DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/8/25 has been entered.
Status of Claims
Claims 1 and 4-27 are pending in this 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 and 4-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 and 4-27 are directed to a method which is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method claim 1 as the claim that represents the claimed invention for analysis. Claim 1 recites the limitations of assessing, underwriting, and pricing risk by classifying/categorizing risk and comparing/processing it against data.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity and mental processes (i.e., concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Identifying “risk classes” (“risk classes” includes “risk characteristics parameter values” defined by “a class-specific risk parameter value range”, and each risk class has “a class-specific risk exposure parameter range”); the risk characteristics parameters = object characteristics + data characteristics; object characteristics = the relationship between classes; data characteristics = data characteristics of each class; rule mining = two threshold values defining a minimum and a minimum confidences to generate a reliability value and availability value for each of the rules; if the support of the rule is greater than the minimum support, the rule is considered to be a frequent item set by the system; mining if satisfies the minimum support and the minimum credibility and the degree of action is greater than 1; detecting and assigning a risk class by triggering the risk class to be added (risk characteristics parameter values being in “the class-specific risk exposure parameter range”); measuring physical events (using (a) measuring stations, (b) station in loco, OR (c) satellite image processors); assessing a relative risk measure based on actual risk exposure to the newly added risk-transfer; adding the new relative risk measure (new/occurring risk is measured by means of (1) measuring stations or (2) sensors in loco and/or (3) by satellite image processing (note, the examiner interprets “by means of” is a measurement of the average value. See specification page 12 line 15-16 (“the mean value of the sector which equals the risk quintile is taken”), and page 27 line 32-33 (“the true mean of the loss distribution with the probability approaching 1”)); providing a rule-based risk parameter capturing using a set of questions (questions assessed by underwriter via GUI); each of the underwriter’s response is weighted based on added or benchmarked risk-transfer; providing impact measured; optimizing (by machine learning) of rule-based captured risk parameter; machine learning trained by a random risk events quintile; In other cases, quality quantile are allocated as out-of-sample data; for each training set, a multi-fold cross-validation is applied searching for the best combination of hyperparameters for a respective machine-learning structure; decision function is applied with the best combination of hyperparameters on measured risk events; machine-learning structure being based on deep neural network; for each training set, applying a multi-fold cross-validation; applying optimal hyperparameters on the measured risk events; hyperparameters tuned for model during validation step; structure of the deep neural network being realized as a feedforward architecture; generating “preliminary risk score”; indexing risk quintile based on the ruled-based captured risk parameter (each risk quintile representing a range of risk quality score, with “risk quality score” transformable into base rate modifier for automated pricing (including optimization (by machine learning) of rule-based captured risk parameter (machine learning trained by a random risk events)); risk quality quintile comprise at least five risk quality quintiles (ranges from excellent, good, average, fair, and poor); each quality range at the value of 0.2 of a density distribution normed; adding/adjusting the risk-transfer to portfolio is the “relative risk measure” is within a desired value range;
assigning trigger parameters defining a range of risk characteristics; a risk-transfer of the portfolio selectable from the portfolio as comparable based on the assigned trigger parameters; wherein the risk characteristics parameters at least comprise the class, similarity of size, similarity of construction type and similarity of the geographic location; benchmarking the newly to be added risk-transfer to the risk-transfers of the portfolio; benchmarking a risk-transfer of the portfolio is detected as comparable if its characteristics parameters are within the defined similarity value range; newly to be added risk-transfer risk allocated to a new quality quintile or moved within the same quintile of the comparable to a higher or lower position, or being removed – specifically, the claim recites: “an automated machine-based underwriting method… for an automated underwriting platform capturing and assessing line of business risks based on relative risk measurements using a portfolio that includes is a container capturing a plurality risk-transfers interlinked by a mutual relationship provided by the portfolio where each risk-transfer captures at least a part of a risk associated with an underwriter, capturing a risk including measuring and assigning physically measurable quantity values to the risk for a probability for an impacting physical event causing a physical impact or damage to a real-world object timely and locally exposed to the occurring of