displayed as a pecuniary curve”); and at least an advanced pecuniary score wherein the at least an advanced pecuniary score comprises a future predicted pecuniary score (claim 1, fig. 6, paragraph 41, the score generated using a predictive model based on at least one of a meta-analysis of existing data sets and a health claims analysis. For example, if there is change in cost (e.g., a health care cost, prescription medicine cost or change in health funding), the PHAPE 140 can re-compute the FS (Financial state), Projected Health Cost (PHC) and WhealthScore (WS). If there is a change with respect to the originally computed score, the system can be updated accordingly. Examiner notes that the score generated using a predictive model based on the future projected financial state and the future projected health cost can be considered as “a future predicted pecuniary score”);
identify a plurality of focus areas as a function of the plurality of pecuniary scores (Fig. 4 and 6, paragraphs 43 and 46, The future action plan summary 408 can display enumerated “Ways to Improve Your Wealth Health Score,” e.g., as depicted (1) Create a budget; (2) Get smart about saving for your children; and (3) Set up automatic deposits to your savings account. In a third step 615, the computing device develops a score-based recommendation and coaching plan. At this stage the computing device can have a projected anticipated health and wealth index to the individual's anticipated trajectory);
generate a holistic pecuniary strategy as a function of the plurality of focus areas and the plurality of pecuniary scores (paragraph 5, Whealth planning “flips the script” on traditional financial planning—which is typically focused on financial matters only, such as accumulation of assets, savings, investments, and estate planning—by taking a 360° view of a customer's circumstances to develop a holistic financial plan into the future. The invention's “WhealthCare Plan” generates a comprehensive and holistic lifetime retirement plan based on a connection between physical and financial health to develop a better-informed plan and to create a financial safety net.); and
project a pecuniary strength metric of the user as a function of the holistic pecuniary strategy using a pecuniary strength machine-learning process (Fig. 6, paragraphs 12-13 and 46, At this stage the computing device can have a projected anticipated health and wealth index to the individual's anticipated trajectory), wherein the pecuniary strength metric comprises an impact of the holistic pecuniary strategy on the user (Fig. 4, paragraphs 43 and 46, The future action plan summary 408 can display enumerated “Ways to Improve Your Wealth Health Score,” e.g., as depicted (1) Create a budget; (2) Get smart about saving for your children; and (3) Set up automatic deposits to your savings account; and provide further details in a smaller print write-up below. The personal progress meter 412 can display a graph (e.g., a line graph) showing the progress of the Wealth Health score over time and/or a synopsis of progress made since the customer's last visit to the portal (e.g., “+4 Since your last visit 2 weeks ago,” as shown). Examiner notes that key areas for anticipated impact (e.g. lifestyle, fitness, wealth preparation for medical interventions etc.) can be proposed and tracked for progression, which is considered as “an impact of the holistic pecuniary strategy on the user”. Examiner notes that progression on Whealth score based on the anticipated impact of the user is tracked/projected, which is considered as “the pecuniary strength metric comprises an impact of the holistic pecuniary strategy on the user and projecting the pecuniary strength metric”) ;
projecting the pecuniary strength metric as a function of the trained pecuniary strength machine-learning process (paragraphs 13-15 and 46, the health trigger module is periodically updated and trained using updated user survey data comprising at least one of health issues or health cost issues. the system includes a probabilistic health anomaly prediction engine in electronic communication with the health trigger module. the invention includes a computerized method of training a probabilistic health anomaly prediction engine. The computerized method also includes developing, by the computing device, a score-based recommendation and coaching plan. Examiner notes that a trained probabilistic health anomaly prediction engine based on the trained health trigger module can be used to generate/develop a score-based recommendation and coaching plan); and
display, on a graphical user interface, the pecuniary strength metric comprising a simulation of a previous iteration of a generated holistic pecuniary strategy (Fig. 4, paragraphs 43, Examiner notes that the Wealth Health score at a past timeframe is generated/simulated based on past impacts (past lifestyle, past planning), which is considered as “display … the pecuniary strength metric comprising a simulation of a previous iteration of a generated holistic pecuniary strategy”.); and an impact of the holistic pecuniary strategy on the user (Fig. 4, paragraphs 43 and 46, The personal progress meter 412 can display a graph (e.g., a line graph) showing the progress of the Wealth Health score over time. If the computing device determines that the individual is off track, key areas for anticipated impact (e.g. lifestyle, fitness, wealth preparation for medical interventions etc.) can be proposed and tracked for progression against the individualized plan. Examiner notes that the personal progress meter based on improvement in anticipated impact such as lifestyle…can be displayed over time, which is considered as “display, on a graphical user interface, the pecuniary strength metric comprising an impact of the holistic pecuniary strategy on the user”).
