DETAILED ACTION
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/24/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by examiner.
Status of Claims
This Office action is in reply to filing by applicant on 07/24/2025.
Claims 1 – 20 are currently pending and have been examined.
This action is made non-final.
Notice Regarding Double Patenting
Examiner notes that there is a potential double patenting rejection (as to claims 1 – 20 herein) based upon the allowed claims of the parent patent (US12380387B2) to this application. Presently, the allowed independent claims of the parent read on the initial independent claims herein, and, thus, all claims herein are preliminarily anticipated by the parent’s claims. Examiner will reserve final judgment on any such rejection however until the final claims herein are clearly established, noting that the present initial claims of 07/24/2025 are still subject to being amended by Applicant.
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 pursuant to 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 – 9 are directed to a method (process), claims 10 – 18 are directed to a method (process), and claims 19 and 20 are directed to a method (process). The claims therefore constitute eligible statutory categories of an invention. (Step 1: YES).
Independent claim 1 is here analyzed (claim 1 is also representative of independent claims 10 and 19):
Claim 1 recites the limitations of:
A method of proactively managing risk, the method comprising: receiving input data comprising event data; generating, using one or more machine learning models (MLM), associated data from the input data by assigning one or more levels of similarity to the input data above a first threshold; generating, using the one or more MLMs, correlated data based on the associated data and the event data; generating, using the one or more MLMs, one or more risk event predictions based on at least the correlated data by assigning probabilistic attributes to the correlated data; outputting the one or more risk event predictions; receiving user input associated with the one or more risk event predictions; training the one or more MLMs with at least the associated data and the user input to modify the first threshold; updating the one or more risk event predictions by: generating updated associated data with the one or more MLMs based on the modified first threshold; generating updated correlated data with the one or more MLMs based on the updated associated data; generating one or more updated risk event predictions with the one or more MLMs based on the updated correlated data; and outputting the one or more updated risk event predictions.
The claims recite the abstract idea of:
Using various risk indicia and probabilities to generate risk predictions
The above abstract idea recites a fundamental economic practice and/or commercial interaction, i.e, accessing risks / probabilities in transactions. This analysis concentrates on the fundamental economic practice and/or commercial interaction nature of the above bulleted abstract idea.
The above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice and/or commercial interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, independent claims 1, 10, and 19 recite an abstract idea.
The above bolded terms of independent claim 1 recite: one or more MLMs. Said bolded independent claim terms are just applying generic computer driven systems to perform the above noted abstract idea limitations. The recitation of generic computer components (e.g., the several generic references to “machine learning models”) in a claim does not necessarily preclude that claim from reciting an abstract idea. Moreover, those MLM’s only serve to generally link the above noted abstract idea to them, without more. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
As to the dependent claims 2 – 9, 11 – 18, and 20, they further refine the above noted abstract idea set forth by the independent claims.
Examiner further notes that there are no additional (to the computer related hardware as noted above) computer / computer driven terms set forth in the dependent claims: These dependent claims are also being applied as tools to the abstract idea, without more. Only through dependency do the dependent claims generally link the abstract idea articulated herein as above to general machine learning computer technology.
The computer hardware/software above bolded are recited at a high-level of generality (i.e., generic machine learning models) all performing generic computer functions, and the same amounts to no more than mere instructions to apply the exception using a generic computer component(s). 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 and are at a high level of generality. That said, the claims are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additionally claimed elements in the claims do not integrate the abstract idea into a practical application).
All claims above reviewed also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. When considered separately and as an ordered combination, the claims 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 perform a judicial exception by applying generic computer components and thereby automating the process cannot provide an inventive concept.
This Application's lack of providing significantly more than the judicial exception is also referred to as its claims lacking an “inventive concept. See MPEP 2106.05(f) where applying a computer as a tool to the abstract idea is not indicative of significantly more. The above detailed non-computer related elements do not change the outcome of the analysis, as they simply further limit ways which the abstract idea may be performed. (Step 2B: NO. The claims do not provide significantly more than the judicial exception).
In summary, the claim set reviewed as above does not include any additional elements that integrate its abstract idea into a practical application, or that are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Claims 1 – 20 are not patent-eligible pursuant to 35 USC 101.
Allowable Subject Matter
Claims 1 – 20 would be allowable if rewritten or amended to overcome the additional rejection herein pursuant to 35 U.S.C. 101. The following is a statement of reasons for the indication of allowable subject matter: Independently, while the claims' limitations most recently set forth herein may individually be disclosed by the prior art, the claims as a whole are not obvious because the examiner would have to improperly use their separate limitations as a road map to combine them.
CONCLUSION
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Spiro (US20180174067A1) – Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.
Mo (US20190034846A1) – A multiplier is utilized to quantify a cybersecurity risk level of a portfolio of entities (e.g., companies) and enable actions to mitigate that quantified risk. In doing so, features or attributes of one or more companies in a portfolio are compared to features or attributes of one or more companies that experienced an adverse cybersecurity event (e.g. a data breach). Further, a degree of dependency, such as a matrix of a number of shared vendors and the proximity of those vendors to the companies, can be measured between (1) portfolio companies and one or more companies that experienced a cybersecurity event, and/or (2) the portfolio companies themselves to better quantify the risk. That is, to more meaningfully analyze a cybersecurity event that occurred at one or more companies and better predict the likelihood of an occurrence at portfolio companies, embodiments can determine an n-degree interdependency between companies.
Clifford (US20210398683A1) – Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.
Eric (US11901080B1) – Techniques are disclosed for determining a risk assessment corresponding to a level of risk that a user of a service organization has or will develop a condition. The techniques include receiving user data associated with a user record of a user. The techniques further include inputting the user data into a trained prediction model of a user condition prediction system. The user data may include a plurality of data points, weighted relative to each other, that respectively correspond to one of a plurality of categories. The techniques further include determining, by the trained prediction model, a risk assessment of a user condition based on the user data. The risk assessment may then be provided to a user device of a user service agent for use coordinating user service.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to call examiner Cobb as above, or to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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