DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 3/23/2026. Claims 1-2,4-12 and 14-20 are pending in this Office Action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claim and specification objections have been withdrawn by the examiner in response to the amendments that overcome the objection.
In view of applicant’s amendments and arguments. The 101 abstract idea rejection has been withdrawn.
Response to Arguments
Applicant's arguments filed on 3/23/26 with respect to rejection of claims under 35 USC 102 and 103 have been considered but are moot in view of the new ground of rejection.
Examiner notes
In the following rejection, the claim limitation with bold text signifies the portion that the prior art teaches and the text in square brackets [] signifies the portion that the prior art fails to expressly teach.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2,4 , 11-12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Gilmore et al.(US Patent Application Publication 2017/0032400 A1, hereinafter “Gilmore”) and further in view of Lagoni et al.,(US Patent Application Publication 2017/0124576 A1, hereinafter “Lagoni”)
As to claim 1 Gilmore teaches a computer-implemented method and system for predicting one or more expected outcomes using a machine learning model, the method comprising: accessing a set of data items, wherein each data item is associated with a respective one or more characteristics; generating or training a machine learning model using the set of data items and associated characteristics (Gilmore par [0060]-[0064] teaches obtaining and optimizing data and par [0065] teaches using the dataset resulting from the optimization process, a set of models may be generated );
Receiving, via the graphical user interface, a set of simulation parameters from a user, wherein the set of simulation parameters includes one or more items of information associated with an event ( Gilmore par [0066]-[0067] teaches user selects particular vehicle configuration using one or more menu)
generating a supplemented set of simulation parameters, wherein generating the supplemented set of simulation parameter includes filling in or supplementing the set of simulation parameters with simulation parameters not specified by the user ( Gilmore par [0068] teaches pricing data associated with the specified vehicle configuration may then be determined. This data may include adjusted transaction prices, pricing data associated with specified vehicle configuration ) ; and wherein generating the supplemented set of simulation parameters comprises determining, for an unspecified item of information and based on historical events (Gilmore par [0068] teaches historical data may be grouped into a series of “bins” of historical sales data and a data is determined by choosing the bin of historical sales data for the transactions most analogous to the parameters specified by the user), [at least one of: a historical average of the unspecified item of information, a historical minimum of the unspecified item of information, a historical maximum of the unspecified item of information, or a historical aggregate of the unspecified item of information;]
applying the machine learning model to the supplemented set of simulation parameters, including the filled in or supplemented simulation parameters, to generate an expected outcome based on the set of simulation parameters; and updating the graphical user interface to include an indication of the expected outcome.(Gilmore par [0071] teaches an interface for presentation of the determined market trend data associated with the specified vehicle configuration may then be generated)
Gilmore fails to expressly teach determining, for an unspecified item of information and based on historical events , at least one of: a historical average of the unspecified item of information, a historical minimum of the unspecified item of information, a historical maximum of the unspecified item of information, or a historical aggregate of the unspecified item of information;
However, Lagoni teaches determining, for an unspecified item of information and based on historical events , at least one of: a historical average of the unspecified item of information, a historical minimum of the unspecified item of information, a historical maximum of the unspecified item of information, or a historical aggregate of the unspecified item of information; (Lagoni par [0062] teaches examples of historical data that can be used include historical unit sales of a product, including statistical measures thereof such as average, median, variance, standard deviation, skewness, and kurtosis)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Lagoni with the teaching of Gilmore to achieve the claimed invention. One would have been motivated to make such combination to improve the accuracy, consistency, and reliability of the estimation.(Lagoni par [0055])
As to claim 2 , Gilmore and Lagoni teach the computer-implemented method of Claim 1, wherein the one or more characteristics include at least one of: type of event, time of event, date of event, location of event, amount of transaction, quantity of transaction, location of transaction, person associated with transaction, category of transaction, terms or conditions of transaction, temperature, altitude, meteorological conditions, load, or output. (Gilmore par [0066]-[0067] teaches user selects particular vehicle configuration using one or more menu)
As to claim 4, Gilmore and Lagoni teach the computer-implemented method of Claim 1 wherein generating the supplemented set of simulation parameters comprises determining the historical average of the unspecified item of information from the historical events . (Lagoni par [0062] teaches examples of historical data that can be used include historical unit sales of a product, including statistical measures thereof such as average, median, variance, standard deviation, skewness, and kurtosis).
Claims 11-12 and 14 merely recites a computing system to perform the method of claims 1-2 and 4 respectively. Accordingly, Gilmore and Lagoni teach every limitation of claims 11-12 and 14 as indicates in the above rejection of claims 1-2 and 4 respectively.
