DETAILED ACTION
Acknowledgments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the application filed on 06/14/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statements filed 06/14/2024 and 10/28/2025 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 non-patent eligible subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
Step 1:
The claims recite a process, system, apparatus, article of manufacture, and/or a nontransitory storage medium with instructions, each of which are proper statutory categories.
Step 2A (prong 1):
Claim 1 (representative of claim 12):
The claim limitations are grouped as shown immediately following:
A computer-implemented method for revising process endpoints by updating strategies based on predicting impacts of exogenous events on original endpoints, comprising: (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
generating, by one or more processors, scenarios based on cognitively analyzing external events; (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
cognitively analyzing, by the one or more processors, the process and segmenting the process into components, for each component: (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
determining an endpoint for each component; (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
determining, by the one or more processors, the scenarios relevant to the component; (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
adjusting, by the one or more processors, the endpoint of the component based on the scenarios relevant to the component; (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
applying, by the one or more processors, reinforcement learning to the scenarios relevant to the component to validate impacts of the scenarios on the endpoint and to select a most likely scenario; (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
implementing, by the one or more processors, process changes to terminate the component at the adjusted endpoint. (Certain Methods Of Organizing Human Activity - managing personal behavior or relationships or interactions between people including following rules or instructions)
Additional dependent claims 4, 5, 15, and 16 appear remedy the deficiency.
Step 2A (prong 2):
Claim 1 (representative of claim 12):
… one or more processors
… a computer system
… a memory
These remaining claim limitations are delineated as shown immediately preceding. The abstract idea is not integrated into a practical application. There are no improvements to the functioning of a computer, other technology or technical field, a particular machine is not cited, nothing is transformed to a different state or thing, the abstract idea is not more than a drafting effort designed to monopolize the abstract idea. The claim merely uses a computer as a tool to perform the abstract idea, which is generally linked to a particular field of use, in this case, marketing and advertising. Thus, these limitations are recited at a high-level of generality (i.e., as a generic processor and memory performing a generic computer function of processing and storing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component – MPEP 2106.05(f). Further, receiving data, evaluating data and distributing data are data gathering and data outputting, which has no effect on technology and does no more than generally link the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B:
The claim limitations do not provide an Inventive Concept. The claim limitations do not recite additional elements that amount to significantly more that the abstract idea because the additional elements of the system comprising a computer processor, computer readable storage medium with instructions, and a memory configured to store information, each recited at a high level of generality in a computer network which only perform the universal computer functions of accessing, receiving, storing, and processing data, transmitting and presenting information. Taking the elements both individually and as an ordered combination, the function performed by the computer at each step of the process is purely orthodox. Using a computer to obtain and display data are some of the most basic functions of a computer. As shown, the individual limitations claimed are some of the most rudimentary functions of a computer. The technical solution described in this invention does not alter hardware structure or its routine, does not transform the character of the information being processed, does not identify a novel source or type of data, does not advance the functionality of a computer as a tool, and does not incorporate specific rules enabling the computer to accomplish innovative utilities. In summary, the individual step and/or component does no more than require a general computer to perform standard computer functions. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a computer devices amounts to no more than mere instructions to apply the exception using a generic computer component - requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 2nd Paragraph, Failure To Particularly Point out and Distinctly Claim (Indefinite)
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 and 12 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, 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 pre-AIA the Applicant regards as the invention. The Examiner cannot determine the metes and bounds of the term cognitively analyzing. Clarification is requested.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Non-Patent Literature:
Ian A. Scott et al. “The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians.” (07 May 2024). Retrieved online 08/16/2026. The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians - Scott - 2024 - Internal Medicine Journal - Wiley Online Library
Relevancy: “Foundation machine learning models are deep learning models capable of performing many different tasks using different data modalities such as text, audio, images and video. They represent a major shift from traditional task-specific machine learning prediction models. Large language models (LLM), brought to wide public prominence in the form of ChatGPT, are text-based foundational models that have the potential to transform medicine by enabling automation of a range of tasks, including writing discharge summaries, answering patients questions and assisting in clinical decision-making. However, such models are not without risk and can potentially cause harm if their development, evaluation and use are devoid of proper scrutiny. This narrative review describes the different types of LLM, their emerging applications and potential limitations and bias and likely future translation into clinical practice.” (Abstract/Introduction)
Elastic Docs. “Overview of data frame analytics with Elastic machine learning.” (August 07, 2020). Retrieved online 08/16/2026.
