DETAILED ACTION
Introduction
This Non-Final Office Action is in response to the application with serial number 19/191,023, filed on April 28, 2025.
Claims 1-14 are pending.
Information Disclosure Statement
The information disclosure statement filed on April 28, 2025, has been considered.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-14 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 1-14 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: risk analysis (as evidenced by the preamble of exemplary independent claim 1), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). Additionally, note that mathematical concepts, including mathematical relationships and formulas, are ineligible abstract ideas. See Id. The limitations of exemplary claim 1 include: “defining a plurality of normal-time objective functions;” “defining an emergency-time objective function . . . [with] input being a predetermined equation including . . . the plurality of normal-time objective functions each multiplied by a second contribution degree;” and “outputting the second contribution degrees in the emergency-time objective function.” The steps are all steps for managing personal behavior related to the abstract idea of risk analysis that, when considered alone and in combination, are part of the abstract idea of risk analysis. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of risk analysis. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes defining objective functions that characterize risk.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a processor in independent claims 1 and 8). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20100185557 A1 to Hunter et al. (hereinafter ‘HUNTER’) in view of US 11615473 B2 to Shakfeh (hereinafter ‘SHAKFEH’).
Claim 1
HUNTER discloses a risk analysis method executed by a risk analysis system that executes risk analysis for reducing a risk that occurs in an emergency (see ¶[0007]-[0009] and [0105]-[0108]; describe how assets behave in times of crisis using an objective function. Maximize return while minimizing the overall risk), the risk analysis method comprising: by a processor of the risk analysis system (see ¶[0014]; the steps of the method are performed in a processor which has access to storage in which are stored historic returns data for the assets and programs implementing a plurality of objective functions and a plurality of adjustments to the objective functions),
defining a plurality of normal-time objective functions each of which is an objective function representing an objective to be normally achieved (see abstract; optimization may involve different objective functions) and has, as input to a predetermined function that outputs a predetermined value in response to the input, a predetermined equation that includes, as first-order terms or factors, a plurality of explanatory factors each multiplied by a first contribution degree relating to the relevant explanatory factor (see ¶[0158]-[0160]; a linear objective function that uses option values as weights. See also ¶[0170]-[0171]; external factors such as price or broad market index); and
defining an emergency-time objective function that is an objective function representing an objective to be achieved in order to reduce the risk (see ¶[0106] and [0173]; describe how assets behave during a time of crisis. Incorporate elasticity of liquidity into the objective function).
HUNTER does not specifically disclose, but SHAKFEH discloses, as a function that outputs a value in response to input, the input being a predetermined equation including, as first-order terms or factors, the plurality of normal-time objective functions each multiplied by a second contribution degree relating to the relevant normal-time objective function (see abstract and col 2, ln 51-col 2, ln 2; cost analysis includes risk minimization. Apply different weights to categories of threats); and
outputting the second contribution degrees in the emergency-time objective function through machine learning of second sample data relating to the emergency-time objective function (see again abstract; apply different weights to categories of threats. Measure how a population or area responds to threats. See also col 1, ln 29-44; use machine learning analysis).
HUNTER further discloses and extracting, on a basis of the second contribution degrees, a prioritized normal-time objective function to be optimized with priority from the normal-time objective functions (see optimize a set of assets using an objective function across scenarios).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim 8
HUNTER discloses a risk analysis system that executes risk analysis for reducing a risk that occurs in an emergency (see ¶[0007]-[0009] and [0105]-[0108]; describe how assets behave in times of crisis using an objective function. Maximize return while minimizing the overall risk), wherein a processor of the risk analysis system (see ¶[0014]; the steps of the method are performed in a processor which has access to storage in which are stored historic returns data for the assets and programs implementing a plurality of objective functions and a plurality of adjustments to the objective functions) defines a plurality of normal-time objective functions each of which is an objective function representing an objective to be normally achieved (see abstract; optimization may involve different objective functions) and has, as input to a predetermined function that outputs a predetermined value in response to the input, a predetermined equation that includes, as first-order terms or factors, a plurality of explanatory factors each multiplied by a first contribution degree relating to the relevant explanatory factor (see ¶[0158]-[0160]; a linear objective function that uses option values as weights. See also ¶[0170]-[0171]; external factors such as price or broad market index);
defines an emergency-time objective function that is an objective function representing an objective to be achieved in order to reduce the risk (see ¶[0106] and [0173]; describe how assets behave during a time of crisis. Incorporate elasticity of liquidity into the objective function).
