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 .
DETAILED ACTION
Status of Claims
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/08/2025 has been entered.
Claims 1, 6 have been amended.
Claims 5,12, have been canceled.
Claims 21-22 have been added.
Claims 1-4, 6-11, 13-22 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on 12/08/2025 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1-11 and 13-20 rejection under 35 USC § 101:
Step 2A, Prong One: The claims are directed to an abstract idea
Applicant argues that “ as amended, claims 1, 11 and 20 are directed to a concrete, computer-implemented system that incorporates specific data structures, model parameters, training/serving architecture, and model dynamics to solve a technical problem encountered in computer-based unique-user forecasting: inter-day user de-duplication and non-linear overlap growth across time at serving latencies compatible with real-time forecasting. The claims do not simply "apply" advertising logic. Rather, they constrain the system to train a machine learning model via a defined pipeline that uses a reference impression-to-user ratio and a predicted impression-to-user ratio informed by a training overlap factor; employ an exponentially saturating overlap model to compute, asynchronously offline and on a periodic cadence, a parameter encoding "a rate of size increase of an overlap user set between unique users on different days"; use that parameter to compute, also asynchronously offline, an overlap factor between users on successive days; and use the trained model together with the asynchronously derived parameter and overlap factor to generate the forecast. These are not field-of-use recitations of marketing activity. They are specific, technical solutions that materially limit data representations (overlap user set), model dynamics (exponential saturation), system architecture (offline parameter computation with periodic updates decoupled from online inference), and serving behavior (use of trained model with the parameter at inference). By prescribing how the computer must structure, train, and deploy the model-including which quantities are computed where and when-the claims are directed to a technical solution and not to a bare marketing concept. Even if the Examiner continues to view the claims as implicating "certain methods of organizing human activity," the amended claims 1, 11 and 20 integrate any such concept into a practical application. The claims do not merely "receive, analyze, and display" information. It imposes concrete technological constraints on model training and serving that effect a technical improvement in the functioning of the forecasting system. The exponentially saturating overlap model addresses a known technical challenge in de-duplicating unique users across days because overlap growth is non-linear and saturating, and encoding that behavior in a parameter learned offline and periodically refreshed yields improved fidelity and stability in the forecasted unique- user counts. The asynchronous offline computation and periodic updates of the parameter and overlap factor reorganize computation between offline and online paths to reduce online latency and compute, enabling real-time forecasting with non-trivial inter-day de-duplication, which is a performance and scalability improvement in system operation, not a business rule. The trained model expressly incorporates the reference and predicted impression-to-user ratios in conjunction with the overlap factor derived from the offline parameter, which is a particular training-and-serving coupling that changes how the computer structures and processes inputs to produce outputs. These concrete, claim-level constraints integrate the concept into a specific technological implementation. The claims are thus "tied to" a particular processing architecture and model class configured in a particular way to effect inter-day user deduplication under real-time constraints, rather than being a result-focused invocation of generic computer (page 2-3/8)”.
Examiner disagrees. uses a reference impression-to-user ratio and a predicted impression-to-user ratio informed by a training overlap factor; employ an exponentially saturating overlap model to compute, asynchronously offline and on a periodic cadence, a parameter encoding "a rate of size increase of an overlap user set between unique users on different days"; use that parameter to compute, also asynchronously offline, an overlap factor between users on successive days; and use the trained model together with the asynchronously derived parameter and overlap factor to generate the forecast is directed to is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes).
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (unique-user forecasting: inter-day user de-duplication and non-linear overlap growth across time at serving latencies compatible with real-time forecasting ) and not in the operations of any additional elements or technology.
As such, the examiner finds that any improvement obtained by practicing the claimed invention is an improvement to a business process. Second, under Step 2a, Prong 2, the improvement to a technology or technological field must be rooted in the additional element. In order to overcome a 35 USC 101 rejection, the technical solution to a technical problem must be rooted in the "additional elements". Additional elements are defined as those elements outside the identified abstract idea itself. As thus, the only additional elements of “memory , device, a processor and ML model ” in the claim(s) that would be capable of overcoming the 101 rejection. These additional elements are a general-purpose computer with generic computer components upon which an abstract idea is merely being applied as evident by Applicant’s specification,” The one or more processors 201 can include any processing circuitry operable to control operations of the user forecast computing device 102 [43]” and “In some embodiments, the communication port(s) 209 allow for the transfer (e.g., uploading or downloading) of data, such as machine learning model training data [ 50]”. The machine learning 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 35 USC § 101.
