DETAILED ACTION
This Office Action is in response to Applicants Request for Continued Examination received on January 20, 2026. Claim(s) 1-20 is/are currently pending in the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Examiner acknowledges the Applicants amendments to claims 1, 3, 8-10, and 13 in the response filed on July 16, 2025. No claims are canceled at this time
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 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 natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process”, or product or “article of manufacture” and is delivery aware audience segmentation which is a process (Step 1: YES).
The Examiner has identified independent method Claim 8 as the claim that represents the claimed invention for analysis and is similar to apparatus Claim 13. Claim 8 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
obtaining activity data for a user;
assigning, using a selector of a machine learning model, the user to a user segment based on
the activity data;
generating, using a reach predictor of the machine learning model, a reach prediction for the
user segment, wherein the machine learning model is trained using conversion data and content reach data to generate the reach prediction by:
training, using the conversion data, a selector of the machine learning model by computing a conversion loss based on the conversion data,
computing a reach loss based on the content reach data, wherein the content reach data comprises a product of a segment-specific match rate and a segment-specific exposure rate over a plurality of media channels, and
training the machine learning model using the reach loss
selecting a media channel, from the plurality of media channels for communicating with the user
based on the user segment and the reach prediction; and
providing content to the user via the selected media channel.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Obtaining activity data, assigning a user to a user segment, determining reach prediction based on conversion and reach data, training a selector by computing conversion loss, computing reach loss based on reach data and match rate, training a model (algorithm) using reach loss, recites a concept performed in the human mind. But for the “machine learning model” and the “media channel”, the claim encompasses collecting data, developing algorithms or equations, generating clusters and calculations based on the clusters, computing reach, and developing a model by computing reach loss using his/her mind and/or pen and paper. The mere nominal recitation of reach prediction being performed by generic trained machine learning and communications with a user over a media channel does not take the limitations out of the mental processes grouping. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The processor and memory including instructions executed by the processor in Claim 13 is just applying generic computer components to the recited abstract limitations. The method using a machine learning model in Claim 8 appears to be just software. Claims 8 and 13 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Obtaining activity data, assigning a user to a user segment, determining reach prediction based on conversion and reach data, training a selector by computing conversion loss, computing reach loss based on reach data and match rate, training a model (algorithm) using reach loss, and sending content over a media channel recites a commercial interaction. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The processor and memory including instructions executed by the processor in Claim 13 is just applying generic computer components to the recited abstract limitations. The method using a machine learning model in Claim 8 appears to be just software. Claims 8 and 13 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a machine learning model and a media channel (Claims 1 and 8) a processor and a memory storing instructions (claim 13). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 8, and 13 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0031] about implantation using general purpose or special purpose computing devices [a server comprises a general purpose computing device, a personal computer, a laptop computer, a mainframe computer, a super computer, or any other suitable processing apparatus.] and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 8, and 13 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7, 9-12, and 14-20 further define the abstract idea that is present in their respective independent claims 1, 8, and 13 and thus correspond to Mental Processes and/or Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception. Therefore, the claims 2-7, 9-12, and 14-20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Response to Arguments
The Applicants remarks begin on page 8 of the response on January 20, 2026. The Applicant begins with a status of the claims and a summary of the interview on December 16, 2025.
Moving on, the arguments being on page 8 with the Applicant summarizing the 35 U.S.C § 101 and the Applicant cites the August 4, 2025 memorandum on “reminders on evaluating subject matter eligibility of claims under 35 U.S.C § 101” regarding AI and machine learning arts. The arguments cite the memo where it cites example 39’s training of a neural network as not reciting a judicial exception (remarks page 9). Applicants argue that their training steps do not recite specific mathematical algorithms and therefore do not recite a judicial exception.
The arguments also cite that the invention is not directed to a business method and take the position of the claims reciting user segmentation and content delivery through media channels, neither of which is inherently commercial. Applicants argue that the field of use in the specification does not transform a technical improvement into a business method.
The Examiner is not in agreement with the Applicant. First, while the Office did issue a memo with respect to inventions involving machine learning or artificial intelligence, it does not indicate or equate to every parent application which involves the words “machine learning”, “artificial intelligence”, or “neural networks” to be instantly allowable simply based on the subject matter. Additionally, the details and remarks of the In re Desjardins decision are based on several aspects of the application, claim language, and the substance of the disclosure of the application. Specifically, the application was dealing with a very specific problem which arises in the area of machine learning where during retraining or updates to the model it can inadvertently lose the ability or knowledge it had previously learned. The details of the training in the disclosure explain how the point of the invention was to mitigate this issue which arises specifically from machine learning or artificial intelligence. That being said, plain and ordinary application of machine learning techniques using generic computer hardware does not instantly rise to practical application.
