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 in response to amendment filed on 19 December 2025. Claims 1, 11 and 20 have been amended. Claims 1-20 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 1-10 are a method, claims 11-19 are an apparatus and claim 20 is a media. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-20 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, 11 and 20) recite an online auction system ,receiving, campaign data including a total budget, target resource utilization curve, and maximum bid for each auction opportunity of the publication application; analyzing historical performance dta to identify camping’s with constrained resource maintaining, dynamic adjustment factor for each campaign; applying, a resource conservation algorithm by calculating an adjusted bid using the dynamic adjustment factor; tracking, in real-time, resource utilization for each campaign for the publication application; updating, the dynamic adjustment factor based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments; using the updated dynamic adjustment factor in a subsequent calculation of tan adjusted bid to create a feedback loop that adapts to change auction condition; dynamically adjusting bid through the specific time period and outputting, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods. These limitations fall within “Certain Methods Of Organizing Human Activity” for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Simply put, these limitation merely describe efficiently manage and distribute limited advertising budgets across multiple time periods, which is clearly a business arrangement in its purest form. Claims 3-4, 6-10, 13-14, 16-19 merely provide additional abstract concepts and narrow the abstract idea of claims 1, 11 and 20. 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.
Claims 3, 5, 12 and 15 recite “updated using a computationally efficient formula that reduces processing time: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ where μk,t is a current dynamic adjustment factor, εk,t is a step size, ρk,t is a target resource utilization rate, Bk is a total budget, and z̃k,t is a realized resource utilization” Thus, the claim recites a mathematical concept. Note
that, in this example, the “ step is determined to recite a mathematical concept because the claim explicitly recites a mathematical formula or calculation. Thus, an abstract idea.
Step 2A-Prong 2: The claims recite additional elements: of receiving a campaign data by one or more processor, memory for maintaining information. receiving campaign data for racking resource utilization, using for calculating an adjusted bid, updating the adjustment factor and for outputting the adjustment factor. The receiving step is recited at a high level of generality (i.e., as a general means of gathering network traffic data for use in the applying, updating and outputting steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The one or more processor that performs the receiving, applying and updating steps is also recited at high level of generality, and merely automates the generic computer function. The memory is also recited at a high-level of generality (i.e., as general means of maintaining or storing information/data) storage devices does not take the claim out of the methods of organizing human interactions grouping. The network interface is also recited at high-level of generality performs simply presenting information to user. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
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 collecting 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). The claims uses a particular tool; thus, it offers no more than efficiently manage and distribute limited advertising budgets across multiple time periods,
In sum, the combination of steps that receives data, maintaining data, updating and outputting or presents information are 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 receives data, maintaining data, updating and outputting or presents information 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jordan (US Pub., 2013/0325589 A1) in view of Jalali et al (US Pub., No., 2015/0134462 A1)
With respect to claim 1, Jordan teaches a computer-implemented method for optimizing resource allocation in a real-time online auction system of a publication application (Fig. 1, 116, discloses real-time bid management module, 127, campaign allocation optimization results, Fig. 8, 830 discloses program code for using advertising campaign allocation optimization paragraph [0007], discloses a method, system and computer program product for using advertising campaign allocation optimization result and paragraph [0094], discloses ad network environment including a real-time bid management module ), comprising:
receiving, by one or more processors, campaign data including a total budget, target resource utilization curve, and maximum bid for each auction opportunity of the publication application(paragraph [0007], discloses receiving a candidate impression opportunity corresponding an advertising campaign that has both branding objective and performance-based objective, contract that can be satisfied by serving the candidate impression opportunity .., and paragraphs [0022]-[0029], discloses an NGD contract is an agreement between an advertiser and originator.., NGD contract includes a contract fulfillment system.., NGD contract can be described in this following form :L Deliver X number of action [utilization curve] for $Y [budget], starting at time S, ending at time E, with constraints C [camping data] and paragraph [0114], discloses collect information regarding a branding campaign regarding a branding campaign seeks to guide an advertiser through a series of question for defining camping objective and constrains.., collect information about an advertiser’s spending limit .., collect information about the advertiser’s maximum bid constraint ..);
maintaining, in a memory, a dynamic adjustment factor for each campaign(Fig. 8, 840 dislcies program code for calculating a bid for the set of the matching contracts .., paragraph [0007], discloses using advertising campaign allocation optimization result to calculate bids, paragraph [0023], discloses calculation that prescribe how to bid, paragraph [0035], dislcies bidding landscape change overtime[adjustment] , paragraph [0112], discloses the bid price might be increased to a bid price higher than .., paragraph [0113], discloses the real-time bid management module calculates the actual bid and supplies the bid value to the auction engine paragraph [0124], discloses at least one memory, the memory serving to store program instruction corresponding to the operation of the system); and
tracking, in real-time, resource utilization for each campaign for the publication application(paragraph [0107], discloses techniques, each contract has a single budget specify how that budget is be sent over time (e.g., over the duration of the camping) and with respect to supply (e.g., constraints on maximum bids). Budget splitting is handled by the optimizing algorithm (e.g., in real time) rather than having budget splitting being impose as a prior constraint, and paragraph [0115], discloses the campaign daily spending limit” ..).
