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 . 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.
DETAILED ACTION
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 10/10/2025.
Status of Claims
Claims 1, 11, and 20 are independent and have been amended by Applicant.
Claims 2,12 have been canceled with their subject matter incorporated into parent Claims 1,11.
Claims 9,10,19 were previously canceled by Applicant.
Claims 1,3-8, 11,13-18 and 20 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/10/2025 has been entered.
Response to amendments
Applicant’s 10/10/2025 amendment necessitated new grounds of rejection in this action.
Response to Applicant’s rebuttal arguments on 35 USC 101 rejection
Remarks 10/10/2025 p.11 ¶1 argues by narrowing the claim scope to “target computing system comprising a plurality of interconnected computing devices”, the amended independent Claims 1,11,20 are anchored in a concrete technical environment, not abstract risk management. Further, by tying the recommendation output by the generative model to a reconfigur[ation] of operation parameters of the target computing system”, the independent Claims 1,11,20 are elevated above a mere abstract process or entrepreneurial method, especially since this limitation changes how the target computing system operates.
Examiner fully considered the 101 rebuttal arguments but respectfully disagrees finding them unpersuasive. First, the Examiner notes that the Original Specification does not appear to provide clear, deliberate and sufficient support to show the Applicant had possession for the newly added matter of “target computing system comprising a plurality of interconnected computing devices” as argued by Applicant at Remarks 10/10/2025 p.11 ¶1. Even if it did, Examiner submits in the arguendo, that narrowing or limiting the “given risk” to limited applications “associated with an operation of a target computing system comprising a plurality of interconnected computing devices” does not render independent Claims 1,11,20 less abstract and eligible. This is because MPEP 2106.04 I ¶3 is clear that claims directed to narrow laws that have limited applications, remain patent ineligible. Such narrow laws that have limited applications are argued by Applicant above vis-a-vis the “target computing system comprising a plurality of interconnected computing devices” as amended at each of independent Claims 1,11,20. Also, in Fairwarning Ip, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016), as cited by MPEP 2106.04, the Federal Circuit ruled that an analogous accessing, compiling and combining information sources to make it possible to generate a full picture of a activity, identity, frequency of activity, and the like in a computer environment, did not differentiate their process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information based category of abstract ideas, further citing Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4. It follows that here, the analogous accessing or receiv[ing] “input of a control opportunity score, a numerical status score, and one or a plurality of risk impact values for a respective plurality of target objectives for a given risk associated with an operation of a target computing system comprising a plurality of” [combined or] “interconnected computing devices” as amended at independent Claims 1,11,20 would also not preclude the claims from reciting, describing or setting forth the abstract exception. Simply put MPEP 2106.04(a)(2) III C #2, cites, among others, FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016), to argue that performing a mental process in a computer environment, still recites, describes or sets forth the abstract exception. Such computer environment is argued here with as “target computing system comprising a plurality of interconnected computing devices”, while the underlining abstract process remains that of “risk management” as tested per MPEP 2106.04(a)(2) III C #2.
Next, with respect to the “reconfigur[ation] of operation parameters of the target computing system”, as also argued by Applicant at Remarks 10/10/2025 p.11 ¶1, the Examiner again finds that the Original Disclosure does not provide a clear, deliberate and sufficient evidence to show that Applicant had possession for such newly added matter. Moreover, the Examiner submits that such expression, as grammatically introduced by the subordinating conjunctional phrase “so as to”, as recited in the limitation “output the recommendation received from the generative model so as to implement the recommendation and reconfigure the operation parameters of the target computing system to reduce the given risk” of independent Claims 1,11,20, can be argued to represent a case of intended use or intended result having limited to no patentable weight1. Yet, Examiner submits in arguendo, that even if the Disclosure would provide clear, deliberate support for the contested limitation, and, even if the contested limitation would raise above a mere intended use or intended result it would still not render the claims less abstract and eligible. This is because the output[ted] recommendation still falls within the confines of a cognitive observation (or “output”) and the associated judgment (or “recommendation”) remain abstract examples of computer-aided processes, exemplified by MPEP 2106.04(a)(2) III ¶2, and MPEP 2106.04(a)(2) III C #2. The fact that the recommendation and reconfigurat[ion] [of] the operation parameters of the target computing system are implement[ed] for the benefit of the abstract process, namely “to reduce the given risk”, still represents a mere computer environment (here “target computing system”) upon which the abstract process is being implemented. Yet, Examiner again stresses that according to MPEP 2106.04(a)(2) III C #2, performing such an abstract process in a computer environment does not preclude the claims from reciting the abstract exception.
