DETAILED ACTION
This is in reference to communication received 25 February 2026. Claims 1 – 20 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Independent claim 1 and representative claims 16 and 20 recites the limitation(s):
receiving, at a computing device, a request for service directed to an online service provider;
identifying, via the computing device, information associated with the received request and an online response of the service provider to the received request;
determining, via the computing device, a feature vector for the received service request based on the identified request and response information;
However, these limitation are confusing. Applicant has not positively claimed what is the response from the service provider associated with, or, what is the service provider responding to.
Dependent claims 2 – 15 and 17 – 19 inherit the deficiencies of parent claims 1 and 16 they claim dependency from and are also rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 16 and 20, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to managing service request from service provider based on determination whether or not to respond to the received service request. When a service-request is received from a service-provider, information associated with the received service-request and response of the service-provider is identified, based upon which, a feature vector for the received for the received service-request is determined. The received request is analyzed and a win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider's response is determined. Based upon the determined win probability and a threshold probability, determination is made whether to throttle providing the response, and managing the service request based on throttling determination to determine whether or not to respond to the received service-request with the online service provider's response.
These limitations describe marketing/sales/advertising activities. When a request is received from a service-provider, marketing team or a person analyze the received request from a service-provider to determine attributes in the received-request, determine the likelihood of an outcome if a response is provided, and based upon the likelihood, made a determination whether a response should be sent for the received-request, or just ignore the received-request by not responding
The independent claims further recite the additional functional element of “analyzing the received request using a trained outcome prediction model and the feature vector that is based on the identified request and response information, and determining a win probability based on the analysis, the win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider's response”. Not only do these features fail to integrate the abstract idea into a practical application, but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Representative claims 16 and 20, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 20), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 16).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
As for dependent claims 2 – 15 and 17 – 19, dependent on the aforementioned independent claims, and include all the limitations contained therein. These claims do not recite any additional technical elements, and simply disclose additional limitations that further limit the abstract idea with details indicating that the request throttling determination indicates that the win probability satisfies the threshold probability; conditions when to throttle the provisioning of the content provided by the supplier in response to the received request; historical data will be used to train some outcome prediction model; what information will be considered to determine the feature vectors; content of the service request, who is considered to be a service provider and the requester, and defining that the requested content is an advertising content. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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.
Claims 1 – 9 and 13 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Raush et al. US Publication 2023/0214876 in view of Wang US Patent 10,878,450.
Regarding claim 1 and representative claims 16 and 20, Raush teaches system and method for real-time user response prediction for content presentations on client devices [Raush, 0003] comprising:
receiving, at a computing device, a request for service directed to an online service provider (Raush, At block 205, the user response prediction module 118 can receive a request (e.g., a bid request or the like for an auction or auction-like process from the content presentation exchange 126 via the network 110) to display information on a client device of a user) [Raush, 0031];
identifying, via the computing device, information associated with the received request (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible.) [Raush, 0031] and an online response of the service provider to the received request (Raush, the user response prediction module 118 can retrieve content presentations from the user response data database 122. At block 220, the model development module 116 can generate a predicted response of the user to the content presentation using a predictive model and the feature vector.) [Raush, 0031];
determining, via the computing device, a feature vector for the received service request based on the identified request and response information (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible.) [Raush, 0031];
analyzing, via the computing device, the received request using a trained outcome prediction model (Raush, any suitable predictive machine learning model can be appropriately trained and used to predict user responses in realtime) [Raush, 0022] and the feature vector that is based on the identified request and response information (Raush, the user response prediction module 118 can retrieve content presentations from the user response data database 122. At block 220, the model development module 116 can generate a predicted response of the user to the content presentation using a predictive model and the feature vector.) [Raush, 0031];
Raush does not explicitly teach throttling determination (e.g., pacing control). However, Raush teaches when content that was selected based on feature-vector in the request and the predicted response of the user does not satisfy threshold, said content is not transmitted as a response to the received request (i.e., transmitting of said content is throttled) [Raush, Fig. 2 and associated disclosure]. Wang teaches online system calibrates the pacing of content in a content campaign by adjusting the probability that the content campaign will participate in opportunities to present content to viewing users [Wang, col. 1, lines 57 – 60]. Wang further recites, It may be desirable to the sponsor that the online system ensures that the content campaign budget be spent smoothly for the duration of the content campaign. Over spending may occur when the content campaign wins too many opportunities for presentation early in the campaign, and this may result in the budget being spent before the end of the campaign [Wang, col. 7, line 65 – col. 8, line 4].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Raush by adopting teachings of Wang to help a service provider or advertiser achieve a desired total budget spent during a period of time by using a pacing factor is as a feedback mechanism to adjust the bid for content upwards or downwards in the auction to increase or decrease the likelihood that the content will win the auction.
