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 .
Response to Amendment
The Amendment filed on 07/02/2026 has been entered. Claims 1-20 remain pending in the application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stoner et al. (hereinafter Stoner), US 20220101441 A1, in view of Charnley, US 20030130923 A1, further in view of Manning et al. (hereinafter Manning), US 20050262002 A1.
Regarding independent claim 1, Stoner teaches a computer-implemented method for determining a beta value of at least one candidate relative to a class, the method comprising:
receiving, using at least one processor (Fig. 7, 754; [0145]), historical data for the at least one candidate (Fig. 15; [0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities) and historical data for the class ([0176] Energy data integrator 1506 may be configured to receive energy-related data from among environmental data source(s) 108. In a non-limiting example, the energy-related data may include simulated future price, supplier cost and demand for a number of energy sources (e.g., oil, gas, coal, biofuels, renewable fuel, renewable electric, hydroelectric, new technology and nuclear). In general, the type of energy-related data may include any suitable type of energy data, and the number of energy sources may include one or more energy sources that, together, may be useful for determining a climate transition risk. Energy data integrator 1506 may be further configured to convert the received energy-related data into one or more energy returns (see eq. 7 below). The energy return data may be used (after further processing described below) for building HLMs 1514, as part of determining the transition risk);
determining, using the at least one processor, a first set of regression coefficients comprising a first series of returns for the at least one candidate derived from the historical data for the at least one candidate ([0094] Financial data integrator 118 may pull time series data on stock prices for a benchmark, calculate the returns from the time series data on stock prices for the benchmark; [0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities. In some examples, the stock return data may include quarterly return data for one or more securities), and a second set of regression coefficients comprising a second series of returns for the class derived from the historical data for the class ([0105] FIG. 10 is a screen shot illustrating example GUI 1000, according to one exemplary embodiment. As illustrated, GUI 1000 may include one or more graphical representations of returns by subsector from 2016 to 2018; [0194] model training module 1602 may be configured to perform one or more groupings of historical stock return data 1614 into one or more hierarchies, such as by sector and/or by industry.
Stoner does not explicitly teach calculating, using the at least one processor, the beta value for the at least one candidate relative to the class as a covariance between the first set of regression coefficients and the second set of regression coefficients divided by a variance of the second set of regression coefficients; generating, using the at least one processor, an indication that the beta value for the at least one candidate is one of positive, negative, or zero; and transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the at least one candidate, wherein an indication of a negative beta value is a recommendation to add the at least one candidate to the class to create an updated class.
However, in the same field of endeavor, Charnley teaches calculating, using the at least one processor, the beta value for the at least one candidate relative to the class as a covariance between the first set of regression coefficients and the second set of regression coefficients divided by a variance of the second set of regression coefficients ([0047] The model uses a relative measure of returns variance--"beta"--that is calculated as the covariance between that investment's periodic returns and those of the asset class average or associated market-index, divided by the variance of the periodic returns for that the asset class average or associated index).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of a relative measure of returns variance--"beta" as suggested in Charnley into Stoner’s system because both of these systems are addressing selecting investments from a population of book-valued collective funds based on a determination of the past average investment performance of funds within an asset class. This modification would have been motivated by the desire to provide a method of selecting for investments within an asset class that is strongly predictive, and is reliable regardless of market conditions, and produces a magnitude of superior future performance that justifies the cost of practicing the selection process (Charnley, [0017]-[0018]).
The combination of Stoner and Charnley does not explicitly teach generating, using the at least one processor, an indication that the beta value for the at least one candidate is one of positive, negative, or zero; and transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the at least one candidate, wherein an indication of a negative beta value is a recommendation to add the at least one candidate to the class to create an updated class.