Thus, Examiner asserts that here, a case can be made that recitations of “target computing system comprising a plurality of interconnected computing devices”, and reconfigur[ation] “of operation parameters of the target computing system”, as amended at Claims 1,11,20, represent computer environment, computer aids, or tools, in performing the abstract idea, which do not preclude the claims from reciting, describing or setting forth the abstract idea. Step 2A prong one.
Yet, the Examiner submits in the arguendo, that even if such elements would be construed as additional computer-based elements at Step 2A prong two and Step 2B of the analysis, they would represent mere invocation of machinery as tools to perform abstract processes which according to MPEP 2106.05(f) would correspond to a mere application of the abstract idea incapable to integrate the abstract idea of “risk management” into a practical application (Step 2A prong two) or provide significantly more (Step 2B). Alternatively, the computerization of elements, as argued by Applicant above, could also be argued as attempts at narrowing the abstract “risk management” to a field of use or technological environment, which according to MPEP 2106.05(h) would fail to integrate said abstract idea into a practical application or provide significantly more.
In conclusion, Examiner has provided a preponderance of legal evidence showing the claims still recite, describe or set forth the abstract exception (Step 2A prong 1) with no additional, computer-based elements capable to integrate the abstract exception into a practical application (Step 2A prong 2) or provide significantly more (Step 2B). Thus, the claims remain ineligible.
With respect to the lack of prior art rejections and the implications on the 35 USC 101 abstract idea rejection, as raised by Applicant at Remarks 10/10/2025 p.11 ¶3-p.12 ¶2, the Examiner reincorporates all findings and rationales of Final Act 07/16/2025 p.25 last ¶, and p.26 last ¶: “To be clear, the Examiner submits that novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel (35 USC 102) and non-obviousness (35 USC 103) rationale above do not necessarily render the claims patent eligible (35 USC 101). See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo, 566 U.S. 71, 101 USPQ2d at 1965); Flook, 437 U.S. at 591-92, 198 USPQ2d at 198 "the novelty of the mathematical algorithm is not a determining factor at all”.” MPEP 2106.04(A)(2) II A ¶1-¶2 similarly finds that term fundamental is not used in the sense of necessarily being old or well-known but rather as a building block of modern economy. Here, the “risk management” can be argued as such building block of modern economy, no matter of its further narrowing or limiting to the limited applications of the target computing system and reconfigur[ation] of operation parameters, as tested above with respect to MPEP 2106.04 I ¶3 and/or MPEP 2106.05(h).
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Response to Applicant’s rebuttal arguments on 35 USC 103 rejection
Remarks 10/10/2025 p.12 ¶3-¶6, argues that since independent Claims 1,11 have been amended to recite the allowable subject matter of he now canceled dependent Claims 2,12, the independent Claims 1,11 now overcome the prior art.
Examiner considered the Applicant’s rebuttal argument on 103 rejection and finds it persuasive.
Examiner reincorporates all findings and rationales at Final Act 07/16/2025 mid-p.25 with respect to overcoming the prior art, by resubmitting that the closest prior art remains Zhang et al US 20090299896 A1 as identified above and mapped with respect to independent Claims 1,11.
Examiner however submits that neither Zhang, nor any other prior art in record, teaches either alone or together with adequate rationales the combination of: “wherein the residual risk value is calculated by calculating a quotient of the control opportunity score divided by a first predetermined constant, multiplying the quotient by the inherent risk value, then summing the resulting product with an additional quotient of the control opportunity score divided by the first predetermined constant” as now amended at independent Claims 1,11 and similarly recited at independent Claim 20 as “calculating a residual risk value by calculating a quotient of the control opportunity score divided by a first predetermined constant, multiplying the quotient by the inherent risk value, then summing the resulting product with an additional quotient of the control opportunity score divided by the first predetermined constant”.
Claims 3-8 are dependent and overcome the prior art rejection by dependency to parent claim 1.
Claims 13-18 are dependent and overcome the prior art rejection by dependency to parent claim 11.