Jordan teaches the above elements including applying, by the one or more processors, a resource conservation algorithm by calculating an adjusted bid using the dynamic adjustment factor (paragraph [0023], discloses techniques include algorithm and calculation that prescribed how to bid (e.g., when to bid , how much to bid, etc.,), paragraph [0102], discloses an advertismetn bidding module 109 in combination with a real-time bid management module.., and paragraph [0113], discloses a real-time bid management module calculates the actual bid… ); prices and budgeting strategies , camping daily spending limit (Fig. 5), an advertising camping having at least one branding objective and at least one performance-based objective (Fig. 8, 820), using advertising campaign allocation optimization results to calculate bids (paragraph [0094] and defining total budget for this campaign (paragraph [0117]). Jordan filed to teach analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion; a resource conversion algorithm only to the identified campaign, the dynamic adjustment factor for the identified campaign using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion; updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments and outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods.
However, Jalali teaches analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion(paragraph [0076], discloses historical data form one or more previous time period may be identified, historical data may use to make prediction.., paragraph [0103], discloses success probability maybe calculated exactly or estimated , the success probability may be determined by analyzing camping history data or measuring the performance of the camping during previous time periods ) ; resource conservation algorithm only to the identified campaigns by calculating an adjusted bid using the dynamic adjustment factor for the identified campaigns(paragraph [0109], discloses the probability of success may be defined in various ways based on the strategic objectives of the advertising campaign);
using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition(paragraph [0036], discloses feedback loop may be applied , paragraph [0058], discloses dynamic bidding price may be formulated using a multi-armed bandit framework collect feedback quickly from the environment in order to update utility function, and paragraph [0060], discloses dynamic CPM campaigns, in which the bid price is dynamically determined ) ;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion (paragraph [0036], discloses adjust the bid price based on the prior performance distribution to maximum the performance goal);
updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments (paragraph [0058], discloses update the utility function.., paragraph [0087], dislcies pacing rate is updated;
outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods (Figs. 8, 9 and paragraphs [0150]- [0151], discloses histogram of the bucketized bid value ratios.., bid quality multiplier may be selected..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with dynamic bid price selection feature of Jalali in order provide improved expected value for successful bids(see, Jalali, paragraph [0039]).
With respect to claim 2, Jordan in view of Jalali teaches elements of claim 1, furthermore, Jordan teaches the method wherein the dynamic adjustment factor (paragraph [0007], discloses to calculate bids of the selected target contract, and paragraph [0010], discloses using advertising camping allocation optimizations results to calculate bids). Jordan failed to teach updated using a computationally efficient formula that reduces processing time: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ where μk,t is a current dynamic adjustment factor, εk,t is a step size, ρk,t is a target resource utilization rate, Bk is a total budget, and z̃k,t is a realized resource utilization.
However, Jalali teaches updated using a computationally efficient formula that reduces processing time: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ where μk,t is a current dynamic adjustment factor, εk,t is a step size, ρk,t is a target resource utilization rate, Bk is a total budget, and z̃k,t is a realized resource utilization (paragraphs [0116], and [0129] discloses determine a bid multiplier for adverting opportunity .., bid placement and advertising budget .., to reduce system computational load). Therefore, it would Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with a feature of with dynamic bid price adjustment feature of Jalali in order to reducing the system’s computational load (see, Jalali paragraph [0129]).
