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 are presented for examination.
This Office action is Non-Final.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 08/19/2025 has been considered by the Examiner and made of record in the application file.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krause et al. discloses a generative-discriminative (GeDi) language modeling for determining a next token in a text sequence. A class conditional language model and a positive control code determine a first class conditional probability for each token candidate. The class conditional language model and a negative control code determine a second class conditional probability for the each token candidate. A logarithmic probability difference between the first class conditional probability and the second class conditional probability is determined for each token candidate. An unconditional language model determines an unconditional probability for each token candidate. A combined probability is determined by combining the unconditional probability and the logarithmic probability difference for each token candidate. The next token is selected from the token candidates based on the combined probabilities of the token candidates.
Kramer et al. discloses computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output. A treatment effect is then computed for a treatment action by generating a simulated treatment matrix where all values for the treatment action are set to true, computing an average of outputs from the machine learning system using the simulated treatment matrix, generating a simulated control matrix where all values for the treatment action are set to false, computing an average of outputs from the machine learning system using the simulated control matrix, and computing a difference between the two average outputs.
Ozcaglar et al. discloses systems are provided to select targets for communications, designed to cause or provoke the targets to perform a desired action, using bias-corrected models that predict the effect of such communications on the targets. Data regarding previous actions taken by potential targets for communications—also referred to as “candidates”—can be analyzed to determine whether there is a difference in actions taken by candidates who have received prior communications in comparison with candidates who have not received the prior communications. Biases in the selection of candidates to receive the prior communications can be corrected by weighting the data associated with the selected candidates to more closely match the distribution of candidates not selected to receive the prior communications.
Liu discloses in a marketing campaign, an individual whom we market to may be a swing buyer, a self buyer, or a non-persuadable non-buyer. A cost effective marketing strategy may focus on the swing buyers, who make a purchase when treated by the marketing campaign and do not purchase the product otherwise. Utilizing a randomized test and control data set including individuals randomly divided between a treatment group and a control group, three methods for predicting swing customers in a marketing campaign are proposed. One such method includes developing a first model corresponding to a likelihood that a member of control group is a buyer of the product, developing a second model corresponding to the likelihood that a non-buyer of the product is a member of control group, and determining a score corresponding to the likelihood that an individual is a swing buyer, using the first model and the second model.
Obviousness Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being -unpatentable over claims 1-15 of U.S. Patent No. 12,417,439 (‘439). Although the claims at issue are not identical, they are not patentably distinct from the claims of the ‘439 patent.
With respect to independent claim 1, claims 1 and 2 of the ‘439 patent collectively recite a machine learning method for automatic action determination including a first action prediction model trained with a desired-outcome subset of a historical dataset and configured to output an action selection probability under an assumption of a first desired outcome, and a second action prediction model trained with the entire historical dataset and configured to output an unconditional action selection probability. Claim 2 further recites using the respective models with a new state and determining, as a future action, an action from the set of possible actions that maximizes a pairwise ratio of the action selection probability under the assumption of the first desired outcome over the unconditional action selection probability for the new state.
Instant claim 1 differs principally in reciting the previously trained status of the first and second action prediction models rather than positively reciting the training operations and in reciting that the future action “optimizes” the pairwise ratio rather than “maximizes” the pairwise ratio.
It would have been obvious to one of ordinary skill in the art, in view of claims 1 and 2 of the ‘439 patent, to perform the claimed action-determination operations using the first and second action prediction models after the models have been trained as expressly required by patent claim 1, because use of the trained models to determine the respective probabilities and select the future action is expressly recited by patent claim 2 and represents the expected use of the trained models. Further, optimization of the pairwise ratio encompasses or, at minimum, would have been an obvious variation of maximizing the pairwise ratio as expressly recited by patent claim 2.
Accordingly, instant claim 1 merely claims an obvious variation of the subject matter already claimed in the ‘439 patent and does not define a patentably distinct invention.
Independent claims 15 and 20 recite substantially corresponding subject matter in system and non-transitory computer-readable-medium form. The change in statutory form does not render the otherwise corresponding machine-learning operations patentably distinct. Accordingly, claims 15 and 20 are likewise unpatentable over the corresponding subject matter of claims 1, 2, 13-15 of the ‘439 patent.
The additional limitations of dependent claims 2-14 and 16-19 likewise do not render the claimed subject matter patentably distinct. The patent claims expressly encompass additional desired-outcome models, undesirable-outcome models, execution and iterative determination of actions, and particular applications of the historical dataset and determined actions. To the extent a presently claimed dependent limitation differs in wording or scope from the patent claims, the limitation represents no more than an obvious variation of the claimed machine-learning action-selection process and does not impart patentable distinctness.
This rejection is based on nonstatutory obviousness-type double patenting and is intended to prevent an unjustified extension of patent exclusivity for claims that are not patentably distinct from those of the ‘439 patent.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2 and 16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 2 and 16 recite, respectively, that “the first action prediction model is configured to predict a first action of a multi-action process,” and that “the second action prediction model is configured to predict a second action of the same multi-action process,” wherein the future action is a single action for the multi-action process.
The Specification [see, e.g., ¶0063, 0067 and 0069-0072]provides support for using first and second action prediction models in connection with a multi-action process. In particular, the Specification discloses that the first action prediction model is trained using records having a desired outcome and predicts an action selection probability under an assumption of the desired outcome, while the second action prediction model is trained using the entire historical dataset and predicts an unconditional action selection probability. The outputs of the two models are then compared using a pairwise ratio to determine a future action.
The Specification [see, e.g., ¶0067] further discloses that the disclosed systems and methods may determine a single action in processes involving several actions.
However, the Specification does not reasonably convey to one of ordinary skill in the art that Applicant was in possession, as of the filing date, of the specifically claimed arrangement in which the first action prediction model predicts a first action of a multi-action process while the second action prediction model predicts a second action of that same multi-action process.
Rather, the disclosed first and second models are described as predicting different probabilities associated with the actions based on different training datasets or conditions. The first model predicts action selection probabilities under an assumption of a desired outcome, whereas the second model predicts unconditional action selection probabilities. The Specification thereafter combines or compares the corresponding model outputs to select an action. The disclosure that a selected action may constitute a single action of a process involving multiple actions does not, without more, disclose the claimed assignment of a first action of the multi-action process to the first prediction model and a second action of the same multi-action process to the second prediction model.
Accordingly, the originally filed disclosure does not reasonably convey possession of the particular model-to-action relationship presently recited in claims 2 and 16.
Allowable Subject Matter
Claims 1-20 would be allowed pending the above Obviousness Double Patenting and 35 USC 112 rejections.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record and considered during examination fails to teach or suggest, either alone or in combination, the claimed arrangement of using a first action prediction model trained with a desired outcome subset of a historical dataset to determine an action selection probability under an assumption of a desired outcome, using a different second action prediction model trained with the entire historical dataset to determine an unconditional action selection probability, and determining a future action from a set of possible actions by optimizing a pairwise ratio of the action selection probability under the assumption of the desired outcome over the unconditional action selection probability for a new state, as recited by independent claims 1, 15 and 20.
When taken into context the claims as a whole were not uncovered in the prior art, even further, dependent claims 2-14 and 16-19 are allowed as they depend upon the allowable independent claims 1 and 15.
Conclusions/Points of Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CASANOVA whose telephone number is (571)270-3563. The examiner can normally be reached M-F: 9 a.m. to 6 p.m. (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CASANOVA/Primary Examiner, Art Unit 2165
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