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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 20, 2026 has been entered.
In response to Applicant’s claims filed on February 20, 2026, claims 1-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 02/20/2026. In this action claim(s)
1-2, 6, 8-9, 13, 15-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mayenberger (US Pub. No. 20150073956) and Murikipudi et al. (US Pub. No. 20170011466) and Kurtz et al. (US Pub. No. 20160232465) in further view of Pai et al. (US Pub. No. 20170140312). The Pai et al. reference has been added to address the amendment of based on the first score and the second score, generating a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model.
Applicant’s arguments:
In regards to claim 1 on Page(s) 9, applicant argues “Here, any observations, evaluations, or judgements that may be involved in the claim limitations are not recited in the claim. For example, current claim 1 does not recite any step of performing a determination or any other observation, evaluation, or judgement, let alone recite any determination that can be performed within the human mind.”
Examiner’s Reply:
The examiner respectfully disagrees and would like to point out that human mind using computer as a tool is fully capable of generating models, scores, and interfaces with various inputs. These steps are also directed towards mental processes and mathematical concepts.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues “Here, a technical problem exists in the field of configuring computational models across data types, weight values, threshold values, and other configuration settings. As explained in spec., para. [0022]-[0023].”
Examiner’s Reply:
The examiner notes that the computer (being used as a generic tool) as recited in the claims is
being used for risk modeling. The use of mathematical
calculations even with various factors does not improve the functioning of a computer. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim medium (claims 1-7), a method (claims 8-14), a system (claims 15-20), are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mathematical Concepts & Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 8, and 15 are directed towards the Mathematical Concepts & Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1, 8, and 15 recites the following limitations directed towards a Mathematical Concepts & Mental Processes:
generating the first computation model configured with the first set of configuration settings (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a model);
generating the second computation model configured with the second set of configuration settings (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a score);
using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a score);
using the second computational model to generate a second score based on the second set of data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a score); and
based on the first score and the second score, generating a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a GUI).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 8, and 15:
a set of processing units; and
a non-transitory machine-readable medium storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to:
in response to receiving a request from a client device for an overall score for an entity, retrieving a first set of data associated with the entity and a second set of data associated with the entity (recites insignificant extra solution activity that amounts to data gathering);
receiving a first set of configuration settings for a first computational model (recites insignificant extra solution activity that amounts to data gathering);
receiving a second set of configuration settings for a second computational model (recites insignificant extra solution activity that amounts to data gathering);
storing the first computational model with the first set of the data and the second computational model with the second set of the data (recites insignificant extra solution activity that amounts to storing data); and
providing the GUI to the client device, wherein a display of the overall score in the GUI is updated based on one or more user inputs to the one or more controls (recites insignificant extra solution activity that amounts to transmitting data).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include
additional elements that are sufficient to amount to significantly more than the judicial
exception. Because the additional elements of the independent claims amount to insignificant
extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 8, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 9, and 16:
Step 2A, prong one of the 2019 PEG:
wherein the second score is for the category, wherein the overall score is determined by selecting one of the first and second scores having the higher score as the overall score (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to select a score).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3, 10, and 17:
Step 2A, prong one of the 2019 PEG:
using a third computational model to generate a third score for a second category based on the first set of data associated with the entity (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a score); and
using a fourth computational model to generate a fourth score for the second category based on the second set of data associated with the entity (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a score), wherein determining the overall score comprises:
selecting one of the first and second scores having the higher score as a first high score for the first category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to select a score);
selecting one of the third and fourth scores having the higher score as a second high score for the second category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to select a score); and
calculating a weighted average of the first high score for the first category and the second high score for the second category as the overall score ((The limitation recites a mathematical concept); calculating an average).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4, 11, and 18:
Step 2A, prong one of the 2019 PEG:
determining a first level for the first category from a plurality of levels based on a first defined threshold, a second defined threshold, and the first high score for the first category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine a level); and
determining a second level for the second category from the plurality of based on a third defined threshold, a fourth defined threshold, and the second high score for the second category (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determine a level).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5, 12, and 19:
Step 2A, prong one of the 2019 PEG:
wherein the first score is for a first category, wherein the second score is for a second category, wherein the overall score is determined by calculating a weighted average of the first score and the second score as the overall score (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a score).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 13, and 20:
Step 2A, prong one of the 2019 PEG:
using the first computational model to generate a third score based on the third set of the data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a score);
using the second computational model to generate a fourth score based on the fourth set of data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by generating a score); and
determining the second overall score for the second entity based on the third score and the fourth score (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a score).
