DETAILED ACTION
Response to Amendment
This action is in response to the response to the amendment filed on 05/11/2026. Claims 1, 8, 9, 11, 18, and 19 have been amended and claims 7, 10, 17, and 20 have been canceled. Claims 1-6, 8, 9, 11-16, 18, and 19 are pending and currently under consideration for patentability.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
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 1-6, 8, 9, 11-16, 18, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1-6, 8, 9, 11-16, 18, and 19 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1-6, 8, and 9 recite a system for real-time bidding and claims 11-16, 18, and 19 recite a method for real-time bidding. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1 and 11 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1 and 11 recite limitations directed to the abstract idea including “receiving user input data; generates a predicted expected performance based on the user input data, wherein the predicted expected performance indicates an expected performance for at least one bid and at least one target, wherein the at least one target includes at least one of at least one keyword and at least one product associated with at least one marketplace; generates a predicted expected cost based on the user input; and adjusting the at least one bid on the at least one of the at least one keyword and the at least one product associated with the at least one marketplace, based on the predicted expected performance and the predicted expected cost.” These further limitations are not seen as any more than the judicial exception. Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted performance based on the user input data; wherein the first machine learning model is fine-tuned based on identified user performance preferences; generating a second machine learning model that generates a predicted expected cost based on the user input; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” The claims are considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as adjusting bids of keywords and product based on predicted expected performance and predicted expected cost. The claims are also considered to be an abstract idea under Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to receiving data (i.e. input data); predicting data (i.e. expected performance and expected cost); and adjusting data (i.e. bids based on predicted expected performance and predicted expected cost). Therefore, under Step 2A, Prong I, claims 1 and 11 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted performance based on the user input data; wherein the first machine learning model is fine-tuned based on identified user performance preferences; generating a second machine learning model that generates a predicted expected cost based on the user input; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” The additional limitations reciting – “generating a first machine learning model that generates a predicted performance based on the user input data; and generating a second machine learning model that generates a predicted expected cost based on the user input.” These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception (i.e. adjusting bids based on predicted expected performance and predicted expected cost) to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted performance based on the user input data; wherein the first machine learning model is fine-tuned based on identified user performance preferences; generating a second machine learning model that generates a predicted expected cost based on the user input; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” The additional limitations reciting – “generating a first machine learning model that generates a predicted performance based on the user input data; and generating a second machine learning model that generates a predicted expected cost based on the user input” do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1 and 11 also recite additional limitations including “wherein the first machine learning model is fine-tuned based on identified user performance preferences; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” Merely, fine tuning or modifying parameters of a machine learning model based on user preferences is seen as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) because these are a well-understood, routine, and conventional computer functions. Fine-tuning a machine learning model or modifying the parameters of the machine learning model based on user preferences is exactly the function of a machine learning model. For example, according to ¶ [0010] of U.S. Publication 2024/0273189 to Shavit; “Conventional approaches to finding a proper balance between false positives and false negatives make use of different threshold adjustments when detection is based on machine learning or statistics or a rule combination that is strong enough to flag attacks as accurately as possible when a rules-based approach is used for detection.” It is a well-understood, routine, and conventional function in machine learning model to fine-tune or adjust the machine learning model based on goals/objectives/preferences of the operator. Claims 1 and 11 do not include additional elements or a combination of elements that result in the claims 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 listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe a “any machine learning or neural network methodology known in the art or developed in the future”, ¶ [0107], for implementing the machine learning model, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 2-6, 8, 9, 11-16, 18, and 19 further recite the method of claim 1 and system of claim 11, respectively. Dependent claims 2-6, 8, 9, 11-16, 18, and 19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1 and 11. For example, claims 2-4 and 12-14 further describe the limitations for the user input that is used to predict expected performance and expected cost in order to adjust bids– which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-6, 8, 9, 11-16, 18, and 19, there are no additional elements introduced. For example, dependent claims 5, 6, 8, 9, 15, 16, 18, and 19 further describe the type of machine learning model being used. These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,).
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 102(a)(1)
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.
Claim(s) 1-4 and 11-14 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Publication 2015/0066661 to Bhattacharjee.