However, in the same field of endeavor, Manning teaches
generating, using the at least one processor, an indication that the beta value for the at least one candidate is one of positive, negative, or zero ([0023] FIG. 1 shows an exemplary embodiment of a system 5 according to the present invention which determines a level of risk in the market, and possibly adjusts user's market exposure based on the level of the market risk (e.g., "Beta"), the user's time horizon and risk tolerance. Beta can be defined as a measure of systematic risk based on the covariance of a portfolio in relation to a given market. In other words, Beta is a measurement of a portfolio's sensitivity to the volatility of a given market (e.g., the stock market). For example, a Beta of zero to the United States stock market may indicate that the user owns no equity-type financial assets (e.g., stocks), but rather holds only cash. Whereas, a Beta of 1.0 may indicate that the user owns 100% in equity-type financial assets with similar financial characteristics to the overall market; Fig. 2; [0050] Next, in step 230, the arrangement 10 performs an assessment of the market risk of negative returns based on the retrieved economic and market indicators. In particular, certain combinations of the above-referenced indicators can be used to generate the Traffic light signal data for determining the user's Beta 500 (see FIG. 6). These signals can be identified as "Red", "Yellow" or "Green" traffic light signals, as described below. The "Red" light signal promotes caution in the market by indicating, e.g., a high-risk scenario in the economy, and possibly signaling a reduction of Beta for the user's portfolio. The "Green" light signal promotes an opportunity in the market by indicating a low-risk scenario in the economy, and thus signaling an increase of Beta for the user's portfolio. The "Yellow" light signal generally indicates a neutral environment, and affirming that Beta for the user should remain unchanged. When both "Red" and "Green" traffic lights are turned on at the same time, or if neither one of these signal is triggered, the "Yellow" light is turned on by the arrangement 10; [0078] Referring to the process of the present invention shown in FIG. 2, in step 240, the arrangement 10 determines the appropriate Beta target for the respective user based on the user's guidelines provided in step 200 and the assessed market risk determined in step 230); and
transmitting to at least one display window of a graphical user interface (GUI) a graphical symbol corresponding to the indication of the beta value for the at least one candidate ([0050] Next, in step 230, the arrangement 10 performs an assessment of the market risk of negative returns based on the retrieved economic and market indicators. In particular, certain combinations of the above-referenced indicators can be used to generate the Traffic light signal data for determining the user's Beta 500 (see FIG. 6). These signals can be identified as "Red", "Yellow" or "Green" traffic light signals, as described below. The "Red" light signal promotes caution in the market by indicating, e.g., a high-risk scenario in the economy, and possibly signaling a reduction of Beta for the user's portfolio. The "Green" light signal promotes an opportunity in the market by indicating a low-risk scenario in the economy, and thus signaling an increase of Beta for the user's portfolio. The "Yellow" light signal generally indicates a neutral environment, and affirming that Beta for the user should remain unchanged. When both "Red" and "Green" traffic lights are turned on at the same time, or if neither one of these signal is triggered, the "Yellow" light is turned on by the arrangement 10; [0080] Thereafter, the arrangement 10 can provide recommendations to the user via the user devices 30, 40 for adjusting his or her Beta. In addition or alternatively, the arrangement may adjust the user's portfolio, if warranted, based on the determined Beta for the user. (Step 250)),
wherein an indication of a negative beta value is a recommendation to add the at least one candidate to the class to create an updated class ([0080] Thereafter, the arrangement 10 can provide recommendations to the user via the user devices 30, 40 for adjusting his or her Beta. In addition or alternatively, the arrangement may adjust the user's portfolio, if warranted, based on the determined Beta for the user. (Step 250)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of generating signal data for determining user’s beta value and using indicators to determine user portfolio’s sensibility as suggested in Manning into Stoner’s system because both of these systems are addressing determining a level of risk in the market, and for adjusting user's market exposure based on the level of risk. This modification would have been motivated by the desire to provide a financial engine for determining the level of risk in the market the economic and market indicators to identify which asset type is expected to provide the highest return over a predetermined time period (Manning, [0004]-[0005]).