Step 2A Prong Two Analysis:
wherein the request is a first request, wherein the overall score is a first overall score for a first entity, wherein the program further comprises sets of instructions for:
in response to receiving a second request from the client device for a second overall score for a second entity, retrieving a third set of data associated with the second entity and a fourth set of data associated with the second entity (recites insignificant extra solution activity that amounts to data gathering).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7 and 14:
Step 2A, prong one of the 2019 PEG:
wherein the program further comprises a set of instructions for determining an overall level from a plurality of levels based on a first defined threshold, a second defined threshold, and the overall score (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by determining a level).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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) 1-2, 6, 8-9, 13, 15-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mayenberger (US Pub. No. 20150073956) and Murikipudi et al. (US Pub. No. 20170011466) and Kurtz et al. (US Pub. No. 20160232465) in further view of Pai et al. (US Pub. No. 20170140312).
With respect to claim 1, Mayenberger teaches a non-transitory machine-readable medium storing a program executable by at least one processing unit of a device, the program comprising sets of instructions for:
in response to receiving a request from a client device for an overall score for an entity, retrieving a first set of data associated with the entity and a second set of data associated with the entity (“The algorithm may select each LOB from among a plurality of LOBs operated by an entity. The algorithm may calculate a total potential risk measure for each of a plurality of model-application pairs associated with the selected LOB,” See Paragraph 73);
using the second computational model to generate a second score based on the second set of data (“A potential risk score or rating may be determined for each model.sub.i . . . I-application.sub.j . . . J pair. The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated,” See Paragraph 171); and
determining the overall score for the entity based on the first score and the second score (“identify a total potential risk due to models used by applications of the LOB,” See Paragraph 34). Mayenberger does not disclose configuring a second computation model based on a second set of configuration settings.
However, Murikipudi et al. teaches receiving a first set of configuration settings for a first computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the first computation model configured with the first set of configuration settings for a second computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
receiving a second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the second computation model configured with the second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger with Murikipudi et al. This would have facilitated dynamic risk detection. See Murikipudi et al. Paragraph(s) 1. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. does not disclose using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity.
However, Kurtz et al. discloses using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity (Paragraph 33 discloses determine risk scores for a number of entities and Paragraph 35 discloses the risk model generator 210 uses at least one of the following risk factors in the risk model to calculate risk of entity)
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. with Kurtz et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. and Kurtz et al. does not disclose based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model.
However, Pai et al. discloses based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model (Paragraph 9 discloses generating a dynamic graphical user interface for the forecasted risk for the risk factors and the forecasted vulnerability scores for third parties within the geographic region);
store the first computational model with the first set of the data and the second computational model with the second set of the data (Paragraph 43 discloses engines may include a risk network model engine 420 (see FIG. 2A), baseline risk model engine 422 (see FIG. 5), risk signal detection model engine 424 (see FIG. 6), risk projection model engine 426 (see FIG. 7), and third party vulnerability score forecast engine 428 (see FIG. 8). Each of these models/engines 420-428 provide for certain aspects of the overall risk model to assess risk of third parties of a user); and
provide the GUI to the client device, wherein a display of the overall score in the GUI is updated based on one or more user inputs to the one or more controls (Paragraph 65 discloses financial risk 812 may be updated on a quarterly basis or otherwise, and the geographic risk may be updated on a real-time or up-to-date basis. A graph or other report 820 may be presented to a risk manager or other user. It should be understood that the above weights and updates are illustrative and that alternative weights and updates may be utilized. The vulnerability score may be a projection for a third party as it is a function of the risk projections for the active risk factors that exists within that geographic region).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Pai et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
The Mayenberger reference as modified by Murikipudi et al. and Kurtz et al. and Pai et al. teaches all the limitations of claim 1. With respect to claim 2, Mayenberger teaches the non-transitory machine-readable medium of claim 1, wherein the first score is for a category, wherein the second score is for the category, wherein the overall score is determined by selecting one of the first and second scores having the higher score as the overall score (“potential risk score or rating may be determined for each model.sub.i . . . I-application.sub.j . . . J pair. The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated. The aggregated risk score may correspond to a potential risk exposure associated with all model.sub.i . . . I-application.sub.j . . . J pairs,” See Paragraph 171).