Claims 1-6, 8, 9 and 11-16, 18, 19 are system and method claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 1:
Bhattacharjee teaches:
A real-time bidding system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to (Bhattacharjee: ¶ [0121]):
receive user input data (i.e. receive user’s bid for keywords) (Bhattacharjee: ¶ [0042] “The mechanics of this text ads channel often requires such users to select a set of keywords, bid on those keywords, set up the appropriate creative and landing pages, and advertise to the right users, as well as perform other tasks. Optimal choices need to be made in all these dimensions ( e.g., keyword selection, bidding, landing page) in order to profitably bring in the right set of people to the website.” Furthermore, as cited in ¶ [0052] “In some embodiments, for any keyword k in the user's portfolio, the bid k is generated by an optimization routine Opt( ... ), which can depend on any combination of various parameters:”);
generate a first machine learning model that generates a predicted expected performance based on the user input data, wherein the predicted expected performance indicates an expected performance for at least one bid and at least one target, wherein the at least one target includes at least one of at least keyword and at least one product associated with at least one marketplace (i.e. generate an RPC prediction model or machine learning model that generates predicted expected performance or RPC for each keyword or user’s input of keyword, wherein expected revenue or performance is for target bid including keyword and wherein target includes keyword and product associated with marketplace) (Bhattacharjee: ¶¶ [0052] [0053] “In some embodiments, for any keyword k in the user's portfolio, the bid k is generated by an optimization routine Opt( ... ), which can depend on any combination of various parameters:… Some of the parameters, such as the RPC and the CPC, can be learned by a machine learning based prediction model. In some embodiments, an RPC prediction model 410 can be used to supply the expected RPC for any keyword.” Furthermore, as cited in ¶ [0061] “A predicted RPC and a predicted CPC can be determined using an RPC prediction model 410 and a CPC prediction model 420, respectively. The predicted RPC and the predicted CPC can be determined using daily or other period-based feedback about the performance results of the corresponding keyword on one or more search engines 470. This feedback can be provided by the search engine(s) 470 in the form of one or more daily or other period-based search reports 480. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be a part of or otherwise incorporated into the bid optimization system 400. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be implemented as their own modules, distinct from the bid optimization system 400.” Furthermore, as cited in ¶ [0064] “Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”), and
wherein the first machine learning model is fine-tuned based on identified user performance preferences (i.e. algorithm of RPC model is adjusted based on user-identified profit-based increase or performance preferences) (Bhattacharjee: ¶¶ [0078] [0079] “These bid update functions represent greedy algorithm techniques, as they make a locally optimal choice for each keyword in updating the bid value for each keyword. In some embodiments, the exposure/exploration function can also have some randomization component to it. For example, the optimization algorithm can randomly choose to bid high ( e.g., a random bid value) for certain keywords in order to collect performance information ( e.g., impression data, click data, revenue data), which can be particularly useful for keywords that do not have sufficient historical data available…It is noted that the algorithm is also able to bias one objective function compared to the other. For example, if the increase in bid is more aggressive in case of profit as compared to the bid increase in case of exploration requirements, then that would imply that the algorithm "favors" profit over exploration. The bid optimization system 400 can be configured to enable the user to configure the degrees or amounts of the different increases for the profit-based increase and the exposure-based increase. For example, the bid optimization system 400 can provide a user interface that can be accessed and used by the user to adjust the respective increase amounts.”);
generate a second machine learning model that generates a predicted expected cost based on user input (i.e. generate a CPC prediction model or machine learning model that generates predicted expected cost or CPC for each keyword or user’s input of keyword) (Bhattacharjee: ¶ [0061] “A predicted RPC and a predicted CPC can be determined using an RPC prediction model 410 and a CPC prediction model 420, respectively. The predicted RPC and the predicted CPC can be determined using daily or other period-based feedback about the performance results of the corresponding keyword on one or more search engines 470. This feedback can be provided by the search engine(s) 470 in the form of one or more daily or other period-based search reports 480. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be a part of or otherwise incorporated into the bid optimization system 400. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be implemented as their own modules, distinct from the bid optimization system 400.”),
