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 .
THIS ACTION IS MADE FINAL.
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 extension fee 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.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 05/06/2026, Applicant, on 07/24/2026.
Status of Claims
Claims 1-7, 12 and 14 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered, but they are not persuasive.
The Examiner respectfully disagrees.
In view of the present amendments, the Examiner submits that claims can recite an abstract idea even if they are claimed as being performed on a computer (using a computer processor). The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer’).
Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory in Content Extraction is according to the court an abstract idea that is similar to other concepts that have been identified as abstract by the courts. Present claim 1 is collecting and analyzing data using a generic computer processor. Claim 1 is applying generic machine learning to score, prioritize, safety issues, and provide recommendations. The claimed steps usually recognized as abstract by the court. Therefore, it is reasonable to conclude based on the similarity of the idea described in this claim to several abstract ideas found by the courts that claim 1 is directed to an abstract idea.
Further, and following the identification of abstract ideas in the claims, the present claims lack existence of additional elements (not even a processor) in order to be considered by the Examiner. As a result, there is no additional elements to integrate the abstract idea into a practical application, Step 2A Prong Two.
The claims do not disclose a new machine learning architecture or a new hardware controller. It uses a "trained machine learning model" as a black box to automate a process human previously calculated using spreadsheets or risk matrices.
The claims do not improve the function of the machine learning model itself, nor does it improve the physical mechanism of the equipment. It merely applies generic computing tools to execute an abstract risk-management rule. As such the claims do not integrate the judicial exception into practical application as argued by Applicant on page 4.
Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step,’ rendering the claim eligible at Step 2B” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354. As discussed above, there is no evidence in the record that the steps of providing safety recommendation in a workplace using generic machine learning are accomplished in a non-conventional way. The Examiner therefore concludes that the claims used generic, conventional, technology to implement the abstract idea of providing safety recommendation in a workplace and that there is no improvement to an “existing technology.”
The claims are a generic, computer-implemented abstraction of a standard workplace hazard analysis. Gathering environmental metrics, processing them through a standard statistical model, and sorting the output to trigger a conventional mechanical override relies entirely on well-known, routine, and conventional prior art components. It fails to offer an inventive concept under §101 and is rendered obvious under §103 by the widespread integration of predictive machine learning in industrial Safety Instrumented Systems.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
Response to Arguments - 35 USC § 103
The arguments have been fully considered, but they are not persuasive.
The Examiner respectfully disagrees.
The claimed steps are routine in industrial automation and predictive maintenance.
Obtaining data from a "plurality of sources" (safety data, incident reports, operational parameters, etc.) is fully anticipated by enterprise risk management platforms and standard networks.
"Cleaning and normalizing" data are foundational, non-novel steps mandatory for virtually every machine learning pipeline in existence. This step lacks any proprietary mathematical or algorithmic constraints.
Inputting preprocessed industrial data into a "trained machine learning model" to output predictive variables, risk scores, and recommendations is heavily anticipated by modern Predictive Maintenance and Industrial frameworks.
Automatically performing equipment operations based on a prioritized score (e.g., triggering a shutdown, safety valve activation, or slowdown in order of severity) is a standard function of Automated Safety Instrumented Systems.
Even if the examiner cannot find a single document containing every single constraint, combining two well-known prior art domains makes this claim legally obvious.
A person having ordinary skill in the art would find it entirely obvious to combine traditional workplace risk registers, maintenance logs, and incident reports with standard machine learning and classification.
The claim simply takes existing industrial safety parameters, passes them through a generic machine learning model, and uses the output to trigger standard mechanical operations. The combination produces no unexpected results beyond the sum of its parts.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 103 in the present office action.