the impacting physical event, the method comprising: identifying risk classes in the portfolio, each of the risk classes of the portfolio including risk-transfers having defined risk characteristics parameter values in a class-specific risk parameter value range associated with the risk class, and each of the risk classes of the portfolio being associated with a class-specific risk exposure parameter range, wherein the risk characteristics parameters at least comprise two types of characteristics parameters including object characteristics and data characteristics, in which object characteristics are used to represent the relationship between classes, and data characteristics are used to represent the data characteristics of each class, and wherein for the classification of a risk-transfer to a risk class an association rule mining method is applied, the rule mining comprise two threshold values defining a minimum and a minimum confidences to generate a reliability value and availability value for each of the rules, and wherein if the support of the rule is greater than the minimum support, the rule is considered to be a frequent item set by the system, mining by said association rule mining from the database a strong association rule that satisfies the minimum support and the minimum credibility and the degree of action is greater than 1, wherein the selected rule is applied to import data and then adapted to capture and realize risk correlation analysis and mining of the data, detecting and assigning, for a new risk-transfer to be added to the portfolio, a risk class or risk cohort of the portfolio by triggering the risk class or risk cohort based on the risk characteristics parameter values of the risk-transfer to be added, the risk characteristics parameter values of the risk-transfer being in the class-specific risk exposure parameter range of the triggered risk class or risk cohort, measuring, in response to the triggering of the risk class or risk cohort based on the risk characteristics parameter values of the risk-transfer to be added… occurring physical events, assessing a relative risk measure based on an actual risk exposure associated with the newly to be added risk-transfer, the relative risk measure measuring an association between a total risk exposure and an actual portfolio-specific outcome upon adding the new risk-transfer, the occurring physical events being measured… being assigned to a historic event set including event parameters for each of a plurality of the impacting physical event… measuring parameters of an occurring impacting physical event profiling an occurrence and/or a style and an environmental condition of the occurring impacting physical event based upon the triggered, captured, and monitored measuring parameters or environmental parameters, providing, for assessing the relative risk measure, a rule-based risk parameter capturing using a preselected set of risk questions, the set of risk questions being assessed by an underwriter of the risk-transfer newly to be added or benchmarked… and each response value of the underwriter to a question of the set of risk questions being weighted based on to the newly to be added or benchmarked risk-transfer, the weighted response values and/or the rule-based captured risk parameters being assigned to the risk characteristics parameter values, dynamically providing, during risk assessment, an impact measure to the relative risk measure of the portfolio, automatically optimizing a set of the rule-based captured risk parameters by applying a machine-learning structure trained using a randomly taken set of physical events of a risk quality quintile as an in-sample training set, while the other cases, measured to be outside the corresponding risk quality quintile are allocated as out-of-sample data, wherein for each training set, a multi-fold cross-validation is applied searching for the best combination of hyperparameters for a respective machine-learning structure, and wherein a decision function is applied with the best combination of hyperparameters on measured risk events, the machine learning structure being based on a deep neural network, wherein for the decision function for each training set, a multi-fold cross-validation is applied to capture the best combination of hyperparameters for the respective model, the decision function being applied with the optimal hyperparameters on the measured risk events and the hyperparameters that being tuned for the selected model during the validation step, and wherein the structure of the deep neural network being realized as a feedforward architecture trained by a stochastic gradient descent algorithm with dropout layers after each of the dense layers, before using the output as activation functions, generating a preliminary risk score measure measuring and indexing risk quality quantiles based on the rule-based captured risk parameters optimized by the machine-learning structure, each risk quality quintile representing a range of risk quality scores transformable into base rate modifiers for automated pricing, wherein the risk quality quintile comprise at least five risk quality quintiles represent quality ranges of the qualities excellent, good, average, fair, and poor, and wherein the quality quintile indicating the quality excellent includes a noise level associated with each quality range at the value of 0.2 of a density distribution normed to 1 and/or at a standard score measure at a value of approximately o-~0.1, adding the newly to be added risk-transfer to the portfolio if the