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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/08/2026 has been entered.
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 .
Examiner’s Comment
This Action is in response to the Request for Continued Examination filed on 01/08/2026 with Amended Claims and Applicant's Remarks filed on 01/08/2026.
Applicant has amended claims 1, 6, 12, 17, and 20 according to Amendments filed on 01/08/2026. Claims 1-18, 20, and 21 are pending and currently under consideration for patentability.
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 1-18, 20, and 21 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 1-18, 20, and 21 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 1-11 and 21 recite a method, claims 12-18 recite a system, and claim 20 recites a computer-readable storage medium. 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, 12, and 20 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1, 12, and 20 recite limitations directed to the abstract idea including “obtaining an advertisement state of each candidate advertisement of a plurality of candidate advertisements corresponding to a current exposure request, the advertisement state representing a competition condition in response to that the candidate advertisement competes for the current exposure request, and obtaining an overall state of an advertising platform in response to the current exposure request, the overall state representing a current exposure task performance situation of the advertising platform, wherein the plurality of candidate advertisements comprises a contract advertisement and a bid advertisement, the contract advertisement is associated with a contract specifying at least a predetermined playing amount, a selling price, and a targeting condition, the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub-parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub- parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement, the bid advertisement is associated with an advertisement effect and a pre-offered bid, the contract advertisement and the bid advertisement are mixed in the plurality of candidate advertisements for the determining of the competition score; determining probabilities of each candidate advertisement belonging to different reference advertisement types; determining a competition score of each candidate advertisement for the current exposure request according to the advertisement state corresponding to the candidate advertisement and the overall state based on the probabilities of the candidate advertisement belonging to different reference advertisement types; determining a target advertisement exposed by the current exposure request according to the competition score of each candidate advertisement for the current exposure request; and transmitting the target advertisement.” These further limitations are not seen as any more than the judicial exception: The limitations reciting “obtaining an advertisement state of each candidate advertisement of a plurality of candidate advertisements corresponding to a current exposure request, the advertisement state representing a competition condition in response to that the candidate advertisement competes for the current exposure request, and obtaining an overall state of an advertising platform in response to the current exposure request, the overall state representing a current exposure task performance situation of the advertising platform, wherein the plurality of candidate advertisements comprises a contract advertisement and a bid advertisement, the contract advertisement is associated with a contract specifying at least a predetermined playing amount, a selling price, and a targeting condition, the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub-parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub- parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement, the bid advertisement is associated with an advertisement effect and a pre-offered bid, the contract advertisement and the bid advertisement are mixed in the plurality of candidate advertisements for the determining of the competition score; determining a target advertisement exposed by the current exposure request according to the competition score of each candidate advertisement for the current exposure request; and transmitting the target advertisement” is considered to be an abstract idea, specifically, certain methods of organizing human activity; such as commercial interactions, advertising, marketing, and sales because the claims are directed to advertising interactions between different parties. The limitations reciting “obtaining an advertisement state of each candidate advertisement of a plurality of candidate advertisements corresponding to a current exposure request; determining probabilities of each candidate advertisement belonging to different reference advertisement types; determining a competition score of each candidate advertisement; and determining a target advertisement exposed by the current exposure request according to the competition score of each candidate advertisement for the current exposure request” is directed to another abstract idea, specifically, mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the steps in the claims are directed to receiving/obtaining data (i.e. advertisement state and overall state) and determining data (i.e. probabilities, competition score, and target advertisement). The limitations reciting “determining probabilities of each candidate advertisement belonging to different reference advertisement types; determining a competition score of each candidate advertisement for the current exposure request according to the advertisement state corresponding to the candidate advertisement and the overall state based on the probabilities of the candidate advertisement belonging to different reference advertisement types;” is directed to another abstract idea, specifically, mathematical concepts such as mathematical relationships / formulas or equations / calculations because the claims are directed to determining probabilities and a score (i.e. competition score). Claims 1, 12, and 20 recite additional limitations including “by a classification / scoring network in a scoring model; to a terminal