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 .
Status of Claims
This action is in response to amendment filed on 10 December 2025. Claims 1, 2, 4, 5, 12, 13, 15, 16 and 20 have been amended. Claims 3 and 15 have been cancelled. Claims 1-2, 4-13 and 15-20 are currently pending and have been examined.
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.
Step 1: The claims 1-2, 4-11 are a method, claims 12, 13 and 15-19 are system and claim 20 is a medium. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-2, 4-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: independent claims (1, 12 and 20) extracting feature information of a target media item from relevant data of the target media item; obtaining, using a prediction model, predicted values of quantity of requisite resources for a plurality of placements in competitive placement of the target media item based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed; and determining, from the predicted values of the quantity of requisite resources for the plurality of placements, quantity of requisite resources for placing the target media item, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources for the plurality of placements. The determining limitation, as drafted is a process, that under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is other than recited a computer components (i.e., processor, memory) , nothing in the claim elements preclude the step from practically being performed in the mind. For example, but for the “computer components ” language, the claims encompass the user manually calculating the quantity resources for plurlity of replacement. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. Simply put, these limitation merely describe determining quantity of requisite resource for placing media item which is clearly a business arrangement in its purest form. Claims 2, 4-11, 13, and 15-19 merely provide additional abstract concepts and narrow the abstract idea of claims 1 and 12 . Further, claims 1-20, are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can select content that meets a specified criteria, acknowledge an agreement to promote content and authorize compensation. Thus, the claim recites a mental process.
Step 2A-Prong 2: The only additional elements in independent claims is some form of
computerized system. These computerized systems are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data and a generic memory storing data) such that it amounts no more 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).
Step 2B: The claim does 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 using the claimed
computer systems amount to no more than “apply” a selection of content on the systems.
Further, the courts have consistently recognized that merely presenting the results of
abstract processes of obtaining and analyzing information, without more (such as identifying a
particular tool for presentation), is abstract as an ancillary part of such collection and analysis.
See, e.g., Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709,
715 (Fed. Cir. 2014). Examiner asserts that “extracting feature information and obtaining predicted value” is not particular tool, thus it offers no more than placement anything that includes the content that resulted from the predicted value and any third party.
In sum, the combination of steps that extracts data, obtain data, determined and places content and at best is doing no more than generally linking the claims to network environment that sends and receives communications– see MPEP 2106.05(h). See also, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
TLI Communications provides an example of a claim invoking computers and other
machinery merely as a tool to perform an existing process. The court stated that the claims
describe steps of recording, administration and archiving of digital images, and found them to be
directed to the abstract idea of classifying and storing digital images in an organized manner. 823
F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of
performing these functions using a telephone unit and a server and noted that these elements
we’re being used in their ordinary capacity (i.e., the telephone unit is used to make calls and
operate as a digital camera including compressing images and transmitting those images, and the
server simply receives data, extracts classification information from the received data, and stores
the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at
1747-48.
Similarly, these claims extracts data, obtain data, determined and places content by invoking computer systems as tools being used in an ordinary capacity to execute the abstract
idea. Thus, these additional elements do not add significantly more to the abstract idea because
they were simply applying the abstract idea on a computer system without sufficient recitation of
details of how to carry out the abstract idea. The claims merely offer conventional computer
systems to organizing Human Activity.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cui (US Pub., No.,. 2015/0332310 A1) in view of Liu et al (US Patent ., No., 10,846,587 B2)
With respect to claim 1, Cui teaches a method for determining quantity of requisite resources for placing a media item (Fig. 8 and paragraph [0019], discloses determine an expected reach and expected number of impression for the sampled users of the target audience), comprising:
extracting feature information of a target media item from relevant data of the target media item (paragraph [00040], discloses extract a bid amount and a specification of a target audience [feature] for a received advertisement request (e.g., from the advertisement request sore 240); and
determining, from the predicted values of the quantity of requisite resources for the plurality of placements, quantity of requisite resources for placing the target media item, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources for the plurality of placements (paragraph [0072], discloses determined based on a likelihood (or statistically measured likelihood) of one or more users of the target audience performing a conversion event after being presented with advertisement content .., target audience is extrapolated form a statistical measure .., and paragraph [0088], discloses the system advertisement system evaluates an efficacy of the specified bid value based on the return on investment metric and specified bid value , ) wherein the placement efficiency measures are adjusted using proportional-integral-derivative (PID) control parameters to stabilize selection under varying auction dynamics(paragraph [0010], discloses total reach of the adveritment content for the target audience is adjusted based on the budget specified in the advertismetn request to limit a total price charged to the advertiser.., and paragraph [0043], discloses adjust the bid amounts for the various advertisement request by factor that depend on attributes of advertisement content ..)) and satisfy a constraint that each predicted value does not exceed a multiple of an expected cost per mile (eCPM) value measure of target media item(paragraph [0064]-[0065], discloses normalizing the retrieved cost (eCPM values)…, normalizing winning bid value or eCPM value) in order to determine a reach count for the give user and paragraph [0141], discloses the ad system defines a return on investment metric for the bid outcome landscape) .
