DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/15/2026 has been entered.
Response to Amendment
Claims 1, 9, and 15 are currently amended.
Claims 1-20 are currently pending and addressed below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a nature phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-20 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Step 2A Prong One:
Claim 1 recites (additional elements underlined):
A computer-implemented method for optimizing electronic content delivery for non-measurable users, the method comprising:
receiving, at an optimization control system, a feature vector for each electronic content impression opportunity among a plurality of electronic content impression opportunities;
receiving a feature vector for each delivered item of electronic content among a plurality of previously-delivered items of electronic content for measurable users;
receiving an in-target indication for each delivered item of electronic content among the plurality of previously-delivered items of electronic content for measurable users;
training, by a machine learning model, a prediction model by associating historical in-target indications with feature vectors for historical content impressions;
estimating, by the trained prediction model, a probability that an electronic content impression opportunity among the plurality of electronic content impression opportunities with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications;
generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, the measurable users being a subset of all users receiving the electronic content;
generating, by a beta actuator of the optimization control system, a bid allocation signal for delivering a new item of electronic content for the electronic content impression opportunity among the plurality of electronic content impression opportunities based on a static mapping between the bid allocation signal and the estimated probability for a given in-target control signal, wherein the bid allocation signal is a value between 0 and 1 representing a probability that the electronic content impression opportunity proceeds to bidding;
submitting, responsive to the bid allocation signal, a bid to a content delivery network for delivering the new item of electronic content for the electronic content impression opportunity;
receiving, from a market clearing module, an indication of awarded impressions delivered to measurable users for winning bids;
dynamically updating, by the optimization control system, the in-target rate control signal based on the awarded impressions to maintain a campaign-level in-target rate that meets or exceeds a specified in-target rate threshold; and
delivering the new item of electronic content to a user based on the awarded impressions.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgment, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth an advertising/marketing activity. Advertising/marketing fall within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because advertising/marketing is related to commerce and economy. The limitations outlined above also describe or set forth a commercial interaction (e.g., advertising, marketing or sales activities or behaviors, business relations). The limitations outlined above also describe or set forth the managing of personal behavior or relationships or interactions between people. Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
The limitations outlined above that describe or set forth the abstract idea are also considered mathematical concepts. These limitations are similar to “organizing information and manipulating information through mathematical correlations” which was determined to be a mathematical concept in MPEP 2106.04(a)(2)(II). Therefore, the claim recites a mathematical concept (Step 2A Prong One, Yes).
Step 2A Prong Two:
In Step 2A Prong Two, the additional element(s) outlined above are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. The Examiner notes that “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent eligible subject matter" (see pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., in a computer environment). The courts have found that simply limiting the use of the abstract idea to a particular environment does not integrate the judicial exception into a practical application. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation (Step 2A Prong Two, No).
Step 2B:
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A Prong Two overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Their collective functions merely provide generic computer implementation (Step 2B, No).
Claim(s) 2-8 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claim 2-3, 5, and 7-8 recite the additional element “electronic.” Claim 6 recites the additional elements “device,” “a device,” and “hashed email address.” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 4 does not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claim 4 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claim 9 recites (additional elements underlined):
A system for optimizing electronic content delivery for non-measurable users, the system comprising:
a data storage device storing instructions for optimizing electronic content delivery for non-measurable users in an electronic storage medium; and
a processor configured to execute the instructions to perform a method including:
receiving a feature vector for each electronic content impression opportunity among a plurality of electronic content impression opportunities;
receiving a feature vector for each delivered item of electronic content among a plurality of previously-delivered items of electronic content for measurable users;
receiving an in-target indication for each delivered item of electronic content among the plurality of previously-delivered items of electronic content for measurable users;
training, by a machine learning model, a prediction model by associating historical in-target indications with feature vectors for historical content impressions;
estimating, by the trained prediction model, a probability that an electronic content impression opportunity among the plurality of electronic content impression opportunities with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications;
generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, the measurable users being a subset of all users receiving the electronic content;
generating a bid allocation signal for delivering a new item of electronic content for the electronic content impression opportunity among the plurality of electronic content impression opportunities based on a static mapping between the bid allocation signal and the estimated probability for a given in-target control signal, wherein the bid allocation signal is a value between 0 and 1 representing a probability that the electronic content impression opportunity proceeds to bidding;
submitting, responsive to the bid allocation signal, a bid to a content delivery network for delivering the new item of electronic content for the electronic content impression opportunity;
receiving, from a market clearing module, an indication of awarded impressions delivered to measurable users for winning bids;
dynamically updating, by the optimization control system, the in-target rate control signal based on the awarded impressions to maintain a campaign-level in-target rate that meets or exceeds a specified in-target rate threshold; and
delivering the new item of electronic content to a user based on the awarded impressions.