wherein the second machine learning model is fine-tuned based on identified user budget preferences (i.e. algorithm of CPC model is adjusted based on identified user budget constraints or preferences) (Bhattacharjee: ¶¶ [0079] [0080] “It is noted that the algorithm is also able to bias one objective function compared to the other. For example, if the increase in bid is more aggressive in case of profit as compared to the bid increase in case of exploration requirements, then that would imply that the algorithm "favors" profit over exploration. The bid optimization system 400 can be configured to enable the user to configure the degrees or amounts of the different increases for the profit-based increase and the exposure-based increase. For example, the bid optimization system 400 can provide a user interface that can be accessed and used by the user to adjust the respective increase amounts…The objective functions ( maximize profit and maximize exposure) can be optimized subject to soft and hard constraints. The daily budget, a user override, a minimum bid value (min_bid), and a maximum bid value (max_bid) can be used as hard constraints, while user suggested bids and external suggestions can be used as soft constraints to guide the optimization.”);
adjust the at least one bid on the at least one of the at least one keyword and the at least one product associated with the at least one marketplace, based on the predicted expected performance and the predicted expected cost (i.e. adjust bid on keywords based on expected revenue or performance and expected cost, wherein keywords relate to products associated with marketplace and keywords/products based on CPC prediction model) (Bhattacharjee: ¶¶ [0104] [0105] “In some embodiments, the exposure determination can be based on a determination that a ratio of a number of impressions for the published content on the search engine to a total search volume on the search engine for a specified period of time is less than a predetermined value. The exposure determination can be based on a determination that a click-through rate for the keyword on the search engine is a predetermined amount greater than a click-through rate for a population of keywords on the search engine. The exposure determination can be based on a determination that a predetermined amount of inventory on an online marketplace of the user has a predetermined level of affinity with the keyword. The exposure determination can be based on a determination that a period-based budget of the user has increased by at least a predetermined amount…In some embodiments, the update operation can comprise adjusting the corresponding bid for a keyword based on an external suggestion from an API of a search engine.” Furthermore, as cited in ¶ [0061] “Referring back to FIG. 4, the bid optimization system 400 can use any combination of the above-mentioned parameters to determine optimal bids for keywords. A predicted RPC and a predicted CPC can be determined using an RPC prediction model 410 and a CPC prediction model 420, respectively. The predicted RPC and the predicted CPC can be determined using daily or other period-based feedback about the performance results of the corresponding keyword on one or more search engines 470. This feedback can be provided by the search engine(s) 470 in the form of one or more daily or other period-based search reports 480. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be a part of or otherwise incorporated into the bid optimization system 400. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be implemented as their own modules, distinct from the bid optimization system 400.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.” Furthermore, as cited in ¶ [0077] “bid update - if predicted RPC > predicted CPC, increase bid; else, decrease bid.”)).
With respect to Claim 11:
All limitations as recited have been analyzed and rejected to claim 1. Claim 11 recites “A real-time bidding method comprising:” the steps of system claim 1. Claim 11 does not teach or define any new limitations beyond claim 1. Therefore it is rejected under the same rationale.
With respect to Claim 2:
Bhattacharjee teaches:
The system of claim 1, wherein the user input data includes a desired total bid value and a bid period (i.e. user’s input bid includes user’s daily budget for the bid, wherein the budget represents desired total bid value and daily or other time period represents bid period) (Bhattacharjee: ¶ [0057] “In yet another example, a determined variation in a daily budget of a user can inform the bid optimization system 400 of an exposure appetite of a user for a keyword. For example, if the user has increased a daily budget by at least a certain degree or amount within a predetermined period of time for a portfolio of keywords that has remained the same or within a predetermined degree of similarity ( e.g., at least 95% of the keywords in the portfolio have remained the same from the time the daily budget was increased), then the bid optimization system 400 can interpret such an occurrence as an indication that the user wants to increase the exposure level of the keywords in the user's portfolio.”).
With respect to Claim 12:
All limitations as recited have been analyzed and rejected to claim 2. Claim 12 does not teach or define any new limitations beyond claim 2. Therefore it is rejected under the same rationale.