Raush in view of Wang teaches system and method further comprising:
analyzing, via the computing device, the received request using a trained outcome prediction model (Raush, any suitable predictive machine learning model can be appropriately trained and used to predict user responses in realtime) [Raush, 0022] and the feature vector that is based on the identified request and response information (Raush, the user response prediction module 118 can retrieve content presentations from the user response data database 122. At block 220, the model development module 116 can generate a predicted response of the user to the content presentation using a predictive model and the feature vector.) [Raush, 0031], and determining a win probability based on the analysis, the win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider’s response (Wang, The pace adjustment module 225 adjusts the pacing during the content campaign based on the current spending of the campaign and the amount of time remaining in the campaign by adjusting a probability of bidding. The probability of bidding adjusts a likelihood that, for a particular opportunity to present content to a viewing user, the content campaign will compete for presentation.) [Wang. Col. 6, line 66 – col. 7, line 5];
making, via the computing device, a request throttling determination based on the win probability and a threshold probability (Wang, The pace adjustment module 225 adjusts the pacing during the content campaign based on the current spending of the campaign and the amount of time remaining in the campaign by adjusting a probability of bidding. The probability of bidding adjusts a likelihood that, for a particular opportunity to present content to a viewing user, the content campaign will compete for presentation.) [Wang. Col. 6, line 66 – col. 7, line 5, also see at least Raush 0030)]; and
managing, via the computing device, the service request in connection with the service provider based on the request throttling determination, the managing comprising using the request throttling determination to determine whether or not to respond to the received request for service with the online service provider's response (Wang, The pace adjustment module 225 adjusts the pacing during the content campaign based on the current spending of the campaign and the amount of time remaining in the campaign by adjusting a probability of bidding. The probability of bidding adjusts a likelihood that, for a particular opportunity to present content to a viewing user, the content campaign will compete for presentation.) [Wang. Col. 6, line 66 – col. 7, line 5].
Regarding claim 2 and representative claim 17, as combined and under the same rationale as above, Raush in view of Wang teaches system and method further comprising: causing, via the computing device, the service provider to generate the response to the service request where the request throttling determination indicates that the win probability satisfies the threshold probability (Wang, The pace adjustment module 225 adjusts the pacing during the content campaign based on the current spending of the campaign and the amount of time remaining in the campaign by adjusting a probability of bidding. The probability of bidding adjusts a likelihood that, for a particular opportunity to present content to a viewing user, the content campaign will compete for presentation.) [Wang. Col. 6, line 66 – col. 7, line 5].
Regarding claim 3, as combined and under the same rationale as above, Raush in view of Wang teaches system and method further comprising: causing, via the computing device, the service provider to forego generating the response to the service request where the request throttling determination indicates that the win probability fails to satisfy (less than) the threshold probability (Raush, when content that was selected based on feature-vector in the request and the predicted response of the user does not satisfy threshold, said content is not transmitted as a response to the received request (i.e., transmitting of said content is throttled) [Raush, Fig. 2 and associated disclosure, also see Wang. Col. 6, line 66 – col. 7, line 5].
Regarding claim 4 and representative claim 19, as combined and under the same rationale as above, Raush in view of Wang teaches system and method further comprising: causing, via the computing device, the service provider to deprioritize generating the response to the service request where the request throttling determination indicates that the win probability fails to satisfy (less than) the threshold probability (Wang, online system calibrates the pacing of content in a content campaign by adjusting the probability that the content campaign will participate in opportunities to present content to viewing users. Wang further recites, It may be desirable to the sponsor that the online system ensures that the content campaign budget be spent smoothly for the duration of the content campaign. Over spending may occur when the content campaign wins too many opportunities for presentation early in the campaign, and this may result in the budget being spent before the end of the campaign [Wang, Wang, col. 1, lines 57 – 60; col. 7, line 65 – col. 8, line 4].