Also, that Examiner dose not find this application similar to Example 39 as the example involves a multi-layer training where after the first training is performed, there is a subset of data which was incorrectly identified. The system takes this data with additional data that has been collected or provided and runs a second training using the new data as well as the problem data from the first training. This second step showed clear improvement in the model and not just an initial and mandatory training of machine learning. In the instant claims the training of the machine learning is the initial and mandatory training all models are required to undergo.
Regarding the argument that the improvement is not a business method. The Examiner points out that the specific area where this application has landed is Operations Research which is one area within Business Methods. Other areas include, Finance, Insurance, E-shopping, Banking, Healthcare, Coupons, Inventory Management, Point of Sale, Cost/Price, Reservations, Shipping and Transportation, Agriculture, Fishing, Mining, Forestry, Wells, and Vehicle Navigation to name a few. Additionally, the applicants disclosure mentions that conversion rate is related to customers. This would be seen as marketing strategies to potential customers with the end goal of having them purchase a product or service. This is where the invention is classified in commercial interactions sub-grouping. The Examiner also points out that target advertising also is under commercial interactions but may also end up in the managing personal behaviors and relationships grouping.
The arguments move on to 35 U.S.C § 101 under Step 2A, Prong 1 (remarks page 10) where the Applicant argues the claims do not recite a mental process. This argument reiterates the prior argument regarding the August 2025 memo. The Applicant goes on to discuss multi-objective optimization as a technical challenge for machine learning where a solution is a joint optimization of two objectives where an improved objective for one may degrade the solution for the other.
The Examiner has already answered the arguments regarding the August 2025 memo. Regarding the position of multi-objective solutions related to the terms “conversion and reach”, the Examiners position is that based on the disclosure the conversion and reach are related to customers. The use of the word customers aligns this with reach and communication over a media channel for a commercial interaction. Additionally, the idea of a multi-objective solution is not new and is heavily rooted in mathematical equations. The position of using machine learning, which as a starting point is more of complex mathematics by a computer, is using a series of algorithms to handle to complex calculations and computations to solve a multi-variable problem. This concept is still something which can be performed by a human using pen and paper. The mere application of well known machine learning techniques using a computer processor is not indicative of practical application, but instead performance of the judicial exception using the computer as a tool.
The arguments move to Step 2A, Prong 2 (remarks page 11) and submit that the claims are a practical application with improvements to the machine learning and a multi phase training including a selector based on conversion loss rom conversion data and then training the machine learning based on reach loss based on predicted reach and content reach data. Applicants positions is it’s not a single training pass, rather a structured training process of different components optimized for different objectives. Applicants submit that the practical application is evident based on the claimed training methods (remarks page 12).
The Examiner disagrees. The Applicant is arguing the optimization of two objectives through a multi-phase training but the claims don’t specifically address optimization. The claim language is training a portion of the machine learning based on conversion data. And a second portion of the model based on reach loss. The selection of the medial channel is based on the user segment which is not performed with the machine learning model and the conversion and reach data.
Claims 8 and 13 also have fewer limitations than claim 1 and simply have collection of data for training a model. Claim 8 for example has training a selection based on conversion loss and training a the machine learning based on reach loss as a comparison of predicted reach and content. This is claimed in a very basic manner and is more of applying a computer as a tool using well known learning techniques.
The arguments conclude with Step 2B arguing the inventive concept is significantly more than the judicial exception. Applicant call Examiners position of use of certain mathematical equations as a concept of linear programming an improper characterization. Applicant argues that the disclosed equations in the specification are novel training methods and not fundamental mathematics akin to thermodynamics or basic linear algebra. It is further stated that one or ordinary skill in the art would recognize deep nonlinear complexity and cannot be reduced to “linear programming” since such models are capable of complex implicit relationships where the training is a significant contributor to the efficacy of the final model.
The Examiner is not in agreement as the position that the algorithms and machine learning is heavily rooted in mathematics and the application of a machine leaning model on a computer is simply use of the computer as a tool to perform the complex mathematics. The application does not equate to significantly more than the judicial exception.
In summary, the claims remain rejected under 35 U.S.C § 101. The claims are not in condition for allowance at this time.
Conclusion
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 March 20, 2026