With respect to claim 3, Jordan in view of Jalali teaches elements of claim 1, furthermore, the method further comprising:
conducting, by the one or more processors, a multi-slot auction using the adjusted bids(paragraph [0071], discloses online advertising marketplace advertiser may bid in connection with placement of advertisements .., selection or ranking bids .., paragraph [0071], dislcies ad spaces supplying from publishers and paragraph [0110], discloses the bid price might be increased to a bid price higher than ..); and
determining, in real-time, optimal resource allocation and pricing based on auction results(paragraph [0110], discloses bidding components uses the value form the optimization components to calculate an actual bid and supplies the bid value to the real-time bid management module.., bid constructed for bidding on candidate impression opportunity and paragraph [0113], dislcies the real-time bid management module calculate the actual bid and supplies the bid value to the auction engine) .
With respect to claim 4, Jordan in view of Jalali teaches elements of claim 1, furthermore, the method further comprising: implementing, by the one or more processors, a minimum-utilization constraint by limiting a specified percentage of the resource utilizations(paragraph [0115], discloses camping spending limit and value serv to collecting information about an advertiser’s spending limit ..); and
adjusting the dynamic adjustment factor update by balancing resource conservation objectives and system performance goals, and optimizing system resource allocation(paragraph [0004], discloses optimize overall goals of the campaign.., and paragraph [0036], discloses recommend targeting attribute changes in order for NGD campaigns to meet advertiser optimization goals. Such recommendations are based (at least in part) on advanced data analysis and data mining over multiple data sources.).
With respect to claim 5, Jordan in view of Jalali teaches elements of claim 4, furthermore, Jordan teaches the method wherein the dynamic adjustment factor (paragraph [0007], discloses to calculate bids of the selected target contract, and paragraph [0010], discloses using advertising camping allocation optimizations results to calculate bids) Jordan failed to teach wherein the minimum-utilization constraint is implemented by introducing a utilization factor γk, and updating both a dynamic adjustment factor μk and a utilization factor γk using computationally efficient formulas: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ γk,t+1 = [γk,t - ε'k,t (z̃k,t - αk · ρk,tBk)]+ where αk is a minimum percentage of resources to be utilized.
However, Jalali teaches u wherein the minimum-utilization constraint is implemented by introducing a utilization factor γk, and updating both a dynamic adjustment factor μk and a utilization factor γk using computationally efficient formulas: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ γk,t+1 = [γk,t - ε'k,t (z̃k,t - αk · ρk,tBk)]+ where αk is a minimum percentage of resources to be utilized(paragraph [0164], dislcies a cling factor which the based bud price is divided to determine the bid price various mathematical technique may use for modify the base bid prices based on the bid multiplier). Therefore, it would Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with a feature of with dynamic bid price adjustment feature of Jalali in order to reducing the system’s computational load (see, Jalali paragraph [0129]).
With respect to claim 6, Jordan in view of Jalali teaches elements of claim 1, furthermore, the method further comprising: analyzing, by the one or more processors, historical performance data(paragraph [0048], discloses advertising analytic gather may be transmitted to location remote to an advertising presentation system);
identifying campaigns with constrained resources or a high likelihood of resource depletion; and selectively applying the resource conservation algorithm to campaigns based on their utilization patterns by optimizing system performance and reducing unnecessary computations(paragraph [0036], discloses recommend targeting attributes changes in order to NGD campaign to meet advertising optimizing goal and paragraph [0115], discloses campaigns having bot branding objective (e.g., total impression fulfilled) ).
With respect to claim 7, Jordan in view of Jalali teaches elements of claim 6, furthermore, the method further comprising: wherein selectively applying the resource conservation algorithm comprises: applying the resource conservation algorithm only to campaigns that have utilized over a predetermined percentage of their resources in previous periods by reallocating focusing computational resources on the campaigns that require active management(paragraph [0110], discloses using advertising campaign allocation optimization result to determine a set of matching.., and paragraph [0112], discloses using advertising campaign allocation optimization results to calculate bid) .
With respect to claim 8, Jordan in view of Jalali teaches elements of claim 1, furthermore, the method further comprising:
dynamically adjusting bids throughout a specified time period to maintain consistent competition levels(paragraph [0022], discloses a differentiation mechanism for altering the price by applying a user-based differentiation policy to the price and paragraph [0110], dislcies the bidding component use the value from the optimization component to calculate an actual bid .., bid value real-time bid management); and
balancing the resource allocation across different time periods to improve system stability and user experience(paragraph [0113], discloses likelihood that additional inventory would be available in a future timeframe ( e.g., a future timeframe during the remaining duration of the campaign)).