Cui teaches the above elements including obtaining, using a prediction model predicted values of quantity of requisite resources for a plurality of placements in competitive placement of the target media item based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed (paragraph [0040], discloses prediction module 255 predicts or estimates an expected measure or number of target users to whom the advertisement request is likely to be presented, paragraph [0041], discloses the advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total each for the target audience (obtained from the reach prediction module 255) and empirical conversion rate for the advertismtn request ) and subsequent distribution of content to different users (paragraph [0037]). Cui failed teach the predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network;
However, Lui teaches predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario (Col. 8, lines 8-16, discloses information regarding one or more bid prices and optionally, other information associated with the content items .., information such as the minimum winning bid or the highest bid of the content items …, Col. 9, lines 10-18, discloses train artificial neural network to evaluate the suitable of content item such as .., emit relevant score, Col. 15, lines 56-59, dislcies iterative training based on occurrences from history adjustments .., improve the accuracy of relevance scores that artificial neural network emits.., and Col. 16, lines 58-61, discloses relevance scoring based on historical training and/or dedicated neural networks…, emit high relevance score for most content item) and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network(Col. 3, lines 12-18, dislcies individual neural networks may be cloned and separately trained and Col. 16, lines 58-67, discloses relevance scoring is based on historical training and/or dedicated neural networks..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for c advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total of Cui with deep neural network for tarted content distribution of Liu in order to generate relevance score that indict a relative suitability of the content item for the target and expend target based on the relevance score generated for each of expended target ( see, Liu, abstract).
With respect to claim 2 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method wherein obtaining the predicted values of the quantity of requisite resources for the plurality of placements (paragraph [0017], disclose bid-reach landscape for a give target audience ss a representation of reach and paragraph [0020], discloses an extrapolation of a predicted reach from a sample of a targeted audience to a total reach for the target audience) comprises:
determining, using the prediction model, a fused distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities, based at least on the feature information(paragraph [0037], discloses identify users having specific characteristics, simplifying subsequent distribution of content to different users); and
determining the predicted values of the quantity of requisite resources for the plurality of placements based on the fused distribution respectively(paragraph [0061], discloses recent auction result or ranking based on bids received for the given user with ..).
With respect to claim 4 Cui in view of Liu teaches elements of claim 2, furthermore, Cui teaches the method wherein the prediction model comprises a second network, the second network describing a second correlation between a presentation of a second reference media item in a second reference competitive placement of the second reference media item and second reference feature information of the second reference media item(paragraph [0128], discloses discrepancy between the number of impressions of the advertisement and a number of conversion events for the advertisement ) ; and
determining the fused distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities (paragraph [0138], discloses estimates or predict reach measure (e.g., reach counts or percentage reach for a target audience) comprises:
determining, using the second network, a second distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities (paragraph [0139], discloses desired reach count or number of impressions or number of conversion ).
With respect to claim 5 Cui in view of Liu teaches elements of claim 4, furthermore, Cui teaches the method wherein obtaining the predicted values of the quantity of requisite resources for the plurality of placements comprises: determining the predicted values of the quantity of requisite resources for the plurality of placements based on the first distribution and the second distribution by applying a weighting of the first distribution and the second distribution(paragraph [0061], discloses recent auction result or ranking based on bids received for the given user with ..).
With respect to claim 6 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method , wherein the plurality of placement efficiency measures are determined based on the predicted values of the quantity of requisite resources for the plurality of placements, a value measure of the target media item, and the plurality of predetermined probabilities (paragraph [0138], discloses estimates or predict reach measure (e.g., reach counts or percentage reach for a target audience and paragraph [0139], discloses desired reach count or number of impressions or number of conversion ).