For the same reasons explained above with respect to claim 1, claim 9 also recites an abstract idea in Step 2A Prong One. For the same reasons explained above with respect to claim 1, claim 9 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claim(s) 10-14 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 9 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claim 10 recites the additional elements “wherein the system is further configured for” and “electronic.” Claims 11-12 and 14 recite the additional element “electronic.” Claim 13 recites the additional elements “device,” “a device,” and “hashed email address.” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 15 recites (additional elements underlined):
A non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform a method for optimizing electronic content delivery for non-measurable users, the method including:
receiving a feature vector for each electronic content impression opportunity among a plurality of electronic content impression opportunities;
receiving a feature vector for each delivered item of electronic content among a plurality of previously-delivered items of electronic content for measurable users;
receiving an in-target indication for each delivered item of electronic content among the plurality of previously-delivered items of electronic content for measurable users;
training, by a machine learning model, a prediction model by associating historical in-target indications with feature vectors for historical content impressions;
estimating, by the trained prediction model, a probability that an electronic content impression opportunity among the plurality of electronic content impression opportunities with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications;
generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, the measurable users being a subset of all users receiving the electronic content;
generating, by a beta actuator of the optimization control system, a bid allocation signal for delivering a new item of electronic content for the electronic content impression opportunity among the plurality of electronic content impression opportunities based on a static mapping between the bid allocation signal and the estimated probability for a given in-target control signal, wherein the bid allocation signal is a value between 0 and 1 representing a probability that the electronic content impression opportunity proceeds to bidding;
submitting, by a bid computer responsive to the bid allocation signal, a bid to a content delivery network for delivering the new item of electronic content for the electronic content impression opportunity;
receiving, from a market clearing module, an indication of awarded impressions delivered to measurable users for winning bids;
dynamically updating, by the optimization control system, the in-target rate control signal based on the awarded impressions to maintain a campaign-level in-target rate that meets or exceeds a specified in-target rate threshold; and
delivering the new item of electronic content to a user based on the awarded impressions.
For the same reasons explained above with respect to claim 1, claim 15 also recites an abstract idea in Step 2A Prong One. For the same reasons explained above with respect to claim 1, claim 15 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claim(s) 16-20 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 15 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claims 16-18 and 20 recite the additional element “electronic.” Claim 19 recites the additional elements “device,” “a device,” and “hashed email address.” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Prior Art
The Examiner notes that after a thorough search on the claims as currently amended, the claims are found to recite novel and non-obvious subject matter. The closest prior art are the following:
Goksel et al. (US 2019/0205919 A1) discloses systems and methods for developing and optimizing target audiences by analyzing consumption patterns of known users, and assigning user characteristics probability scores to anonymous users based on their patterns.
Els et al. (US Patent No. 10,282,758 B1) discloses methods and systems for controlling a pace of purchasing online advertisements in real-time bidding (RTB) environments.
Karlsson et al. (US 2018/0158095 A1) discloses systems and methods for allocating bids for providing content within a segmented campaign which is controlled to ensure that an event rate associated with the provided content meets or exceeds a threshold rate. A campaign-level event rate, associated with the provided content, is estimated and provided as a feedback signal. This feedback signal is employed to dynamically update bid allocations for each of the segments, which in turn varies the number or rate of provided impressions and events. Such feedback enables the control of the campaign-level rate and ensures that the campaign-level rate meets or exceeds the rate threshold.