relative risk measure is within a desired value range, assigning trigger parameters defining a range of risk characteristics parameters, a risk-transfer of the portfolio being selectable from the portfolio as comparable based on the assigned trigger parameters, wherein the risk characteristics parameters at least comprise the class, similarity of size, similarity of construction type and similarity of the geographic location, and benchmarking the newly to be added risk-transfer to the risk-transfers of the portfolio, wherein for benchmarking a risk-transfer of the portfolio is detected as comparable, if its characteristics parameters are within the defined similarity value range of the risk characteristics parameter to the newly to be added risk-transfer, the newly to be added risk-transfer being allocated to a new quality quintile or moved within the quintile of the comparable to a higher or lower position, or being removed” recites a fundamental economic practice. Similarly, “identifying risk classes in the portfolio” is a type of observation; “detecting and assigning, for a new risk-transfer to be added to the portfolio, a risk class” is a type of evaluation and judgment; and “generating a preliminary risk score measure measuring” is a type of opinion. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activity and Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The “a graphical user interface”, “measuring stations”, “sensors”, “satellite image processors”, “central core circuit”, “trigger-driven score module”, and “a machine-learning structure”, in claim 1, are just applying generic computer components to the recited abstract limitations. Feedforward architecture is an abstract mathematic concept. The recitation of generic computer components and mathematics in a claim do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as a central core circuit and satellite image processors; a communication device such as measuring stations, sensors, and a graphical user interface; data and data types such as a machine-learning structure; and software module and algorithm such as trigger-driven score module. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claim 1 is not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claim 4 discloses the limitation of matching the risk-transfer newly to be added or benchmarked with the risk-transfers of the portfolio or historical risk-transfers, wherein a risk-transfer is detected as comparable if its risk characteristics parameters are within a defined similarity value range to the new risk- transfer, and providing access to an underwriter of the risk-transfer newly to be added, wherein upon detection and selection of a comparable, the risk-transfer newly to be added is matched and benchmarked against the selected comparable, which further narrows the abstract idea.
Dependent claim 5 discloses the limitation of assigning trigger parameters defining a range of the risk characteristics parameters, wherein the comparable is selected from the portfolio and/or from other conducted risk-transfers based on the assigned trigger parameters, and only risk from a same class or cohort that are compared are triggered by the assigned trigger parameters, which further narrows the abstract idea.
Dependent claim 6 discloses the limitation of matching the risk-transfer newly to be added or benchmarked with the risk-transfers of the portfolio or historical risk-transfers, wherein a risk-transfer is detected as comparable if its risk characteristics parameters are within a defined similarity value range to the new risk- transfer, providing access to an underwriter of the risk-transfer newly to be added, wherein upon detection and selection of a comparable, the risk-transfer newly to be added is matched and benchmarked against the selected comparable, upon benchmarking, the underwriter being enabled to remove one or more of the benchmarked risk-transfers, and measuring a resulting risk quality score value of the risk-transfers of the portfolio, the risk being allocated to a new quality quintile or moved within the same quintile to a higher or lower position, which further narrows the abstract idea.
Dependent claim 7 discloses the limitation of measuring a reference risk value for each specific risk classes or cohorts, wherein the risk-transfer newly to be added or benchmarked is matched and benchmarked against the reference risk value, and the benchmark is provided to the underwriter of the risk-transfer to be added or benchmarked, which further narrows the abstract idea.
Dependent claim 8 discloses the limitation of dynamically providing, during risk assessment, an impact measure of an individual risk quality score to the portfolio, which further narrows the abstract idea.
Dependent claim 9 discloses the limitation of the risk characteristics parameter values are characteristic for each risk class or cohort, and each specific risk class or cohort have a dedicated set of risk questions assigned based on the risk characteristics parameter values characteristic for said specific risk class or cohort, which further narrows the abstract idea.
Dependent claim 10 discloses the limitation of predefined responses are provided to each dedicated set of risk questions, and upon selection of the predefined responses the risk assessment is processed, which further narrows the abstract idea.
Dependent claim 11 discloses the limitation of the risk classes or cohorts are automatically classified by applying internal risk codes and/or external industry codes, which further narrows the abstract idea.
