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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on August 10, 2026, in which claims 1, 2, 5-9, 12-16, 19, and 20 are currently amended. Claims 1-20 are currently pending.
Specification
Applicant's amendments made to the specification are acknowledged. Examiner’s objection to the specification are hereby withdrawn, as necessitated by Applicant’s amendments made to the specification.
Response to Arguments
Applicant’s arguments with respect to rejection of claims 1-20 under 35 U.S.C. 101 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on p. 10 of the Remarks submitted 8/10/2026 that "as a practical matter, one cannot independently calibrate two or more outputs and combine the calibrated two or more outputs entirely within the mind," Examiner respectfully disagrees. As a non-limiting example, it would be trivial for one of ordinary skill in the art to take a model like y=mx+b, choose variables (calibrate the model output by selecting the variables that form the output), obtain the output (for example y=(2)(2)+(2)=6) and then combine that output with another model calibrated similarly. There is nothing in the instant claims that would require more than this.
With respect to Applicant's arguments on p. 12 of the Remarks submitted 8/10/2026 that the claims present a technical improvement, Examiner respectfully disagrees. As noted above the claims appear to be directed wholly to a mental process which cannot be relied upon to present a technical improvement (MPEP 2106.05(a) "It is important to note, the judicial exception alone cannot provide the improvement").
With respect to Applicant's arguments on p. 12 of the Remarks submitted 8/10/2026 that the claims present a technical improvement by combining "unsupervised," "supervised," and "reinforcement learning", Examiner notes that none of the instant claims are limited to requiring three distinct training paradigms, only claim 3 requires two different model types, and claim 3 recites these models at a high level of generality such that they appear to be mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)) or an attempt to generally link the judicial exception to a particular field or technology (See MPEP 2106.05(h)).
For at least these reasons and those further detailed below Examiner asserts that it is reasonable and appropriate to maintain the rejection under 35 USC 101.
Applicant’s arguments with respect to rejection of claims 1, 2, 5-9, 12-16, 19, and 20 under 35 U.S.C. 102 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on p. 15 of the Remarks submitted 8/10/2026 that "Lin nonetheless fails to disclose independently calibrating the two or more outputs," Examiner respectfully disagrees. Neither the instant claims or the instant specification explicitly limit what it means to "independently calibrat[e]" two or more outputs. One of ordinary skill in the art would recognize that the entire purpose of machine learning model training is to calibrate the outputs to match the training data, such that Lin's explicit recitation of training each predictive model (independently [Col. 2 l. 10-15] "processing the feature vector includes submitting the entire training dataset to each untrained predictive model of the subset" [Col. 3 l. 1-4] "a predictive model can be constructed (or “trained”) using a training dataset in conjunction with a machine learning algorithm" [Col. 4 l. 31-35] "The boosting operations can include training a set of predictive models in series" where model 1 is explicitly trained/calibrated in a first step independent of the second training step) is seen as independently calibrating the respective model outputs. Examiner asserts that this is a very reasonable interpretation that should be maintained.
Applicant’s arguments with respect to rejection of claims 3, 4, 10, 11, 17, and 18 under 35 U.S.C. 103 based on amendment have been considered, however, are not persuasive.
With respect to Applicant's arguments on p. 17 of the Remarks submitted 8/10/2026 that Chow fails to disclose independently calibrating the two or more outputs, Examiner respectfully disagrees. Chow explicitly reinforces Examiners above interpretation ([¶0043] "the training data can include one or more ground truth utterances. The systems and methods can then evaluate a loss function that evaluates a difference between a particular candidate utterance of the plurality of candidate utterances and the one or more ground truth utterances. One or more parameters of at least one of the plurality of machine-learned language models can be adjusted based at least in part on the loss function.").
For at least these reasons and those further detailed below, Examiner asserts that it is reasonable and appropriate to maintain the rejections under 35 USC 103.
Claim Rejections - 35 USC § 101
101 Rejection
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 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
Independently calibrating the two or more outputs (observation, evaluation, and judgement)
combining the two or more outputs (observation, evaluation, and judgement),
generating a final output based on the combining (observation, evaluation, and judgement)
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “receiving two or more outputs from two or more models” which amounts to gathering and outputting data which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i)).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 8 and 15, which recite a system and a computer program product, respectively, as well as to dependent claims 2-7, 9-14, and 16-20.