While the prior art teach some of the elements of the claimed invention, they do not teach the following limitations when viewing the claimed invention as a whole: “estimating, by the trained prediction model, a probability that an electronic content impression opportunity among the plurality of electronic content impression opportunities with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications; generating an in-target rate control signal based on a number of total delivered items of electronic content for measurable users and a number of in-target delivered items of electronic content for measurable users, the measurable users being a subset of all users receiving the electronic content; generating, by a beta actuator of the optimization control system, a bid allocation signal for delivering a new item of electronic content for the electronic content impression opportunity among the plurality of electronic content impression opportunities based on a static mapping between the bid allocation signal and the estimated probability for a given in-target control signal, wherein the bid allocation signal is a value between 0 and 1 representing a probability that the electronic content impression opportunity proceeds to bidding; submitting, responsive to the bid allocation signal, a bid to a content delivery network for delivering the new item of electronic content for the electronic content impression opportunity; receiving, from a market clearing module, an indication of awarded impressions delivered to measurable users for winning bids; dynamically updating, by the optimization control system, the in-target rate control signal based on the awarded impressions to maintain a campaign-level in-target rate that meets or exceeds a specified in-target rate threshold.” Additionally, while each of the individual features may have been known per se, there is no teaching or suggestions absent Applicant’s own disclosure to combine these features in the specific manner claimed other than with impermissible hindsight.
Response to Arguments
Applicant's arguments filed 06/15/2026 have been fully considered but they are not persuasive. In the Remarks, Applicant argues:
Argument: “The Office Action alleged that the claim limitations ‘cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s).’ (Office Action at 4.) Applicant respectfully disagrees. The steps recited in each independent claim, when viewed as a whole, cannot practically be performed in the human mind. The specification explicitly confirms this: ‘The marketplace for providing electronic impressions to users may involve providing many millions of such impressions within short periods of time. As such, a human operator cannot practically compete with automated systems for bidding upon and providing electronic impressions to users.’ (Specification at [0084].). Furthermore, the specification states that the actions of the automated in-target controller are performed ‘at speeds and in numbers that cannot be performed in the human mind or with pencil and paper’ and that ‘application of an automated in-target controller may allow for the control of a large number of campaigns - tens of thousands or more.’ (Specification at [0084].) The amended claims recite an ordered combination of steps performed by specific system components, e.g., an optimization control system comprising an in- target rate controller, a beta actuator, a bid computer, and a market clearing module, operating in a real-time feedback loop to dynamically control bid allocations across a content delivery network. The claim further requires submitting bids to a content delivery network, receiving indications of awarded impressions from a market clearing module, and dynamically updating the in-target rate control signal based on the awarded impressions. These are not steps that can practically be performed in the human mind or with pencil and paper. The claim recites a specific technical system operating at machine speed to process millions of impression opportunities in real time. This is a task that is beyond human capability.”
In response, the Examiner respectfully disagrees. First, the limitations that exclude the additional elements, can be practically performed in the human mind as explained above. Second, the claims do not explicitly recite that millions of impressions are being provided within a short period of time, or the control of tens of thousands of campaigns.
Argument: “The amended claims do not recite advertising, marketing, or sales activities.”
In response, the Examiner respectfully disagrees. The claims explicitly recite a method for content delivery. Paragraph 37 of the Specification states that “content may include, but is not otherwise limited to images, audio, video, advertisements, or advertising content.” Therefore, the claims do recite a certain method of organizing human activity in Step 2A Prong One.
Argument: “the claims do not merely recite mathematical relationships, formulas, or calculations in the abstract.”