Regarding dependent claim 2, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Stoner further teaches further comprising:
using a trained machine learning model to select the at least one candidate based on the historical data for the class ([0008] In one embodiment, the system may generate public equity indices and portfolio analysis tools. In one embodiment, the generated public equity indices may be benchmarked to one or more public indices such as the S&P 500 or the Russell 1000. In one embodiment, the system may uniquely leverage machine learning to neutralize climate change risk and integrate energy economics and financial analysis to optimize portfolio performance; Figs. 2-6; [0069] FIG. 2 is a flow diagram illustrating a method 200 of climate data processing and impact prediction, according to one exemplary embodiment. As illustrated, method 200 may have four phases; [0070] At phase 202, climate data analytics module 114 may generate one or more environmental metrics for one or more energy sources based on a selected scenario. The one or more operations implemented at phase 202 are discussed in further detail below, in conjunction with FIG. 3; [0071] At phase 204, climate data analytics module 114 may generate one or more profitability indicators for each of the one or more energy sources. For example, climate data analytics module 114 may generate the one or more profitability indicators based on the one or more environmental metrics generated during phase 202. The one or more operations implemented at phase 204 are discussed in further detail below, in conjunction with FIG. 4; [0072] At phase 206, climate data analytics module 114 may correlate each energy source with a financial subsector based on the one or more profitability indicators. For example, climate data analytics module 114 may downward deploy the profitability indicators generated in phase 204 to one or more financial subsectors down to one or more companies within each financial subsector. The one or more operations implemented during phase 206 are discussed in further detail below, in conjunction with FIG. 5; [0073] At phase 208, climate data analytics module 114 may generate an output data set for a user. For example, climate data analytics module 114 may provide a scoring based on a company's exposure to the energy industry and climate change using environmental and social and governance (ESG) metrics. The one or more operations implemented during phase 208 are discussed in further detail below, in conjunction with FIG. 6).
Regarding dependent claim 3, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Stoner further teaches wherein the machine learning model is trained on the historical data for the class and a set of parameters provided by a user ([0180] T-Risk generator 1512 may be configured to receive energy factor(s) from energy factor analyzer 1510 and historical stock return data from financial data integrator 1508. Based on the energy factor(s) and historical stock return data, T-Risk generator 1512 may train HLMs 1514, may predict future stock returns (via the trained HLMs 1514), and may generate one or more T-Risk scores for the predicted future returns based on multiple climate scenarios (e.g., using one or more parameters stored in storage 1516, such as Carbon BAU, Carbon Paris Aligned). In some examples, the T-Risk score(s) may be adjusted via one or more carbon emission parameters (stored for example in storage 1516). In a non-limiting example, T-Risk score(s) may also be scaled, such that a unit of output may indicate the number of universe interquartile ranges from a universe median. T-Risk generator 1512 is described further below with respect to FIG. 16; [0181] HLMs 1514 may be configured to predict future stock returns for security(s) that take into account energy factor(s). HLMs 1514 may first be trained on historical stock return data (via financial data integrator 1508) with respect to the identified energy factor(s) (identified via energy factor analyzer 1510). The trained HLMs 1514 may then be used to predict future stock returns (in accordance with the energy factor(s)). use more training data which can reduce the variance of the coefficients' estimates).
Regarding dependent claim 4, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Stoner further teaches wherein the at least one candidate comprises a plurality of candidates, each candidate of the plurality of candidates including a respective set of regression coefficients ([0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities. In some examples, the stock return data may include quarterly return data for one or more securities.); the method further comprising:
calculating, using the at least one processor, a respective beta value for each candidate of the plurality of candidates ([0207] As part of the model training, a beta estimation (vâr({circumflex over (β)}j) shown in eq. 9) is performed, and may provide an indication of efficacy of the model (as trained); [0209] At 1718, at least one T-Risk score may be determined).