The Mayenberger reference as modified by Murikipudi et al. and Kurtz et al. and Pai et al. teaches all the limitations of claim 1. With respect to claim 6, Mayenberger teaches the non-transitory machine-readable medium of claim 1, wherein the request is a first request, wherein the overall score is a first overall score for a first entity, wherein the program further comprises sets of instructions for:
in response to receiving a second request from the client device for a second overall score for a second entity, retrieving a third set of data associated with the second entity and a fourth set of data associated with the second entity (“a raw risk score. The raw risk score may correspond to: M.sub.iC*A.sub.jC*M.sub.i,j=Raw Risk Score Equation 1 [0038] In equation 1, M.sub.iC represents a complexity of a model I,” See Paragraph 37-38);
using the first computational model to generate a third score based on the third set of the data (“a raw risk score may be calculated for each model that is used by the application,” See Paragraph 40);
using the second computational model to generate a fourth score based on the fourth set of data (“a raw risk score may be calculated for each model that is used by the application,” See Paragraph 40); and
determining the second overall score for the second entity based on the third score and the fourth score (“total potential risk exposure for a plurality of model-application pairs,” See Paragraph 71).
With respect to claim 8, Mayenberger teaches a method comprising:
in response to receiving a request from a client device for an overall score for an entity, retrieving a first set of data associated with the entity and a second set of data associated with the entity (“The algorithm may select each LOB from among a plurality of LOBs operated by an entity. The algorithm may calculate a total potential risk measure for each of a plurality of model-application pairs associated with the selected LOB,” See Paragraph 73);
using the second computational model to generate a second score based on the second set of data (“A potential risk score or rating may be determined for each model.sub.i . . . I-application.sub.j . . . J pair. The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated,” See Paragraph 171); and
determining the overall score for the entity based on the first score and the second score (“identify a total potential risk due to models used by applications of the LOB,” See Paragraph 34). Mayenberger does not disclose configuring a second computation model based on a second set of configuration settings.
However, Murikipudi et al. teaches receiving a first set of configuration settings for a first computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the first computation model configured with the first set of configuration settings for a second computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
receiving a second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the second computation model configured with the second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger with Murikipudi et al. This would have facilitated dynamic risk detection. See Murikipudi et al. Paragraph(s) 1. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. does not disclose using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity.
However, Kurtz et al. discloses using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity (Paragraph 33 discloses determine risk scores for a number of entities and Paragraph 35 discloses the risk model generator 210 uses at least one of the following risk factors in the risk model to calculate risk of entity)
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. with Kurtz et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. and Kurtz et al. does not disclose based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model.
However, Pai et al. discloses based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model (Paragraph 9 discloses generating a dynamic graphical user interface for the forecasted risk for the risk factors and the forecasted vulnerability scores for third parties within the geographic region);
store the first computational model with the first set of the data and the second computational model with the second set of the data (Paragraph 43 discloses engines may include a risk network model engine 420 (see FIG. 2A), baseline risk model engine 422 (see FIG. 5), risk signal detection model engine 424 (see FIG. 6), risk projection model engine 426 (see FIG. 7), and third party vulnerability score forecast engine 428 (see FIG. 8). Each of these models/engines 420-428 provide for certain aspects of the overall risk model to assess risk of third parties of a user); and
provide the GUI to the client device, wherein a display of the overall score in the GUI is updated based on one or more user inputs to the one or more controls (Paragraph 65 discloses financial risk 812 may be updated on a quarterly basis or otherwise, and the geographic risk may be updated on a real-time or up-to-date basis. A graph or other report 820 may be presented to a risk manager or other user. It should be understood that the above weights and updates are illustrative and that alternative weights and updates may be utilized. The vulnerability score may be a projection for a third party as it is a function of the risk projections for the active risk factors that exists within that geographic region).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Pai et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
With respect to claim 9, it is rejected on grounds corresponding to above rejected claim 2, because claim 9 is substantially equivalent to claim 2.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 6, because claim 13 is substantially equivalent to claim 6.
With respect to claim 15, Mayenberger teaches a system comprising:
a set of processing units (See Fig. 1); and
a non-transitory machine-readable medium (See Fig. 1) storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to:
in response to receiving a request from a client device for an overall score for an entity, retrieving a first set of data associated with the entity and a second set of data associated with the entity (“The algorithm may select each LOB from among a plurality of LOBs operated by an entity. The algorithm may calculate a total potential risk measure for each of a plurality of model-application pairs associated with the selected LOB,” See Paragraph 73);
using the second computational model to generate a second score based on the second set of data (“A potential risk score or rating may be determined for each model.sub.i . . . I-application.sub.j . . . J pair. The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated,” See Paragraph 171); and
determining the overall score for the entity based on the first score and the second score (“identify a total potential risk due to models used by applications of the LOB,” See Paragraph 34). Mayenberger does not disclose configuring a second computation model based on a second set of configuration settings.