device for playback; and the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type, each of the multiple scoring networks being configured to score each candidate advertisement based on the corresponding reference advertisement type of the different reference advertisement types.” Therefore, under Step 2A, Prong I, claims 1, 12, and 20 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, 12, and 20 recite additional limitations including “by a classification / scoring network in a scoring model; to a terminal device for playback; and the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type, each of the multiple scoring networks being configured to score each candidate advertisement based on the corresponding reference advertisement type of the different reference advertisement types.” The additional elements reciting – ““by a classification / scoring network in a scoring model; to a terminal device for playback;” in claims 1, 12, and 20 are not found to integrate the judicial exception into a practical application. Merely 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 and Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(f) is not indicative of integration into a practical application. 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. scoring model and device, 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, 12, and 20 recite the following additional limitations including “by a classification / scoring network in a scoring model; to a terminal device for playback; and the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type, each of the multiple scoring networks being configured to score each candidate advertisement based on the corresponding reference advertisement type of the different reference advertisement types.” The additional elements reciting – “by a classification / scoring network in a scoring model; to a terminal device for playback” do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 12, and 20 also recite additional elements – “the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type, each of the multiple scoring networks being configured to score each candidate advertisement based on the corresponding reference advertisement type of the different reference advertisement types.” Merely describing a model that has different scoring networks corresponding (i.e. one-to-one relationship) to different advertisement types, wherein the different scoring networks are trained according to samples associated with the different advertisement types are not indicative of integration into a practical application because the limitation is adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). The courts have noted that “performing repetitive calculations” is seen as a well-understood, routine, and conventional computer function (See: Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")). Furthermore, merely applying different sets of training data to a model in order to improve/update the model or generate a different scoring network (i.e. model with different/updated parameters) 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. It has been well-known since at least 1996 that the “Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.” (See Wikipedia: Machine learning: The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.”). Furthermore, utilizing a subset of training data in order to improve/update the model or generate a different scoring network (i.e. model with different/updated parameters) is exactly the function of a machine learning model. Feedback or updated data is needed to retrain the learning model because the learning model develops and learns as it goes through the iterations. Claims 1, 12, and 20 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 generic computer-based elements, ¶¶ [0011] [0025], for implementing the “apparatus” or “processor”, 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 2-11, 21 and 13-18 further recite the method and system of claims 1 and 12, respectively. Dependent claims 2-11, 13-18, and 21 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, 12, and 20. For example, dependent claims 2-11, 13-18, and 21 further describe the limitations for determining a competition score for a candidate advertisement in order to determine an advertisement state – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-11, 13-18, and 21, there are no additional elements introduced that integrate the claims into a practical application. For example, dependent claims 2-11, 13-18, and 21 recite further describe the environment in which the abstract idea takes place such as an advertising platform with scoring models and scoring networks. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. For example, dependent claim 7 recites – “inputting the feedback information into the initial scoring model as reference information in response to that the initial scoring model scores the training candidate advertisement corresponding to the training exposure request in a next round, so as to assist in adjusting a model parameter of the initial scoring model.” Merely describing a model that continually adjusts model parameters based on feedback in a multi-round process (i.e. model is updated each round) is not indicative of integration into a practical application because the limitation is adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). The courts have noted that “performing repetitive calculations” is seen as a well-understood, routine, and conventional computer function (See: Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")). Examiner would additionally like to note that merely communicating training data to a model in order to improve/update the model 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. It has been well-known since at least 1996 that the “Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.” (See Wikipedia: Machine learning: The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.”). Furthermore, utilizing a subset of training data in order to improve/update the model is exactly the function of a machine learning model. Feedback or updated data is needed to retrain the learning model because the learning model develops and learns as it goes through the iterations. 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.