Independent claim 8 recites additional instructions to apply the judicial exception using generic computer components “one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method” which does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)).
Independent claim 15 recites additional instructions to apply the judicial exception using generic computer components “one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method” which does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)).
The additional limitations of the dependent claims are addressed briefly below:
Dependent claims 2, 9, and 16 recite additional observation, evaluation, and judgement “calibrating the final output”
Dependent claims 3, 10, and 17 recite additional instructions to apply the judicial exception using generic computer components “the two or more models include a reinforcement learning model and a supervised learning model”
Dependent claims 4, 11, and 18 recite additional observation, evaluation, and judgement “wherein the two or more models identify an intent of a user within a virtual assistant program”
Dependent claims 5, 12, and 19 recite additional insignificant extra-solution activity “applying a pairwise weighted average to the two or more calibrated outputs”.
Dependent claims 6, 13, and 20 recite additional observation, evaluation, and judgement “the pairwise weighted average is 100% of one of the two or more calibrated outputs”
Dependent claims 7 and 14 recite additional observation, evaluation, and judgement “applying customizable rules to the two or more calibrated outputs”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-20 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 5-9, 12-16, 19, and 20 are rejected under U.S.C. §102(a)(1) as being anticipated by Lin (US8370280B1).
Regarding claim 1, Lin teaches A method for combining model outputs, the method comprising:([Col. 1 l. 13-14] "This specification relates to predictive modeling technology." [Col. 1 l. 38-40] "output from a number of distinct predictive models can be combined to achieve a superior prediction.")
receiving two or more outputs from two or more models;([Col. 1 l. 44-67] "selecting, from a set of predictive models, a subset of one or more predictive models based on the element types and one or more performance indicators associated with each predictive model in the set of predictive models; processing the feature vector using the subset of predictive models, each predictive model of the subset of predictive models generating an output based on the feature vector to provide a plurality of outputs")
independently calibrating the two or more outputs;([Col. 2 l. 10-15] "processing the feature vector includes submitting the entire training dataset to each untrained predictive model of the subset" [Col. 1 l. 44-67] "processing the feature vector using the subset of predictive models, each predictive model of the subset of predictive models generating an output based on the feature vector to provide a plurality of outputs" [Col. 3 l. 1-4] "a predictive model can be constructed (or “trained”) using a training dataset in conjunction with a machine learning algorithm" Lin is explicit that the training dataset is independently provided to each untrained predictive model)
combining the two or more calibrated outputs; and([Col. 2 l. 1-3] "the final output is generated using a decision maker, the decision maker receiving the plurality of outputs; the plurality of outputs are combined")
generating a final output based on the combining.([Col. 2 l. 1-3] "the final output is generated using a decision maker, the decision maker receiving the plurality of outputs; the plurality of outputs are combined").
Regarding claim 2, Lin teaches The method of claim 1, further comprising: calibrating the final output(Lin [Col. 3 l. 14-22] "the accuracy of the predicted outcomes ultimately generated by a predictive model can be augmented by increasing the number of training examples and/or the number of features per training example.").
Regarding claim 5, Lin teaches The method of claim 1, wherein the combining further comprises: applying a pairwise weighted average to the two or more calibrated outputs.(Lin [Col. 9 l. 50-60] "The category receiving a majority of votes is considered the final predicted outcome. Other comparable fixed combining rules (e.g., the Maximum rule, the Median rule, the Mean, rule, the Minimum rule, the Product rule, the Borda Count rule, etc.) can also be used for combining output from predictive models" [Col. 14 l. 4-6] "the first plurality of predictive outputs is equally weighted when generating the first combined predictive output." For two outputs the combination rule would always necessarily be pairwise).
Regarding claim 6, Lin teaches The method of claim 5, wherein the pairwise weighted average is 100% of one of the two or more calibrated outputs.(Lin [Col. 9 l. 50-60] "The category receiving a majority of votes is considered the final predicted outcome. Other comparable fixed combining rules (e.g., the Maximum rule, [...] the Minimum rule" For two outputs the combination rule would always necessarily be pairwise and the maximum or minimum rule would give 100% weight to one of the outputs.).