generating, using the at least one processor, a beta matrix comprising the plurality of candidates and the respective beta values for each of the plurality of candidates ([0159] In some examples, transition risk may represent a directional climate risk matrix providing information to improve financial and environment performance of a portfolio of securities (such as equity investment portfolios, fixed income investment portfolios, etc.). The transition risk may represent a matrix in that T-Risk metrics may indicate both climate sensitivity and a direction of the climate sensitivity);
storing, using the at least one processor, the beta matrix in a beta database ([0188] Storage 1516 may be configured to store one or more parameters associated with one or more climate scenarios, such as, without being limited to, a Carbon BAU scenario, a 1.5-degree scenario, a 2.0-degree scenario and the like. In general the climate scenario parameters may be associated with any desired climate scenario, associated with one or more predetermined goals (such as a Carbon reduction goal) certain carbon goals or targets (e.g., goals established by UNFCCC), IPCC, and the like). Storage 1516 may also be configured to store one or more carbon emission parameters. In some examples, storage 1516 may also be configured to store one or more parameters associated with one or more of energy data integrator 1506, financial data integrator 1508, energy factor analyzer, T-Risk generator 1512, HLMs 1514, output module 1518, optional portfolio generator 1520 and optional backtesting module 1522); and
providing user interaction functionality with the beta database via the display window of the GUI ([0190] In some embodiments, output module 1518 (similar to output module 122 of FIG. 1) may produce a website, accessible by one or more users via application 110 (FIG. 1) executing on client device 102. The website may provide a dashboard that allows users to view results generated by T-Risk generator 1512, and/or optional portfolio generator 1520. In some embodiments, output module 1518 may generate one or more data files for electronic transfer to client device 102; [0191] Optional portfolio generator 1520 may be configured to use the results from T-Risk generator 1512 to project a portfolio performance relative to multiple climate scenarios. Portfolio generator 1520 may optimize the portfolio based on the selected climate scenarios, and the transition risk scores and/or metric(s) associated with the selected scenarios. In some examples, a user, via output module 1518 may be configured to interact with portfolio generator 1520, such as to adjust energy and/or financial characteristics. Portfolio generator 1520 may update the projected portfolio based on the adjusted characteristics and transition risk metric(s)).
Regarding dependent claim 5, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 4 that is incorporated. Stoner further teaches further comprising:
determining, by the at least one processor, a smallest negative beta candidate from among the plurality of candidates ([0110] In some embodiments, output module 122 may generate a spread of a security's expected performance using expected returns. A higher spread indicates a higher sensitivity towards economic and policy changes. A low spread indicates lower risk towards these changes);
transmitting to the at least one display window the smallest negative beta candidate ([0111] In some embodiments, output module 122 may generate a min max draw down score. The min max draw down score may reflect the spread of a constituent's expected performance as computed using Expected Returns for the Carbon Minimum and Carbon Maximum scenarios. A higher spread may be indicative of a higher risk towards economic and policy changes); and
automatically adding the smallest negative beta candidate to the class to create an updated class ([0191] Portfolio generator 1520 may update the projected portfolio based on the adjusted characteristics and transition risk metric(s)).
Regarding dependent claim 6, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Stoner teaches wherein the class is comprised of a plurality of items, the method further comprising:
receiving, using the at least one processor, historical data for each of the plurality of items (Fig. 15; [0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities; [0176] Energy data integrator 1506 may be configured to receive energy-related data from among environmental data source(s) 108. In a non-limiting example, the energy-related data may include simulated future price, supplier cost and demand for a number of energy sources (e.g., oil, gas, coal, biofuels, renewable fuel, renewable electric, hydroelectric, new technology and nuclear). In general, the type of energy-related data may include any suitable type of energy data, and the number of energy sources may include one or more energy sources that, together, may be useful for determining a climate transition risk. Energy data integrator 1506 may be further configured to convert the received energy-related data into one or more energy returns (see eq. 7 below). The energy return data may be used (after further processing described below) for building HLMs 1514, as part of determining the transition risk);
determining, using the at least one processor, a third set of regression coefficients for the historical data for each of the plurality of items ([0183] In general, HLM models 1514 of the present disclosure may include (a) one or more regression coefficients for various stocks (more specifically β coefficients, discussed further below) that represent a cohort common fixed effect for a stock and (b) one or more regression coefficients that represent a random effect of a stock on an industry group. For example, FIG. 21 illustrates an example graph of regression coefficients of a hierarchical linear model for two petroleum entities (e.g., Exxon Mobil and HollyFrontier) with respect to a supplier cost of gas and a price of oil, according to an exemplary embodiment. In this example, the combination of two petroleum entities represent an example industry group. In this example, both entities may include common cohort 2102 having a fixed effect for a stock. Each of the two entities may have their own separate random effect 2104-1, 2104-2 (e.g., due to their respective supplier cost for gas, price of oil and/or any combination thereof). In this example, the regression coefficients of the HLM model may include coefficient(s) based on cohort common fixed effect 2102 and separate coefficients based on random effects 2104-1 and 2104-2);
calculating, using the at least one processor, a beta value for each of the items relative to the class based on the third set of regression coefficients and the second sets of regression coefficients ([0185] In level-2 models, the level-1 regression coefficients (αij, βij) may be used as outcome variables and may be related to level-2 predictors. In the following example, the case of an intercept only model is utilized with the below equations:
αij=γoj +r 0ij (eq. 2)
βij=γ1j +r 1ij (eq. 3)
where: αf represents an intercept for an i-th stock in a j-th industry group; βij represents a slope for an i-th stock in a j-th industry group; γoj represents an overall intercept for an j-th industry group; γ1j represents an overall coefficient for a j-th industry group; r0ij represents a random effect of an i-th stock in a j-th industry group on the intercept; r1ij represents a random effect of an i-th stock in a j-th industry group on the slope; E(r0ij)=0; E(r1ij)=0; var(r0ij)=τ00; var(r1ij)=τ11; cov(r0ij, r1ij)=τ01; and cov represents a covariance).