With respect to Claim 3:
Bhattacharjee teaches:
The system of claim 1, wherein the user input data includes information associated the at least one product (i.e. user’s input includes bids related to product) (Bhattacharjee: ¶ [0059] “The parameter "eventCalendar" can comprise indications of events associated with a desired increase in exposure. For example, an indication of a particular holiday, such as Christmas, can be used by the bid optimization system 400 to increase a bid for a keyword having an association with that holiday, in order to increase its exposure and take advantage of the event.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”).
With respect to Claim 13:
All limitations as recited have been analyzed and rejected to claim 3. Claim 13 does not teach or define any new limitations beyond claim 3. Therefore it is rejected under the same rationale.
With respect to Claim 4:
Bhattacharjee teaches:
The system of claim 1, wherein the user input includes information associated with the at least one marketplace (i.e. user’s input includes bids related to product in marketplace) (Bhattacharjee: ¶ [0059] “The parameter "eventCalendar" can comprise indications of events associated with a desired increase in exposure. For example, an indication of a particular holiday, such as Christmas, can be used by the bid optimization system 400 to increase a bid for a keyword having an association with that holiday, in order to increase its exposure and take advantage of the event.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”).
With respect to Claim 14:
All limitations as recited have been analyzed and rejected to claim 4. Claim 14 does not teach or define any new limitations beyond claim 4. Therefore it is rejected under the same rationale.
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.
Claim(s) 5, 6, 8, 9, 15, 16, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharjee in view of U.S. Publication 2014/0289017 to Trenkle.
With respect to Claim 5:
Bhattacharjee does not explicitly disclose the system of claim 1, wherein the first machine learning model includes a tree-based machine learning model.
However, Trenkle further discloses wherein the first machine learning model includes a tree-based machine learning model (i.e. machine learning model is decision tree model) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s first machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a first machine learning model that generates a predicted expected performance based on the user input data (See claim 1). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 15:
All limitations as recited have been analyzed and rejected to claim 5. Claim 15 does not teach or define any new limitations beyond claim 5. Therefore it is rejected under the same rationale.
With respect to Claim 6:
Bhattacharjee does not explicitly disclose the system of claim 1, wherein the first machine learning model includes a Bayesian machine learning model.
However, Trenkle further discloses wherein the first machine learning model includes a Bayesian machine learning model (i.e. machine learning model utilizes Bayesian techniques) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s first machine learning model includes a Bayesian machine learning model to Bhattacharjee’s generate a first machine learning model that generates a predicted expected performance based on the user input data (See claim 1). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 16:
All limitations as recited have been analyzed and rejected to claim 6. Claim 16 does not teach or define any new limitations beyond claim 6. Therefore it is rejected under the same rationale.
With respect to Claim 8:
Bhattacharjee does not explicitly disclose the system of claim 7, wherein the second machine learning model includes a tree-based machine learning model.
However, Trenkle further discloses wherein the second machine learning model includes a tree-based machine learning model (i.e. machine learning model is decision tree model, wherein a many machine learning models may be utilized) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s second machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a second machine learning model that generates a predicted expected cost based on the user input (See claim 7). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 18:
All limitations as recited have been analyzed and rejected to claim 8. Claim 18 does not teach or define any new limitations beyond claim 8. Therefore it is rejected under the same rationale.
With respect to Claim 9:
Bhattacharjee does not explicitly disclose the system of claim 7, wherein the second machine learning model includes a Bayesian machine learning model.
However, Trenkle further discloses wherein the second machine learning model includes a Bayesian machine learning model (i.e. machine learning model utilizes Bayesian techniques, wherein a many machine learning models may be utilized) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s second machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a second machine learning model that generates a predicted expected cost based on the user input (See claim 7). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 19:
All limitations as recited have been analyzed and rejected to claim 9. Claim 19 does not teach or define any new limitations beyond claim 9. Therefore it is rejected under the same rationale.