With respect to claim 9, Jordan in view of Jalali teaches elements of claim 1, including the advertisement bidding module might codify predicted action rates (e.g., click-through rates) (paragraph [0113]). Jordan failed to teach the corrosinding click-through rates defined based on a traffic curve.
However, Jalali teaches wherein the target resource utilization curve is based on one of: a traffic curve, a uniform utilization curve, or a response rate curve, to allow for flexible adaptation to different system requirements(Figs. 8, 9 and paragraphs [0150]- [0151], discloses histogram of the bucketized bid value ratios.., bid quality multiplier may be selected..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with dynamic bid price selection feature of Jalali in order provide improved expected value for successful bids(see, Jalali, paragraph [0039]).
With respect to claim 10, Jordan in view of Jalali teaches elements of claim 1, furthermore, the method further comprising: further comprising: implementing the method within a resource management controller (Fig. 1, and paragraph [0094], discloses and advertising network for using advertising campaign allocation optimization result to calculate bids);
integrating the resource management controller with an existing online auction system(Figs. 1, 4, 104 discloses an auction engine aggregator and paragraph [0098], discloses real-time to an auction engine aggregator and paragraph [0111], discloses a bidding component 340 submits the bid to a media exchange (e.g., through an auction engine aggregator 104)) ; and
providing a feedback mechanism for updating adjustment signals based on real-time utilization data, by creating a self-optimizing system that continuously improves its performance and efficiency(paragraph [0082], discloses social networking or social networking website .., potentially, additional relationship may subsequently be formed as a result of social interactions via the communication network .., and paragraph [0083], discloses individuals with similar experiences, opinions, education levels or backgrounds).
With respect to claim 11, Jordan teaches a computer apparatus ( paragraph [0007], discloses a method, system and computer program product for using advertising campaign allocation optimization result and paragraph [0094], discloses ad network environment including a real-time bid management module ), comprising:
processor; and a memory storing instructions that, when executed by the processor(Fig. 8, 810 dislcies a computer processor to executed a set of program code instructions, Fig. 11, 1108 discloses processor , 1126 instructions , 1110 main memory , 1112 static memory ) , configure the apparatus to:
receive campaign data including a total budget, target resource utilization curve, and maximum bid for each auction opportunity of the publication application(paragraph [0007], discloses receiving a candidate impression opportunity corresponding an advertising camping that has both branding objective and performance-based objective, contract that can be satisfied by serving the candidate impression opportunity .., and paragraphs [0022]-[0029], discloses an NGD contract is an agreement between an advertiser and originator.., NGD contract includes a contract fulfillment system.., NGD contract can be described in this following form :L Deliver X number of action [utilization curve] for $Y [budget], starting at time S, ending at time E, with constraints C [camping data] and paragraph [0114], discloses collect information regarding a branding campaign regarding a branding campaign seeks to guide an advertiser through a series of question for defining camping objective and constrains.., collect information about an advertiser’s spending limit .., collect information about the advertiser’s maximum bid constraint ..);
maintain in a memory, a dynamic adjustment factor for each campaign(Fig. 8, 840 dislcies program code for calculating a bid for the set of the matching contracts .., paragraph [0007], discloses using advertising campaign allocation optimization result to calculate bids, paragraph [0023], discloses calculation that prescribe how to bid, paragraph [0035], dislcies bidding landscape change overtime[adjustment] , paragraph [0112], discloses the bid price might be increased to a bid price higher than .., paragraph [0113], discloses the real-time bid management module calculates the actual bid and supplies the bid value to the auction engine paragraph [0124], discloses at least one memory, the memory serving to store program instruction corresponding to the operation of the system); and
track, in real-time, resource utilization for each campaign for the publication application(paragraph [0107], discloses techniques, each contract has a single budget specify how that budget is be sent over time (e.g., over the duration of the camping) and with respect to supply (e.g., constraints on maximum bids). Budget splitting is handled by the optimizing algorithm (e.g., in real time) rather than having budget splitting being impose as a prior constraint, and paragraph [0115], discloses the campaign daily spending limit” ..).