However, Schmitt does not disclose training a pecuniary strength machine-learning process using a pecuniary strength training data applied to an input layer of nodes… ; adjusting the one or more connections and one or more weights…; and calculating a weighted sum of the input layer of nodes by adding a bias to the weighted sum of the input layer of nodes; wherein the pecuniary curve comprises a multidimensional space comprising a plurality of axis representing at least one category, and wherein the pecuniary curve is constructed in the multidimensional space by: minimizing a vertical displacement of at least one discrete pecuniary score from the pecuniary curve; and locating at least one new pecuniary score on the pecuniary curve based on a range of a discrete set of the plurality of pecuniary scores; and wherein generating the holistic pecuniary strategy further comprises: receiving a plurality of pecuniary strategy training data comprising a plurality of pecuniary score examples and a plurality of holistic pecuniary strategy examples, wherein each pecuniary score example further comprises a pecuniary score vector having a plurality of pecuniary score numerical fields; training a pecuniary strategy machine-learning process using the pecuniary strategy training data; and generating the holistic pecuniary strategy using the trained pecuniary strategy machine-learning process and the plurality of pecuniary scores, wherein the plurality of pecuniary scores are input as a vector of numerical fields and each numerical field represents a pecuniary score of the plurality of pecuniary scores; and wherein the plurality of focus areas comprises at least excessive debt and unessential large purchases.
However, Bloom teaches implementing, using the at least a processor, the pecuniary strength machine-learning process comprising: iteratively training the pecuniary strength machine-learning process using a pecuniary strength training data applied to an input layer of nodes, wherein the input layer of nodes further comprise a plurality of holistic pecuniary strategies, one or more intermediate layers, and an output layer of nodes, wherein the output layer of nodes comprise a plurality of pecuniary strength metrics, by creating one or more connections between the input layer of nodes and the output layer of nodes; adjusting the one or more connections and one or more weights between nodes in adjacent layers of the pecuniary strength machine- learning process to iteratively update the output layer of nodes by updating the pecuniary strength training data applied to the input layer of nodes; and calculating a weighted sum of the input layer of nodes by adding a bias to the weighted sum of the input layer of nodes (A neural network 400 also known as an artificial neural network, is a network of “nodes,” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes 404, one or more intermediate layers 408, and an output layer of nodes 412. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. Node may perform a weighted sum of inputs using weights wi that are multiplied by respective inputs xi. Additionally or alternatively, a bias b may be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. col. 22, lines 30- col. 23, lines14, Fig. 4-5), wherein the pecuniary strength training data includes a plurality of historical holistic pecuniary strategies, wherein the historical holistic pecuniary strategies are holistic pecuniary strategies generated previously using a plurality of historical pecuniary scores, wherein the training data is iteratively updated by feeding back the output of the pecuniary strength machine- learning process to update the training data (receive a user input relating to a user; receive pecuniary data relating to the user; identify a plurality of trends in the pecuniary data; generate a first training data set comprising: at least a priority scoring criteria; and a plurality of a plurality of identified trends in the pecuniary data relating to the user; classify at least an element of the user input to a priority score using a first machine-learning model. updating the first training data set with input and output results from the first machine machine-learning model; and retraining the first machine machine-learning with an updated first training data set. The pecuniary scoring criteria may include exemplary user input and pecuniary data relating to ideal financial stability for the average user is, such as, ideal income, savings, investments, debt balance. Identified trends in pecuniary data 128 may be used to correlate user's goal to the priority scoring criteria. Trends may represent a positive and/or negative financial history of the user. A positive trend may show that a user is financially stable and responsible with a financial matter. Examiner notes that updating the first training data set with input and output results from the first machine machine-learning model, wherein the training data set includes a priority score and identified trends in pecuniary data, which is considered as “the pecuniary strength training data includes a plurality of historical holistic pecuniary strategies…updated by feeding back the output of the pecuniary strength machine- learning process to update the training data“, claim 1, col. 5, lines 54-col. 6, lines 12, and col. 9, lines 18-55);
