DETAILED ACTION
This is the initial Office action based on the application filed on June 3, 2025. Claims 1-20 are currently pending and have been considered below.
Priority
The instant application relies on a provisional application 63/655,116 for its priority date. However, the provisional application does not contain detailed information such as is being described and claimed in the instant application. As such, the instant application will not be given consideration to the earlier effective filing date of its provisional application.
Claim Objections
Claims 7 and 17 are objected to because of the following informalities:
The Claims recite “a SHAP plot.” However, no definition is provided for the SHAP acronym. For expedited prosecution, the Examiner will assume, in view of the instant specification that SHAP means “Shapley Additive Explanations.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 state “generating, at the framework, a prediction score based on the sequence of functionality steps.” The instant specification at [0022] states that “a sequence of functionality steps that can be executed including, for example, training an LLM, evaluating the trained model, training and evaluating…”
However, it is unclear based on which functionality steps, a prediction score can be generated. For example, can a prediction score be generated based only on evaluation of a model and training a model? Moreover, it is unclear how the prediction score can be generated based on evaluation of the model; Furthermore, it is unclear what is being predicted. Some kind of output? A relevance of a model? As such, the metes and bounds of the claims cannot be determined.
Dependent Claims 2-10 and 12-20 do not remedy the above issue.
Claims 9-10 and 19-20 state “regenerating a new LLM…” However, it is unclear how a model can be regenerated if it was not previously generated. For example, neither the rejected claims nor their respective parent claims describe any generation of LLMs. As such, it is unclear if the regeneration is of a previously generated model or whether the claims describe some other LLMs.
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 an abstract idea without significantly more.
Claims 1 and 11 recite:
receiving a user instruction at an application executed by an electronic device – Receiving information is considered insignificant extra-solution activity as discussed in MPEP 2106.05.
generating, at a framework executed by a server, one or more feature groups based on profile data – Grouping data is something that may be done in the mind and/or with help of pen and paper.
generating, at the framework, a data configuration file – Generating the configuration file seems to be gathering of information such as various parameters. That is something that may be done in the mind and/or with help of pen and paper. Also, gathering information may be considered insignificant extra-solution activity as discussed in MPEP 2106.05.
generating, at the framework, a model configuration file – Generating the model configuration file seems to be gathering of information such as information about the model. That is something that may be done in the mind and/or with help of pen and paper. Also, gathering information may be considered insignificant extra-solution activity as discussed in MPEP 2106.05.
specifying, at the framework, a sequence of functionality steps for execution; generating, at the framework, a prediction score based on the sequence of functionality steps – Identifying a probable output based on gathered information is something that may be done in the mind and/or with help of pen and paper.
saving and executing, at the framework, the sequence of functionality steps – Storing and executing data is generic functionality of a computer. Furthermore, such functionality may also be considered a well-understood, routine and conventional activity of the device as discussed in MPEP 2106.05.
generating, at the electronic device, one or more outputs including, for example, a prediction based on the sequence of functionality steps - Identifying a probable output based on gathered information is something that may be done in the mind and/or with help of pen and paper.
This judicial exception is not integrated into a practical application. Other, the abstract idea, the claims recite additional elements of hardware executing the abstract idea. The additional elements such a processor, storage device, etc are recited at a high level of generality, i.e. as generic computer components performing generic computer functions of information processing. 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.
Dependent Claims 2-10 and 12-20 recite further mental processes that may be completed with aid of pen and paper and as such are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5-6, 11 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243).
Claims 1 and 11: Brown discloses a method and a system comprising:
receiving a user instruction at an application executed by an electronic device [0041]. [See at least “A round of dialog as used herein may refer to a user input…”]
generating, at a framework executed by a server, one or more feature groups based on profile data [0055]. [Feature groups seem to be information collected about the user as described at least in para [0030] of the instant specification. In view of that, see at least Brown [0055] for information collected from a user’s online profile.]
generating, at the framework, a data configuration file [0073]. [See at least “a config file.”]
generating, at the framework, a model configuration file [0101]. [See at least “a configuration file for one or more of the machine learning models.”]
specifying, at the framework, a sequence of functionality steps for execution [0105]. [See at least “sequence of steps, tasks, items, and/or milestones that are part of the plan.”]
generating, at the framework, a prediction score based on the sequence of functionality steps [0222]. [“In a scoring mode, the output of the machine learning model 915 includes a score, which corresponds to a probability of the predicted output.”]
generating, at the electronic device, one or more outputs including, for example, a prediction based on the sequence of functionality steps [0105]. [See at least the output. Also, the term for example seems to be presenting an optional feature.]