With respect to claim 7 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method further comprising: adjusting the plurality of placement efficiency measures based on a proportional integral differential control parameter(Fig. 5, 545, discloses adjust the estimated total reach of the advertismetn content based on an advertiser specific budget [control parameter]).
With respect to claim 8 Cui in view of Liu teaches elements of claim 6, furthermore, Cui teaches the method wherein a relationship between the predicted values of the quantity of requisite resources for the plurality of placements and the value measure of the target media item satisfies a predetermined constraint condition (paragraph [0037], discloses identify users having user profile information, edges or actions satisfying at least one of the targeting criteria).
With respect to claim 9 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method wherein the value measure of the target media item is determined based on quantity of requisite resources for placement of the target media item, a predicted value of a click through rate of the target media item, and a predicted value of a conversion rate of the target media item (paragraph [0138], discloses estimates or predict reach measure (e.g., reach counts or percentage reach for a target audience and paragraph [0139], discloses desired reach count or number of impressions or number of conversion ).
.
With respect to claim 10 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method, wherein determining the quantity of requisite resources for placement based on the plurality of placement efficiency measures comprises: in response to determining that a first placement efficiency measure among the plurality of placement efficiency measures is higher than a second placement efficiency measure, selecting a first predicted value corresponding to the first placement efficiency measure from the predicted values of the quantity of requisite resources for the plurality of placements(paragraph [0094], discloses these prices may be a specified ( e.g., a predetermined) amount higher or lower than bid amounts in ad requests that won bid auctions and resulted in successful impressions for those users).
With respect to claim 11 Cui in view of Liu teaches elements of claim 1, furthermore, Cui teaches the method, wherein the feature information of the target media item comprises at least any of: a type of the target media item, a platform on which the target media item is to be placed, an operating system of a platform on which the target media item is to be placed, a client device of a platform on which the target media item is to be placed, and a region in which the target media item is to be placed(paragraph [0064], discloses location of ad placement based on a type of computing device).
With respect to claim 12, Cui teaches an electronic device (Fig. 8 and paragraph [0019], discloses determine an expected reach and expected number of impression for the sampled users of the target audience), comprising:
at least one processing unit(Figs. 1-3); and
at least one memory coupled to the at least one processing unit and storing instructions executable by at one processing unit, the instructions, when executed by the at least one processing unit causing the electronic device to perform acts (Figs. 1-3 and paragraph [0146], discloses computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, )comprising:
extracting feature information of a target media item from relevant data of the target media item (paragraph [00040], discloses extract a bid amount and a specification of a target audience [feature] for a received advertisement request (e.g., from the advertisement request sore 240); and
determining, from the predicted values of the quantity of requisite resources for the plurality of placements, quantity of requisite resources for placing the target media item, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources for the plurality of placements (paragraph [0072], discloses determined based on a likelihood (or statistically measured likelihood) of one or more users of the target audience performing a conversion event after being presented with advertisement content .., target audience is extrapolated form a statistical measure .., and paragraph [0088], discloses the system advertisement system evaluates an efficacy of the specified bid value based on the return on investment metric and specified bid value , ) wherein the placement efficiency measures are adjusted using proportional-integral-derivative (PID) control parameters to stabilize selection under varying auction dynamics(paragraph [0010], discloses total reach of the adveritment content for the target audience is adjusted based on the budget specified in the advertismetn request to limit a total price charged to the advertiser.., and paragraph [0043], discloses adjust the bid amounts for the various advertisement request by factor that depend on attributes of advertisement content ..)) and satisfy a constraint that each predicted value does not exceed a multiple of an expected cost per mile (eCPM) value measure of target media item(paragraph [0064]-[0065], discloses normalizing the retrieved cost (eCPM values)…, normalizing winning bid value or eCPM value) in order to determine a reach count for the give user and paragraph [0141], discloses the ad system defines a return on investment metric for the bid outcome landscape) .
