DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/4/26 has been entered.
Status of the Claims
This office action is submitted in response to the RCE filed on 2/4/26.
Examiner notes that this application is a continuation of 18/316784, which is now US Patent #11966949.
Examiner further notes that 18/316784 is a continuation of three other cases, which are now US Patent Nos. 11687969, 11288704, and 10937058.
Examiner further notes Applicant’s priority date of 7/2/19, which stems from the aforementioned parent applications.
Examiner further notes the previous withdrawal of prior art on 7/16/25.
Claims 1, 6, 8, 11, 15, and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Double Patenting
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11966949. Although the claims at issue are not identical, they are not patentably distinct from each other because they both disclose a method for targeting bid and position for a keyword, comprising: receiving keyword performance information from a first search engine in response to user interaction; receiving historical keywork information associating an application, an account, a conversion, or a value with the keywork performance information; combining the keyword performance information and the historical keyword information to generate a keyword dataset; generating a feature dataset in response to generating the keyword dataset, the dataset comprising position values for the keyword; determining a prediction function and associated uncertainties based on the position-value for the keyword; determining a target position for the keyword based on applying a selection operation to the prediction function; determining a target position for the keyword based on applying the prediction function; utilizing historical bid position data; determining bid information based on the target position and the bid-to-position function; and transmitting a bid message to a second search engine, the message including the bid information.
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.
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.
Independent claims 1, 15, and 20, in part, describe an invention comprising: (1) "receiving . . . keyword performance information"; (2) "retrieving . . . historical keyword information associating an application, an account, a conversion, or a value with the keyword performance information"; (3) "joining . . . the keyword performance information and the historical keyword information to generate a keyword dataset" and "generating . . . a feature dataset . . . comprising a plurality of position-value-time sets for the keyword, wherein each position-value-time set . . . includes a respective profit-per-impression"; (4) "filtering . . . the plurality of position-value-time sets for the keyword to identify one or more position-value-time sets . . . associated with a period of time"; (5) "receiving . . . a non-linear prediction function and associated uncertainties"; (6) "receiving . . . a target position for the keyword"; and (7) "determining . . . bid information based on the target position for the keyword and the bid-to-position function." As such, the invention is directed to the abstract idea of collecting keyword performance and historical bid data, computing profit-per-impression across position-value-time sets, predicting an optimal bid position based on performance data and uncertainty, and determining a corresponding bid amount — which is aptly categorized as a method of organizing human activity (managing and optimizing a commercial keyword bidding transaction). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
Next, the aforementioned claims recite additional elements that are associated with the judicial exception, including: "receiving, via a network interface of a computing device, keyword performance information from a first search engine based on interactions with webpages associated with the first search engine"; "transmitting, via the network interface of the computing device, a bid message to a second search engine, the bid message including the bid information"; and "receiving, by the computing device and after transmitting the bid message, a result of the bid message for the keyword." Examiner understands these limitations to be insignificant extra-solution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle into a patentable process.")).
The aforementioned claims also recite additional elements including: (1) "a computing device" with a "network interface" and "data store" (claims 1, 15, 20); (2) a "join information section" and "feature generation section" of the computing device (claims 1, 15, 20); and (3) a "backwards filtering model of a bid model" of the computing device (claims 1, 15, 20). These limitations are recited at a high level of generality, and appear to be nothing more than field-of-use limitations (applying the abstract idea of mathematical bid optimization to keyword advertising using generic computing infrastructure), with generic computer components (a networked computing device, data store, and functionally-labeled software sections) used to automate the abstract mathematical processes of modeling, optimization, and bid determination. 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 2358, 110 USPQ2d at 1983.
