DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 3/11/2026, Applicant, on 7/1/2026, amended Claims 1, 8, and 15. Claims 1-20 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant recites the amended limitations of the claims and states that this invokes a machine learning model or another model which is a feedback loop in which the results of the actual outcome database are provided back to the machine learning model and thus an improvement to the specification, stating that this is an integration of the judicial exception and that similar to Example 39 the claims do not recite a mental process and that there is an improvement of predictions of a machine-learned language mode, and thus are eligible under 101. Examiner disagrees as two of the amended limitations of the Claims recite a Mental Process as per the rejection below, and further all Claims recite a Commercial Interaction, as the Claims recite limitations for outputting analyzed historical product data, which is a Certain Method of Organizing Human Activity. The treatment here is a display/presentation of information, with use of a machine learning model or another model, which does not require a machine learned language model as Applicant has argued, and Example 39 has no similarities to this case, other than potential use of a machine learning model which does not need to be used as it is part of a conditional “or”. Applicant has asserted that there is an improvement to predictions which is completely abstract, and any purported improvement would be part of the abstraction. There is no improvement any additional element, alone or in combination, nor to any technology, or technological process, this is utilization of current technologies such as possibly a machine learning model, and thus this is “Applying It” similar to Alice and not eligible by the MPEP.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
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.
Alice – Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 are directed at limitations for obtaining a user history database, wherein the user history database describes at least a user’s past behavior (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), obtaining at least two treatments, wherein the treatment comprises a candidate interaction the user engages with, wherein the at least two treatments include a first treatment and a second treatment that vary (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), generating a prompt for a machine learning language model including the at least two treatments, the user history database, and a request to generate predictions of an output of a desired output type, wherein the output type indicates how the user will interact with the treatment (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), providing the prompt to a model serving system, hosting the machine learned language model, for execution, wherein the machine learned language model is configured as a transformer architecture including an attention operation, the attention operation coupled to receive input data and generate queries, keys, and values, and generate an attention output from the queries, the keys, and the values, (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), receiving a response from the model serving system, wherein the response includes an output, and wherein the output indicates a prediction of the desired output type on how the user will respond to each received treatment (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), selecting, a treatment based on the received prediction of the user (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), responsive to selecting the treatment based for the user, presenting the selected treatment to a user device associated with the user (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), obtaining an actual treatment database, wherein the actual treatment database includes at least an actual outcome of the user responsive to presentation of the first treatment on the client device (Receiving Information, an observation, a Mental Process; a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), determining a difference between the prediction to the actual outcome (Analyzing the Information, an evaluation, a Mental Process; a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), generating a finetuned prompt by invoking the machine learning language model or another model with at least the actual outcome and the prediction, a request to generate the finetuned prompt based on the difference such that subsequent predictions align with actual outcomes (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), and simulating, using the finetuned prompt, predictions for a second user for the at least two treatments (a Commercial Interaction, i.e. analyzing product information ; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of determining an output of analyzed product information, a Commercial Interaction, but for the recitation of generic computer components. That is, other than reciting a user history database, model serving system, user device, computer-readable medium, computer processor, computer system, and varying a layout or design of a user interface of an application or presentation of an interface element associated with the application, nothing in the claim element precludes the step from practically being performed for the purposes of a Commercial Interaction. The claims recite limitations for a Commercial Interaction, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, user history database, computer processor, computer-readable medium, user device, and model serving system are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0016] The picker client device 110 is a client device through which a picker may interact with the customer client device 100, the retailer computing system 120, or the online concierge system 140. The picker client device110 can be a personal or mobile computing device, such as a smartphone, a tablet, a laptop computer, or desktop computer. In some embodiments, the picker client device110 executes a client application that uses an application programming interface (API) to communicate with the online concierge system 140.”
Which states that any type of device or computer may be used, such as any personal computer, laptop, mobile phone, tablet, etc., to perform the abstract limitations, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. Further, the “vary a layout or design of a user interface (UI) of an application, or presentation of an interface element associated with the application” in Applicant’s Specification is:
“[0036]In one embodiment, the online concierge system 140 described herein simulates A/B testing experiments in conjunction with one or more large-scale machine-learned models deployed by the online concierge system140. Companies utilize A/B testing to compare variations of an element to improve customer’s experiences and increase engagement. A/B testing evaluates variations in color schemes, button placement, navigation menus, or even entire layouts. For example, such companies might run an A/B test to evaluate the click-through-rates of different email campaigns or website designs. Companies may also run A/B tests to improve site navigation and user ability by enhancing the sequence of interfaces presented to the user during the user’s experience with the online concierge system 140. For example, the A/B tests can test several iterations of the interface elements presented to the user during a single experience with the online concierge system 140”
Which states that these interface elements are presented on a system, but has nothing to do with the treatments and are broadly connected to them, but rather are just elements displayed on a computer, with no description on how this is performed, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that are generic, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmission steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, user device, processors, etc., nor the receiving and transmission steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-7, 9-14, and 16-20 contain the identified abstract ideas, further narrowing them, with no additional elements to be considered as part of a practical application or under prong 2 of the 2019 PEG, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-20 are objected to as being dependent upon a rejected base claim, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection.
The closest prior art of record are Neumann (U.S. Publication No. 2020/032,1116), Wexler (U.S. Publication No. 2021/038,3925), and Mikkulainen (U.S. Publication No. 2017/019,3366}. Neumann, a method and system for generating an alimentary instruction set identifying an individual prognostic mitigation plan, teaches obtaining a user history database, wherein the user history database data describes at least a user’s past behavior, receiving at least two treatments, wherein the treatment comprises a candidate interaction the user engages with, receiving a response from the model serving system, wherein the response includes an output, and wherein the output indicates a prediction of the desired output type on how the user will respond to each received treatment, responsive to selecting the treatment based for the user, presenting the selected treatment to a user device associated with the user, generating a prompt for use with a machine learning language model including the at least two treatments, the user history database where the history information and treatments are used with a prompt and machine learning to come up with a best predicted output, it does not explicitly state this is the desired output type or how the user will interact with the treatment, nor does it teach selecting, a treatment based on the received prediction of the user, varying of a layout or design based on the treatments, nor the machine learning language model configured as transformer architecture in the manner claimed. Wexler, a system and method for adaptive healthcare support and behavioral intervention, teaches a request to generate predictions of an output of a desired output type, wherein the output type indicates how the user will interact with the treatment and prediction of treatments, but it does not teach the varying of a layout or design based on the treatments, nor the machine learning language model configured as transformer architecture in the manner claimed. Mikkulainen, a system and method for webinterface generation and testing using artificial neural networks, teaches using targeted user behaviors to convert end users, changing the property of interface elements based on clusters stemming from a machine learning model which customizes the interface, and use a machine learning for multivariate, A/B testing, but not the specific varying of a layout or design based on the treatments, nor the machine learning language model configured as transformer architecture in the manner claimed. None of the prior art explicitly teaches this varying of a layout or design based on the treatments, nor the machine learning language model configured as transformer architecture in the manner claimed, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record, and are objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 9/21/2026