To be clear, Examiner resubmits that novelty (35 USC 102) and non-obviousness (35 USC 103) still pertain to features that are mostly abstract that do not render the claims patent eligible (35 USC 101). Simply said the novel (35 USC 102) and non-obviousness (35 USC 103) rationale above do not necessarily render the claims patent eligible (35 USC 101). See for example MPEP 2106.04 I ¶5, 3rd sentence citing Mayo 566 U.S. 71,101 USPQ2d at 1965); Flook,437 U.S. at 591-92,198 USPQ2d at 198 "novelty of the mathematical algorithm is not a determining factor at all”.
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1,3-8,11,13-18 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1,11,20 are independent and have each been amended to recite, among others:
… “a target computing system comprising a plurality of interconnected computing devices”
Original Specification does not appear to provide clear, deliberate and sufficient support to show the Applicant had possession for the newly added matter of “target computing system comprising a plurality of interconnected computing devices” as argued by Applicant at Remarks 10/10/2025 p.11 ¶5 with respect to the Original Specification ¶ [0031]: … the recommendation 42 includes deploying additional portable fans or air conditioning units near servers, investing in a Heating, Ventilation, and Air Conditioning (HVAC) system, and reorganizing server racks into a hot/cold aisle layout to optimize air flow and cooling efficiency.
However, at no point does the Original Specification ¶ [0031] provide any clear deliberate and sufficient disclosure to how that Applicant had support for the “interconnected computing devices” because at no point does Original Specification ¶ [0031] indicate that the “portable fans or air conditioning units near servers”, the “investing in a Heating, Ventilation, and Air Conditioning (HVAC) system”, and “reorganizing server racks into a hot/cold aisle layout to optimize air flow and cooling efficiency” are interconnected with each other. Applicant is reminded: “One shows that one is in possession of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious” “Lockwood v. American Airlines, Inc., 41 USPQ2d 1961, No. 96-1168, 107 F3d 1565.
Claims 1,11,20 are independent and have each been amended to recite, among others:
… “reconfigure operation parameters of the target computing system to reduce the given risk”. [bolded emphasis added] Remarks 10/10/2025 p.10 ¶4 points to Original Specification ¶ [0018]-[0020], [0031] as support. Yet, at no point does the Disclosure provide a clear, deliberate and sufficient evidence to show that Applicant had possession for such newly added matter to … “reconfigure operation parameters of the target computing system to reduce the given risk”.
Applicant is again reminded: “One shows that one is in possession of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious” “Lockwood v. American Airlines, Inc., 41 USPQ2d 1961, No. 96-1168, 107 F3d 1565.
Claims 1,11,20 are thus rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 3-8 are dependent and rejected based on rejected parent independent Claim 1.
Claims 13-18 are dependent and rejected based on rejected parent independent Claim 11.
Clarification and/or correction is/are required.
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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 1,3-8,11,13-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract grouping(s)2 of Certain methods of Organizing human activities [MPEP 2106.04(a)(2) II] implementable through equally abstract Mental Processes [MPEP 2106.04(a)(2) III] and Mathematical Relationships expressed in words [MPEP 2106.04(a)(2) I A].
First, when read in light of the Background and ¶ [0001] of the Original Specification, the claims are found to recite, describe or set forth the abstract risk management which is reflected at independent Claims 1,11,20 as “generate a recommendation” “to reduce the given risk” and corroborated by the Invention’s Title. Such risk mitigation is listed by MPEP 2106.05(a)(2) II A (i), as an example of fundamental economic practices or principles to fall within the abstract Certain Methods of organizing Human Activities. Further, MPEP 2106.04(A)(2) II A ¶1-¶2 clarifies that the term fundamental is not used in the sense of being old or well-known but as a building block of modern economy. Here, the “generate a recommendation” “to reduce the given risk” at Claims 1,11,20 can be argued as such building block of modern economy, no matter of its narrowing or limiting to the limited applications, set forth here as “associated with an operation of a target computing system comprising a plurality of interconnected computing devices” and “reconfigure operation parameters” as recited at Claims 1,11,20 and tested on MPEP 2106.04 I ¶3.