Jordan teaches the above elements including applying, by the one or more processors, a resource conservation algorithm by calculating an adjusted bid using the dynamic adjustment factor (paragraph [0023], discloses techniques include algorithm and calculation that prescribed how to bid (e.g., when to bid , how much to bid, etc.,), paragraph [0102], discloses an advertismetn bidding module 109 in combination with a real-time bid management module.., and paragraph [0113], discloses a real-time bid management module calculates the actual bid… ); prices and budgeting strategies , camping daily spending limit (Fig. 5), an advertising camping having at least one branding objective and at least one performance-based objective (Fig. 8, 820), using advertising campaign allocation optimization results to calculate bids (paragraph [0094] and defining total budget for this campaign (paragraph [0117]). Jordan filed to teach analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion; a resource conversion algorithm only to the identified campaign, the dynamic adjustment factor for the identified campaign using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion; updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments and outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods.
However, Jalali teaches analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion(paragraph [0076], discloses historical data form one or more previous time period may be identified, historical data may use to make prediction.., paragraph [0103], discloses success probability maybe calculated exactly or estimated , the success probability may be determined by analyzing camping history data or measuring the performance of the camping during previous time periods ) ; resource conservation algorithm only to the identified campaigns by calculating an adjusted bid using the dynamic adjustment factor for the identified campaigns(paragraph [0109], discloses the probability of success may be defined in various ways based on the strategic objectives of the advertising campaign);
using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition(paragraph [0036], discloses feedback loop may be applied , paragraph [0058], discloses dynamic bidding price may be formulated using a multi-armed bandit framework collect feedback quickly from the environment in order to update utility function, and paragraph [0060], discloses dynamic CPM campaigns, in which the bid price is dynamically determined ) ;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion (paragraph [0036], discloses adjust the bid price based on the prior performance distribution to maximum the performance goal);
updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments (paragraph [0058], discloses update the utility function.., paragraph [0087], dislcies pacing rate is updated;
outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods (Figs. 8, 9 and paragraphs [0150]- [0151], discloses histogram of the bucketized bid value ratios.., bid quality multiplier may be selected..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with dynamic bid price selection feature of Jalali in order provide improved expected value for successful bids(see, Jalali, paragraph [0039]).
With respect to claim 12, Jordan in view of Jalali teaches elements of claim 11, furthermore, Jordan teaches computing apparatus wherein the dynamic adjustment factor (paragraph [0007], discloses to calculate bids of the selected target contract, and paragraph [0010], discloses using advertising camping allocation optimizations results to calculate bids). Jordan failed to teach updated using a computationally efficient formula that reduces processing time: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ where μk,t is a current dynamic adjustment factor, εk,t is a step size, ρk,t is a target resource utilization rate, Bk is a total budget, and z̃k,t is a realized resource utilization.
However, Jalali teaches updated using a computationally efficient formula that reduces processing time: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ where μk,t is a current dynamic adjustment factor, εk,t is a step size, ρk,t is a target resource utilization rate, Bk is a total budget, and z̃k,t is a realized resource utilization (paragraphs [0116], and [0129] discloses determine a bid multiplier for adverting opportunity .., bid placement and advertising budget .., to reduce system computational load). Therefore, it would Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with a feature of with dynamic bid price adjustment feature of Jalali in order to reducing the system’s computational load (see, Jalali paragraph [0129]).
With respect to claim 13, Jordan in view of Jalali teaches elements of claim 11, furthermore, the computing apparatus further comprising:
conduct, by the one or more processors, a multi-slot auction using the adjusted bids(paragraph [0071], discloses online advertising marketplace advertiser may bid in connection with placement of advertisements .., selection or ranking bids .., paragraph [0071], dislcies ad spaces supplying from publishers and paragraph [0110], discloses the bid price might be increased to a bid price higher than ..); and
determine, in real-time, optimal resource allocation and pricing based on auction results(paragraph [0110], discloses bidding components uses the value form the optimization components to calculate an actual bid and supplies the bid value to the real-time bid management module.., bid constructed for bidding on candidate impression opportunity and paragraph [0113], dislcies the real-time bid management module calculate the actual bid and supplies the bid value to the auction engine) .
With respect to claim 14, Jordan in view of Jalali teaches elements of claim 11, furthermore, the computing apparatus further comprising: implement, by the one or more processors, a minimum-utilization constraint by limiting a specified percentage of the resource utilizations(paragraph [0115], discloses camping spending limit and value serv to collecting information about an advertiser’s spending limit ..); and
adjust the dynamic adjustment factor update by balancing resource conservation objectives and system performance goals, and optimizing system resource allocation(paragraph [0004], discloses optimize overall goals of the campaign.., and paragraph [0036], discloses recommend targeting attribute changes in order for NGD campaigns to meet advertiser optimization goals. Such recommendations are based (at least in part) on advanced data analysis and data mining over multiple data sources.).
