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 .
DETAILED ACTION
The following is a Non-Final Office Action in response to communications received September 13, 2023. Claims 1-9 are pending and examined.
Drawings
The drawings are objected to because Figures 1-7 contain grayscale features. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "bund". There is insufficient antecedent basis for this limitation in the claim. For examination purposes, bund will be interpreted as “bond”.
Dependent claims 3-8 are rejected based on their dependency on a rejected claim.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claims recite a system and method. For the purposes of this analysis, representative claim 2 is addressed.
(Step 2A, prong 1) Abstract ideas are in bold below, and represents certain methods of organizing human activity, as a method of improving the accuracy of forecasts. Improving the accuracy of forecasts is akin to certain methods of organizing human activity.
A digital method for accuracy-enhanced expert forecasting based on an audit-linked, digital best-estimation framework by applying a controlled decision-making architecture technically supporting human experts to become less biased, wherein a digital platform provides a digital channel for automated audit-based best-estimation forecast of base rate values for underwriting in complex contextual environments covering heterogenous risk sources and risk-exposure classes, wherein the digital channel being provided with two or more execution members assessing the digital platform using network-enabled devices via a data transmission network, wherein the digital platform at least comprises processing circuitry configured to capture data from a plurality of sensors and the network-enabled devices, the digital method comprising: executing, by the two or more execution members using the network-enabled devices at least the steps of: (i) determining a forecasted value for a definable future time window benchmarking the audit-based, digital best-estimation framework to a starting point value based on one or more historical databases, (ii) determining a 90% confidence interval range for the determined starting point value by a lower bund value and an upper bond value of the interval range, wherein the starting point value is part of the confidence interval value range, and wherein an actually measured value of the forecasted value in the future time window is forecasted to measurably deviate with a 90% probability within the 90% confidence interval range, and (iii) selecting one or more possible scenarios each having a definable probability distribution and applying a sensitivity analysis by at least varying a time-based range of an observation window, wherein if the forecasted value deviates further from the starting point value as a predefined threshold value by the variation, the starting point value is adjusted, in that the forecasted values of the at least two execution member are transmitted and captured by a best-estimation engine determining a best-estimation forecasted value based on the captured forecasted values of the at least two execution member, and in that the plurality of sensors is coupled to the data processing engine and connected to the digital platform, wherein the accuracy the forecasted base rates is measured by the plurality of sensors providing a digital loop-back process by monitoring and verifying the different forecasted base rates via the sensory link of the plurality of sensors to the real physical world.
(Step 2A prong 2) The additional elements are considered as follows:
“a digital platform provides a digital channel”. This is merely “apply it” this sever is claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results.
“digital platform using network-enabled devices via a data transmission network, wherein the digital platform at least comprises processing circuitry”. This is merely “apply it” this sever is claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results.
“configured to capture data from a plurality of sensors and the network-enabled devices”. This is an extra solution activity, akin to data gathering.
“network-enabled devices”. This is merely “apply it” this sever is claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results.
“plurality of sensors is coupled to the data processing engine and connected to the digital platform”. This is an extra solution activity, akin to data gathering and data transmitting.
“plurality of sensors providing a digital loop-back process…via the sensory link of the plurality of sensors to the real physical world”. This is an extra solution activity, akin to data gathering and data transmitting.
(Step 2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of improving the accuracy of forecasts, over a generic computer network with generic computing elements, and generic hardware. The additional elements do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (i.e. sensors).
Analysis of dependent claims 3-8, recited additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more. For example, Claims 3, 4, & 7 further narrow the limitation “method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework” of Claim 2 by “determining”, which is “apply it”. Claims 5 & 6 further narrow the limitation “method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework” of Claim 2 by “applying”, which is “apply it”. Claim 8 further narrows the limitation “two or more execution members” of Claim 2. System Claim 1 and method Claim 9 are similarly rejected as the method Claim 2.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crabtree et al. (Publication No.: US 2021/0295447 A1) in view of Sudjianto et al. (Publication No.: US 2013/0073481 A1).
Claim 1 is rejected under the same reasoning as Claim 2.