As such, any purported improvement in what the applicant calls a technical field is an improvement in ineligible subject matter. In order for an improvement to a technology or technological filed to overcome a 35 USC 101 rejection, the purported improvement must be rooted in the "additional elements" which in this case they are not. The claimed additional elements are merely a general purpose computer and generic machine learning upon which an abstract idea is merely being applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-20 under USC § 101 is maintained.
Claim 1 for instance recites “ a system, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor to:train a machine learning model using historical user data and historical campaign data based at least in part by performing some steps”. The recitation of “ to : train”, is an indent use of the machine learning model. Even if the machine learning model is actually trained to perform an action (s). “using such a trained machine learning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Claim 1 also recites “generate, using trained the machine learning model and a parameter”. The machine learning 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 35 USC § 101. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
Under Step 2B,
Applicant argues that “the amended limitations provide "significantly more" than any alleged abstract idea. The Office Action itself indicates that the claims would be allowable over the prior art if the § 101 rejection were overcome and that the Examiner has "been unable to find prior art that discloses the combination of the claimed features." That acknowledgement is probative that the claimed combination-training with specific impression-to-user ratios and a training overlap factor, together with an exponentially saturating overlap model whose parameter and day-to-day overlap factor are computed asynchronously offline and periodically updated for use by the trained model at inference-is not well-understood, routine, or conventional (page 3/8)”.
Examiner disagrees. Examiner statement of “ the Examiner has "been unable to find prior art that discloses the combination of the claimed features” with regard to prior art, is not an admission and / or automatically mean that the claims are allowable under 35 USC § 101. Examiner asserts that the rejection of claims under 35 USC 101 is separate and distinct from rejections for anticipation and obviousness. Rejecting the claims under 35 U.S.C. § 102 and/or 35 U.S.C. § 103 has no bearing or consequence on the materiality of a rejection under 35 U.S.C. § 101. Therefore, the claim rejection of claims 1-11 and 13-20 under 35 USC § 101 is maintained.
Applicant argues that “the claims do not broadly recite "using a model" or "doing calculations offline." It requires an exponentially saturating overlap model with a defined semantic parameter, computed asynchronously offline and periodically updated, that encodes the rate of size increase of the overlap user set and is then used to compute an inter-day overlap factor also asynchronously offline. This offline parameterization and factor are expressly consumed by the trained model in generating the forecast. That architecture and model dynamic-as claimed-are non- conventional and yield specific, technological benefits: accurate de-duplication across days represented by a saturating process, while enabling real-time serving through offline computation and periodic refresh. Taken individually and as an ordered combination, these elements recite a specific technical solution, not a generic application of an abstract idea on a computer. The ordered combination of: (i) constrained training using reference and predicted impression-to-user ratios with a training overlap factor, (ii) an exponentially saturating overlap model with a semantically defined parameter, (iii) asynchronous offline computation and periodic update of that parameter and the successive-day overlap factor, and (iv) use of the trained model and the parameter to produce the forecast in real time, amounts to significantly more than an abstract idea. It improves the operation of the forecasting computer system by reducing online compute, improving latency, and increasing accuracy and stability in unique-user forecasting under non-linear overlap conditions (page 4/8)”.
Examiner disagrees. the recitation of: an exponentially saturating overlap model with a defined semantic parameter, computed asynchronously offline and periodically updated, that encodes the rate of size increase of the overlap user set and is then used to compute an inter-day overlap factor also asynchronously offline is directed to is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes).
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (accurate de-duplication across days represented by a saturating process, while enabling real-time serving through offline computation and periodic refresh) and not in the operations of any additional elements or technology.
As such, the examiner finds that any improvement obtained by practicing the claimed invention is an improvement to a business process. Second, under Step 2a, Prong 2, the improvement to a technology or technological field must be rooted in the additional element. In order to overcome a 35 USC 101 rejection, the technical solution to a technical problem must be rooted in the "additional elements". Additional elements are defined as those elements outside the identified abstract idea itself.