Regarding claim 5, as combined and under the same rationale as above, Raush in view of Wang teaches system and method further comprising:
generating, via the computing device, a training dataset based on a corpus of unthrottled service request traffic representing past negative and positive service request outcomes (Raush, the predictive model built by the model development module 116 can be trained (e.g., using a suitable optimizer, such as, for example, mini-batch Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) or the like) on suitable data and then periodically updated with incremental data as it is received by the server system114.) [Raush, 0023]; and
training, via the computing device, using the training dataset, the outcome prediction model to determine the win probability indicating a likelihood of a predefined outcome in connection with the service request and the service provider’s response (Raush, the predictive model may be a Bayesian Logistic Regression Model or the like. In embodyments, the predictive model may be built by the model development module 116 and trained using, for example, mini-batch Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) or the like.) [Raush, 0034].
Regarding claim 6, as combined and under the same rationale as above, Raush in view of Wang teaches system and method, wherein a training data instance of the training dataset comprises a feature vector generated for a respective service request of the corpus of unthrottled service request traffic and a label indicating whether or not the service request resulted in the predefined outcome (Raush, any suitable predictive machine learning model can be appropriately trained and used to predict user responses in real time; The user response data database 122 can include data associated with users, user responses to content presentations, user information ( e.g., age, gender, device identifier, etc.), content presentation response or bid history ( e.g., publisher, category, bids, impressions, sampled auctions, etc.), user response history ( e.g., clicks, installs, in-app purchases, in-app revenue, number of purchases, content presentation interactions (e.g., view completions), etc.), content presentation performance metrics (e.g., click through rate, cost per action, etc.) …. ) [Raush, 0022]
Regarding claim 7, as combined and under the same rationale as above, Raush in view of Wang teaches system and method, wherein the respective service request’s feature vector comprises information associated with the respective service request and information associated with the respective service request’s response (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible) [Raush, 0031].
Regarding claim 8, as combined and under the same rationale as above, Raush in view of Wang teaches system and method for determining a feature vector for the received service request further comprising:
determining, via the computing device, a first feature vector based on a set of features determined for the received service request (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible) [Raush, 0031];
determining, via the computing device, a second feature vector based on a set of features determined for a user (Raush, At block 215, the user response prediction module 118 can select a content presentation to display on the client device of the user based on the feature vector; These features may include at least one feature characterizing the user and at least one additional feature characterizing the client device.) [Raush, 0031, 0032]; and
determining, via the computing device, the feature vector for the received service request based on the first and second feature vectors (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible) [Raush, 0031].
Regarding claim 9, as combined and under the same rationale as above, Raush in view of Wang teaches system and method, further comprising:
incrementally training, via the computing device, the threshold probability using historical information comprising a number of win probabilities determined for a corresponding number of received service requests (Raush, the predictive model built by the model development module 116 can be trained (e.g., using a suitable optimizer, such as, for example, mini-batch Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) or the like) on suitable data and then periodically updated with incremental data as it is received by the server system114.) [Raush, 0023].
Regarding claim 13, as combined and under the same rationale as above, Raush in view of Wang teaches system and method, wherein the service request comprises a request for content (Raush, At block 205, the user response prediction module 118 can receive a request (e.g., a bid request or the like for an auction or auction-like process from the content presentation exchange 126 via the network 110) to display information on a client device of a user) [Raush, 0031], the response comprising content responsive to the service request (Raush, If the predicted response satisfies (e.g., is at or above) the threshold, then at block 230 the user response prediction module 118 can generate a content presentation impression value using the predicted response and a target value associated with the user.) [Raush, 0031].
Regarding claim 14, as combined and under the same rationale as above, Raush in view of Wang teaches system and method wherein the service provider is a Supply-Side-Platform (SSP) service provider, the service request is received from a publisher of a website and comprises a request for content for a page of the website (Raush, In some implementation of the present invention, the model development module 116 can build a model for a single entity (e.g., an advertiser or the like) or a group of entities (e.g., a group of advertisers or the like) that desire to display one or more content presentations on the client devices of users.) [Raush, 0022], and the predefined outcome comprises inclusion of the requested content in the page published to at least one end user of the website (Raush, The information that is output or displayed on a client device of the user may include digital advertisements, creative assets, and so forth, which may include text, images, video, audio, and/or the like. …. a computer can interact with a user by sending resources to and receiving resources from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.) [Raush, 0032, 0048].