As to Claim 2, Crabtree teaches the a digital method for accuracy-enhanced expert forecasting based on an audit-linked, digital best-estimation framework by applying a controlled decision- making architecture technically supporting human experts to become less biased, wherein a digital platform provides a digital channel for automated audit-based best- estimation forecast of base rate values for underwriting in complex contextual environments covering heterogenous risk sources and risk-exposure classes (see ¶[0048] – “The enterprise operating system 100…when programmed to operate as an insurance decision platform, is very well suited to perform advanced predictive analytics and predictive simulations to produce risk predictions needed required by actuaries and underwriters to generate accurate tables for later pricing at step 202. Data forming the basis of these calculations may be drawn from a set comprising at least: inspection and audit data on the condition and worth of the customer's equipment and infrastructure to be insured at step 203”),
wherein the digital channel being provided with two or more execution members assessing the digital platform using network-enabled devices via a data transmission network (see ¶[0045] – “Client access to system 105 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system's distributed, extensible high bandwidth cloud interface 110”),
wherein the digital platform at least comprises processing circuitry configured to capture data from a plurality of sensors and the network-enabled devices, the digital method comprising:
executing, by the two or more execution members using the-network- enabled devices at least the steps of (see ¶[0045] – “Much of the enterprise data analyzed by the system both from sources within the confines of the client enterprise, and from cloud based sources 107, public or proprietary such as, but not limited to: subscribed enterprise field-specific data services, external remote sensors…sensors that may be of several different types. Multiple dimension time series data store module 120 may also store any time series data encountered by system 100 such as, but not limited to, environmental factors at insured client infrastructure sites, component sensor readings”):
(i) determining a forecasted value for a definable future time window benchmarking the audit-based, digital best-estimation framework to a starting point value based on one or more historical databases (see Figure 18 – (34) databases, and ¶[0084] – “recommended offers based on historical underwriting”),
and
in that the plurality of sensors is coupled to the data processing engine and connected to the digital platform, wherein the accuracy the forecasted base rates is measured by the plurality of sensors providing a digital loop-back process by monitoring and verifying the different forecasted base rates via the sensory link of the plurality of sensors to the real physical world (see ¶[0045] – “ Much of the enterprise data analyzed by the system both from sources within the confines of the client enterprise, and from cloud based sources 107, public or proprietary such as, but not limited to: subscribed enterprise field-specific data services, external remote sensors”).
Although Crabtree substantially teaches the invention of Claim 2, it does not explicitly teach (ii) determining a 90% confidence interval range for the determined starting point value by a lower bund value and an upper bond value of the interval range, wherein the starting point value is part of the confidence interval value range, and wherein an actually measured value of the forecasted value in the future time window is forecasted to measurably deviate with a 90% probability within the 90% confidence interval range, and (iii) selecting one or more possible scenarios each having a definable probability distribution and applying a sensitivity analysis by at least varying a time- based range of an observation window, wherein if the forecasted value deviates further from the starting point value as a predefined threshold value by the variation, the starting point value is adjusted, in that the forecasted values of the at least two execution member are transmitted and captured by a best-estimation engine determining a best-estimation forecasted value based on the captured forecasted values of the at least two execution member. Sudjianto does teach (ii) determining a 90% confidence interval range for the determined starting point value by a lower bund value and an upper bond value of the interval range, wherein the starting point value is part of the confidence interval value range, and wherein an actually measured value of the forecasted value in the future time window is forecasted to measurably deviate with a 90% probability within the 90% confidence interval range, and (iii) selecting one or more possible scenarios each having a definable probability distribution and applying a sensitivity analysis by at least varying a time-based range of an observation window, wherein if the forecasted value deviates further from the starting point value as a predefined threshold value by the variation, the starting point value is adjusted, in that the forecasted values of the at least two execution member are transmitted and captured by a best-estimation engine determining a best-estimation forecasted value based on the captured forecasted values of the at least two execution member (see ¶[0094] – “In accordance with at least one embodiment, an automated feedback loop may be utilized to assess assumptions made in the risk reward assessment mechanism. By utilizing an automated feedback loop, a determination can be made as to whether a forecast was inaccurate due to an error associated with the overall process, risk reward assessment mechanism model, itself or due to an error associated with an input variable. Ascertaining the reason behind the inaccuracy in the forecast is helpful when trying to evaluate the impact of strategy and policy, i.e., the vintage effect. In accordance with at least one embodiment, there may be a defined cut-off between use of the modeled vintage effect on a vintage, where vintage is equal to or older/greater than 15 months, and use of a business assumption, where vintage is younger/less than 15 months. The accuracy of the first transition month may be assessed to determine how accurate the risk reward assessment mechanism process is operating. The immediate feedback may have an impact on the confidence in the model and it may provide a confidence interval of future forecasts. “). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to use a fund manager’s portfolio turnover factors as in Sudjianto as part of the qualitative factors of Crabtree as both the inventions are using these factors to improve the accuracy of a forecast.