Next, it could be also argued that here, such risk mitigation could be practically achieved through equally abstract computer-aided observation, evaluation and judgement as listed by MPEP 2106.04(a)(2) III. For example here, as in Electric Power Group v Alstom, S.A., 830 F.3d 1350,1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), as cited by MPEP 2106.04(a)(2) III A, 5th bullet point, the claims recite the abstract combination of collecting information, analyzing it, followed by displaying certain results of the collection and analysis3 that correspond to the abstract grouping of “Mental Processes”. Specifically:
#1 The collection and/or observation is/are set forth here as “receive input of a control opportunity score, a numerical status score, and one or a plurality of risk impact values for a respective plurality of target objectives for a given risk associated with an operation of a target computing system comprising a plurality of interconnected computing devices” (independent Claims 1,11,20), “wherein further input of an impact score and a likelihood score are received” (dependent Claims 3,13).
#2 The evaluation or analysis is/are set forth here as “calculate a residual risk value for the given risk based on the control opportunity score and an inherent risk value”; “calculate a relative risk value for the given risk based on the residual risk value, the numerical status score, and the one or the plurality of risk impact values”, “wherein the residual risk value is calculated by calculating a quotient of the control opportunity score divided by a first predetermined constant, multiplying the quotient by the inherent risk value, then summing the resulting product with an additional quotient of the control opportunity score divided by the first predetermined constant” (independent Claims 1,11,20); “wherein the relative risk value is calculated by multiplying the residual risk value by a summed value weight, multiplying the resulting product by a quotient of the numerical status score divided by a second predetermined constant, and then summing the resulting product with an additional quotient of the numerical status score divided by the second predetermined constant” (dependent Claims 5,15, and independent Claim 20), “wherein the summed value weight is calculated by summing current averaged value weights for each of the one or the plurality of risk impact values for the respective plurality of target objectives” (dependent Claims 6, 16, independent Claim 20); “wherein the numerical status score is one of a plurality of values on a scale from lowest risk to highest risk” (dependent Claims 7,17); “the description is a qualitative description of the one or the plurality of risk impact values” (dependent Claims 8,18)
#3 The opinion / displaying of certain results of the collection and analysis / judgment is recited, described or set forth as “output the recommendation received from the generative model
so as” [or intended] “to4 implement the recommendation and reconfigure the operation parameters of the target computing system to reduce the given risk” (independent Claims 1,11,20)
Further, it would appear that here, such mental processes of evaluation and judgement can be achieved through equally abstract mathematical calculat[ions] of “residual risk” and “relative risk” values and “calculating a quotient of the control opportunity score divided by a first predetermined constant, multiplying the quotient by the inherent risk value, then summing the resulting product with an additional quotient of the control opportunity score divided by the first predetermined constant” (independent Claims 1,1120); “wherein the relative risk value is calculated by multiplying the residual risk value by a summed value weight, then multiplying the resulting product between the residual risk value and the summed value weight, by a quotient of the numerical status score divided by a second predetermined constant, and then summing the resulting product between the residual risk value, the summed value weight, and the quotient with an additional quotient of the numerical status score divided by the second predetermined constant” (dependent Claims 5,15, independent Claim 20); “wherein the summed value weight is calculated by summing current averaged value weights for each of the one or the plurality of risk impact values for the respective plurality of target objectives” (dependent Claims 6,16 and similarly independent Claim 20). This finding is important since MPEP 2106.04(a)(2) I.A. iv. cites Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) to stress that generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form, was found to describe an abstract process of organizing information and manipulating information through mathematical correlations. Similarly, MPEP 2106.04(a)(2) I, ¶4 and C i., cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018), to state that performing a resampled statistical analysis to generate resampled distribution, was also found to describe the abstract mathematical concepts. Thus, the current calculations above, would similarly set forth the abstract exception by at least similar reasons as those articulated by the Federal Circuit in Digitech and SAP supra.
Equally important, MPEP 2106.04(a)(2) III C states that: #1 Performing mental process on generic computer, #2 Performing mental process in computer environment, #3 Using computer as tool to perform a mental process, does not preclude a claim from reciting the abstract idea.
It would then follow that here, as in MPEP 2106.04(a)(2) III C #1,#2, #3 the nominal recitation of “risk associated with an operation of a target computing system comprising a plurality of interconnected computing devices”; “generate a prompt including the relative risk value and a description of the given risk”, “input the prompt into a generative model to generate a recommendation that, when implemented, reconfigures operation parameters of the target computing system to reduce the given risk” at independent Claims 1,11,20, could be argued as a computerized environment or tool from which the data is obtained, for manipulation, which would not preclude the claims, from reciting, describing or setting forth the abstract exception.