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 1-7, 12 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-7, 12 and 14 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-7, 12 and 14 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of providing safety recommendation in a workplace.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “A method comprising: obtaining data from a plurality of sources associated with a work environment, the data comprising historical safety data, a plurality of incident reports, a plurality of operational parameters, a safety risk register, and a plurality of maintenance records; preprocessing, the obtained data, wherein preprocessing comprises cleaning and normalizing the obtained data; and for each scenario among a plurality of scenarios: inputting the preprocessed data into a trained machine learning model, predicting a plurality of predictive variables associated with each scenario of a process performed in the work environment, a risk exposure prioritization score associated with each scenario, and a safety recommendation associated with each scenario from the trained machine learning model, and performing, in order of decreasing risk exposure prioritization score, an operation on an equipment associated with each scenario based on the risk exposure prioritization score and the safety recommendation”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for safety recommendation in a workplace. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 12 recites substantially similar limitations to those presented with respect to claim 1. As a result, claim 12 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-7 and 14 recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for safety recommendation in a workplace. As a result, claims 2-7 and 14 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 12 recites substantially similar limitations to those recited with respect to claim 1. Although claim 12 further recites “a non-transitory computer readable medium”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 12 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-7 and 14 do not include any additional elements beyond those recited by independent claims 1 and 12. As a result, claims 2-7 and 14 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claims 12 and 17 recite substantially similar limitations to those recited with respect to claim 1. Although claim 12 further recites “a non-transitory computer readable medium”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 12 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-7 and 14 do not include any additional elements beyond those recited by independent claims 1 and 12. As a result, claims 2-7 and 14 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-7, 12 and 14 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-4, 6-7, and 12 are rejected under 35 U.S.C. 103 as being un-patentable over Grant et al. (US 20210182748 A1) in view of Honey et al. (US 20240378534 A1).
Regarding claim 1. A method comprising: [Grant, claim 1, Grant teaches “A method comprising:”] obtaining data from a plurality of sources associated with a work environment, the data comprising historical safety data, a plurality of incident reports, a plurality of operational parameters, a safety risk register, and a plurality of maintenance records; [Grant, claim 1, Grant teaches “continuously capturing one or more pieces of internal data for an entity about a workplace hazard including safety compliance data, environmental conditions data, personnel health data and personnel geospatial monitoring data and one or more pieces of external data about the workplace hazard for the entity including public safety and risk data” wherein obtaining data from multiple sources and wherein the type of data is a non-functional descriptive materials per MPEP 2111.05. Further, para. 0017 teaches “Current workplace hazard and incident reporting systems” and para. 0075 teaches “Based on OSHA historical records, the predominant injury types experienced in these types of manufacturing locations are:” wherein incident data] preprocessing, the obtained data, wherein preprocessing comprises cleaning and normalizing the obtained data; [Grant, claim 5, Grant teaches “assesses a set of behaviors of the entity for workplace risk against a similar sized company” wherein normalizing data]
Grant does not specifically teach, however; Honey teaches and for each scenario among a plurality of scenarios: inputting the preprocessed data into a trained machine learning model, predicting a plurality of predictive variables associated with each scenario of a process performed in the work environment, a risk exposure prioritization score associated with each scenario, and a safety recommendation associated with each scenario from the trained machine learning model, and performing, in order of decreasing risk exposure prioritization score, an operation on an equipment associated with each scenario based on the risk exposure prioritization score and the safety recommendation [Honey, claim 1, Honey teaches “determining, based on the asset health scores and the criticality scores, operational risk scores for the plurality of assets in the facility, wherein each of the operational risk scores indicate a risk posed to the ongoing operation of the facility or to the enterprise by the corresponding asset; determining one or more actions and corresponding action prioritizations to recommend for each of the plurality of assets based, at least in part, on the operational risk scores; ranking the plurality of assets based on the operational risk scores; and outputting, in a user interface, information identifying the plurality of assets ranked based on the operational risk scores, wherein the information includes the operational risk scores, the one or more actions for each of the plurality of assets, and the action prioritizations for the one or more actions” wherein the “operational risk scores for the plurality of assets in the facility” is equivalent to “a risk exposure prioritization score associated with each scenario” and wherein “a user interface, information identifying the plurality of assets ranked based on the operational risk scores” is equivalent to “a safety recommendation associated with each scenario from the trained machine learning model, and performing, in order of decreasing risk exposure prioritization score”]
It would have been obvious at the time of the invention to one of ordinary skill in the art to modify the method and system of Grant to include the teachings of Honey (by calculating a risk exposure prioritization score for each scenario and providing recommendation) since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately and one of ordinary skill in the art would have recognized that the results of the combination were predictable while providing maintenance prioritization.