Manning teaches
transmitting to the at least one display window the beta value for each of the items ([0050] Next, in step 230, the arrangement 10 performs an assessment of the market risk of negative returns based on the retrieved economic and market indicators. In particular, certain combinations of the above-referenced indicators can be used to generate the Traffic light signal data for determining the user's Beta 500 (see FIG. 6). These signals can be identified as "Red", "Yellow" or "Green" traffic light signals, as described below. The "Red" light signal promotes caution in the market by indicating, e.g., a high-risk scenario in the economy, and possibly signaling a reduction of Beta for the user's portfolio. The "Green" light signal promotes an opportunity in the market by indicating a low-risk scenario in the economy, and thus signaling an increase of Beta for the user's portfolio. The "Yellow" light signal generally indicates a neutral environment, and affirming that Beta for the user should remain unchanged. When both "Red" and "Green" traffic lights are turned on at the same time, or if neither one of these signal is triggered, the "Yellow" light is turned on by the arrangement 10).
Regarding dependent claim 7, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Stoner teaches wherein the at least one candidate comprises a plurality of candidates, each candidate of the plurality of candidates including a respective set of regression coefficients and the class is comprised of a plurality of items ([0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities. In some examples, the stock return data may include quarterly return data for one or more securities; [0176] Energy data integrator 1506 may be configured to receive energy-related data from among environmental data source(s) 108. In a non-limiting example, the energy-related data may include simulated future price, supplier cost and demand for a number of energy sources (e.g., oil, gas, coal, biofuels, renewable fuel, renewable electric, hydroelectric, new technology and nuclear). In general, the type of energy-related data may include any suitable type of energy data, and the number of energy sources may include one or more energy sources that, together, may be useful for determining a climate transition risk. Energy data integrator 1506 may be further configured to convert the received energy-related data into one or more energy returns (see eq. 7 below). The energy return data may be used (after further processing described below) for building HLMs 1514, as part of determining the transition risk); the method further comprising:
calculating, using the at least one processor, a respective beta value for each candidate of the plurality of candidates ([0183] In general, HLM models 1514 of the present disclosure may include (a) one or more regression coefficients for various stocks (more specifically β coefficients, discussed further below) that represent a cohort common fixed effect for a stock and (b) one or more regression coefficients that represent a random effect of a stock on an industry group. For example, FIG. 21 illustrates an example graph of regression coefficients of a hierarchical linear model for two petroleum entities (e.g., Exxon Mobil and HollyFrontier) with respect to a supplier cost of gas and a price of oil, according to an exemplary embodiment. In this example, the combination of two petroleum entities represent an example industry group. In this example, both entities may include common cohort 2102 having a fixed effect for a stock. Each of the two entities may have their own separate random effect 2104-1, 2104-2 (e.g., due to their respective supplier cost for gas, price of oil and/or any combination thereof). In this example, the regression coefficients of the HLM model may include coefficient(s) based on cohort common fixed effect 2102 and separate coefficients based on random effects 2104-1 and 2104-2);
determining, by the at least one processor, a smallest negative beta candidate from among the plurality of candidates ([0110] In some embodiments, output module 122 may generate a spread of a security's expected performance using expected returns. A higher spread indicates a higher sensitivity towards economic and policy changes. A low spread indicates lower risk towards these changes);