wherein the pecuniary curve comprises a multidimensional space comprising a plurality of axis representing at least one category ( Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. col. 11, lines 23-31); and
wherein generating the holistic pecuniary strategy further comprises: receiving a plurality of pecuniary strategy training data comprising a plurality of pecuniary score examples and a plurality of holistic pecuniary strategy examples, wherein each pecuniary score example further comprises a pecuniary score vector having a plurality of pecuniary score numerical fields (the language processing module may use vector similarity comparison including cosine similarity and/or other geometric measures of vector similarity to compare data in documents to a threshold number, wherein the threshold number indicates if data contained in a document or plurality of documents is a positive or negative pecuniary trend. col. 5, lines 54-col. 6, lines 12, col. 6, lines 65-col. 7, lines 27); training a pecuniary strategy machine-learning process using the pecuniary strategy training data (In some embodiments, output from the language processing module may be used as training data in machine learning models described throughout this disclosure. col. 5, lines 54-col. 6, lines 12, col. 6, lines 65-col. 7, lines 27); and generating the holistic pecuniary strategy using the trained pecuniary strategy machine-learning process and the plurality of pecuniary scores, wherein the plurality of pecuniary scores are input as a vector of numerical fields and each numerical field represents a pecuniary score of the plurality of pecuniary scores (Still referring to FIG. 1, computing device 104 is configured to generate a first training data set 132 including at least a priority scoring criteria and identified trends in pecuniary data 128 relating to the user. As used in this disclosure, a “priority scoring criteria” is a plurality of ranked exemplary user inputs and pecuniary data based on priority. The pecuniary scoring criteria may include exemplary user input and pecuniary data relating to ideal financial stability for the average user is, such as, ideal income, savings, investments, debt balance. first training data set 132 may include the priority scoring criteria, pecuniary data 128, and identified trends in pecuniary data 128. User input 120 may include goals such as building a diverse financial portfolio in 1 year and building a retirement savings of $100 k in 1 year. The goals may be weighed against each other using first training data set 132 to prioritize building retirement savings over diversifying a financial portfolio based on the negative trend of low savings and the average retirement budget in the user's geographic location. Still referring to FIG. 1, computing device 104 is configured to classify at least an element of the user input to a priority score 140 using a first machine-learning model 136, wherein classifying the at an element of the user input 120 includes training a first machine machine-learning model, as a function of the first training data set. A “priority score,” as used in this disclosure is a number that represents the relative level of importance or urgency. computing device 104 may be configured to generate a classifier using a Naïve Bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. col. 9, lines 18-col 11. Lines 57).
It would have been obvious to one of ordinary still in the art to include in the personalized retirement plan system of Schmitt the ability to utilize a machine learning technique to train a pecuniary strength machine-learning process using a pecuniary strength training data applied to an input layer of nodes…; adjusting the one or more connections and one or more weights…; calculating a weighted sum of the input layer of nodes by adding a bias to the weighted sum of the input layer of nodes, wherein the pecuniary curve comprises a multidimensional space comprising a plurality of axis representing at least one category, and…receiving a plurality of pecuniary strategy training data …each pecuniary score example further comprises a pecuniary score vector having a plurality of pecuniary score numerical fields; training a pecuniary strategy machine-learning process…and generating the holistic pecuniary strategy using the trained pecuniary strategy machine-learning process and the plurality of pecuniary scores, wherein the plurality of pecuniary scores are input as a vector of numerical fields and each numerical field represents a pecuniary score of the plurality of pecuniary scores as taught by bloom since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Schmitt disclose receive and process the pecuniary assessment via a survey module including a set of survey questions (paragraph 8). However, Schmitt does not disclose wherein the plurality of documents are a plurality of paystubs of the user; and process the pecuniary assessment using an optical character recognition (OCR) process.