As thus, the only additional elements of “system, memory , device, a processor and ML model ” in the claim(s) that would be capable of overcoming the 101 rejection. These additional elements are a general-purpose computer with generic computer components upon which an abstract idea is merely being applied as evident by Applicant’s specification,” The one or more processors 201 can include any processing circuitry operable to control operations of the user forecast computing device 102 [43]” and “In some embodiments, the communication port(s) 209 allow for the transfer (e.g., uploading or downloading) of data, such as machine learning model training data [ 50]”. The machine learning 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 35 USC § 101.
As such, any purported improvement in what the applicant calls a technical field is an improvement in ineligible subject matter. In order for an improvement to a technology or technological filed to overcome a 35 USC 101 rejection, the purported improvement must be rooted in the "additional elements" which in this case they are not. The claimed additional elements are merely a general purpose computer and generic machine learning upon which an abstract idea is merely being applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
Additional considerations within the § 101 analysis:
Applicant argues that “first, the Office Action characterizes the claims as merely "gathering, analyzing, determining, generating, and transmitting." That characterization omits the claim's specific model class, parameter semantics, and architectural constraints. The claims do not generically "analyze data"; it requires a particular exponentially saturating overlap model with offline- learned parameters that encode inter-day overlap growth, and it requires the trained model to use those artifacts. Those are concrete, non-generic improvements to how the computer is configured to process data (page 5/8)”.
Examiner disagrees. the PTO’s preliminary guidelines, which have been followed by Examiner, are completely in line with Supreme Court and Federal Circuit precedent, and provide substantive criteria to both examiners and applicants in defining an abstract idea within a claim and determining overall subject matter eligibility. As such, it is Examiner's position that the office's action is supported by fact and analysis and has provided sufficient rationale and explanation.
As thus, the office action did not omit the claim's specific model class, parameter semantics, and architectural constraints.
The recitation of : a particular exponentially saturating overlap model with offline- learned parameters that encode inter-day overlap growth, and it requires the trained model to use those artifacts fails to (a) improve another technology or technical field and (b) improve the functioning of the computer itself and (c) applies the abstract idea with or by use of, a particular machine, which is a generic computer performing generic computer functions and are not seen to recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself.
Claim 1 for instance recites “ a system, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor to:train a machine learning model using historical user data and historical campaign data based at least in part by performing some steps”. The recitation of “ to : train”, is an indent use of the machine learning model. Even if the machine learning model is actually trained to perform an action (s). “using such a trained machine learning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Claim 1 also recites “generate, using trained the machine learning model and a parameter”. The machine learning 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 35 USC § 101. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
Applicant argues that “Second, the Office Action states there is "no improvement to another technology or technical field" and "no improvement to the functioning of the computer itself." Respectfully, that conclusion overlooks the explicit claim recitations of asynchronous offline computation with periodic updates of the overlap parameter and factor, which reorganize the computational workload to enable real-time inference with more accurate de-duplication. This is a technological improvement to the functioning of the computer-based forecasting system, not a business improvement. The use of an exponentially saturating overlap model further improves the model's technical behavior in representing non-linear overlap dynamics that standard linear ratios cannot capture (page 5/8)”.
Examiner disagrees. the recitations of “asynchronous offline computation with periodic updates of the overlap parameter and factor, which reorganize the computational workload to enable real-time inference with more accurate de-duplication and / or the use of an exponentially saturating overlap model” to (a) improve another technology or technical field and (b) improve the functioning of the computer itself and (c) applies the abstract idea with or by use of, a particular machine, which is a generic computer performing generic computer functions and are not seen to recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself.
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method (representing non-linear overlap dynamics that standard linear ratios cannot capture) and not in the operations of any additional elements or technology.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
Applicant argues that “Third, the Office Action suggests that reciting "machine learning" merely "links" the claims to a technological environment. That would be true if the claims only invoked a model in name. But here, the claims specify how the model is trained, what intermediate quantities are computed, how those quantities are derived offline with periodic refresh, the structure of the overlap model, the semantic meaning of the parameter, and how the trained model uses that parameter to generate the forecast. That specificity distinguishes the claims from generic use of ML (page 5/8)”.