Also, separate from the above findings, it is also noted that the wherein limitation of “output the recommendation received from the generative model so as to implement the recommendation and reconfigure the operation parameters of the target computing system to reduce the given risk” at independent Claims 1,11,20, could be argued to have limited patentable weight, because when tested per MPEP 2111.04 I5 they would simply expresses the intended result or result. In an abundance of caution, the Examiner will more granularly test the level of computerization at the subsequent steps. For now, given the preponderance of legal evidence above, it is clear that the claims’ character as whole, is undeniably abstract. Step 2A prong one.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements is/are found to apply the already recited abstract idea and/or narrow it to a field of use or technological environment. Here, aside from the computer-aided test, at the preceding step, the recited computer components could be argued as additional computer-based elements, namely the “memory storing instructions which are executed by the processing circuitry” (independent Claim 1) that executes the abstract processes above. It could also be argued that the “generative model” and its associated “prompt” (independent Claims 1,11,20) and “event log database” (independent Claim 20) could be also argued as additional, computer-based elements. Specifically here, when tested per MPEP 2106.05(f)(2), such additional computer-based elements are merely used as tools to apply the mitigative business actions and their underlining algorithm6, as well as to perform economic tasks or other tasks to receive, store and transmit data7 and to monitor audit log data executed on a general-purpose computer8 and possibly use a computer component or other computer components to tailor information9. Examiner again asserts that the “memory storing instructions which are executed by the processing circuitry” at independent Claim 1, to execute the algorithmic functions above, is such an example of a tool that apply the mitigative business actions and their underlining algorithm identified above. Such “processing circuitry”, if now more granularly tested per MPEP 2106.05(f)(2) (i) at Step 2A prong two, would represent mere invocation of computer as machinery to apply the abstract idea, which would not integrate it into a practical application. Similarly, the “memory storing instructions which are executed by the processing circuitry” at independent Claim 1 to “receive input of a control opportunity score, a numerical status score, and one or a plurality of risk impact values for a respective plurality of target objectives for a given risk associated with an operation of a target computing system comprising a plurality of interconnected computing devices”; “generate a prompt including the relative risk value and a description of the given risk”; “input the prompt into a generative model to generate a recommendation that, when implemented, reconfigures operation parameters of the target computing system to reduce the given risk”; “output the recommendation received from the generative model so as to implement the recommendation and reconfigure the operation parameters of the target computing system to reduce the given risk” could be argued as examples of receiving and transmitting data [MPEP 2106.05(f)(2)¶1], monitor audit log data executed on a general-purpose computer [MPEP 2106.05(f)(2)(i)], and to require use of software (here prompt) or other computer components to tailor information [MPEP 2106.05(f)(2)(iii)]. According to MPEP 2106.05(f)(2) none of them integrate the abstract idea into a practical application.
Additionally, and/or alternatively, when tested per MPEP 2106.05(h), the computerized implementation of the enumerated abstract processes via “memory storing instructions which are executed by the processing circuitry” (independent Claim 1) could also be argued, along with use of the “generative model” and its associated “prompt” (independent Claims 1,11,20) and “event log database” (independent Claim 20), as a field of use or technological environment upon which the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis is achieved in a manner similar to in Electric Power Group, LLC v Alstom S.A.,830 F3d 1350,1354,119 USPQ2d 1739,1742 (Fed Cir 2016) cited by MPEP 2106.05(h) vi.
Thus, there is a preponderance of legal evidence showing that that no additional computer-based elements are capable to integrate the abstract idea into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) and/or (h) to submit that as shown above, the additional computer elements merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or narrow the abstract idea to a field of user or technological environment [MPEP 2106.05(h)]. For these reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself considering MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence, without the need to rely on the well-understood, routine and conventional test. Yet, assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further rely on MPEP 2106.05(d) guidelines to demonstrate that said additional elements are also well-understood, routine, conventional. In such case, Examiner would rely as evidence on Applicant’s own Original Specification per MPEP 2106.05(d)(I)(2) as follows:
- Original Specification ¶ [0015] “In general, the processing circuitry 14 may be configured to receive, via an input interface 26 (in some implementations, the prompt interface API), natural language text input 28, which is incorporated into the prompt 36 and provided to the trained generative language model 40. The trained generative language model 40 receives the prompt 36, which includes the natural language text input 28 from the user for the trained generative language model 40 to generate a recommendation 42. The generative language model 40 generates, in response to the prompt 36, the recommendation 42. In turn, processing circuity 14 is configured to output the recommendation 42 received from the generative model 40 to the user, for example via a display, file, electronic message, database entry, API message, etc. It will be understood that the natural language text input 28 may also be generated by and received from a software program, rather than directly from a human user”. [emphasis added].