receiving, using the at least one processor, historical data for each of the plurality of items (Fig. 15; [0177] Financial data integrator 1508 (similar to financial data integrator 118 of FIG. 1) may be configured to retrieve financial data from among financial data source(s) 106. In some examples, the financial data may include historical stock return data associated with one or more financial securities; [0176] Energy data integrator 1506 may be configured to receive energy-related data from among environmental data source(s) 108. In a non-limiting example, the energy-related data may include simulated future price, supplier cost and demand for a number of energy sources (e.g., oil, gas, coal, biofuels, renewable fuel, renewable electric, hydroelectric, new technology and nuclear). In general, the type of energy-related data may include any suitable type of energy data, and the number of energy sources may include one or more energy sources that, together, may be useful for determining a climate transition risk. Energy data integrator 1506 may be further configured to convert the received energy-related data into one or more energy returns (see eq. 7 below). The energy return data may be used (after further processing described below) for building HLMs 1514, as part of determining the transition risk);
determining, using the at least one processor, a third set of regression coefficients for the historical data for each of the plurality of items ([0183] In general, HLM models 1514 of the present disclosure may include (a) one or more regression coefficients for various stocks (more specifically β coefficients, discussed further below) that represent a cohort common fixed effect for a stock and (b) one or more regression coefficients that represent a random effect of a stock on an industry group. For example, FIG. 21 illustrates an example graph of regression coefficients of a hierarchical linear model for two petroleum entities (e.g., Exxon Mobil and HollyFrontier) with respect to a supplier cost of gas and a price of oil, according to an exemplary embodiment. In this example, the combination of two petroleum entities represent an example industry group. In this example, both entities may include common cohort 2102 having a fixed effect for a stock. Each of the two entities may have their own separate random effect 2104-1, 2104-2 (e.g., due to their respective supplier cost for gas, price of oil and/or any combination thereof). In this example, the regression coefficients of the HLM model may include coefficient(s) based on cohort common fixed effect 2102 and separate coefficients based on random effects 2104-1 and 2104-2);
calculating, using the at least one processor, a beta value for each of the items relative to the class based on the third set of regression coefficients and the second sets of regression coefficients ([0185] In level-2 models, the level-1 regression coefficients (αij, βij) may be used as outcome variables and may be related to level-2 predictors. In the following example, the case of an intercept only model is utilized with the below equations:
αij=γoj +r 0ij (eq. 2)
βij=γ1j +r 1ij (eq. 3)
where: αf represents an intercept for an i-th stock in a j-th industry group; βij represents a slope for an i-th stock in a j-th industry group; γoj represents an overall intercept for an j-th industry group; γ1j represents an overall coefficient for a j-th industry group; r0ij represents a random effect of an i-th stock in a j-th industry group on the intercept; r1ij represents a random effect of an i-th stock in a j-th industry group on the slope; E(r0ij)=0; E(r1ij)=0; var(r0ij)=τ00; var(r1ij)=τ11; cov(r0ij, r1ij)=τ01; and cov represents a covariance);
determining, by the at least one processor, a highest positive beta item from among the plurality of items ([0110] In some embodiments, output module 122 may generate a spread of a security's expected performance using expected returns. A higher spread indicates a higher sensitivity towards economic and policy changes. A low spread indicates lower risk towards these changes); and
transmitting to the at least one display window the smallest beta candidate and the highest positive beta item and/or automatically adding the smallest negative beta candidate to the class and removing the highest positive beta item from the class to create an updated class ([0111] In some embodiments, output module 122 may generate a min max draw down score. The min max draw down score may reflect the spread of a constituent's expected performance as computed using Expected Returns for the Carbon Minimum and Carbon Maximum scenarios. A higher spread may be indicative of a higher risk towards economic and policy changes; [0191] Portfolio generator 1520 may update the projected portfolio based on the adjusted characteristics and transition risk metric(s)).