With respect to claim 15, Jordan in view of Jalali teaches elements of claim 14, furthermore, Jordan teaches the computing apparatus wherein the dynamic adjustment factor (paragraph [0007], discloses to calculate bids of the selected target contract, and paragraph [0010], discloses using advertising camping allocation optimizations results to calculate bids) Jordan failed to teach wherein the minimum-utilization constraint is implemented by introducing a utilization factor γk, and updating both a dynamic adjustment factor μk and a utilization factor γk using computationally efficient formulas: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ γk,t+1 = [γk,t - ε'k,t (z̃k,t - αk · ρk,tBk)]+ where αk is a minimum percentage of resources to be utilized.
However, Jalali teaches u wherein the minimum-utilization constraint is implemented by introducing a utilization factor γk, and updating both a dynamic adjustment factor μk and a utilization factor γk using computationally efficient formulas: μk,t+1 = [μk,t - εk,t(ρk,tBk - z̃k,t)]+ γk,t+1 = [γk,t - ε'k,t (z̃k,t - αk · ρk,tBk)]+ where αk is a minimum percentage of resources to be utilized(paragraph [0164], dislcies a cling factor which the based bud price is divided to determine the bid price various mathematical technique may use for modify the base bid prices based on the bid multiplier). Therefore, it would Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with a feature of with dynamic bid price adjustment feature of Jalali in order to reducing the system’s computational load (see, Jalali paragraph [0129]).
With respect to claim 16, Jordan in view of Jalali teaches elements of claim 11, furthermore, the computing apparatus further comprising: analyze, by the one or more processors, historical performance data(paragraph [0048], discloses advertising analytic gather may be transmitted to location remote to an advertising presentation system);
identify campaigns with constrained resources or a high likelihood of resource depletion; and selectively applying the resource conservation algorithm to campaigns based on their utilization patterns by optimizing system performance and reducing unnecessary computations(paragraph [0036], discloses recommend targeting attributes changes in order to NGD campaign to meet advertising optimizing goal and paragraph [0115], discloses campaigns having bot branding objective (e.g., total impression fulfilled) ).
With respect to claim 17, Jordan in view of Jalali teaches elements of claim 16, furthermore, the computing apparatus further comprising: wherein selectively applying the resource conservation algorithm comprises: applying the resource conservation algorithm only to campaigns that have utilized over a predetermined percentage of their resources in previous periods by reallocating focusing computational resources on the campaigns that require active management(paragraph [0110], discloses using advertising campaign allocation optimization result to determine a set of matching.., and paragraph [0112], discloses using advertising campaign allocation optimization results to calculate bid) .
With respect to claim 18, Jordan in view of Jalali teaches elements of claim 11, furthermore, the computing apparatus further comprising:
dynamically adjusting bids throughout a specified time period to maintain consistent competition levels(paragraph [0022], discloses a differentiation mechanism for altering the price by applying a user-based differentiation policy to the price and paragraph [0110], dislcies the bidding component use the value from the optimization component to calculate an actual bid .., bid value real-time bid management); and
balance the resource allocation across different time periods to improve system stability and user experience(paragraph [0113], discloses likelihood that additional inventory would be available in a future timeframe ( e.g., a future timeframe during the remaining duration of the campaign)).
With respect to claim 19, Jordan in view of Jalali teaches elements of claim 11, including the advertisement bidding module might codify predicted action rates (e.g., click-through rates) (paragraph [0113]). Jordan failed to teach the corrosinding click-through rates defined based on a traffic curve.
However, Yakov teaches wherein the target resource utilization curve is based on one of: a traffic curve, a uniform utilization curve, or a response rate curve, to allow for flexible adaptation to different system requirements(Figs. 8, 9 and paragraphs [0150]- [0151], discloses histogram of the bucketized bid value ratios.., bid quality multiplier may be selected..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with dynamic bid price selection feature of Jalali in order provide improved expected value for successful bids(see, Jalali, paragraph [0039]).