As to saving and executing, at the framework, the sequence of functionality steps, Brown discloses generating and executing the sequence of functionality steps at paragraph [0105]. But, Brown does not explicitly disclose that the steps would be saved.
However, the generated steps as in Brown [0105] do not just get generated and then exist in a vacuum. Clearly, the steps would have to be stored, because the steps are used in a subsequent operation for generating an output.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown to save the sequence of steps in order to at least use the steps for processing data using the steps.
Claims 5 and 15: Brown as modified discloses the method and the system of Claims 1 and 11 above, and Brown further discloses supporting, by the framework, batch and real-time processing based on a framework inference [0189].
Claims 6 and 16: Brown as modified discloses the method and the system of Claims 1 and 11 above, and Brown further discloses the profile being in a graph embedding format [0071].
Claims 2, 7, 12, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243) in view of Nawab et al (US Patent Application Publication 2023/0334365).
Claims 2 and 12: Brown as modified discloses the method and the system of Claims 1 and 11 above, and Brown further discloses wherein the data configuration file is based on the profile data [0055], but Brown alone does not explicitly disclose wherein the data configuration file specifies data characteristics including location of LLM training and evaluation data, a data reader type, a data parameter including a number of data points for LLM training and evaluation, and a number of data points used for model explainability.
However, Nawab [0046] discloses generating configuration files for a training model that includes a number of data points for the training model, evaluation and various visualizations, such as at least model explainability (as in [0058]). As to a “data reader type,” the instant specification in paragraph [0032] describes it as at least “interaction data.” Nawab [0066] discloses such interaction data.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Nawab. One would have been motivated to do so in order to provide a learning system with parameters that would better train the system for more relevant results for a particular user.
Claims 7 and 17: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose further comprising generating a SHAP plot for the feature groups including for feature importance and explainability.
However, Nawab [0053-0056] discloses using a SHAP visualization that includes feature importance and explainability.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Nawab. One would have been motivated to do so for “providing information regarding the strengths and weaknesses of the model and enabling users to make informed decisions and take appropriate actions to improve model performance.”
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243) in view of Larkin et al (US Patent Application Publication 2024/0111551).
Claims 3 and 13: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose determining, by a data loader that uploads the profile data, that the each profile data set of the profile data is associated with a customer identifier, satisfies a data quality check that ensures each personalization of the profile data is available and complete, and performs a sanity check to determine whether each personalization of the profile data is within an expected range.
However, Larkin [0115] discloses analyzing an input data object that contains various identifiers and parameters. The analyzing includes at least data completeness and a data score (i.e. within an expected range).
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Larkin. One would have been motivated to do so in order to validate an input object for further processing.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243) in view of Okamoto (US Patent Application Publication 2021/0125106) and further in view of Yip et al (US Patent Application Publication 2025/0280305).
Claims 4 and 14: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose wherein the model configuration file comprises model characteristics include a model type, a model backend, a model directory to export, a model hyperparameter, and a tuning hyperparameter.
However, Okamoto [0057] discloses a configuration file with a hyperparameter; Okamoto [0179] discloses a configuration file “specifying a type… of the model.” Yip [0163] discloses a configuration file that contains the rest of the information about the model characteristics.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Okamoto and Yip. One would have been motivated to do so in order to manage particular AI models.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243) in view of Makhijani et al (US Patent Application Publication 2024/0289645).
Claims 8 and 18: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose regenerating the model based on the prediction being below a threshold, the regenerating being based on updating one or more parameter weights for new profile data.
However, Makhijani [0161] discloses regenerating a model when its score is below a threshold. The regenerating is based at least prioritizing attention (i.e. weighing different) on other parameters.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Makhijani. One would have been motivated to do so in order to optimize a prediction model.
Claims 9 and 19: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose regenerating a new LLM without updated parameters or previous inputs and with new profile data.
However, Makhijani [0116] discloses regenerating a model with the same prior iteration outputs, but by gathering “additional data related to the variable to be predicted including new data.”
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with Makhijani. One would have been motivated to do so in order to optimize a prediction model.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al (US Patent Application Publication 2026/0057243) in view of McDaniel et al (US Patent Application Publication 2025/0252377).
Claims 10 and 20: Brown as modified discloses the method and the system of Claims 1 and 11 above, but Brown alone does not explicitly disclose regenerating a new LLM periodically.
However, McDaniel [0045] discloses periodically regenerating models “on a periodic” basis.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Brown with McDaniel. One would have been motivated to do so in order for the models to be “more accurate as the information evolves over time.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gudla et al (2025/0165513) describes at least periodically retraining models;
Liebkowiz et al (2025/0307228) describes at least parsing a configuration file for LLM processing.
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/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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