- Original Specification ¶ [0017] disclosing commercially available generative language models: “The trained generative language model 40 is a generative model that has been configured through machine learning to receive input that includes natural language text and generate output that includes natural language text in response to the input. It will be appreciated that the trained generative language model 40 can be a large language model (LLM) having tens of millions to billions of parameters, non-limiting examples of which include GPT-3 and BLOOM. The trained generative language model 40 can be a multi-modal generative model configured to receive multi-modal input including natural language text input as a first mode of input and image, video, or audio as a second mode of input, and generate output including natural language text based on the multi-modal input. The output of the multi-modal model may additionally include a second mode of output such as image, video, or audio output. Non-limiting examples of multi-modal generative models include Kosmos-1, GPT-4 VISUAL (and LLaMA). Further, the trained generative language model 40 can be configured to have a generative pre-trained transformer architecture, examples of which are used in the GPT-3 and GPT-4 models”. [emphasis added].
- Original Specification ¶ [0030] 4th-7th sentences reciting at high level of generality: “The generative language model 40 may be trained using a database of risk descriptions, relative risk values, and recommendations. For example, personnel at a data center may maintain logs that chronicle daily operations, incidents, near misses, and observed risks. Risk analysts, quantifiers, and mitigation specialists may review the logs, describe and quantify the observed risks, and make recommendations accordingly. The logs including the risk descriptions, relative risk values, and recommendations may be used to train the generative language model 40 to associate the risk descriptions with their relative risk values and appropriate recommendations for risk mitigation”. [emphasis added].
- Original Specification ¶ [0048] reciting at high level of generality: “Processing circuitry typically includes one or more logic processors, which are physical devices configured to execute instructions. For example, the logic processors may be configured to execute instructions that are part of one or more applications, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions may be implemented to perform a task, implement a data type, transform the state of one or more components, achieve a technical effect, or otherwise arrive at a desired result”. [emphasis added].
Additionally, and/or alternatively, if necessary, the Examiner would further rely on MPEP 2106.05(d) (2)(c) and point to the conventionality of a “generative language model” as follows:
* US 20230023645 A1 ¶ [0040] 4th sentence: training a GNN may occur in stages, first using a generic dataset, whether a publicly available NLP training dataset (e.g., an NLP training dataset, such as those available from commoncrawl or Wikipedia) or a generically pre-trained model (e.g., OpenAI GPT-3, Google BERT, Microsoft CodeBERT, Facebook RoBERTa).
* US 20220366145 A1 ¶ [0008] 1st sentence: Another conventional model is the Bidirectional Encoder Representations from Transformers (BERT) model.
* US 20220300708 A1 ¶ [0021] 4th sentence: A conventional BERT (Bidirectional Encoder Representation from Transformers) model is adopted in the main model.
* US 20220309089 A1 ¶ [0198] 1st sentence: Once a generic language model (e.g., a generic BERT model) has been created, the language model can be further fine-tuned on a specific domain by continuing training the generic language model on domain specific data.
* US 20220237682 A1 [0133] 3rd sentence: evaluation follows the splitting strategy for a comparison with a conventional BERT model. Here each user behavior history is viewed as a single session.
* US 11232358 B1 column 6 lines 53-57: the generic language model may be implemented using a pre-trained language model, such as Google's BERT (Bidirectional Encoder Representations from Transformers) or OpenAI's GPT-3 (Generative Pretrained Transformer). column 7 lines 62-67: Generic language models such as BERT, ALBERT, RoBERTa, and DistilBERT, which employ deep bidirectional transformer architectures, perform well in both sentence-level and token-level tasks.
* US 20220222491 A1 ¶ [0026] 2nd sentence: Some conventional natural language processing techniques use a transformer-based language model (such as Bidirectional Encoder Representations from Transformers or “BERT”) for various language understanding tasks
* US 20230153533 A1 ¶ [0014] 3rd sentence: The conventional way to setup a model for entity extraction is to take a pre-trained language model like BERT (e.g., pre-trained on some generic dataset like Wikipedia) and fine-tune (train) it to handle a particular entity extraction task using labeled training data specific to the desired task. ¶ [0015] 3rd sentence: Some language models like BERT come pre-trained using masked language modelling (e.g., on a generic dataset like Wikipedia), and some techniques continue masked language modeling using data from a target domain.