Allowable Subject Matter
Claims 1-18, 20, and 21 allowable over the prior art:
The Applicant has provided arguments on 01/08/2026; “Independent claim 1, as amended, recites a data processing method including, for example, "obtaining an advertisement state of each candidate advertisement of a plurality of candidate advertisements corresponding to a current exposure request the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub- parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub-parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement; determining, by a scoring network in the scoring model, a competition score of each candidate advertisement for the current exposure request according to the advertisement state corresponding to the candidate advertisement and the overall state based on the probabilities of the candidate advertisement belonging to different reference advertisement types, the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type" (Emphasis added.) The cited references, whether taken alone or in any combination, fail to disclose or suggest at least these elements. In the rejection of claim 1, the Office alleges that Yan and Lin disclose "contract advertisement," referring to ¶¶[0031], [0032], [0039], and [0040] of Yan and ¶[0052] of Lin for support. Office Action at 13-1520, and 21. Further, in the rejection of claim 6, the Office alleges that Yan discloses "the advertisement state corresponding to the contract advertisement further comprises a playing control parameter referring to ¶¶[0031], [0032], [0037], and [0051] of Yan. Id., at 40-42. Yan is directed to an online system that tracks and stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisement performed by online system users. Yan, Abstract. Without acquiescing to the Office's assertions, Applicant notes that Yan discloses that an advertisement request includes advertisement content and an advertiser provided bid amount that can be used to determine an expected value to the online system for presenting the advertisement. Id., at ¶[0031]. An advertisement request may also include advertiser specified targeting criteria that define characteristics of users eligible to be presented with the advertisement content. Id., at ¶[0032]. The correlation module computes a score for each advertisement based at least partly on cosine similarity between third party content accessed by the viewing user and content accessed by additional users who interacted with the ad, and may further weight the score using thresholds on shared content access, overall interaction rates, and revenue so that better performing ads receive higher scores. Id., at ¶[0037]. A content selection module can use bid amounts to determine expected values and rank and select ad requests for presentation, including via a unified ranking with other content items based on relevance measures. Id., at ¶[0039]. A table can store user action and access data by using rows for users and columns for third party content and advertisements, with each cell indicating how many times a user accessed the content or interacted with the advertisement. Id., at [[0040]. In the example table each integer at the intersection of a user row and a content or advertisement column represents how many times that user accessed the corresponding third party content or interacted with the corresponding advertisement, and alternative encodings such as binary or fractional values and restrictions to specified time windows may also be used. Id., at ¶[0051]. Moreover, Lin is directed to balancing advertisement inventory allocation. Lin, Abstract. Without acquiescing to the Office's assertions, Applicant notes that Lin discloses at ¶[0052] that when demand cannot be satisfied with real impressions, artificial supply nodes and penalty arcs can be used so the optimizer supplies artificial impressions only under shortage and can track how many artificial impressions are needed to balance the network for a minimal cost flow solution. Accordingly, at best, Yan discloses user engagement or the number of times the content was accessed and Lin disclose artificial supply nodes and penalty arcs. Clearly Yan and Lin are completely silent on and fail to disclose or suggest "the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub-parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub-parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement," as recited in amended claim 1. Moreover, in the rejection of claim 1, the Office alleges that Sahasi discloses "the scoring model comprising multiple scoring networks corresponding to different reference advertisement types," referring to ¶¶[0154] and [0155] of Sahasi for support. Office Action at 17 and 18. Sahasi is directed to model selection and content recommendations. Sahasi, Abstract. Without acquiescing to the Office's assertions, Applicant notes that Sahasi discloses that the training module extracts a feature set from the training datasets in various ways, including using different classification based feature extraction techniques multiple times, and may generate several machine learning models from different feature sets and select the feature set whose features are most indicative of user interest for training models that predict whether a media asset is of interest. Id., at ¶ [0154]. The training datasets are analyzed to find dependencies and correlations between features and labeled predictions, yielding lists of features associated with different outcomes such as content being of interest or not, where a feature can be defined as any data characteristic, including interest attributes, interest levels, functionality features, or content features, that can help distinguish those outcomes. Id., at ¶[0155]. Thus, to the extent Sahasi discloses generating several machine learning models from different feature sets, Sahasi clearly fails to disclose or suggest "the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type," as recited in amended claim 1.”
