DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) have been considered but are moot in view of the new ground(s) of rejection.
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) 1, 2, 4, 5, 7, 9, 11, 12, 14, 15, 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (hereinafter ‘Huang’, Pub. No. 2020/0074500) in view of Chen et al. (hereinafter ‘Chen’1946’, Pub. No. 2023/0291946).
Regarding claims 1, 11 and 18, Huang teaches system (100, Fig. 1) (with corresponding method and machine-storage medium) comprising:
one or more hardware processors; and a memory storing instructions that, when executed by the one or more hardware processors ([0122]-[0130]), cause the one or more hardware processors to perform operations comprising:
receiving a request for content, in association with an ongoing objective, to be delivered to a user device ([0019]-[0024]; [0040]);
in response to receiving the request, access, by a central control component of a content system, content delivery data and content provider settings associated with the ongoing objective ([0019]; [0028]-[0036], where content delivery campaign and targeting criteria is accessed);
based on the content delivery data and the content provider settings, generating, by the central control component, content delivery settings including frequency capping (Fcap) rules for controlling delivery of content according to the ongoing objective ([0064]-[0069]);
transmitting the content delivery settings to a serving system, the serving system configured to determine content to deliver to the user based on the Fcap rules ([0059]-[0070], where the rules are produced by content delivery );
accessing, by a determination component of the serving system, user data associated with the user that indicates user preferences ([0052]-[0053]);
based on the content delivery settings and the user data, selecting, by the determination component of the serving system, a piece of content to deliver to the user; and triggering, by the determination component, a delivery component of the serving system to cause presentation of the piece of content to the user ([0038]-[0044]).
On the other hand, Huang does not explicitly teach wherein generating the content delivery settings comprises dynamically adjusting the Fcap rules to satisfy the ongoing objective using a machine learning model.
However, in an analogous art, Chen’1946 teaches a system that receives item information and user (account information) ([0021]). Item information such as multiple delivery parameters for each line item (i.e. delivery rules, frequency caps, line item duration, targeting rules, etc.; [0031]) and for user/account data ([0019]). Chen’1946 teaches using deep neural network (DNN) ([0039]) through training two models ([0014]; [0023]; [0024]). The first model generates estimated values for representation parameters of a representation function for a line item, which describes delivery impressions to the number or distinct user accounts in which the line item is reached ([0040]). The second model is trained, separately, to include ‘competition factors’ among the different line item of being selected ([0041]-[0044]). The second model generates modification factors that dynamically adjust the estimated parameters of the first model, to reach delivery goals faster ([0015]; [0052]; [0055]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Huang’s invention with Chen-1946’s feature of dynamically adjusting the Fcap rules to satisfy the ongoing objective using a machine learning model for the benefit of using a second ‘modification model’ “to improve the speed at which the training can be performed and the accuracy of the trained models” (Chen’1946: [0052]).
Regarding claims 2 and 12, Huang and Chen-1946 teach further comprising:
in response to receiving the request, further accessing one or more forecasting curves, wherein the central control component uses the one or more forecasting curves to ensure delivery is at a pace of the one or more forecasting curves (Huang: [0015]; [0049]-[0053]).
Regarding claims 4, 14 and 20, Huang and Chen-1946 teach wherein generating the content delivery settings comprises applying one or more of the content delivery data, the content provider settings, or one or more forecasting curves to a machine-learning model (Huang: [0028]-[0034]; [0040]).
Regarding claims 5 and 15, Huang and Chen-1946 teach further comprising:
training the machine-learning model using past content delivery data; and periodically retraining the machine-learning model using updated past content delivery data (Huang: [0058]-[0063]).
Regarding claims 7 and 17, Huang and Chen-1946 teach wherein the generating content delivery settings further comprises generating a resource usage factor that indicates an amount of resources to apply to a content delivery opportunity (Huang: [0037]).
Regarding claim 9, Huang and Chen-1946 teach wherein the Fcap rules provide frequency capping control for how many times the user will see a same or similar content within a certain time period (Huang: [0064]).
Claim(s) 3, 6, 8, 13, 16 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (hereinafter ‘Huang’, Pub. No. 2020/0074500) in vies of Chen et al. (hereinafter ‘Chen’1946’, Pub. No. 2023/0291946) in further view of Chen et al. (hereinafter ‘Chen’, Pub. No. 2012/0253926).
Regarding claims 3 and 13, Huang and Chen’1946 teach all the limitations of the claims they depend on. On the other hand, they do not explicitly teach generating the one or more forecasting curves using past content delivery data, wherein the one or more forecasting curves comprises a geo curve or a target expression curve.
However, in an analogous art, Chen teaches a system for allocating a minimum number of impression to a content item in order to satisfy a delivery goal for the content item during a certain period time (Abstract). The system uses learning algorithms for forecasting demand and content delivery, as determined on a delivery curve ([0058]-[0060]). The system includes campaign rules for delivering content, including frequency capping for content presentation ([0037]; [0057]-[0061]). Additionally, the system monitors delivery comparing with forecasted and goals. The forecast uses past content delivery data ([0038]; [0039]; [0052]). The system checks if the delivery meets forecasting curves to whether is below or above the forecast curve or target curve ([0044]). The content selection process can be optimized and calculates different factors ([0037]-[0042]), including click-through rate (pCTR), pCTR percentile (PP), satisfaction index (SI), eCPM (estimated cost-per-1000 impressions) ([0044]-[0056]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Huang and Chen-1946’s invention with Chen’s feature of generating the one or more forecasting curves using past content delivery data, wherein the one or more forecasting curves comprises a geo curve or a target expression curve for the benefit of monitoring predetermined delivery curve for a given day.
Regarding claims 8 and 18, Huang and Chen-1946 teach all the limitations of the claims they depend on. On the other hand, they do not explicitly teach wherein the content delivery data comprises a current status of a content delivery process for a content provider, the current status indicating whether the content delivery process meets, is below, or is higher than one or more forecasting curves.
However, in an analogous art, Chen teaches a system for allocating a minimum number of impression to a content item in order to satisfy a delivery goal for the content item during a certain period time (Abstract). The system uses learning algorithms for forecasting demand and content delivery, as determined on a delivery curve ([0058]-[0060]). The system includes campaign rules for delivering content, including frequency capping for content presentation ([0037]; [0057]-[0061]). Additionally, the system monitors delivery comparing with forecasted and goals. The system checks if the delivery meets forecasting curves to whether is below or above the forecast curve or target curve ([0044]). The content selection process can be optimized and calculates different factors ([0037]-[0042]), including click-through rate (pCTR), pCTR percentile (PP), satisfaction index (SI), eCPM (estimated cost-per-1000 impressions) ([0044]-[0056]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Huang and Chen-1946’s invention with Chen’s feature of monitoring a delivery status to determine if is above/below a forecasting curve for the benefit of having instant information in order to correct parameters in order to comply with goals.
Regarding claims 6 and 16, Huang and Chen teaches wherein generating the content delivery settings further comprises generating a throttle factor that indicates a percentage of content delivery opportunities to consider (Chen: [0058]-[0060]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 OMAR S PARRA whose telephone number is (571)270-1449. The examiner can normally be reached M-F: Mostly 10-6PM.
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/OMAR S PARRA/Primary Examiner, Art Unit 2421