Cui teaches the above elements including obtaining, using a prediction model predicted values of quantity of requisite resources for a plurality of placements in competitive placement of the target media item based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed (paragraph [0040], discloses prediction module 255 predicts or estimates an expected measure or number of target users to whom the advertisement request is likely to be presented, paragraph [0041], discloses the advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total each for the target audience (obtained from the reach prediction module 255) and empirical conversion rate for the advertismtn request ) and subsequent distribution of content to different users (paragraph [0037]). Cui failed teach the predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network;
However, Lui teaches predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario (Col. 8, lines 8-16, discloses information regarding one or more bid prices and optionally, other information associated with the content items .., information such as the minimum winning bid or the highest bid of the content items …, Col. 9, lines 10-18, discloses train artificial neural network to evaluate the suitable of content item such as .., emit relevant score, Col. 15, lines 56-59, dislcies iterative training based on occurrences from history adjustments .., improve the accuracy of relevance scores that artificial neural network emits.., and Col. 16, lines 58-61, discloses relevance scoring based on historical training and/or dedicated neural networks…, emit high relevance score for most content item) and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network(Col. 3, lines 12-18, dislcies individual neural networks may be cloned and separately trained and Col. 16, lines 58-67, discloses relevance scoring is based on historical training and/or dedicated neural networks..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for c advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total of Cui with deep neural network for tarted content distribution of Liu in order to generate relevance score that indict a relative suitability of the content item for the target and expend target based on the relevance score generated for each of expended target ( see, Liu, abstract).
With respect to claim 12 Cui in view of Liu teaches elements of claim 12, furthermore, Cui teaches the electronic device wherein obtaining the predicted values of the quantity of requisite resources for the plurality of placements (paragraph [0017], disclose bid-reach landscape for a give target audience ss a representation of reach and paragraph [0020], discloses an extrapolation of a predicted reach from a sample of a targeted audience to a total reach for the target audience) comprises:
determining, using the prediction model, a fused distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities, based at least on the feature information(paragraph [0037], discloses identify users having specific characteristics, simplifying subsequent distribution of content to different users); and
determining the predicted values of the quantity of requisite resources for the plurality of placements based on the fused distribution respectively(paragraph [0061], discloses recent auction result or ranking based on bids received for the given user with ..).
With respect to claim 15 Cui in view of Liu teaches elements of claim 13, furthermore, Cui teaches the electronic device wherein the prediction model comprises a second network, the second network describing a second correlation between a presentation of a second reference media item in a second reference competitive placement of the second reference media item and second reference feature information of the second reference media item(paragraph [0128], discloses discrepancy between the number of impressions of the advertisement and a number of conversion events for the advertisement ) ; and
determining the fused distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities (paragraph [0138], discloses estimates or predict reach measure (e.g., reach counts or percentage reach for a target audience) comprises:
determining, using the second network, a second distribution of the quantity of requisite resources for the plurality of placements associated with the plurality of predetermined probabilities (paragraph [0139], discloses desired reach count or number of impressions or number of conversion ).
With respect to claim 16 Cui in view of Liu teaches elements of claim 15, furthermore, Cui teaches the electronic device wherein obtaining the predicted values of the quantity of requisite resources for the plurality of placements comprises: determining the predicted values of the quantity of requisite resources for the plurality of placements based on the first distribution and the second distribution by applying a weighting of the first distribution and the second distribution(paragraph [0061], discloses recent auction result or ranking based on bids received for the given user with ..).
With respect to claim 17 Cui in view of Liu teaches elements of claim 12, furthermore, Cui teaches the electronic device, wherein the plurality of placement efficiency measures are determined based on the predicted values of the quantity of requisite resources for the plurality of placements, a value measure of the target media item, and the plurality of predetermined probabilities (paragraph [0138], discloses estimates or predict reach measure (e.g., reach counts or percentage reach for a target audience and paragraph [0139], discloses desired reach count or number of impressions or number of conversion ).
With respect to claim 18 Cui in view of Liu teaches elements of claim 17, furthermore, Cui teaches the electronic device further comprising: adjusting the plurality of placement efficiency measures based on a proportional integral differential control parameter(Fig. 5, 545, discloses adjust the estimated total reach of the advertismetn content based on an advertiser specific budget [control parameter]).
With respect to claim 19 Cui in view of Liu teaches elements of claim 17, furthermore, Cui teaches the electronic device wherein a relationship between the predicted values of the quantity of requisite resources for the plurality of placements and the value measure of the target media item satisfies a predetermined constraint condition (paragraph [0037], discloses identify users having user profile information, edges or actions satisfying at least one of the targeting criteria).