The aforementioned claims further recite a "Gaussian process machine learning model,” a "Thompson sampling reinforcement machine learning model" of a "Gaussian process model section"; and a "backwards filtering model of a bid model" of the computing device (claims 1, 15, 20). These models are well-known, off-the-shelf machine learning techniques that are particularly suited for modeling non-linear data and optimizing decisions under uncertainty, and are being applied here as tools to implement the abstract idea — specifically, to process the position-value-time feature dataset and output a prediction function and target position. The claims do not recite the underlying mathematical operations by which data is input to or processed within these models; rather, the models are invoked at a high level of generality as vehicles for carrying out the abstract idea of predicting an optimal bid position. Applying known machine learning models as tools to implement an abstract idea — without reciting any improvement to the models themselves or to the technology underlying them — is the paradigmatic "apply it" scenario and does not render the claims eligible. See MPEP 2106.05(f); Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field; apply the judicial exception with a particular machine; effect a transformation or reduction of a particular article to a different state or thing; or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use generic computer components — including off-the-shelf machine learning models applied as tools — to collect keyword performance and bid history data, apply mathematical modeling and reward-based optimization to select a target position, and determine a bid amount for transmission to a search engine.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements amount to activities that are well-understood, routine, and conventional (See MPEP 2106.05(d)):
Receiving data from and transmitting bid messages to external network services via a generic network interface. buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); MPEP 2106.05(g)(3).
Applying known machine learning models — including Gaussian process and Thompson sampling reinforcement learning models — as tools to process collected data and generate outputs, without reciting any improvement to the models themselves or the technology underlying them. SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168, 127 USPQ2d 1597, 1602 (Fed. Cir. 2018); MPEP 2106.05(f).
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Claims 2–14 and 16–19 are dependent on the aforementioned independent claims, and further limit the abstract idea with non-functional descriptive features as follows: determining profit-per-impression using a profit-by-impression algorithm (claim 2); determining profit-per-impression using a moving average algorithm (claim 3); specifying that each position-value-time set includes a respective position and time associated with the keyword dataset (claim 4); specifying that the Thompson sampling model outputs the target position based on an exploit-explore ratio (claims 5 and 19); specifying that the Thompson sampling agent selects an exploit or explore action based on the exploit-explore ratio (claim 6); specifying that keyword performance information includes clicks, impressions, or average position (claim 7); specifying that the Gaussian process model uses Bayesian inference with a prior function to make a posterior inference (claims 8 and 15 system equivalent); specifying that the prior function is initiated as linear, quadratic, or exponential functions relating to kernels (claim 9); specifying that the prior function is initiated as constant, squared exponential, matern, periodic, or linear kernel functions (claim 10); specifying that the resulting non-linear prediction function is stored as the prior function for subsequent iterations (claim 11); specifying exploit and explore ranges of positions based on expected values (claim 12); specifying that exploit and explore ranges are determined based on expected values and positions (claim 13); and specifying a no-bid range of positions based on expected values below a threshold (claim 14). These claims merely specify particular mathematical implementation details or algorithmic variations on the fundamental abstract process of collecting keyword performance data, computing profit-per-impression, predicting an optimal bid position, and determining a bid amount. They do not recite any additional functional computer operations beyond generic data processing and mathematical modeling, and do not effect an improvement in the functioning of the computer itself, in machine learning technology, or in any other technical field. The dependent claims merely add further mathematical or algorithmic detail to the abstract idea without providing significantly more than the underlying abstract idea.
Therefore, claims 1–20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Relevant Prior Art
The following references are deemed to be relevant to Applicant’s disclosures:
Knapp (20150066639) discloses a method for bidding for ad impressions in an online setting.
Libby (20100004974) discloses a method for determining weighted average success probabilities of internet ads.
Mathew et al. (20090327083) discloses a method for automating online ad placement optimization.
Response to Arguments
Applicant’s arguments regarding the sufficiency of the claims under 35 USC 101 remain unpersuasive.