Torres teaches wherein the plurality of documents are a plurality of paystubs of the user (paragraph 70); process the pecuniary assessment using an optical character recognition (OCR) process to convert at least a portion of the plurality of paystubs into machine-encoded text, wherein converting the at least a portion of the plurality of paystubs into the machine-encoded text comprises converting images of text in the at least a portion of the plurality of paystubs into the machine-encoded text (Moreover, OCR can be processing intensive and/or inefficient, for example in cases wherein an entire image is analyzed to find and identify characters within. Researchers have proposed numerous methods for detecting text in natural images, fig. 1-2, paragraphs 15 and 70) and further comprises: pre-processing image components of the images of text in the at least a portion of the plurality of paystubs by de-skewing at least one of the image components by applying a transform operation to the at least one of the image components (Embodiments described herein may improve pre-processing of images to enhance the effectiveness and/or efficiency of OCR. For example, some embodiments may perform de-skewing and/or cropping of an image before it is processed using OCR. De-skewing and/or cropping may reduce background interference and/or align the documents in an image, thereby enhancing any text characters in the images for improved OCR results. fig. 1-2, paragraphs 16 and 21); implementing an OCR algorithm comprising a matrix matching process by comparing pixels of at least one of the pre-processed images of text in the at least a portion of the plurality of paystubs to pixels of a stored glyph on a pixel-by-pixel basis (he captured portion may be mapped to a lower-dimensional intermediate feature layer 408. FIG. 4C shows an example of the convolution operation 412 performed over the input array, where sliding window 404 includes a filter matrix that computes the convolution operation. fig. 3, paragraphs 26 and 50); and post-processing an output of the matrix matching process to increase OCR accuracy by constraining the output to a lexicon containing a set of words whose occurrence is permitted (These words may then be processed, either individually or in groups of neighboring words, and the OCR results may be recombined in a post-processing procedure. By passing low-quality images initially to RPNs, embodiments described herein may generate accurate word segments even for images that are blurred or skewed or that have additional, non-text background. The character sequence 1022 may be passed through the network 1024 and labeled given the above encoding scheme 1026, and then the tagging sequence may be post-processed to produce concatenated character sequences that correspond to identified entities. fig. 1-2, paragraphs 17-18 and 69); the data collection processed the plurality of documents includes the OCR processed at least a portion of the plurality of paystubs (the same process may be used to identify fields in a W-2 tax document, to identify line item amounts in received invoice document, or to extract information about employees from their past paystubs for payroll filing setup, among other uses, paragraphs 15-16 and 70).
Therefore, 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 pecuniary planner program/system of Schmitt and Bloom to include, wherein the plurality of documents are a plurality of paystubs of the user; and process the pecuniary assessment using an optical character recognition (OCR) process, as taught in Torres, in order to perform OCR processing on image data within a plurality of regions of the image to generate a text result for each region (Torres, abstract).
However, Oleson teaches wherein the plurality of focus areas comprises at least excessive debt and unessential large purchases (purchase of a car can be on an as needed basis (and normally not even every year), major repairs happen sporadically (hopefully not every year), etc. The evaluation service 102 may also include or provide an FHS simulator 134, which may interface with UI 108 to enable a user to change certain scores for individual factors, certain inputs for certain data points, simulate additional purchases or debt reduction strategies and the like, whereby the FHS simulator 134 will output the resulting FHS. In some cases, the debt ratio may be calculated on a monthly basis, which means the consumer's monthly adverse debt payment(s) 804. Once the debt ratio has been determined, the consumer's debt factor can be determined at operation 816, and the debt factor (along with the total adverse debt obligations and adverse debt payment(s)) may be stored in a safe and secure location so it can be used to show the consumer's historical financial health, at operation 818. When it comes to adverse debt, this is very easy and straightforward. To be completely financially healthy, the consumer should always pay off their entire adverse debt obligations every month—and thereby avoid paying any excessive interest rates. Having the ability to do this can allow the end user (e.g., consumer, a financial planner, a loan consultant, or anyone else) to create scenarios of new financial situations in order to better understand the impact of financial decisions on the (financial health) score. paragraphs 30, 38, 228, 230, and 400).
Therefore, 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 combination of references to include, wherein the plurality of focus areas comprises at least excessive debt and unessential large purchases, as taught in Oleson, in order to generate an accurate and balanced evaluation of a consumer or entity's financial health (Oleson, paragraph 13).