HUNTER does not specifically disclose, but SHAKFEH discloses, as a function that outputs a value in response to input, the input being a predetermined equation including, as first-order terms or factors, the plurality of normal-time objective functions each multiplied by a second contribution degree relating to the relevant normal-time objective function (see abstract and col 2, ln 51-col 2, ln 2; cost analysis includes risk minimization. Apply different weights to categories of threats), and
outputs the second contribution degrees in the emergency-time objective function through machine learning of second sample data relating to the emergency-time objective function (see again abstract; apply different weights to categories of threats. Measure how a population or area responds to threats. See also col 1, ln 29-44; use machine learning analysis).
HUNTER further discloses and extracts, on a basis of the second contribution degrees, a prioritized normal-time objective function to be optimized with priority from the normal-time objective functions (see optimize a set of assets using an objective function across scenarios).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim(s) 2-6 and 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20100185557 A1 to HUNTER et al. and US 11615473 B2 to SHAKFEH as applied to claim 1 above, and further in view of US 20230316167 A1 to Suzuki et al. (hereinafter ‘SUZUKI’).
Claim 2
The combination of HUNTER and SHAKFEH discloses the risk analysis method according to claim 1.
The combination of HUNTER and SHAKFEH does not specifically disclose, but SUZUKI discloses, further comprising: by the processor, outputting the first contribution degrees in the prioritized normal-time objective function through machine learning of first sample data relating to the prioritized normal-time objective function and extracting the explanatory factors corresponding to a predetermined number of first contribution degrees in a descending order of the first contribution degrees (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 3
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis method according to claim 2.
HUNTER does not specifically disclose, but SUZUKI discloses, further comprising: by the processor, managing the explanatory factors and countermeasures for reducing the risk in association with each other; and outputting the countermeasures corresponding to the extracted explanatory factors (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 4
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis method according to claim 2.
HUNTER does not specifically disclose, but SHAKFEH discloses, wherein the processor outputs the first contribution degrees through machine learning for each sample of the first sample data (see col 1, ln 28-44; the method include machine learning for risk reduction).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim 5
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis method according to claim 3.
HUNTER does not specifically disclose, but SUZUKI discloses, wherein the processor outputs, together with the countermeasures, priorities of the countermeasures corresponding to the explanatory factors which priorities depend on magnitudes of the predetermined number of first contribution degrees (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 6
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis method according to claim 3.
HUNTER does not specifically disclose, but SHAKFEH discloses, wherein the risk is infection aggravation during an infection pandemic (see col 1, ln 20-25 and col 4, ln 37-65; analyze future events that impact individuals. Stakes may be high when it comes to pandemic potential),
each of the explanatory factors is data relating to a health state of each subject (see col 1, ln 20-25 and col 4, ln 37-65; analyze future events that impact individuals. Stakes may be high when it comes to pandemic potential),
each of the normal-time objective functions represents a relation between an infection situation of disease of each subject and each explanatory factor (see col 18, ln 52-col 19, ln 36; resilience relates to a set of relevant factors and objectives and weights), and
the emergency-time objective function represents a relation between a situation of the infection aggravation during the infection pandemic and each normal-time objective function (see col 18, ln 52-col 19, ln 36; resilience relates to a set of relevant factors and objectives and weights).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim 9
The combination of HUNTER and SHAKFEH discloses the risk analysis system according to claim 8.
The combination of HUNTER and SHAKFEH does not specifically disclose, but SUZUKI discloses, wherein the processor outputs the first contribution degrees in the prioritized normal-time objective function through machine learning of first sample data relating to the prioritized normal-time objective function and extracts the explanatory factors corresponding to a predetermined number of first contribution degrees in a descending order of the first contribution degrees (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 10
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis system according to claim 9.
HUNTER does not specifically disclose, but SUZUKI discloses, wherein the processor manages the explanatory factors and countermeasures for reducing the risk in association with each other, and outputs the countermeasures corresponding to the extracted explanatory factors (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 11
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis system according to claim 9.
HUNTER does not specifically disclose, but SHAKFEH discloses, the risk analysis system according to wherein the processor outputs the first contribution degrees through machine learning for each sample of the first sample data (see col 1, ln 28-44; the method include machine learning for risk reduction).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim 12
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis system according to claim 10.