Regarding claim 2. Grant in view of Honey teaches all of the limitations of claim 1 (as above). Further, Grant teaches wherein the plurality of sources comprises at least one of a plurality of internal and external databases, a plurality of sensors, a plurality of manual reports, a plurality of distributed control systems, or a plurality of engineering workstations [Grant, claim 1, Grant teaches “continuously capturing one or more pieces of internal data for an entity about a workplace hazard including safety compliance data, environmental conditions data, personnel health data and personnel geospatial monitoring data and one or more pieces of external data about the workplace hazard for the entity including public safety and risk data” wherein obtaining data from multiple sources and wherein the type of data is a non-functional descriptive materials per MPEP 2111.05. Para. 0052 teaches “In the example and more generally, this module 404 contextualizes environmental conditions from fixed and personnel mounted locations and weights their impact on the location incident frequency and severity score” wherein sensors to collect data. See also para. 0025 “The external data sources may include government incident reports”].
Regarding claim 3. Grant in view of Honey teaches all of the limitations of claim 1 (as above). Further, Grant teaches wherein the historical safety data comprises a plurality of past records of safety related incidents [Grant, figure 5, the figure shows examples of the different compliance processes (state and federal incident records, experience history, etc.)].
Regarding claim 4. Grant in view of Honey teaches all of the limitations of claim 1 (as above). Further, Grant teaches wherein the plurality of operational parameters comprises temperature data and pressure data [Grant, para. 0088, Grant teaches “For “Building A” an alert will be sent for high temperatures to the project administrator and field workers via the mobile app that is part of the system in FIG. 1. The alert will recommend safety actions to be completed as a result of this exposure”. In addition, figure 7 (2C) of Grant teaches pressure data].
Regarding claim 6. Grant in view of Honey teaches all of the limitations of claim 1 (as above). Further, Grant teaches wherein the plurality of predictive variables comprises at least one of a severity of the risk exposure, probability of occurrence of a major disaster, or an effectiveness of existing measures [Grant, para. 0065, Grant teaches “risk score” wherein the risk score is equivalent to risk of exposure. Also, para. 0065 “The system may use various different known or unknown machine learning algorithms that can be used to categorize risks based on the probability of a workplace incident and each algorithm's effectiveness may be determined based on measurements such as Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) to check the model performance” wherein the probability of a workplace incident is equivalent to probability of occurrence and wherein each algorithm's effectiveness is equivalent to effectiveness of existing measures].
Regarding claim 7. Grant in view of Honey teaches all of the limitations of claim 1 (as above). Further, Grant teaches wherein the safety recommendation comprises at least one of a hazard identification, a plurality of predictive actions, a plurality of improvement suggestions, or a plurality of trend analyses [Grant, para. 0058, Grant teaches “alert will recommend safety actions to be completed” wherein the risk score is equivalent to risk of exposure. Also, para. 0091 “The notification will recommend rest and a reassessment of the work environment. Training may be recommended if the issue persists or is widespread amongst the workforce” wherein trend analysis and improvement suggestions].
Regarding claim 12. the claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise. Claim 1 is a method claim while claim 12 is directed to a non-transitory computer readable medium which is anticipated by Grant para. 0116.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being un-patentable over Grant in view of Honey.
Regarding claim 5. wherein the trained machine learning model comprises a gradient boosting regressor and subsampling Although the invention is not identically disclosed or described as set forth in 35 U.S.C. 103, 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 designer having ordinary skill in the art to which the claimed invention pertains, the invention is not patentable.
In the instant case, gradient boosting regressor and subsampling are design choices that would have been obvious to a skilled in the art to modify/combine with the machine learning of Grant.
Regarding claim 14. the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise. Claim 5 is a method claim while claim 14 is directed to a non-transitory computer readable medium which is anticipated by Grant para. 0116.
Conclusion
The following prior arts made of record and not relied upon are considered pertinent to applicant's disclosure. Amin; Alsumail et al. (US 12429842 B2). Alsumail teaches a method may include determining classified safety data regarding a plant area using the unstructured data and a first machine-learning model that uses natural language processing. The method may further include determining a plant hazard for the plant area using a second machine-learning model, the classified safety data, and the structured safety data. The method may further include obtaining historical hazard data for various plant facilities. The method may further include determining a hazard rate for the plant area based on the plant hazard and the historical hazard data. The method may further include generating, within a graphical user interface on the user device, a hazard map for a plant facility. The method may further include determining a mitigation operation for the plant hazard. The method may further include transmitting a command to a control system to perform the mitigation operation.
Applicant's amendments and arguments dated 02/21/2022 necessitated the updating of the 35 USC § 101 and the 35 USC § 103 rejections of the pending claims presented in the present 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).
Any inquiry concerning this communication from the Examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The Examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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