Regarding claim 7, Lin teaches The method of claim 1, wherein the combining further comprises: applying customizable rules to the two or more calibrated outputs.(Lin [Col. 10 l. 7-10] "a suitable combining technique for generating a final output may be defined" [Col. 10 l. 24-25] "FIG. 6A shows a graph 600 a for implementing an arbitrary fixed output combining rule.").
Regarding claim 8, claim 8 is directed towards a computer program product for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 8. Claim 8 also recites additional elements one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media capable of performing a method, the method comprising:(Lin [Col. 10 l. 52-65] "A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device").
Similarly, regarding claims 9 and 12-14, claims 9 and 12-14 are directed towards a computer program product for performing the methods of claims 2 and 5-7, respectively. Therefore, the rejection applied to claims 2 and 5-7 also applies to claims 9 and 12-14.
Regarding claim 15, claim 15 is directed towards a system for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 15. Claim 15 also recites additional elements one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method, the method comprising:(Lin [Col. 10 l. 52-65] "A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device").
Similarly, regarding claims 16 and 19-20, claims 16 and 19-20 are directed towards a system for performing the methods of claims 2 and 5-6, respectively. Therefore, the rejections applied to claims 2 and 5-6 also apply to claims 16 and 19-20.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3, 4, 10, 11, 17, and 18 are rejected under U.S.C. §103 as being unpatentable over the combination of Lin and Chow (US20230376697A1).
Regarding claim 3, Lin teaches The method of claim 1.
However, Lin doesn't explicitly teach, wherein: the two or more models include a reinforcement learning model
and a supervised learning model.
Chow, in the same field of endeavor, teaches the two or more models include a reinforcement learning model ([¶0028] "The dialogue management model may have been trained with reinforcement learning")
and a supervised learning model. ([¶0046] "One or more machine-learned models may be trained via unsupervised and/or semi-supervised learning").
Lin as well as Chow are directed towards machine learning ensembles. Therefore, Lin as well as Chow are reasonably pertinent analogous art It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Lin with the teachings of Chow by using the output combination method in Lin for the models and application of Chow. Chow provides as additional motivation for combination ([¶0135] “The systems and methods may use open-domain dialogue as a running example to show how MoE-LM-based agents can improve user satisfaction measured by an improvement on a sentiment or engagement. A DM agent may possess different behaviors (e.g., inquisitive, explorative, relevant, soothing, empathetic, complimentary, provoking) and may swiftly decide which intent to use to pivot a conversation, build rapport, pique the user's interests, improve their mood, etc”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 4, Lin teaches The method of claim 1.
However, Lin doesn't explicitly teach wherein the two or more models identify an intent of a user within a virtual assistant program.
Chow, in the same field of endeavor, teaches the two or more models identify an intent of a user within a virtual assistant program. ([¶0007] "The operations can include providing the predicted dialogue response to a user computing system and receiving additional conversation data from the user computing system" [¶0028] "the dialogue management model can be configured to determine a conversation intent based on the language representation and select a particular candidate output based on the particular candidate output being associated with the conversation intent").
Lin as well as Chow are directed towards machine learning ensembles. Therefore, Lin as well as Chow are reasonably pertinent analogous art It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Lin with the teachings of Chow by using the output combination method in Lin for the models and application of Chow. Chow provides as additional motivation for combination ([¶0135] “The systems and methods may use open-domain dialogue as a running example to show how MoE-LM-based agents can improve user satisfaction measured by an improvement on a sentiment or engagement. A DM agent may possess different behaviors (e.g., inquisitive, explorative, relevant, soothing, empathetic, complimentary, provoking) and may swiftly decide which intent to use to pivot a conversation, build rapport, pique the user's interests, improve their mood, etc”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claims 10-11, claims 10-11 are directed towards a computer program product for performing the methods of claims 3-4, respectively. Therefore, the rejections applied to claims 3-4 also apply to claims 10-11.
Regarding claims 17-18, claims 17-18 are directed towards a system for performing the methods of claims 3-4, respectively. Therefore, the rejections applied to claims 3-4 also apply to claims 17-18.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
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