With respect to claim 20, Cui teaches a computer-readable storage medium having a computer program stored instructions executable by at one processing unit, the instructions, when executed by the at least one processing unit causing the electronic device to perform acts (Figs. 1-3 and paragraph [0146], discloses computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, )comprising:
extracting feature information of a target media item from relevant data of the target media item (paragraph [00040], discloses extract a bid amount and a specification of a target audience [feature] for a received advertisement request (e.g., from the advertisement request sore 240); and
determining, from the predicted values of the quantity of requisite resources for the plurality of placements, quantity of requisite resources for placing the target media item, based on a plurality of placement efficiency measures associated with the predicted values of the quantity of requisite resources for the plurality of placements (paragraph [0072], discloses determined based on a likelihood (or statistically measured likelihood) of one or more users of the target audience performing a conversion event after being presented with advertisement content .., target audience is extrapolated form a statistical measure .., and paragraph [0088], discloses the system advertisement system evaluates an efficacy of the specified bid value based on the return on investment metric and specified bid value , ) wherein the placement efficiency measures are adjusted using proportional-integral-derivative (PID) control parameters to stabilize selection under varying auction dynamics(paragraph [0010], discloses total reach of the adveritment content for the target audience is adjusted based on the budget specified in the advertismetn request to limit a total price charged to the advertiser.., and paragraph [0043], discloses adjust the bid amounts for the various advertisement request by factor that depend on attributes of advertisement content ..)) and satisfy a constraint that each predicted value does not exceed a multiple of an expected cost per mile (eCPM) value measure of target media item(paragraph [0064]-[0065], discloses normalizing the retrieved cost (eCPM values)…, normalizing winning bid value or eCPM value) in order to determine a reach count for the give user and paragraph [0141], discloses the ad system defines a return on investment metric for the bid outcome landscape) .
Cui teaches the above elements including obtaining, using a prediction model predicted values of quantity of requisite resources for a plurality of placements in competitive placement of the target media item based at least on the feature information, the predicted values of the quantity of requisite resources for the plurality of placements respectively corresponding to a plurality of predetermined probabilities of the target media item being placed (paragraph [0040], discloses prediction module 255 predicts or estimates an expected measure or number of target users to whom the advertisement request is likely to be presented, paragraph [0041], discloses the advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total each for the target audience (obtained from the reach prediction module 255) and empirical conversion rate for the advertismtn request ) and subsequent distribution of content to different users (paragraph [0037]). Cui failed teach the predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network;
However, Lui teaches predication model comprising a first neural network trained on historical minimum winning bid posteriors and a second neural network trained on send-show rates (SSR) of actual impression to correct for delivery bias in no-return scenario (Col. 8, lines 8-16, discloses information regarding one or more bid prices and optionally, other information associated with the content items .., information such as the minimum winning bid or the highest bid of the content items …, Col. 9, lines 10-18, discloses train artificial neural network to evaluate the suitable of content item such as .., emit relevant score, Col. 15, lines 56-59, dislcies iterative training based on occurrences from history adjustments .., improve the accuracy of relevance scores that artificial neural network emits.., and Col. 16, lines 58-61, discloses relevance scoring based on historical training and/or dedicated neural networks…, emit high relevance score for most content item) and item being placed , by fusing a first distribution from the first neural network and a second distribution from the second neural network(Col. 3, lines 12-18, dislcies individual neural networks may be cloned and separately trained and Col. 16, lines 58-67, discloses relevance scoring is based on historical training and/or dedicated neural networks..). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for c advertisement outcome prediction module 260 computes an expected conversion outcome for an advertisement request based estimated total of Cui with deep neural network for tarted content distribution of Liu in order to generate relevance score that indict a relative suitability of the content item for the target and expend target based on the relevance score generated for each of expended target ( see, Liu, abstract).
Prior arts:
Cui (US Pub., No.,. 2015/0332310 A1) discloses an advertising system predicts advertisement reach for a received advertisement request based on an advertiser-specified
bid amount and a specification of a target audience. The system samples the target audience, and for each sampled user of the target audience, accesses a recent impression
history to obtain costs or bids associated with recent advertisement impressions.