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwj0sYPtz6WWAxX8ElkFHYhaBe4QFnoECBYQAQ&url=https%3A%2F%2Fwww.elastic.co%2Fdocs%2Fexplore-analyze%2Fmachine-learning%2Fdata-frame-analytics%2Fml-dfa-overview&usg=AOvVaw1BdpkR9s-C8s_Hh0hyshIc&opi=89978449
Relevancy: “Elastic supervised learning enables you to train a machine learning model based on training examples that you provide. You can then use your model to make predictions on new data. This page summarizes the end-to-end workflow for training, evaluating and deploying a model. It gives a high-level overview of the steps required to identify and implement a solution using supervised learning.” (Abstract/Introduction)
Diya Li et al. “A reinforcement learning-based routing algorithm for large street networks.” (2023). Retrieved online 08/16/2026. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwj0sYPtz6WWAxX8ElkFHYhaBe4QFnoECBcQAQ&url=https%3A%2F%2Frepository.library.noaa.gov%2Fview%2Fnoaa%2F61350%2Fnoaa_61350_DS1.pdf&usg=AOvVaw29N0rDP6BPEdRQhyifH9he&opi=89978449
Relevancy: “Evacuation planning and emergency routing systems are crucial in saving lives during disasters. Traditional emergency routing systems, despite their best efforts, often struggle to accurately capture the dynamic nature of flood conditions, road closures, and other real-time changes inherent in urban disaster logistics. This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL). The model incorporates a unique RL environment that considers multiple criteria, such as traffic conditions, hazardous situations, and the availability of safe routes. The RL agent in this model learns optimal actions through interaction with the environment, receiving feedback in the form of rewards or penalties. The ReinforceRouting model excels in executing prompt and accurate route planning on large road networks, outperforming traditional RL algorithms and shortest-path-based algorithms. A higher safety score and episode reward of the model are demonstrated when compared to these classical methods. This innovative approach to disaster evacuation planning offers a promising avenue for enhancing the efficiency, safety, and reliability of emergency responses in dynamic urban environments.” (Abstract/Introduction)
Foreign Art:
HUANG et al. “Method For Testing Application With Machine Learning Algorithm, Involves Generating Output Indicative Of Incident Causing Application To Be Inoperable For Incident Detected In Instance Of Instances.” (WO 2022/232139 A1)
Relevancy: “The method involves executing instances of an application, where the instances are configured to receive input from a machine learning algorithm configured to provide input to cause an incident for the application. The algorithm is executed to provide the input to the executed instances. An output indicative of the incident causing the application to be inoperable, is generated for the incident detected in an instance of the instances. The input is restricted based on devices selected for testing on the application, and the input comprises network data transmitted to the application and an operational freeze. The incident is determined from an application log and a debug hook.” (Abstract/Introduction)
DRIESEN et al. “Method For Executing Computer-automated Process Using Trained Machine Learning Models, Involves Accessing Event Data Describing Event, Determining Output Characterization Of First Event And Determining To Deactivate First Rule Set.” (EP 4095770 A1)
Relevancy: “The method involves accessing event data describing an event, and executing a trained machine learning (ML) model (111B) to determine an ML characterization of the event using the event data by hardware processors. A rule set is applied by the hardware processors to the data to generate a rule characterization. An output characterization of an event is determined by the processors based on the rule characterization, and a determination is made whether to deactivate the rule set based on a ML characterization. A proactive detection rule is applied to the event to determine that the event is an untrained event.” (Abstract/Introduction)
CASTIGLIONE et al. “System For Adversarial Vulnerability Testing Of Input Non-differentiable Machine Learning Model, Has Processor That Generates Output Data Object Indicative Of Adversarial Vulnerability Of Input Non-differentiable Machine Learning Model.” (CA 3159935 A1)
Relevancy: “The system has a processor that receives an input data object including one or more data elements representing the nondifferentiable input machine learning model including a set of piece-wise branching nodes. The processor transforms the non-differentiable input machine learning model to generate a smoothed machine learning model by replacing each piece-wise branching node with a sigmoid function data object, conducts a gradient-ascent based adversarial example search on the smoothed machine learning model to attempt generation of a valid adversarial example data object, causes an output classification of the smoothed machine learning model to change relative to an original output classification and generates an output data object indicative of an adversarial vulnerability of the input non-differentiable machine learning model, upon generation of the valid adversarial example data object.” (Abstract/Introduction)
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to James A. Reagan (james.reagan@uspto.gov) whose telephone number is 571.272.6710. The Examiner can normally be reached Monday through Friday from 9 AM to 5 PM. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, John Hayes, can be reached at 571.272.6708.
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/JAMES A REAGAN/Primary Examiner, Art Unit 3697
james.reagan@uspto.gov
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