However, Bria teaches minimizing a vertical displacement of at least one discrete pecuniary score from the pecuniary curve; and locating at least one new pecuniary score on the pecuniary curve based on a range of a discrete set of the plurality of pecuniary scores (A plurality of curve fitting models are applied to the historical data subset to form a plurality of historical data subset fitted curves. A mathematical model can then be used to fit a curve to the data. The curve can enable an analyst to interpolate and predict results in areas where no data actually exists. Larger sets of data can enable more precise curves to be fit to the data, which in turn can provide more accurate interpolations. The accuracy of the interpolations, however, is also limited by the mathematical model used to create the curve to fit the known data. The mathematical model may imbue a certain amount of error in the curve relative to the actual results. The error may be relatively constant throughout the curve, or may be weighted more heavily over one or more sections of the curve. The error can result in a reduced accuracy in interpolating between known sets of data. Examiner notes that in Wikipedia, Curve fitting that minimizes vertical displacement is typically achieved through ordinary least squares (OLS) regression. Finding an estimated new pecuniary (financial) score, often required when calculating intermediate values like NPV, IRR, or ranked data not explicitly listed in tables, is achieved through linear interpolation. paragraphs 3 and 14).
Therefore, 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 combination of references to include, minimizing a vertical displacement of at least one discrete pecuniary score from the pecuniary curve; and locating at least one new pecuniary score on the pecuniary curve based on a range of a discrete set of the plurality of pecuniary scores, as taught in Bria, in order to collect, organize, store, and analyze data to enable a better understanding of a selected topic (Bria, paragraph 2).
With regard to claims 2 and 12, Schmitt discloses the pecuniary datum comprises a vitality datum (Fig. 2, paragraph 32).
With regard to claims 3 and 13, Schmitt discloses receiving the pecuniary datum comprises: accepting the pecuniary assessment from the user; and extracting the pecuniary datum from the pecuniary assessment (Fig. 2, paragraphs 6 and 32).
With regard to claims 4 and 14, Schmitt discloses the pecuniary assessment comprises a data submission of a plurality of documentation from the user (Fig. 2, paragraphs 6 and 32, key documents can be obtained for the user, such as bank account documents, medical directives, or legal documents, to verify and obtain further relevant information about the user).
With regard to claims 5 and 15, Schmitt discloses the plurality of pecuniary scores comprises a pecuniary curve (Fig. 4, paragraph 43, The personal progress meter 412 can display a graph (e.g., a line graph) showing the progress of the Wealth Health score over time).
With regard to claims 6 and 16, Schmitt discloses generating the plurality of pecuniary scores comprises: generating a first pecuniary score representing a first category; and generating a second pecuniary score representing a second category, wherein the first category is different than the second category (Fig. 2, Fig. 4 and Fig. 6, paragraph 6, 32-33 and 37-38, claim 1).
With regard to claims 7 and 17, Schmitt discloses identifying the plurality of focus areas comprises: comparing a first pecuniary score with a second pecuniary score; and identifying a focus area as a function of the comparison of the first pecuniary score and the second pecuniary score (Fig. 4 and 6, paragraphs 43 and 46, Here the system can capture a user's interaction with his or her devices via biometric sensors (e.g., steps, heart rate, blood pressure, skin acid level), as well as their usage (e.g., higher contrast screens, font size, audio levels) and map to a projected index for a typical user of similar age, demographics, and/or location to calculate whether a user is on track or off track to a projected expected Whealth index. If the computing device determines that the individual is off track, key areas for anticipated impact (e.g. lifestyle, fitness, wealth preparation for medical interventions etc.) can be proposed and tracked for progression against the individualized plan).
With regard to claims 9 and 19, Schmitt discloses the pecuniary strength metric comprises a plurality of advanced pecuniary scores (Fig. 4, paragraph 43).
Response to Arguments
Applicants' arguments filed on 02/17/2026 have been fully considered but they are not fully persuasive especially in light of the new prior art used in the rejections.
Applicants remark that “the combination of references does not disclose or teach wherein the pecuniary curve is constructed in the multidimensional space by: minimizing a vertical displacement of at least one discrete pecuniary score from the pecuniary curve; and locating at least one new pecuniary score on the pecuniary curve based on a range of a discrete set of the plurality of pecuniary scores”.
Examiner directs Applicants' attention to the office action above.
Conclusion
Please refer to form 892 for cited references.
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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