Response to Arguments
Applicant’s arguments see pages 6-9 of the Remarks disclosed, filed on 05/11/2026, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 1-20 have been considered but are not persuasive:
The Applicant asserts “First, Applicant respectfully disagrees and submits that, as the Supreme Court has cautioned, "describing the claims at such a high level of abstraction and untethered from the language of the claims all but ensures that the exceptions to § 101 swallow the rule." Alice Corp., 134 S. Ct. at 2354 (citing Mayo Collaborative Servs. V. Prometheus Labs. Inc., 566 US 66, 71, 101 USPQ2d 1961, 1965 (2012)). This is because, at some level, all inventions "embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas." Id. Thus, simply reciting that an abstract idea is allegedly present or recited by limitations in a claim is insufficient to establish that the claim as a whole is directed to an abstract idea under the test set forth in Alice Corp. (See also MPEP § 2106.04(II)(1), citing Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) ("The 'directed to' inquiry, therefore, cannot simply ask whether the claims involve a patent-ineligible concept, because essentially every routinely patent-eligible claim involving physical products and actions involves a law of nature and/or natural phenomenon")). Examiners should accordingly be careful to distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis). Here, Applicant's claims at most merely involve an exception and can only be alleged to recite an abstract idea when considered at too high a level of abstraction; therefore, Applicant's claims are in fact not directed to a judicial exception.” The Examiner respectfully disagrees. Claims 1 and 11 recite limitations directed to the abstract idea including “receiving user input data; generates a predicted expected performance based on the user input data, wherein the predicted expected performance indicates an expected performance for at least one bid and at least one target, wherein the at least one target includes at least one of at least one keyword and at least one product associated with at least one marketplace; generates a predicted expected cost based on the user input; and adjusting the at least one bid on the at least one of the at least one keyword and the at least one product associated with the at least one marketplace, based on the predicted expected performance and the predicted expected cost.” These further limitations are not seen as any more than the judicial exception. The claims are considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as adjusting bids of keywords and product based on predicted expected performance and predicted expected cost. The claims are also considered to be an abstract idea under Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to receiving data (i.e. input data); predicting data (i.e. expected performance and expected cost); and adjusting data (i.e. bids based on predicted expected performance and predicted expected cost). Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted performance based on the user input data; wherein the first machine learning model is fine-tuned based on identified user performance preferences; generating a second machine learning model that generates a predicted expected cost based on the user input; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” The additional limitations reciting – “generating a first machine learning model that generates a predicted performance based on the user input data; and generating a second machine learning model that generates a predicted expected cost based on the user input.” These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception (i.e. adjusting bids based on predicted expected performance and predicted expected cost) to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,).
The Applicant also asserts “Second, even if, arguendo, the claims can be considered to recite a judicial exception, these claims integrate the alleged judicial exception into a practical application. "[A] claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception." MPEP § 2106.04(II)(A)(2). This is because integrating the judicial exception into a practical application will "apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." MPEP § 2106.04(d). Thus, Applicant's claims, as a whole, integrate any alleged abstract idea into a practical application of that judicial exception. Applicant's claims recite additional elements beyond any judicial exception that, at least in combination, integrate the alleged exception into a practical application. Accordingly, Applicant respectfully submits that claim 1 is directed to statutory subject matter. Claim 11 is directed to statutory subject matter for at least similar reasons.” The Examiner respectfully disagrees. Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted performance based on the user input data; wherein the first machine learning model is fine-tuned based on identified user performance preferences; generating a second machine learning model that generates a predicted expected cost based on the user input; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” The additional limitations reciting – “generating a first machine learning model that generates a predicted performance based on the user input data; and generating a second machine learning model that generates a predicted expected cost based on the user input.” These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception (i.e. adjusting bids based on predicted expected performance and predicted expected cost) to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Claims 1 and 11 also recite additional limitations including “wherein the first machine learning model is fine-tuned based on identified user performance preferences; and wherein the second machine learning model is fine-tuned based on