Dependent claim 12 discloses the limitation of the applied internal risk codes at least include Property Industry Code (PIC) risk identification for property risks, which further narrows the abstract idea.
Dependent claim 13 discloses the limitation of wherein the external industry codes at least include North American Industry Classification System (NAICS) and/or German Insurance Association (GDV) classification, which further narrows the abstract idea.
Dependent claim 14 discloses the limitation of in a case of detecting a lack in granular risk information provided by captured risk characteristics parameter values, more granular risk classes or cohorts are automatically grouped high level groups or cohorts, which further narrows the abstract idea.
Dependent claim 15 discloses the limitation of the set of risk questions include a limited number of questions per class or cohort, which further narrows the abstract idea.
Dependent claim 16 discloses the limitation of the set of risk questions is limited to a number from 8 to 12 per class or cohort, which further narrows the abstract idea.
Dependent claim 17 discloses the limitation of a value of the risk score measure is generated based on the weighted responses to the set of risk questions of the risk class or cohort, the value of the risk score measure is normed to score ranges from 0.1 to 5.0 split into 5 segments indicative of the risk quality quintiles, and the lower the value of the risk score the higher the risk measure, which further narrows the abstract idea.
Dependent claim 18 discloses the limitation of the value of the risk score measure is measurable within a quintile from the lower value range of the quantile to the higher end value range, which further narrows the abstract idea.
Dependent claim 19 discloses the limitation of the quintile average is indicative of an averaged expected risk quality value within a class or cohort, which is reflected in a base rate assigned to the respective class or cohort, which further narrows the abstract idea.
Dependent claim 20 discloses the limitation of the comparable is matched and selected based on the following parameters: (i) class or cohort, (ii) similarity of size, (iii) total turnover for CAS lines, (iv) single Total Insurable Value for Property Recovery, (v) for Property Recovery, similarity of construction type and protection level, (vi) for Property Recovery, a geographic location and/or state, and (vii) for Property Recovery, in case of multi-location access, a location with the highest Total Insurable Value, which further narrows the abstract idea.
Dependent claim 21 discloses the limitation of the similarity value range of size for selecting or triggering comparables is 15% of the size of the risk-transfer to be matched, which further narrows the abstract idea.
Dependent claim 22 discloses the limitation of the similarity of construction type and protection level is matched based on the Construction Occupancy Protection Exposure comprising parameters defining a set of risks providing the basis for generate pricing for a risk-transfer covering a property or construction, which further narrows the abstract idea.
Dependent claim 23 discloses the limitation of providing risk assessment information at least including an original score and/or responses selected and/or modified score as p/o benchmarking and/or risk assessment rationale, which further narrows the abstract idea.
Dependent claim 24 discloses the limitation of providing a listing of risk features as defined benchmarks for selection by the underwriter, which either will move the risk to a lower quintile or a higher quintile, which further narrows the abstract idea.
Dependent claim 25 discloses the limitation of the risk-transfer newly to be added or benchmarked is benchmarked against all detected comparables, which further narrows the abstract idea.
Dependent claim 26 discloses the limitation of identifying industry classes and/or sub-classes in the portfolio of risk-transfers, analyzing risk classes for typical risk features, turning features into a small set of risk focused questions and pre-defined responses from which an underwriting return picks answers, weighting questions and feeding responses into a structure generating a default risk assessment allocating an actual risk score into a risk quality quintile, benchmarking by the underwriter, based on experience and knowledge the actual risk against other bound risks belonging to the same risk class, the underwriter further modifying a default risk assessment leading to a different risk opinion, justifying with a rationale the modification of the default risk assessment, and using a final risk assessment as a basis for risk related base rate modifications in a pricing process, which further narrows the abstract idea.
Dependent claim 27 discloses the limitation of the line of business risks at least include general liability and/or professional liability risks and/or worker compensation and/or employers' liability and/or property risks, which further narrows the abstract idea.