Specifically, Applicant argues that the updating limitations of claim 1 — specifically, updating the reward function of the Thompson sampling reinforcement machine learning model based on the bid result, and subsequently updating the monotonically decreasing function using the backwards filtering model — integrate the claimed exception into a practical application, and that these features enable the method to adapt to changing auction environments in a manner analogous to the continual learning improvement recognized in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential). Applicant further argues that the Gaussian process model section improves keyword bidding technology by accounting for the non-linear nature of the position-to-profit relationship. These arguments are not persuasive.
With respect to Applicant's reliance on Desjardins, that decision is inapposite and does not compel eligibility here. In Desjardins, the ARP credited patent eligibility because the claims were directed to a specific technical improvement to how the machine learning model itself operates — namely, a training strategy that selectively adjusted model parameters to preserve performance on earlier tasks while learning new ones, directly addressing the well-recognized technical problem of catastrophic forgetting in continual learning systems. Ex Parte Desjardins, Appeal No. 2024-000567, at 7–10. The specification in Desjardins identified concrete, enumerated improvements to the machine learning model itself, including effective sequential learning while protecting prior task knowledge, reduced storage capacity requirements, and reduced system complexity. Id. Critically, the improvement in Desjardins was an improvement to the model itself — to how the model learns and retains knowledge — not merely an improvement to the downstream output or commercial result the model was used to produce.
The claims here are materially distinguishable. The updating limitations of claim 1 do not improve how the Thompson sampling model or the backwards filtering model themselves operate. Rather, these limitations simply feed the result of a commercial bid transaction back into the existing models as new data inputs — i.e., the reward function is updated based on the bid result, and the monotonically decreasing function is updated based on that same result. This is the ordinary operation of a reinforcement learning feedback loop: receiving a result and updating a parameter accordingly. The specification's own description confirms this — the models are described as adapting to changes in auction environments caused by seasonal effects or competitor bidding behavior. See spec. at paragraph 63. This is not an improvement to the machine learning model's architecture, training methodology, or internal operation; it is the standard application of an off-the-shelf reinforcement learning model to process new data and update its parameters in the ordinary course of its designed function. Updating a reward function with new transactional data is precisely what a Thompson sampling reinforcement learning model is designed to do. No improvement to the model itself — no solution to a technical problem in machine learning — is recited or described. The claims simply leverage the existing, well-understood adaptive capabilities of Thompson sampling and backwards filtering as tools to implement the abstract idea of optimizing keyword bid positions, which is the paradigmatic "apply it" scenario that does not integrate the exception into a practical application. See MPEP 2106.05(f); Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 223–26 (2014).
Applicant's argument regarding the Gaussian process model section fares no better. Applicant contends that using a Gaussian process model to account for non-linearity in the position-to-profit relationship represents a technological improvement over conventional approaches. However, Gaussian process modeling is a well-known mathematical technique for generating non-linear prediction functions with associated uncertainties. The claims do not recite any improvement to how the Gaussian process model operates, any novel training methodology, or any modification to the model's architecture or internal mathematical operations. The claims recite only that an off-the-shelf Gaussian process model is applied to the feature dataset and outputs a prediction function — i.e., the model is used as a tool to implement the abstract mathematical process of predicting bid position performance. The fact that a known mathematical technique is better suited for non-linear data than simpler approaches does not transform the use of that technique into a technical improvement to computer functionality or machine learning technology. Selecting an appropriate off-the-shelf model for a particular data characteristic is an exercise in applying a known tool to an abstract problem, not an improvement to the tool itself. See SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168, 127 USPQ2d 1597, 1602 (Fed. Cir. 2018); MPEP 2106.05(a).
Accordingly, Applicant's arguments do not overcome the §101 rejection, and the rejection is maintained.
Finally, the previous double patenting rejection has been revised and sustained in view of the aforementioned amendments and Applicant’s failure to file a terminal disclaimer.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER BUSCH whose telephone number is (571)270-7953. The examiner can normally be reached M-F 10-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER C BUSCH/Examiner, Art Unit 3621
/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621