HUNTER does not specifically disclose, but SUZUKI discloses, wherein the processor outputs, together with the countermeasures, priorities of the countermeasures corresponding to the explanatory factors which priorities depend on magnitudes of the predetermined number of first contribution degrees (see ¶[0121]; adjust weights so that ranks are arranged in a descending order and match the priority ranks of input constraint conditions).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SUZUKI discloses optimization that arranges weights in descending order. It would have been obvious to arrange the weights as taught by SUZUKI in the system executing the method of HUNTER with the motivation to optimize a portfolio of assets.
Claim 13
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis system according to claim 10.
HUNTER does not specifically disclose, but SHAKFEH discloses, wherein the risk is infection aggravation during an infection pandemic (see col 1, ln 20-25 and col 4, ln 37-65; analyze future events that impact individuals. Stakes may be high when it comes to pandemic potential),
each of the explanatory factors is data relating to a health state of each subject (see col 1, ln 20-25 and col 4, ln 37-65; analyze future events that impact individuals. Stakes may be high when it comes to pandemic potential),
each of the normal-time objective functions represents a relation between an infection situation of each disease of each subject and each explanatory factor (see col 18, ln 52-col 19, ln 36; resilience relates to a set of relevant factors and objectives and weights), and
the emergency-time objective function represents a relation between a situation of the infection aggravation during the infection pandemic and each normal-time objective function (see col 18, ln 52-col 19, ln 36; resilience relates to a set of relevant factors and objectives and weights).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. SHAKFEH discloses resilience measurement using objective functions that describe the ability to respond to threats. It would have been obvious for one of ordinary skill in the art at the time of invention to include the different weights for categories of threats as taught by SHAKFEH in the system executing the method of HUNTER with the motivation to minimize risk and optimize objectives over a time period that includes threats and crises.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20100185557 A1 to HUNTER et al., US 11615473 B2 to SHAKFEH , and US 20230316167 A1 to SUZUKI et al. as applied to claims 1 and 3 above, and further in view of US 20230316193 A1 to Coburn et al. (hereinafter’ COBURN’).
Claim 7
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis method according to claim 3.
The combination of HUNTER, SHAKFEH, and SUZUKI does not specifically disclose, but COBURN discloses, wherein the risk is delay in delivery of a product in an event of an earthquake (see ¶[0022] and [0042]; a number of products delayed. An earthquake), each of the explanatory factors is data relating to an operation of each factory that produces a component forming the product (see ¶[0166] industrial incidents),
each of the normal-time objective functions represents a relation between number of days of delay in delivery of each component supplied by each factory and each explanatory factor (see ¶[0022]; downtime), and
the emergency-time objective function represents a relation between number of days of delay in the delivery of the product in the event of the earthquake and each normal- time objective function (see again ¶[0022]; downtime).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. COBURN discloses estimating risks on technical systems in times of an earthquake. It would have been obvious for one of ordinary skill in the art at the time of invention to include the estimation of risks as taught by COBURN in the system executing the method of HUNTER with the motivation to model risks in times of crisis.
Claim 14
The combination of HUNTER, SHAKFEH, and SUZUKI discloses the risk analysis system according to claim 10.
The combination of HUNTER, SHAKFEH, and SUZUKI does not specifically disclose, but COBURN discloses, wherein the risk is delay in delivery of a product in an event of an earthquake see ¶[0022] and [0042]; a number of products delayed. An earthquake), each of the explanatory factors is data relating to an operation of each factory that produces a component forming the product (see ¶[0166] industrial incidents),
each of the normal-time objective functions represents a relation between number of days of delay in delivery of each component supplied by each factory and each explanatory factor (see ¶[0022]; downtime), and
the emergency-time objective function represents a relation between number of days of delay in the delivery of the product in the event of the earthquake and each normal- time objective function (see again ¶[0022]; downtime).
HUNTER discloses resource allocation techniques that includes objective functions that are optimized for risk that consider how assets behave during a time of crisis. COBURN discloses estimating risks on technical systems in times of an earthquake. It would have been obvious for one of ordinary skill in the art at the time of invention to include the estimation of risks as taught by COBURN in the system executing the method of HUNTER with the motivation to model risks in times of crisis.
Conclusion
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624