The Examiner agrees and would also like to note that the independent claims, as filed of 01/08/2026, recite – “obtaining an advertisement state of each candidate advertisement of a plurality of candidate advertisements corresponding to a current exposure request, the advertisement state representing a competition condition in response to that the candidate advertisement competes for the current exposure request, and obtaining an overall state of an advertising platform in response to the current exposure request, the overall state representing a current exposure task performance situation of the advertising platform, wherein the plurality of candidate advertisements comprises a contract advertisement and a bid advertisement, the contract advertisement is associated with a contract specifying at least a predetermined playing amount, a selling price, and a targeting condition, the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub-parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub- parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement, the bid advertisement is associated with an advertisement effect and a pre-offered bid, the contract advertisement and the bid advertisement are mixed in the plurality of candidate advertisements for the determining of the competition score; and determining, by a scoring network in the scoring model, a competition score of each candidate advertisement for the current exposure request according to the advertisement state corresponding to the candidate advertisement and the overall state based on the probabilities of the candidate advertisement belonging to different reference advertisement types, the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type, each of the multiple scoring networks being configured to score each candidate advertisement based on the corresponding reference advertisement type of the different reference advertisement types.” The Yan, Sahasi, Lin, Kitts, and Lu references do not disclose “wherein the plurality of candidate advertisements comprises a contract advertisement and a bid advertisement, the contract advertisement is associated with a contract specifying at least a predetermined playing amount, a selling price, and a targeting condition, the advertisement state of the contract advertisement includes a playing control parameter comprising a first sub-parameter that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub- parameter used for internal sorting of contract advertisements, the second sub-parameter being a ratio of the predetermined playing amount of the contract advertisement to a current inventory amount of the contract advertisement, the bid advertisement is associated with an advertisement effect and a pre-offered bid, the contract advertisement and the bid advertisement are mixed in the plurality of candidate advertisements for the determining of the competition score; and determining, by a scoring network in the scoring model, a competition score of each candidate advertisement for the current exposure request according to the advertisement state corresponding to the candidate advertisement and the overall state based on the probabilities of the candidate advertisement belonging to different reference advertisement types, the scoring model comprising multiple scoring networks specialized for and having a one-to-one correspondence relationship with different reference advertisement types, each of the multiple scoring networks is trained using only training candidate advertisement samples belonging to the corresponding reference advertisement type.” Therefore, the rejection(s) of claim(s) 1-18, 20, and 21 under 35 U.S.C. § 103 has been previously withdrawn.
Response to Arguments
Applicant’s arguments see pages 18-20 of the Remarks disclosed, filed on 01/08/2026, with respect to the 35 U.S.C. § 101 rejection(s) of claim(s) 1-18, 20, and 21 have been considered but are not persuasive. The Applicant asserts “The claims are not directed to an abstract idea because they recite a specific improvement to advertising platform computing for real time selection among heterogeneous candidate advertisements. Even if the Office characterizes certain aspects as involving scoring or mathematical relationships, the claims integrate any such concept into a practical application under Step 2A, Prong Two by requiring a particular machine learning based control architecture that operates on platform state data and produces an exposure decision that is immediately used to drive terminal playback. The claims require obtaining, for each exposure request, both an advertisement state that represents a competition condition and an overall state that represents a current exposure task performance situation of the platform, and then determining probabilities of reference advertisement types using a classification network within a scoring model that includes multiple scoring networks adapted for different reference advertisement types. The claims further require determining a competition score for each candidate advertisement using the advertisement state and overall state based on those probabilities, selecting a target advertisement accordingly, and transmitting the target advertisement to a terminal device. This is not a result or a manual mental process because the claimed architecture relies on trained networks operating on multi source state inputs in a way that cannot practically be performed in the human mind for a live advertising platform. As amended, the independent claims further require that the contract advertisement state includes a playing control parameter having a first sub-parameter (i.e., Rate sub-parameter) that indicates a probability that the contract advertisement enters a candidate advertisement queue and a second sub-parameter (i.e., Theta sub-parameter) used for internal sorting of contract advertisements, where the second sub-parameter is defined as a ratio of predetermined playing amount to current inventory amount. This limitation ties the scoring and selection to a concrete platform control mechanism that governs admission and ordering of contract advertisements in the mixed contract and bid scenario, rather than an abstract business objective. Therefore, the claims are patent-eligible at Step 2A, Prong Two… The ordered combination of elements amounts to significantly more than conventional activity. The specification explains that using multiple scoring networks corresponding to reference advertisement types reduces the action space per expert and improves convergence and performance, which is a technical improvement to how the platform computes exposure decisions at scale. The claimed gate plus expert architecture and the specific Rate and Theta playing control parameters provide more than generic use of a computer because they specify how the candidate queue of the platform and internal contract sorting are controlled and incorporated into competition scoring to retain contract advertisements while still serving bid advertisements. The Office has not provided evidence that these elements are well-known, routine, or conventional. Therefore, the claims are patent-eligible at Step 2B.” The Examiner respectfully disagrees. An “improvement to advertising platform computing for real time selection among heterogeneous candidate advertisements” is not an improvement to the computer or another technical field/technology. Furthermore, “using multiple scoring networks corresponding to reference advertisement types reduces the action space per expert and improves convergence and performance, which is a technical improvement to how the platform computes exposure decisions at scale” is not enough for the claims to be integrated into a practical application because the claims do not clearly recite how the scoring model is trained according to candidate advertisement samples corresponding to an advertisement type and how training the scoring model with the different candidate advertisement samples generates a different scoring network. Furthermore, merely applying different sets of training data to a model in order to improve/update the model or generate a different scoring network (i.e. model with different/updated parameters) 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. It has been well-known since at least 1996 that the “Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.” (See Wikipedia: Machine learning: The definition "without being explicitly programmed" is often attributed to Arthur Samuel, who coined the term "machine learning" in 1959, but the phrase is not found verbatim in this publication, and may be a paraphrase that appeared later. Confer "Paraphrasing Arthur Samuel (1959), the question is: How can computers learn to solve problems without being explicitly programmed?" in Koza, John R.; Bennett, Forrest H.; Andre, David; Keane, Martin A. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design '96. Springer, Dordrecht. pp. 151–170. doi:10.1007/978-94-009-0279-4_9.”). Furthermore, utilizing a subset of training data in order to improve/update the model or generate a different model (i.e. model with different/updated parameters) is exactly the function of a machine learning model. Therefore, the rejection(s) of claim(s) 1-18, 20, and 21 under 35 U.S.C. § 101 is maintained above with an updated analysis.