Examiner disagrees. claim 1 for instance recites “ a system, comprising: a processor; and a non-transitory memory storing instructions that, when executed, cause the processor to:train a machine learning model using historical user data and historical campaign data based at least in part by performing some steps”. The recitation of “ to : train”, is an indent use of the machine learning model. Even if the machine learning model is actually trained to perform an action (s). “using such a trained machine learning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Claim 1 also recites “ generate, using trained the machine learning model and a parameter”. The machine learning 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 35 USC § 101. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
Applicant argues “ recent PTAB jurisprudence confirms that the current subject matter is eligible. Of note, Ex parte Desjardins, Appeal 2024-000567 (Sept. 26, 2025) states: "Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology". Here, the Board explained that claims reciting specific machine-learning structures and a concrete deployment architecture that improve system performance can be patent-eligible. Claims 1, 11 and 20 mirror that rationale: it recites an exponentially saturating overlap model, asynchronous offline computation with periodic parameter updates, and use of those artifacts at inference to enable real-time, more accurate inter- day de-duplication. These claim-specific constraints integrate any alleged abstract idea into a practical application under Step 2A, Prong Two, and supply an inventive concept under Step 2B for the reasons set out above (page 6/8)”.
Examiner disagrees. The recitation of “an exponentially saturating overlap model, asynchronous offline computation with periodic parameter updates, and use of those artifacts at inference to enable real-time, more accurate inter- day de-duplication”, is directed to is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. MPEP Revised Step 2A Prong One=Yes).
Furthermore, reciting of machine-learning structures to perform “an exponentially saturating overlap model, asynchronous offline computation with periodic parameter updates, and use of those artifacts at inference to enable real-time, more accurate inter- day de-duplication”, does not qualify under the other non-limiting non-exclusive examples.
In order to overcome a 35 USC 101 rejection, the technical solution to a technical problem must be rooted in the "additional elements". Additional elements are defined as those elements outside the identified abstract idea itself. As thus, the only additional elements of “memory , device, a processor and ML model ” in the claim(s) that would be capable of overcoming the 101 rejection. These additional elements are a general-purpose computer with generic computer components upon which an abstract idea is merely being applied as evident by Applicant’s specification,” The one or more processors 201 can include any processing circuitry operable to control operations of the user forecast computing device 102 [43]” and “In some embodiments, the communication port(s) 209 allow for the transfer (e.g., uploading or downloading) of data, such as machine learning model training data [ 50]”. The machine learning 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 35 USC § 101.
As such, any purported improvement in what the applicant calls a technical field is an improvement in ineligible subject matter. In order for an improvement to a technology or technological filed to overcome a 35 USC 101 rejection, the purported improvement must be rooted in the "additional elements" which in this case they are not. The claimed additional elements are merely a general purpose computer and generic machine learning upon which an abstract idea is merely being applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem.
Hence, Examiner maintains that the claims do not define substantially more than an abstract idea. therefore, the claim rejection of claims 1-11 and 13-20 under USC § 101 is maintained.
With regard to claims 1-11 and 13-20 rejection under 35 USC § 102 (A) (1)/ 103, applicant’s arguments are considered. The claim rejection of claims 102 (A) (1)/ 103 is withdrawn.
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 1-4, 6-11, 13-22 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception subject matter, specifically an abstract idea. The analysis for this determination is explained below:
Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. In this case, Step 1, determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claim(s) 1-4, 7-11 are directed to a machine (i.e. a system); claims 13-19 are directed to a machine (i.e. a system); claim (s) 20-22 are directed to a manufacture (i.e. a non-transitory computer medium). The claimed invention is directed to at least one judicial exception (i.e a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) the following abstract idea:
Claim 1, as exemplary, recites the abstract idea of “ to train a machine learning model via a defined pipeline that uses a reference impression-to-user ratio and a predicted impression-to-user ratio informed by a training overlap factor; employ an exponentially saturating overlap model to compute, asynchronously offline and on a periodic cadence, a parameter encoding "a rate of size increase of an overlap user set between unique users on different days"; use that parameter to compute, also asynchronously offline, an overlap factor between users on successive days; and use the trained model together with the asynchronously derived parameter and overlap factor to generate the forecast”.