Regarding dependent claim 8, the combination of Stoner, Charnley and Manning teaches all the limitations as set forth in the rejection of claim 7 that is incorporated. Stoner teaches, further comprising:
generating, using the at least one processor, a beta matrix comprising the plurality of candidates and the respective beta values for each of the plurality of candidates, and the plurality of items and the respective beta values for each of the plurality of items ([0159] In some examples, transition risk may represent a directional climate risk matrix providing information to improve financial and environment performance of a portfolio of securities (such as equity investment portfolios, fixed income investment portfolios, etc.). The transition risk may represent a matrix in that T-Risk metrics may indicate both climate sensitivity and a direction of the climate sensitivity);
storing, using the at least one processor, the beta matrix in a beta database ([0188] Storage 1516 may be configured to store one or more parameters associated with one or more climate scenarios, such as, without being limited to, a Carbon BAU scenario, a 1.5-degree scenario, a 2.0-degree scenario and the like. In general the climate scenario parameters may be associated with any desired climate scenario, associated with one or more predetermined goals (such as a Carbon reduction goal) certain carbon goals or targets (e.g., goals established by UNFCCC), IPCC, and the like). Storage 1516 may also be configured to store one or more carbon emission parameters. In some examples, storage 1516 may also be configured to store one or more parameters associated with one or more of energy data integrator 1506, financial data integrator 1508, energy factor analyzer, T-Risk generator 1512, HLMs 1514, output module 1518, optional portfolio generator 1520 and optional backtesting module 1522); and
providing user interaction functionality with the beta database via the display window of the GUI ([0190] In some embodiments, output module 1518 (similar to output module 122 of FIG. 1) may produce a website, accessible by one or more users via application 110 (FIG. 1) executing on client device 102. The website may provide a dashboard that allows users to view results generated by T-Risk generator 1512, and/or optional portfolio generator 1520. In some embodiments, output module 1518 may generate one or more data files for electronic transfer to client device 102; [0191] Optional portfolio generator 1520 may be configured to use the results from T-Risk generator 1512 to project a portfolio performance relative to multiple climate scenarios. Portfolio generator 1520 may optimize the portfolio based on the selected climate scenarios, and the transition risk scores and/or metric(s) associated with the selected scenarios. In some examples, a user, via output module 1518 may be configured to interact with portfolio generator 1520, such as to adjust energy and/or financial characteristics. Portfolio generator 1520 may update the projected portfolio based on the adjusted characteristics and transition risk metric(s)).
Regarding independent claim 9, it is a system claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Stoner further teaches the system (Fig. 1, 100; [0041]) comprising:
at least one server (Fig. 1, 104; [0041]; [0046]);
a storage device that stores instructions (Fig. 7, 756; [0147]); and
at least one processor that executes instructions the instructions to perform a method (Fig. 7, 754; [0145]).
Regarding dependent claim 10, it is a system claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Regarding dependent claim 11 it is a system claim that corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claim 3 above.
Regarding dependent claim 12, it is a system claim that corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 13, it is a system claim that corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claim 5 above.
Regarding dependent claim 14, it is a system claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding dependent claim 15, it is a system claim that corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Regarding dependent claim 16, it is a system claim that corresponding to the method of claim 8. Therefore, it is rejected for the same reason as claim 8 above.
Regarding independent claim 17, it is a medium claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Stoner further teaches a non-transitory computer-readable medium storing instructions for determining a beta value of at least one candidate relative to a class, the instructions configured to cause at least one processor to perform a method ([0047]; [0068]; [0138]).
Regarding dependent claim 18, it is a system claim that corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claim 4 above.
Regarding dependent claim 19, it is a system claim that corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claim 6 above.
Regarding dependent claim 20, it is a system claim that corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claim 7 above.
Response to Arguments
Applicant's arguments filed 07/02/2026 have been fully considered. Each of applicant’s remarks is set forth, followed by examiner’s response.
(1) Regarding 35 U.S.C. 101 rejections, Applicant’s amendments to the Claims 1-20 have overcome the rejections. Rejections under 35 U.S.C 101 to claims 1-20 are withdrawn.
(2) Applicant’s prior art arguments with respect to the pending claims have been considered but they are moot in view of the new ground(s) of rejections presented above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Kapoustin (US 20150039652 A1) discloses automatically analyzing performance of a group of entities.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER WELCH can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMY P HOANG/Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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