Thus, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1 and 4-27 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 12/8/25 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“In particular, the existing technical solutions do not disclose automatically benchmarking of risk transfers based on the sensor measurements against corresponding, predefined comparable risk transfers (comparables), whereby the measured risk transfer is benchmarked by means of a classification in quartiles. Thus, the existing systems do not disclose automated portfolio management by technical means, but merely allows simple pricing based on the measured in-arrival time measurements or the measured temporal pattern… automated bench-marking portfolio-assignment of risk transfers against corresponding, predefined comparable risk transfers (comparables),”
the examiner respectfully disagrees. These are not technical. At best, there are procedural improvements (e.g., benchmarking of risk transfers based on the latest measurements, and benchmarking risk against predefined comparable) being carried out by “generic computer”.
In response to applicant's argument that:
“No prior art system therefore does disclose automated portfolio management in the present technical way. Typically, prior art system merely allows simple pricing based on rule-based decisions, and not a complete and reliable allocation and management within an existing portfolio structure,”
the examiner respectfully disagrees. The improvement – if any – is due to the business procedure/idea being implemented. It is not due to any technological improvement. As stated in the prior office actions:
“These “improvements” are not technical improvement. E.g., benchmarking risk by classifying them in quartiles is not a technical improvement. At best, it’s an abstract idea being applied for a particular purpose (risk assessment).”
and
“the “improvements” is in changes in the way risk/data is calculated/classified– “benchmarking of risk transfers based on the sensor measurements against corresponding, predefined comparable risk transfers (comparables), whereby the measured risk transfer is benchmarked by means of a classification in quantiles”. This is not a technological innovation”.
In response to applicant's argument that:
“amended Claim 1 now explicitly recites "assigning trigger parameters defining a range of risk characteristics parameters, a risk-transfer of the portfolio being selectable from the portfolio as comparable based on the assigned trigger parameters, wherein the risk characteristics parameter… (reciting the newly added language)… These features make it clearer that the claims are not related to an underlying business model and they achieve a technical automated system that is not equivalent to any abstract idea,”
the examiner respectfully disagrees. In comparison to the prior version, the added elements (see underlined) and deleted elements (if any, struck out with a line) are essentially:
(1) “assigning trigger parameters defining a range of risk characteristics parameters”;
(2) “a risk-transfer of the portfolio being selectable from the portfolio as comparable based on the assigned trigger parameters”;
(3) “wherein the risk characteristics parameters at least comprise the class, similarity of size, similarity of construction type and similarity of the geographic location”;
(4) “benchmarking the newly to be added risk-transfer to the risk-transfers of the portfolio”;
(5) “wherein for benchmarking a risk-transfer of the portfolio is detected as comparable, if its characteristics parameters are within the defined similarity value range of the risk characteristics parameter to the newly to be added risk-transfer”; and
(6) “newly to be added risk-transfer of the comparable to a higher or lower position, or being removed”.
These changes are not sufficient to overcome the 35 U.S.C. § 101 rejections because: for 101 analysis purpose, this is just stating (corresponding to the numberings above):
adding a procedural step;
explaining what is possible – i.e., user can select a risk-transfer as comparable;
explaining what risk characteristics can be;
adding another procedural step;
a procedural step; and
explaining that the benchmark process involves allocating risk value to its comparables.
These are abstract ideas. There is nothing technical about it.
In response to applicant's argument that:
“Ex Parte Guillaume Desjardins,”
the examiner respectfully disagrees. the examiner respectfully disagrees. First, Ex Parte Guillaume Desjardins is a PTAB’s decision, which does not have the force of legal precedent. More importantly, the claimed invention does not have all the elements and the steps of Ex Parte Guillaume Desjardins. While guided by the PTAB’s teaching, the examiner still must read Ex Parte Guillaume Desjardins narrowly in deference to the Alice Court’s emphatic prohibition against patenting abstract ideas that lack genuine innovation beyond the use of generic computers. To be patent eligible, an abstract idea must be accompanied by recognizable technical innovation/breakthrough. It is well established that in “claim[ing] a technological solution to a technological problem, the patent must actually claim the technological solution.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mike Anderson can be reached on 571 270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK H GAW/Examiner, Art Unit 3693