The following are the abstract idea limitations of: “ determining a reference impression-to-user ratio for a first time period based on historical campaign data and historical user data during the first time period, determining a predicted impression-to-user ratio for a second time period after the first time period based on the reference impression-to-user ratio and a training overlap factor, and algorithm to generate a labelled number of unique users based on the reference impression-to-user ratio and the predicted impression-to-user ratio, train an algorithm using historical user data and historical campaign data, wherein the training comprises: determining a reference impression-to-user ratio for a first time period based on historical campaign data and historical user data during the first time period, determining a predicted impression-to-user ratio for a second time period after the first time period based on the reference impression-to-user ratio and a training overlap factor, and training the algorithm to generate a labelled number of unique users based on the reference impression-to-user ratio and the predicted impression-to-user ratio, receive, from a computing device, a user forecast request, determine, based on the user forecast request, campaign data associated with an advertising campaign, generate, using trained algorithm and a parameter, a total number of unique users forecasted to be reached by the advertising campaign in a future time period based on the campaign data, wherein the parameter is computed asynchronously offline and periodically updated using an exponentially saturating overlap model based on historical user and campaign data, wherein the parameter indicates a rate of size increase of an overlap user set between unique users on different days, wherein an overlap factor between users on successive days is asynchronously computed offline based on the parameter and using the exponentially saturating overlap model, generate forecasted user data based on the total number of unique users, and transmit, in response to the user forecast request, the forecasted user data”.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations, because the merely gather data, analyze the data, determine results based upon the analysis, generate tailored content based on the results, and transmit the tailored content. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes). Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of “system, memory, processor, machine learning (ML) model, display, device” . The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using 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 claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add 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)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes). When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using “a network, device , electronic resource, machine learning (ML) model, display on a display of the device and a user interface” to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “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 FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)).
As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computers communicating over a general purpose network (as evidenced from paragraphs 41-55). Therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Finally, the following limitations are considered insignificant extra solution activity as they are directed to merely receiving, storing and/or transmitting data:
transmit, in response to the user forecast request, the forecasted user data to the computing device.
receive, from a computing device, a user forecast request,
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e. “PEG” Step 2B=No).
For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same analysis is applied here to independent claims 13 and 20.
Dependent claims 2-4, 6-11, 13-22 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more. The claims merely add further details that narrow that abstract idea of, without significantly more. The dependent claims 2-4, 6-11, 13-22 appears to merely further limit the abstract idea of Certain methods of organizing Human Activity” as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations), Thus, the dependent claims further narrows the abstract idea and/or recite additional elements previously rejected in the independent 1,13 and 20.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102 /103
The Examiner is unable to find a prior art that teaches the limitations of : independent claims 1, 13 and 20 of “ determining a reference impression-to-user ratio for a first time period based on historical campaign data and historical user data during the first time period, determining a predicted impression-to-user ratio for a second time period after the first time period based on the reference impression-to-user ratio and a training overlap factor, and algorithm to generate a labelled number of unique users based on the reference impression-to-user ratio and the predicted impression-to-user ratio, train an algorithm using historical user data and historical campaign data, wherein the training comprises: determining a reference impression-to-user ratio for a first time period based on historical campaign data and historical user data during the first time period, determining a predicted impression-to-user ratio for a second time period after the first time period based on the reference impression-to-user ratio and a training overlap factor, and training the algorithm to generate a labelled number of unique users based on the reference impression-to-user ratio and the predicted impression-to-user ratio, receive, from a computing device, a user forecast request, determine, based on the user forecast request, campaign data associated with an advertising campaign, generate, using trained algorithm and a parameter, a total number of unique users forecasted to be reached by the advertising campaign in a future time period based on the campaign data, wherein the parameter is computed asynchronously offline and periodically updated using an exponentially saturating overlap model based on historical user and campaign data, wherein the parameter indicates a rate of size increase of an overlap user set between unique users on different days, wherein an overlap factor between users on successive days is asynchronously computed offline based on the parameter and using the exponentially saturating overlap model, generate forecasted user data based on the total number of unique users, and transmit, in response to the user forecast request, the forecasted user data”
Possible Allowable Subject Matter
Claims 1-4,6-11,13-22 recite subject matter that would be allowable over the prior art if the Applicant were to be able to overcome the claim rejection under 35 USC § 101 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kalish et al, US pub No: 20160379244 A1, teaches method and system for forecasting a campaign performance using predictive modeling.
Wang et al, US pub no: 2012/0158456 A1, teaches forecasting and traffic based on business metric performance
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622