With respect to claim 20 , Jordan teaches a non-transitory computer-readable storage medium including instructions that when executed by a computer, ( paragraph [0007], discloses a method, system and computer program product for using advertising campaign allocation optimization result and paragraph [0094], discloses ad network environment including a real-time bid management module Fig. 8, 810 dislcies a computer processor to executed a set of program code instructions, Fig. 11, 1108 discloses processor , 1126 instructions , 1110 main memory , 1112 static memory ), cause the computer to:
receive campaign data including a total budget, target resource utilization curve, and maximum bid for each auction opportunity of the publication application(paragraph [0007], discloses receiving a candidate impression opportunity corresponding an advertising camping that has both branding objective and performance-based objective, contract that can be satisfied by serving the candidate impression opportunity .., and paragraphs [0022]-[0029], discloses an NGD contract is an agreement between an advertiser and originator.., NGD contract includes a contract fulfillment system.., NGD contract can be described in this following form :L Deliver X number of action [utilization curve] for $Y [budget], starting at time S, ending at time E, with constraints C [camping data] and paragraph [0114], discloses collect information regarding a branding campaign regarding a branding campaign seeks to guide an advertiser through a series of question for defining camping objective and constrains.., collect information about an advertiser’s spending limit .., collect information about the advertiser’s maximum bid constraint ..);
maintain in a memory, a dynamic adjustment factor for each campaign(Fig. 8, 840 dislcies program code for calculating a bid for the set of the matching contracts .., paragraph [0007], discloses using advertising campaign allocation optimization result to calculate bids, paragraph [0023], discloses calculation that prescribe how to bid, paragraph [0035], dislcies bidding landscape change overtime[adjustment] , paragraph [0112], discloses the bid price might be increased to a bid price higher than .., paragraph [0113], discloses the real-time bid management module calculates the actual bid and supplies the bid value to the auction engine paragraph [0124], discloses at least one memory, the memory serving to store program instruction corresponding to the operation of the system); and
track, in real-time, resource utilization for each campaign for the publication application(paragraph [0107], discloses techniques, each contract has a single budget specify how that budget is be sent over time (e.g., over the duration of the camping) and with respect to supply (e.g., constraints on maximum bids). Budget splitting is handled by the optimizing algorithm (e.g., in real time) rather than having budget splitting being impose as a prior constraint, and paragraph [0115], discloses the campaign daily spending limit” ..).
Jordan teaches the above elements including applying, by the one or more processors, a resource conservation algorithm by calculating an adjusted bid using the dynamic adjustment factor (paragraph [0023], discloses techniques include algorithm and calculation that prescribed how to bid (e.g., when to bid , how much to bid, etc.,), paragraph [0102], discloses an advertismetn bidding module 109 in combination with a real-time bid management module.., and paragraph [0113], discloses a real-time bid management module calculates the actual bid… ); prices and budgeting strategies , camping daily spending limit (Fig. 5), an advertising camping having at least one branding objective and at least one performance-based objective (Fig. 8, 820), using advertising campaign allocation optimization results to calculate bids (paragraph [0094] and defining total budget for this campaign (paragraph [0117]). Jordan filed to teach analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion; a resource conversion algorithm only to the identified campaign, the dynamic adjustment factor for the identified campaign using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion; updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments and outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods.
However, Jalali teaches analyzing, by the processors, historical performance data to identify campaigns with constrained resources or a high likelihood of resource depletion(paragraph [0076], discloses historical data form one or more previous time period may be identified, historical data may use to make prediction.., paragraph [0103], discloses success probability maybe calculated exactly or estimated , the success probability may be determined by analyzing camping history data or measuring the performance of the camping during previous time periods ) ; resource conservation algorithm only to the identified campaigns by calculating an adjusted bid using the dynamic adjustment factor for the identified campaigns(paragraph [0109], discloses the probability of success may be defined in various ways based on the strategic objectives of the advertising campaign);
using the updated dynamic adjustment factor in a subsequent calculation of an adjusted bid to create a feedback loop that adapted to change auction condition(paragraph [0036], discloses feedback loop may be applied , paragraph [0058], discloses dynamic bidding price may be formulated using a multi-armed bandit framework collect feedback quickly from the environment in order to update utility function, and paragraph [0060], discloses dynamic CPM campaigns, in which the bid price is dynamically determined ) ;
dynamically adjusting bids throughout the specified time period to maintain consistent competition levels and prevent premature resource depletion (paragraph [0036], discloses adjust the bid price based on the prior performance distribution to maximum the performance goal);
updating, by the one or more processors, the dynamic adjustment factor at predetermined time interval throughout a specified time period based on a difference between target and actual resource utilization, to reduce computational load through adaptive bid adjustments (paragraph [0058], discloses update the utility function.., paragraph [0087], dislcies pacing rate is updated;
outputting, to a network interface, the updated dynamic adjustment factor and the adaptive bid adjustments for use in subsequent auctions, to balance resource utilizations across multiple time periods (Figs. 8, 9 and paragraphs [0150]- [0151], discloses histogram of the bucketized bid value ratios.., bid quality multiplier may be selected..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for Real-Time Bid Management Module of Jordan with dynamic bid price selection feature of Jalali in order provide improved expected value for successful bids(see, Jalali, paragraph [0039]).