identified user budget preferences.” Merely, fine tuning or modifying parameters of a machine learning model based on user preferences is seen as adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g) because these are a well-understood, routine, and conventional computer functions. Fine-tuning a machine learning model or modifying the parameters of the machine learning model based on user preferences is exactly the function of a machine learning model. For example, according to ¶ [0010] of U.S. Publication 2024/0273189 to Shavit; “Conventional approaches to finding a proper balance between false positives and false negatives make use of different threshold adjustments when detection is based on machine learning or statistics or a rule combination that is strong enough to flag attacks as accurately as possible when a rules-based approach is used for detection.” It is a well-understood, routine, and conventional function in machine learning model to fine-tune or adjust the machine learning model based on goals/objectives/preferences of the operator. Claims 1 and 11 do not include additional elements or a combination of elements that result in the claims 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 listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe a “any machine learning or neural network methodology known in the art or developed in the future”, ¶ [0107], for implementing the machine learning model, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Therefore, the rejection(s) of claim(s) 1-6, 8, 9, 11-16, 18, and 19 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Applicant’s arguments see pages 9-12 of the Remarks disclosed, filed on 05/11/2026, with respect to the 35 U.S.C. § 102(a)(1) rejection(s) of claim(s) 1-4, 7, 10-14, 17, and 20 over Bhattacharjee with claims 5, 6, 8, 9, 15, 16, 18, and 19 being rejected in further view of Trenkle have been considered but are not persuasive. The Applicant asserts “Bhattacharjee does not teach or suggest, inter alia, these features. Bhattacharjee describes "for any keyword k in the user's portfolio, the bid is generated by an optimization routine Opt(...), which can depend on any combination of various parameters Some of the parameters, such as the RPC and the CPC, can be learned by a machine learning based prediction model. In some embodiments, an RPC prediction model 410 can be used to supply the expected RPC for any keyword." (See Paragraphs [0052]-[0053]). Nothing in this, or any portion of Bhattacharjee teaches or suggests, at least, to "generate a first machine learning model that generates a predicted expected performance based on the user input data, wherein the predicted expected performance indicates an expected performance for at least one bid and at least one target, wherein the at least one target includes at least one of at least one keyword and at least one product associated with at least one marketplace, and wherein the first machine learning model is fine-tuned based on identified user performance preferences; generate a second machine learning model that generates a predicted expected cost based on the user input, wherein the second machine learning model is fine-tuned based on identified user budget preferences; and adjust the at least one bid on the at least one of the at least one keyword and the at least one product associated with the at least one marketplace, based on the predicted expected performance and the predicted expected cost," as recited in claim 1.” The Examiner respectfully disagrees. The Bhattacharjee reference teaches generating an RPC prediction model or machine learning model that generates predicted expected performance or RPC for each keyword or user’s input of keyword, wherein expected revenue or performance is for target bid including keyword and wherein target includes keyword and product associated with marketplace and generating a CPC prediction model or machine learning model that generates predicted expected cost or CPC for each keyword or user’s input of keyword in ¶¶ [0052] [0053] [0061] [0064] (See pages 8-13 above). The Bhattacharjee reference also teaches algorithm of RPC model is adjusted based on user-identified profit-based increase or performance preferences and the algorithm of CPC model is adjusted based on identified user budget constraints or preferences in ¶¶ [0078]-[0080] (See pages 8-13 above). Therefore, the rejection(s) of claim(s) 1-4 and 11-14 under 35 U.S.C. § 102(a)(1) is provided above with updated citations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are cited to further show the state of the art:
U.S. Publication 2020/0219145 to Kalampoukas for disclosing A system and method for adjusting bid forming in a real-time bidding advertisement auction system. The method may be implemented for a bidding agent or in connection with a campaign database specifying campaign objectives by segment and campaign duration.
U.S. Publication 2023/0069621 to White for disclosing A method and system for enriching bid requests for real-time bidding on a digital advertisement placement are provided. The method comprises processing a received bid request to extract at least one data point, wherein the bid request is received from a website requesting placement of a digital advertisement; causing generation of at least one enriched data point based on the at least one extracted data point; and associating the at least one enriched data point with the received bid request to allow a refined real-time bidding on a placement of a digital advertisement in a webpage of the website in response to the bid on the received bid request.
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 Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
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Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
July 15, 2026
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