As to Claim 3, Sudjianto teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, further comprising:
determining said 90% confidence interval range for the estimated return period based on a market rate and/or an industry rate and/or a market loss ratio value and/or benchmark values of similar transactions or risk-triggered systems (see Figure 28, and ¶[0091] – “ method for forecasting expected values and volatilities of revenue and loss and optimizing a business's portfolio in accordance with at least one aspect of the present disclosure. FIG. 28 illustrates an alternative embodiment to the operation of segment and portfolio prediction 343 in FIG. 3. As shown in FIG. 28, segment and portfolio prediction 343 includes prediction available for the modeled segments as well as the attribute segments 2801.”).
As to Claim 4, Sudjianto teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, further comprising; determining said 90% confidence interval range for the estimated return period by setting an upper and lower bound value equal-distant or not-equal-distant to the starting point value of the forecasted value (see ¶[0181]).
As to Claim 5, Crabtree teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, further comprising:
applying said sensitivity analysis at least by varying a time-based range of an observation window and/or a return period of losses and/or a distribution for fitting and/or different methods for extrapolation (see ¶[0047] – “A significant proportion of the data that is retrieved and transformed by the enterprise operating system, both in real world analyses and as predictive simulations that build upon intelligent extrapolations of real world data, may include a geospatial component. The indexed global tile module 170 and its associated geo tile manager 170a may manage externally available, standardized geospatial tiles and may enable other components of the enterprise operating system, through programming methods, to access and manipulate meta-information associated with geospatial tiles and stored by the system. The enterprise operating system may manipulate this component over the time frame of an analysis and potentially beyond such that, in addition to other discriminators, the data is also tagged, or indexed, with their coordinates of origin on the globe. This may allow the system to better integrate and store analysis specific information with all available information within the same geographical region. Such ability makes possible not only another layer of transformative capability, but may greatly augment presentation of data by anchoring to geographic images including satellite imagery and superimposed maps both during presentation of real world data and simulation runs.”)
As to Claim 6, Crabtree teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, further comprising:
applying said sensitivity analysis additionally by comparing a deviation of the forecasted value over an observation window of 3 years to a yearly forecasted value and/or by comparing the forecasted values by aggregated or separately considered different regions and/or comparing the forecasted values by aggregated or separately considered line of businesses (see ¶[0067] – “Other uses of the rules engine may include, but is not limited to, validating contracts; verifying the legality of a request based on rules, laws, and regulations associated with locality and line of business; evaluating of contract-specific terms and requirements as specified in underwriting guidelines configured in the system; evaluation of peril-specific terms and requirements, such as geolocality restrictions; evaluation of portfolio impact; evaluation against projected deal flow; and the like.”).
As to Claim 7, Crabtree teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, further comprising:
determining different forecasted values by varying the one or more historical databases for detecting possible biases introduced by specific compositions of the one or more historical databases (see ¶[0084).
As to Claim 8, Crabtree teaches the digital method for accuracy-enhanced expert forecasting based on an audit-linked best-estimation framework according to claim 2, wherein the two or more execution members comprise execution members with different role comprising client managers and/or claims experts and/or reserving actuaries and/or casualty R&D members and/or casualty center members and/or faculty underwriting members (see ¶[0045]).
Claim 9 is the method for using the system of Claim 1 and is rejected under the same reasoning as Claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE S KANG whose telephone number is (571)270-3611. The examiner can normally be reached on Monday through Friday between M-F 10am-2pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Gart may be reached at (571)-273-3955. 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.
/IRENE KANG/
Examiner, Art Unit 3695
9/21/2025
/JOSEPH W. KING/Primary Examiner, Art Unit 3696
Prosecution Insights