Liu et al (US Patent ., No., 10,846,587 B2) discloses Herein are techniques to use an artificial neural network to score the relevance of content items for a target and techniques to rank the content items based on their scores
Hoyne (US Pub., No.,. 2016/0050129 A1) discloses systems, methods, and computer-readable storage media that may be used to evaluate performance of resources and/or content campaign impressions are provided. One method includes, for each of a first set of resources: (1) filtering path data to extract a first set of path data items including an interaction with the resource and a second set of path data items including paths excluding an interaction with the resource;
Synett et al (US Pub., No., 2013/0204700 A1) discloses a system operable to computing a performance assessment, the system including: an interface, configured to obtain information of interactions which are included in a series of interactions, wherein at least one of the interactions of the series includes communication of digital media over a network connection.
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claim 1-20 filed on 10 December 2025 have been fully considered but they are not persuasive. Applicants’ arguments of independent claim 1 is directed to a specific, technical improvement in computing system used for real-time bidding (RTB) auction is not persuasive. A real-time bidding (RTB) auction is an "abstract idea" rather than a patent-eligible process, machine, or manufacture. Under the Alice Corp. v. CLS Bank framework, such rejections are upheld if the RTB mechanism is deemed a fundamental business practice (like auctioning or bidding) implemented merely on a generic computer.
Applicant futher argued that the claim recite a particular machine-learing architecture—a dual network prediction model using minimum winning bid posteriors and send-show rate (SSR) telemetry, distribution fusion, and proportional-integral-derivative (PID)-stabilized efficiency selection-that corrects systematic bias is no-return competitive auction environments is not persuasive. Based on the description provided and recent USPTO guidance (including AI update and 2025 Memorandum) the claim is directed to one of the statutory categories of 35 U.S.C. §101 rejections under the Alice framework because the claimed element as drafted interpreted as merely applying generic machine learning to auction data. The claims are directed to an abstract idea, specifically "mathematical concepts" (calculating predicted values/posterior probabilities) or "methods of organizing human activity" (advertising auctions/bidding). The additional elements of
Predictive Model: Training a model on historical data is often considered a well-known, generic machine learning technique.
PID Control/Resource Determination: Determining resources based on a PID controller could be seen as an abstract method of adjusting a budget, rather than a technical improvement.
Applicants arguments’ of claim 1 requires i) a first neural network trained on historical minimum winning bid posteriors, ii) a second neural network trained on SSR (send-show rate) telemetry; iii) fusing a first distribution and a second distribution to obtain predicted value across multiple predetermined placement probability iv) adjusting placement efficiency measures using PID (proportional-integral-derivative) control parameter and enforce a constraint that each predictive value do not exceed a multiple of an eCPM value measure is not persuasive because
argument typically stems from the failure to demonstrate that the invention improves the functional capability of the computer itself, rather than merely applying conventional AI/machine learning to a new data environment. Analysis of abstract idea and lack of inventive concept:
1) First Neural Network trained on historical minimum winning bid posteriors, & a Second Neural Network trained on SSR (send-show rate) telemetry.
These are considered generic applications of machine learning to data analysis. Using neural networks to analyze historical bidding data (posteriors) or performance data (SSR telemetry) is a well-known, conventional technique.
Merely collecting data (bids, SSR) and feeding it into a neural network to output a prediction is generally deemed an abstract idea of "collecting and analyzing data.
2) "Fusing a first distribution and a second distribution to obtain predicted value"
Combining outputs from multiple models ("fusing") is a standard statistical approach. Without a specific, technical improvement in how the neural networks operate (e.g., a new architecture), this is seen as a mathematical manipulation of data.
3) "Adjusting placement efficiency measures using PID control"
Utilizing a Proportional-Integral-Derivative (PID) controller is a well-known method in control theory. Applying this well-known algorithm to adjust "efficiency measures" is often considered an abstract, mathematical operation.
"Enforce a constraint that each predictive value does not exceed a multiple of an eCPM"
This constitutes a rule-based constraint or a constraint based on business logic. Stating a "rule" (eCPM limitation) does not transform an abstract idea into a patent-eligible invention
Therefore, the 35 U.S.C 101 rejection with respect to claims 1-20 is maintained for the above reasons.
Applicant’s arguments of 35 U.S.C 103 rejections filed on 10 December 2025 with respect to claim(s)1-2, 4-13 and 15-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621