Applicant’s arguments see pages 20-25 of the Remarks disclosed, filed on 01/08/2026, with respect to the 35 U.S.C. § 103 rejection(s) of claim(s) 1-18, 20, and 21 over Yan in view of Sahasi in further view of Lin have been considered and are persuasive. See Allowable Subject Matter above. Therefore, the rejection(s) of claim(s) 1-18, 20, and 21 under 35 U.S.C. § 103 has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The following reference are cited to further show the state of the art:
U.S. Publication 2016/0275554 to Yan for disclosure of An online system tracks stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisements performed by online system users. When the online system identifies an opportunity to present an advertisement to a viewing user, the online system identifies content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. A score is computed for various advertisements based at least in part on correlations between content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. The online system selects candidate advertisements to evaluate for presentation to the viewing user based on the scores.
U.S. Publication 2023/0004833 to Sahasi for disclosure of Methods, systems, and apparatuses for improved model selection and content recommendations are described herein. A distribution platform may comprise a system of computing devices, servers, software, etc., that is configured to present media assets ( e.g., content) at user devices. In one example embodiment, an analytics subsystem may provide at least one content recommendation to a user device using a machine learning model. The machine learning model may be selected based on a clustering method using an unsupervised machine learning model.
U.S. Publication 2010/0106605 to Lin for disclosure of A method of balancing advertisement inventory allocation includes constructing a flow network of nodes having impressions connected to contracts through corresponding arcs such as to satisfy demand requests of the contracts; normalizing an impression value of each node to a predetermined cost range; setting a cost of each arc to each corresponding normalized value; iteratively performing a plurality of times: (a) sampling the nodes or the arcs to create sample nodes and arcs, each time starting from a different random seed; (b) optimally allocating impressions from the sample nodes to the contracts with a minimum-cost network flow algorithm; (c) separately allocating impressions from sample arcs of lowest cost before allocating those from sample arcs of higher cost; averaging allocations from iterations (b) to create a first allocation; averaging allocations from iterations (c) to produce a second allocation; and computing a weighted solution of the first and second allocations.
U.S. Publication 2016/0037197 to Kitts for disclosure of Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons. The methods also include enriching the viewing data with the consumer demographic data; identifying a plurality of advertiser industries; enriching the product purchaser data with the consumer demographic data; calculating a relevance of each advertiser industry among the plurality of advertiser industries for each identified program based on demographic attributes of the product purchasers in each advertiser industry and demographic attributes of the viewing persons.
U.S. Publication 2021/0110428 to Lu for disclosure of In some examples, a computing device includes at least one processor and at least one module, operable by the at least one processor to receive, from a client device of a user, a request for one or more advertisements to display at the client device with a set of messages. The set of messages is associated with the user in a social network messaging service. The at least one module may be further operable to determine a probability that the user will select a candidate advertisement using a machine learning model based on point-wise learning and pair-wise learning. The at least one module may be further operable to determine, based on the probability that the user will select the candidate advertisement, a candidate score for the candidate advertisement, determine that the candidate score satisfies a threshold, and send, for display at the client device, the candidate advertisement.
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/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
February 18, 2026