Claims 5-7 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gilmore , Lagoni and further in view of Official Notice.
As to claim 5, Gilmore and Lagoni teach the computer-implemented method of Claim 1 wherein generating the supplemented set of simulation parameters comprises determining the historical [minimum] of the unspecified item of information from the historical events. (Lagoni par [0062] teaches statistical measures thereof such as average, median, variance, standard deviation, skewness, and kurtosis)
Gilmore and Lagoni fail to expressly teach statistical measures of minimum, maximum or aggregate.
However, the statical measures of minimum, maximum and aggregate are all well known in the art and therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to utilize the historical minimum, maximum or aggregate to measure historical events as taught by Gilmore and Lagoni to achieve the claimed invention. One would have been motivated to make such combination to improve the accuracy, consistency, and reliability of the estimation.(Lagoni par [0055])
As to claim 6, Gilmore and Lagoni teach the computer-implemented method of Claim 1, but fail to teach wherein generating the supplemented set of simulation parameters comprises determining the historical maximum of the unspecified item of information from the historical events.
. However, the statical measures of minimum, maximum and aggregate are all well known in the art and therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to utilize the historical minimum, maximum or aggregate to measure historical events as taught by Gilmore and Lagoni to achieve the claimed invention. One would have been motivated to make such combination to improve the accuracy, consistency, and reliability of the estimation.(Lagoni par [0055])
As to claim 7, Gilmore and Lagoni teach the computer-implemented method of Claim 1 but fail to teach wherein generating the supplemented set of simulation parameters comprises determining the historical aggregate of the unspecified item of information from the historical events.
However, the statical measures of minimum, maximum and aggregate are all well known in the art and therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to utilize the historical minimum, maximum or aggregate to measure historical events as taught by Gilmore and Lagoni to achieve the claimed invention. One would have been motivated to make such combination to improve the accuracy, consistency, and reliability of the estimation.(Lagoni par [0055])
.
As to claims 15-17 , see the above rejection of claims 5-7 respectively.
Claims 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gilmore, Lagoni and further in view of Rai et al.(US Patent Application Publication 2014/0343916 A1, hereinafter ‘Rai”)
As to claim 8, Gilmore and Lagoni teach the computer-implemented method of Claim 1 but fail to teach further comprising: applying the machine learning model to a subset of the set of simulation parameters or the supplemented set of simulation parameters to generate baseline outcome; and generating a comparison between the expected outcome and the baseline outcome.
However, Rai teaches applying the machine learning model to a subset of the set of simulation parameters or the supplemented set of simulation parameters to generate baseline outcome; and generating a comparison between the expected outcome and the baseline outcome.(Rai par [0063] teaches the simulation module simulates possible outcomes of a subset of parameters. Rai par [0065] teaches the simulation module 120 may allow for the results of one or more simulated runs to be viewed alongside with benchmark values of specific parameters)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Gilmore and Lagoni with the subset of simulation parameters of Rai to achieve the claimed invention. One would have been motivated to make such modification to optimize the simulation (Rai par [0095])
As to claim 9, Gilmore and Lagoni teach the computer-implemented method of Claim 1 but fail to teach further comprising: aggregating one or more expected values of the expected outcome ; applying the machine learning model to a subset of the set of simulation parameters or the supplemented set of simulation parameters to generate baseline outcome ; aggregating one or more expected values of the baseline outcome; and generating a comparison between the aggregated expected values of the expected outcome and the aggregated expected values of the baseline outcome.
However, Rai teaches further comprising: aggregating one or more expected values of the expected outcome (Rai par [0065] teaches benchmark values) ; applying the machine learning model to a subset of the set of simulation parameters or the supplemented set of simulation parameters to generate baseline outcome( Rai par [0063] teaches the simulation module simulates possible outcomes of a subset of parameters).; aggregating one or more expected values of the baseline outcome; and generating a comparison between the aggregated expected values of the expected outcome and the aggregated expected values of the baseline outcome. (Rai par [0065] teaches the simulation module 120 may be used to display the deviation of simulated values from the target values) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the simulation of Garvey with the teachings of Rai to achieve the claimed invention. One would have been motivated to make such modification to optimize the simulation (Rai par [0095])
As to claim 10, Gilmore ,Lagoni and Rai teach the computer-implemented method of Claim 9 wherein aggregating comprises at least one of: determining an average, determining a minimum, determining a maximum, or determining a median. ( Rai par [0060] teaches the target setting module 118 may provide certain benchmark values that are prescribed as the threshold values or minimum values that have to be achieved)
As to claims 18-20, see the above rejection of claims 8-10.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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