Prior arts:
Jordan (US Pub., 2013/0325589 A1) discloses a method, system, and computer program product for using advertising campaign allocation optimization results to calculate bids. The method commences by receiving a candidate impression opportunity corresponding to an advertising campaign that has both branding objectives and performance-based objectives. Contracts that can be satisfied by serving the candidate impression opportunity use advertising campaign allocation optimization results to determine a set of matching contracts.
Jalali et al (US Pub., No., 2015/0134462 A1) discloses techniques and mechanisms described herein facilitate the dynamic selection of bid price. According to various embodiments, a base bid price may be determined for an advertising opportunity bid request received at a communications interface during a designated time period
Yakov et al (US Pub., No., 2006/0167703 A1) discloses a resource allocation platform for allocating resources between a provider and a plurality of users at a certain price differentiated for different users, the resources being time dependent resources such as communication data capacity, the platform comprising: an agent-based interaction mechanism for allowing said provider and said plurality of users to indicate their requirements and to translate the requirements into offers and bids, and a pricing engine for a
Dong et al (US Patent No., 10,423,456 B2) discloses apparatuses and methods related to dynamical adjustment of thresholds are disclosed. A method for dynamic adjustment of thresholds may include obtaining costs related to computing resources that have been used by a user. A resource utilization threshold may be dynamically adjusted based on at least one parameter.
Response to Arguments
Applicant’s arguments of the 35 U.S.C 101 rejection’s filed on 19 December 2025 with respect to claim(s) 1-20 have been fully considered but they are not persuasive.
Applicants arguments of claims recited a specific technological solution to a technical performance in computer auction system is not persuasive.
Under the Alice/Mayo framework used by the USPTO, the claims are evaluated based on given 35 U.S.C. 101 eligibility guidelines and , a computer-implemented method for optimizing resource allocation in a real-time online auction system is an abstract idea, specifically falling under "method of organizing human activity" (business practices) or "mathematical concepts.
Further, the claims are directed to the abstract idea of managing resource allocation in an auction or calculating dynamic bids:
Fundamental Economic Practice: Resource allocation (budgeting) and bid adjustment are fundamental economic practices, often deemed abstract when implemented on a computer without technological improvement.
Mathematical Concept: The use of a "resource conservation algorithm" and a "dynamic adjustment factor" to calculate bids is often categorized as a mathematical calculation.
Mental Process: Identifying constrained resources and adjusting bids could be performed, in theory, by a human, making it a mental process.
The additional limitation of : "one or more processors" to perform known tasks (analyzing, tracking, calculating) at a high level of generality. Merely using a computer to speed up auction bidding or reduce computational load does not automatically make the invention a technical improvement. The claim defines what is achieved (balanced resource utilization) rather than how the specific, new technical process achieves it. Thus, the 35 U.S.C 101 rejections with respect to claims 1-20 is maintained.
Applicants’ arguments relating to the BASCOM are misguided. The instant Application lacks the filtering internet content (i.e., installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user) of Bascom which proved to the element that was determined to be non-abstract. Therefore, BASCOM is in applicable to the instant case.
Applicants’ arguments relating to the Enfish are misguided. The instant application lacks the self-referential table of Enfish which proved to the element that was determined to be non-abstract idea. Therefore, Enfish is in applicable to the instant case.
Applicants’ arguments relating to the McRO are misguided.
Applicants claimed invention and the McRO has no similarity because as indicted by the Applicant McRO claims is directed to automatically producing accurate and realistic lip synchronization and facial expression in animated character, but Applicant failed to identify the similarly of the Applicants’ claimed invention with McRO’s claims. ... Therefore, the McRO claims and the Applicants claim has no similarity and claims are not patent ineligible.
Applicant’s arguments of the 35 U.S.C 103 rejection’s filed on 19 December 2025 with respect to claim(s) 1-20 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
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