However, Murikipudi et al. teaches receiving a first set of configuration settings for a first computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the first computation model configured with the first set of configuration settings for a second computational model (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
receiving a second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis);
generating the second computation model configured with the second set of configuration settings (Paragraph 100 discloses mathematical and/or statistical formulas and/or models in accordance with the second data processing instructions 842-2 to provide a data processing result based on a second version of a data processing model, such as a second version of an insurance product risk analysis).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger with Murikipudi et al. This would have facilitated dynamic risk detection. See Murikipudi et al. Paragraph(s) 1. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. does not disclose using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity.
However, Kurtz et al. discloses using the first computational model to generate a first score for a category based on the first set of the data, wherein the first computational model is an entity computational model and the first set of data is related to a plurality of attributes of the entity (Paragraph 33 discloses determine risk scores for a number of entities and Paragraph 35 discloses the risk model generator 210 uses at least one of the following risk factors in the risk model to calculate risk of entity)
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. with Kurtz et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
Mayenberger reference as modified by Murikipudi et al. and Kurtz et al. does not disclose based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model.
However, Pai et al. discloses based on the first score and the second score, generate a graphical user interface (GUI) comprising the overall score for the entity and one or more controls for updating at least one of the first set of configuration settings of the first computational model or the second set of configuration settings of the second computation model (Paragraph 9 discloses generating a dynamic graphical user interface for the forecasted risk for the risk factors and the forecasted vulnerability scores for third parties within the geographic region);
store the first computational model with the first set of the data and the second computational model with the second set of the data (Paragraph 43 discloses engines may include a risk network model engine 420 (see FIG. 2A), baseline risk model engine 422 (see FIG. 5), risk signal detection model engine 424 (see FIG. 6), risk projection model engine 426 (see FIG. 7), and third party vulnerability score forecast engine 428 (see FIG. 8). Each of these models/engines 420-428 provide for certain aspects of the overall risk model to assess risk of third parties of a user); and
provide the GUI to the client device, wherein a display of the overall score in the GUI is updated based on one or more user inputs to the one or more controls (Paragraph 65 discloses financial risk 812 may be updated on a quarterly basis or otherwise, and the geographic risk may be updated on a real-time or up-to-date basis. A graph or other report 820 may be presented to a risk manager or other user. It should be understood that the above weights and updates are illustrative and that alternative weights and updates may be utilized. The vulnerability score may be a projection for a third party as it is a function of the risk projections for the active risk factors that exists within that geographic region).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Pai et al. This would have facilitated dynamic risk detection. See Kurtz et al. Paragraph(s) 3-5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 2, because claim 16 is substantially equivalent to claim 2.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 6, because claim 20 is substantially equivalent to claim 6.
Claim(s) 3-5, 7, 10-12, 14, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mayenberger (US Pub. No. 20150073956) and Murikipudi et al. (US Pub. No. 20170011466) and Kurtz et al. (US Pub. No. 20160232465) in further view of Yan et al. (US Pub. No. 20140143134).
The Mayenberger reference as modified Murikipudi et al. and Kurtz et al. teaches all the limitations of claim 1. Regarding claim 3, Mayenberger teaches the non-transitory machine-readable medium of claim 1, wherein the first score is for a first category, wherein the second score is for a second category, wherein the program further comprises sets of instructions for:
using a third computational model to generate a third score for the first category based on the first set of data associated with the entity (“The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated,” See Paragraph 171); and
using a fourth computational model to generate a fourth score for the second category based on the second set of data associated with the entity (“The potential risk score for all model.sub.i . . . I-application.sub.j . . . J pairs may be aggregated,” See Paragraph 171). Mayenberger as modified by Laroche et al. and Murikipudi et al. does not disclose selecting a higher of the first and third scores as a first high score for the first category; selecting a higher of the second and fourth scores as a second high score for the second category; and calculating a weighted average of the first high score for the first category and the second high score for the second category as the overall score.