In response, the Examiner respectfully disagrees. Claims 1, 9, and 15 recite the limitations: receive a feature vector for each content impression, receive a feature vector for each delivered item among a plurality of previously-delivered items, receive in-target indications for each delivered item, use a prediction model to associate historical in-target indications with feature vectors for historical content impressions, estimate a probability that a content impression opportunity with a specified feature vector will meet targeting requirements based on the received feature vectors and the received in-target indications, generate an in-target rate control signal based on a number of total delivered items for measurable users and a number of in-target delivered items for measurable users, generating a bid allocation signal based on a static mapping between the bid allocating signal and the estimated probability for a given in-target control signal, and updating the in-target rate control signal based on the awarded impressions to maintain a campaign-level in-target rate that meets or exceeds a specified in-target rate threshold. At least these limitations recite mathematical concepts. The Examiner notes that “a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation” (MPEP 2106.04(a)(2)). Therefore, the claims recite mathematical concepts in Step 2A Prong One.
Argument: “The above-recited elements of independent claim 1 describe a method that
provides a technical solution to a specific technical problem in the field of electronic
content delivery systems. The technical problem is identified in the specification: ‘[O]nline content providers have traditionally measured the behavior and attributes of individual users through the use of mechanisms such as cookies, tracking pixels in images, etc., for example, to improve the 'in-target' rate of delivered impressions. However, the use of such mechanisms may be less available to online content providers in view of, for example, an increased ability of users to opt-out of such mechanisms, desire to allow users to expressly opt-in to such mechanisms, and so on. That is, online content providers face a need to measure and optimize the efficiency of an online content campaign even when the behavior of many users is less measurable.’ (Specification at [0002].) This is a technical problem rooted in the architecture of electronic content delivery systems, namely, the loss of user-level tracking mechanisms (such as cookies and tracking pixels) creates a technical gap in the data available to optimization control systems, rendering traditional optimization approaches ineffective. The specification discloses a specific technical solution to this problem: ‘Systems and methods are disclosed that adaptively allocate bids for providing the online content within the campaign…. The specification further describes a specific technical improvement provided by the beta actuator component of the optimization control system. In particular, the specification explains that applying a binary threshold condition to the control signal "may result in an abrupt change in observed in-target rate" and that "it may be desired to provide a mechanism to make the relationship between the campaign-level control signal and estimated campaign-level in-target rate smooth.’ (Specification at [0087].) The beta actuator addresses this technical deficiency: ‘[R]ather than applying a threshold condition based on the control signal, the actuator may employ a continuous function, or map, to update the offer or bid allocations for the content based on a control signal.... [T]he various embodiments may provide smooth, predictable, and responsive control of 'in-target' rates for an online campaign.’ (Specification at [0058].)”
In response, the Examiner respectfully disagrees. Unlike in DDR in which the claimed invention solved the business challenge of retaining website visitors that is particular to the Internet, here the claimed invention amounts to merely reciting the performance of a business practice (e.g., optimizing content delivery for non-measurable users) along with the requirement to perform it on the Internet. The claimed invention here is not necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. “We caution, however, that not all claims purporting to address Internet-centric challenges are eligible for patent” (see p. 22 of DDR Holdings, LLC v. Hotels.com, L.P. (Fed. Cir. 2014)).
Similar to SAP America, the claims here are ineligible because their innovation is an innovation in ineligible subject matter (i.e., improvements to advertising/marketing). The advance here lies entirely in the realm of the abstract idea, with no plausibly alleged innovation in the non-abstract application realm.
Moreover, the claims use of a machine learning model amounts to adding the words “apply it”. “[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101” (p. 18 of Recentive Analytics Inc. v. Fox Corp. (Fed. Cir. 2025)).
Argument: “Here, the amended claims recite a specific ordered combination of technical
steps performed by identified system components, not generic computer functions. The optimization control system, with its in-target rate controller, beta actuator, bid computer, and market clearing module, operates as a closed-loop feedback control system that provides smooth, predictable, and responsive control of in-target rates for electronic content delivery. The claims reflect the specific technical improvements described in the specification, such as the dynamic feedback loop that continuously updates the control signal based on real-time market clearing results. These are not generic computer functions. Rather, they are specific technical solutions to the specific technical problem of optimizing content delivery when most users cannot be directly measured.”
In response, the Examiner respectfully disagrees. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation. Therefore, the claims still do not integrate the judicial exception into a practical application or amount to significantly more.
Conclusion
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/SAM REFAI/Primary Examiner, Art Unit 3621
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