Regarding claim 15, as combined and under the same rationale as above, Raush in view of Wang teaches system and method, wherein the requested content is advertising content (Raush, the information that is output or displayed on a client device of the user may include digital advertisements, creative assets, and so forth, which may include text, images, video, audio, and/or the like) [Raush, 0032].
Claims 10 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Raush et al. US Publication 2023/0214876 in view of Wang US Patent 10,878,450 and Data Origami published article “Percentile and Quantile Estimation of Big Data: The t-Digest”.
Regarding claim 12, Raush in view of Wang does not explicitly recite t-Digest. However, DataOrigami teaches that first published in 2013, by the uber-practical and uber-intelligent Ted Dunning, the t-Digest is a probabilistic data structure for estimating the median (and more generally any percentile) from either distributed data or streaming data [DataOrigami, page 1].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Raush in view of Wang by adopting teachings of DataOrigami to represent interesting points of cumulative distribution.
as combined and under the same rationale as above, Raush in view of Wang and DataOrigami teaches system and method, wherein the OPE mechanism is a t-Digest approach (DataOrigami, first published in 2013, by the uber-practical and uber-intelligent Ted Dunning, the t-Digest is a probabilistic data structure for estimating the median (and more generally any percentile) from either distributed data or streaming data [DataOrigami, page 1].
Regarding claim 11, as combined and under the same rationale as above, Raush in view of Wang and DataOrigami teaches system and method teaches system and method, wherein the OPE mechanism is a Quantile Regression (QR) approach (DataOrigami, Running a small test locally, I streamed 8mb of pareto-distributed data into a t-Digest. Theresulting size was 5kb, and I could estimate any percentile or quantile desired. Accuracy was on the order of 0.002%) (DataOrigami, page 3].
Regarding claim 10, as combined and under the same rationale as above, Raush in view of Wang and DataOrigami teaches system and method, wherein an online percentage estimation (OPE) mechanism is used with the historical information to incrementally train the threshold probability (DataOrigami, Running a small test locally, I streamed 8mb of pareto-distributed data into a t-Digest. The resulting size was 5kb, and I could estimate any percentile or quantile desired. Accuracy was on the order of 0.002%) (DataOrigami, page 3].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent because cited combination of prior art does not teach receiving information associated with both the received request and an online response of the online service provider to the received request is identified, where the claimed feature vector is determined based on information identified for the received service request and the information identified for the online response of the service provider to the received service request. Raush's description of using a feature vector that is based solely on its received request information to select content to present at a client device fails to disclose or suggest at least this claimed subject matter.
However, cited reference teaches identifying, via the computing device, information associated with the received request (Raush, At block 210, the user response prediction module 118 can extract a plurality of features associated with the request to generate a feature vector. For purposes of illustration and not limitation, the feature vector can include categorical features such as, for example, publisher ( e.g., app, category, etc.), placement, user (e.g., age, gender, device identifier, etc.), time of day, day of week, week of year, geographical location (e.g., country, region, DMA, etc.), and/or other like information, although other features are possible.) [Raush, 0031] and an online response of the service provider to the received request (Raush, the user response prediction module 118 can retrieve content presentations from the user response data database 122. At block 220, the model development module 116 can generate a predicted response of the user to the content presentation using a predictive model and the feature vector.) [Raush, 0031];
Applicant's argument that pending claimed amended invention is eligible for patent because cited prior art Bhalgat a pacing factor to set a presentation pace for ad content and to determine what ad content to provide in response to the request. Bhalgat does not teach whether or not to respond to the claimed request for service, is acknowledged and considered.
However, while performing an update search, new prior art “Wang” was found and cited in this office action. Cited prior art Raush in view or Wang teaches to make determination whether or not to respond to the claimed request for service. Therefore, applicant’s arguments are moot under new grounds of rejection.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because
Claimed invention is not directed to the abstract idea of performing commercial transactions, rather, the claims are directed to a specific technical solution to a specific technical problem, and the claimed invention recite specific technological components working together in a specific way to achieve a specific technical result is acknowledged and considered.
However, upon further review, it is deemed that the invention as currently claimed in not eligible for patent under 35 USC 101 and have been responded to in updated Rejection under 35 USC 101 section.
Conclusion
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/NARESH VIG/Primary Examiner, Art Unit 3622
June 18, 2026
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