However, Yan et al. teaches selecting a higher of the first and third scores as a first high score for the first category (Paragraph 88 discloses the individual models 110 to the received data to generate risk scores from the models. At a block 346, the generated scores are selected, depending on the combined model that is created or in use);
selecting a higher of the second and fourth scores as a second high score for the second category (Paragraph 88 discloses the individual models 110 to the received data to generate risk scores from the models. At a block 346, the generated scores are selected, depending on the combined model that is created or in use); and
calculating a weighted average of the first high score for the first category and the second high score for the second category as the overall score (Paragraph 104-111 discloses each of the risk indicator scores for a particular entity using a weighted average or other suitable combining calculation to generate an overall entity score).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Yan et al. This would have facilitated dynamic risk detection. See Yan et al. Paragraph 5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment. The close relation between both of the references highly suggest an expectation of success.
The Mayenberger reference as modified Murikipudi et al. and Kurtz et al. teaches all the limitations of claim 3. Regarding claim 4, Mayenberger teaches the non-transitory machine-readable medium of claim 3, wherein the program further comprises sets of instructions for:
determining a first level for the first category from a plurality of levels based on a first defined threshold, a second defined threshold, and the first high score for the first category (Paragraph 103 discloses A second stage of scoring is defined by thresholds 0<T.sub.L<T.sub.M in conjunction with an admissible model risk score S); and
determining a second level for the second category from the plurality of based on a third defined threshold, a fourth defined threshold, and the second high score for the second category (Paragraph 103 discloses A second stage of scoring is defined by thresholds 0<T.sub.L<T.sub.M in conjunction with an admissible model risk score S).
The Mayenberger reference as modified Murikipudi et al. and Kurtz et al. teaches all the limitations of claim 1. Regarding claim 5, Mayenberger as modified Murikipudi et al. and Kurtz et al. does not disclose wherein the first score is for a first category, wherein the second score is for a second category, wherein the overall score is determined by calculating a weighted average of the first score and the second score as the overall score.
However, Yan et al. teaches wherein the first score is for a first category, wherein the second score is for a second category, wherein determining the overall score comprises calculating a weighted average of the first score and the second score as the overall score (Paragraph 104-111 discloses each of the risk indicator scores for a particular entity using a weighted average or other suitable combining calculation to generate an overall entity score).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Yan et al. This would have facilitated dynamic risk detection. See Yan et al. Paragraph 5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment. The close relation between both of the references highly suggest an expectation of success.
The Mayenberger reference as modified Murikipudi et al. and Kurtz et al. teaches all the limitations of claim 1. Regarding claim 7, Mayenberger as modified Murikipudi et al. and Kurtz et al. does not disclose wherein the program further comprises a set of instructions for determining an overall level from a plurality of levels based on a first defined threshold, a second defined threshold, and the overall score.
However, Yan et al. teaches the non-transitory machine-readable medium of claim 1, wherein the program further comprises a set of instructions for determining an overall level from a plurality of levels based on a first defined threshold, a second defined threshold, and the overall score (Paragraph 103 discloses A second stage of scoring is defined by thresholds 0<T.sub.L<T.sub.M in conjunction with an admissible model risk score S).
Therefore, it would have been obvious before the effective filing date of invention was made to a person having ordinary skill in the art to modify Mayenberger and Murikipudi et al. and Kurtz et al. with Yan et al. This would have facilitated dynamic risk detection. See Yan et al. Paragraph 5. In addition, the references teach features that are directed to analogous art and they are directed to the same field of endeavor: risk assessment. The close relation between both of the references highly suggest an expectation of success.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 3, because claim 10 is substantially equivalent to claim 3.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 4, because claim 11 is substantially equivalent to claim 4.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 5, because claim 12 is substantially equivalent to claim 5.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 7, because claim 14 is substantially equivalent to claim 7.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 3, because claim 17 is substantially equivalent to claim 3.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 4, because claim 18 is substantially equivalent to claim 4.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 5, because claim 19 is substantially equivalent to claim 5.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20160012235 is directed to ANALYSIS AND DISPLAY OF CYBERSECURITY RISKS FOR ENTERPRISE DATA: [0033] Automated techniques for estimating potential financial loss from cybersecurity incidents allow the modeling or assessment of various risk mitigation policies. Modeling tools according to specific embodiments allow management to avoid overspending (e.g., adopting policies that are more costly than their associated reduction in risk), under spending (e.g., adopting policies that devote insufficient resources to cybersecurity threats) and misallocation (i.e., devoting resources to the wrong areas).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154
Prosecution Insights