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 . Claims 1-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 06/17/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The amended claims overcome the 103 rejections and is therefore withdrawn.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the recite an improvement to the technical field of machine learning. Applicant further contends the claims compare different models architectures to predict outcomes.
Examiner respectfully disagrees. The claims do not recite any machine learning elements and as such are abstract. Examiner further points to the Applicant’s specification. While the Specification describes the item selection model and availability models as machine learning model (Spec, [39, 41]. However, with respect to the claimed marketing mix model is not defined as a machine learning with the Specification describing marketing models as multi-variate regression model (Spec, [02]). Further the Specification merely describes that the model may be used by the online system and trained by a machine-learning training module (Spec, [56]). As such the claimed improvement is not commensurate with the scope of the claims. Further the Examiner notes that if the claims were amended to include machine learning models, the training steps would be treated as additional elements which are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
The 101 rejection is updated and maintained below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent claim(s), (additional elements bolded)
Regarding Claim(s) 1 and 19, A method, performed at a computer system comprising a processor and a computer-readable medium, comprising. A system comprising: a processor that executes instructions; and a non-transitory computer-readable storage medium having instructions executable by the processor for::
identifying a set of experiments, each experiment including a set of experiment input features and an experimental outcome, wherein the set of experiment input features for an experiment comprises a varied input feature and controlled input features;
for each experiment in the set of experiments:
training a plurality of marketing mix models with training data that excludes data for the respective experiment, wherein each of the plurality of marketing mix models has a different model architecture of a plurality of model architectures and is trained to predict an outcome based on a set of input features,
applying each trained marketing mix model to the set of input features to predict an outcome, and
generating an experiment score for each trained marketing mix model by comparing the predicted outcome to the experimental outcome;
computing a causality score for each of the plurality of model architectures by scoring plurality of model architectures associated with the plurality of marketing mix models based on the experiment scores;
selecting a model architecture of the plurality of model architectures based on the computed causality scores; and
training a new marketing mix model based on the selected model architecture,
wherein the new marketing mix model is trained on new training data to predict an outcome based on another set of input features, wherein training the new marketing mix model comprises:
accessing a plurality of training examples from the new training data, wherein each training example comprises the other set of input features and an outcome label indicating an outcome; and for each of the plurality of training examples:
generating, using the new marketing mix model, a predicted outcome based on the other set of input features from the training example;
comparing the predicted outcome to the outcome label from the training example; and
updating parameters of the new marketing mix model based on the comparison.
Regarding Claim(s) 10. A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:
identifying a set of experiments, each experiment including a set of experiment input features and at least one an experimental outcome, wherein the set of experiment input features for an experiment comprises a varied input feature and controlled input features;
for each experiment in the set of experiments:
training a plurality of marketing mix models with training data that excludes data for the respective experiment, wherein each of the plurality of marketing mix models has a different model architecture of a plurality of model architectures and is trained to predict an outcome based on a set of input features,
applying each trained marketing mix model to the set of input features to predict an outcome, and
generating an experiment score for each trained marketing mix model by comparing the predicted outcome to the experimental outcome;
computing a causality score for each of the plurality of model architectures by scoring plurality of model architectures associated with the plurality of marketing mix models based on the experiment scores;
selecting a model architecture of the plurality of model architectures based on the computed causality scores; and
training a new marketing mix model based on the selected model architecture,
wherein the new marketing mix model is trained on new training data to predict an outcome based on another set of input features, wherein training the new marketing mix model comprises:
accessing a plurality of training examples from the new training data, wherein each training example comprises the other set of input features and an outcome label indicating an outcome; and
for each of the plurality of training examples:
generating, using the new marketing mix model, a predicted outcome based on the other set of input features from the training example;
comparing the predicted outcome to the outcome label from the training example; and
updating parameters of the new marketing mix model based on the comparison.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards identifying a set of experiments, training a model, applying a model to inputs, generating an experiment score, generating a model score, and deploying a model all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards evaluating marketing mix models (See Specification,[04]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least an a computer system comprising a processor and a computer-readable medium; A system comprising: a processor that executes instructions; and a non-transitory computer-readable storage medium having instructions executable by the processor for; A computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 2-9, 11-18, and 20, the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Examining Claims with Respect to Prior Art
Claims 1-20, though directed to non-statutory subject matter, are deemed to define over the currently known prior art under 35 USC 102 and 103. Examiner interprets based upon the claim limitations that there is no currently known prior art that discloses the features relating to: “identifying a set of experiments, each experiment including a set of experiment input features and at least one an experimental outcome, wherein the set of experiment input features for an experiment comprises a varied input feature and controlled input features; for each experiment in the set of experiments: training a plurality of marketing mix models with training data that excludes data for the respective experiment, wherein each of the plurality of marketing mix models has a different model architecture of a plurality of model architectures and is trained to predict an outcome based on a set of input features, applying each trained marketing mix model to the set of input features to predict an outcome, and generating an experiment score for each trained marketing mix model by comparing the predicted outcome to the experimental outcome; computing a causality score for each of the plurality of model architectures by scoring plurality of model architectures associated with the plurality of marketing mix models based on the experiment scores; selecting a model architecture of the plurality of model architectures based on the computed causality scores; and training a new marketing mix model based on the selected model architecture, wherein the new marketing mix model is trained on new training data to predict an outcome based on another set of input features, wherein training the new marketing mix model comprises: accessing a plurality of training examples from the new training data, wherein each training example comprises the other set of input features and an outcome label indicating an outcome; and for each of the plurality of training examples: generating, using the new marketing mix model, a predicted outcome based on the other set of input features from the training example; comparing the predicted outcome to the outcome label from the training example; and updating parameters of the new marketing mix model based on the comparison.”
The reason to withdraw the 35 USC 103 rejection of claims 1-20 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Known Prior Art (patent)
US 20250225375 A1
MACHINE LEARNING SYSTEMS AND TECHNIQUES FOR AUDIENCE-TARGETED CONTENT GENERATION
US 20200245009 A1
UTILIZING A DEEP GENERATIVE MODEL WITH TASK EMBEDDING FOR PERSONALIZED TARGETING OF DIGITAL CONTENT THROUGH MULTIPLE CHANNELS ACROSS CLIENT DEVICES
US 20200387849 A1
MODULAR MACHINE-LEARNING BASED MARKET MIX MODELING
US 20240346289 A1
BAYESIAN NEURAL NETWORK POINT ESTIMATOR
US 20250390895 A1
ATTENTION-BASED DATA-DRIVEN ATTRIBUTION
US 20220292542 A1
MACHINE LEARNING WITH DATA SYNTHESIZATION
US 20240152696 A1
BUILDING AND USING TARGET-BASED SENTIMENT MODELS
US 20230076243 A1
MACHINE LEARNING ARCHITECTURE FOR QUANTIFYING AND MONITORING EVENT-BASED RISK
US 20210042590 A1
MACHINE LEARNING SYSTEM USING A STOCHASTIC PROCESS AND METHOD
Known Prior Art (NPL)
R. Takahashi et al., "Multi-period marketing-mix optimization with response spike forecasting," in IBM Journal of Research and Development, vol. 58, no. 5/6, pp. 1:1-1:13, Sept.-Nov. 2014, doi: 10.1147/JRD.2014.2337131
Known Prior Art (foreign)
IN202411038012A
A system and method for optimizing target functions in marketing mix modeling
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 JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEREMY L GUNN/ Primary Examiner, Art Unit 3624
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