* US 20220198327 A1 ¶ [0027] 3rd sentence: A conventional pre-training model is for example a Bidirectional Encoder Representations from Transformers (BERT) model
* US 20210357441 A1 ¶ [0049] 4th sentence: the semantic machine learning model may include a pre-trained language model, which may be based on a Bidirectional Encoder Representations from Transformers (BERT) model or any other suitable conventional pre-trained language model.
* US 20210383068 A1 ¶ [0010] last sentence: …models and/or techniques may be implemented for the semantic machine learning model, including, but not limited to, generic language model(s) and/or natural language processing technique(s), FastText approach (based on subwords information), Bidirectional Encoder Representations from Transformers (BERT) model, Biomedical Bidirectional Encoder Representations from Transformers (BioBERT) model, and/or the like. Similarly, ¶ [0102] last sentence,
¶ [0143] last sentence.
* US 20210375277 A1 ¶ [0019] 1st sentence: Several pre-trained language models, such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), have been used on many natural language processing tasks. ¶ [0040] 3rd sentence: conventional techniques included full BERT-based model, StateNet, GCE, GLAD, BERT-DST PS, Neural Belief Tracker-CNN, and Neural Belief Tracker-DNN. ¶ [0030] Fig.2 demonstrates an exemplary input representation of a multi-layer bidirectional transformer encoder (e.g., BERT model), according to one or more embodiments. BERT is a language representation model pre-trained on vast amounts of unlabeled text corpora, consisting of multiple transformer layers, each with a hidden size of 768 units and 12 self-attention heads. An input to BERT model is a sequence of tokens (i.e., words or pieces of words) and a corresponding output is a sequence of vectors, one for each input token. ¶ [0033] During pre-training, BERT model may be trained using two self-supervised tasks: masked language modeling (masked LM) and next sentence prediction (NSP). In masked LM, some tokens in the input sequence are randomly selected and replaced with a special token denoted [MASK], for the purpose of predicting the original vocabulary identifiers of the masked tokens. In NSP, BERT model may be configured to predict whether two input segments follow each other in the original text. Positive examples are created by taking consecutive sentences from the text corpus, whereas negative examples are created by picking segments from different documents. After the pre-training stage, BERT model can be applied to various downstream tasks such as question answering and language inference, without substantial task-specific architecture modifications.
* US 11361571 B1 column 5 lines 39-40: conventional systems often make use of multiple BERT models.
* US 20220382565 A1 ¶ [0116] 1st sentence: Similar to how BERT is used as a generic feature representation for different NLP downstream tasks, fine-tuning the interface prediction model for a variety of UI understanding tasks is relatively easy and does not require substantial task-specific architecture changes nor a large amount of task-specific data
* US 20230028664 A1 ¶ [0074] 1st sentence: Many deep learning models (e.g., the BERT architecture) are pre-trained on a big corpus of English, generic-domain texts so they can handle generic-domain applications well enough.
* US 20230029829 A1 ¶ [0051]: The first model may be a deep learning language model, such as BERT (Devlin et al., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv:1810.04805 [cs.CL], October 2018), or any other conventional language model. Although the following discussion utilizes certain BERT-related terminology for reasons of convenience, those of skill in the art will readily recognize how to adapt this discussion to any other conventional language model.
* US 20220237776 A1 claims 4,12: predetermined generic models include for said report texts and said structured data one or more chosen from a model of the Transformers family such as BERT, RoBERTa, DistillBert, Word2Vec and Elmo word embeddings, and for said images at least ImageNet.
* US 20200372341 A1 ¶ [0050] 1st sentence noting: conventional BERT and DFGN models.
All these demonstrate that the additional computer-based elements fail to provide anything significantly more than what is already well-understood, routine and conventional in light of MPEP 2106.05(d).
In conclusion, Claims 1,3-8, 11,13-18 and 20, although directed to statutory categories (here “system” or machine at Claims 1, 3-8, “method” or process at Claims 11,13-18 and Claim 20), they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more (Step 2B). Thus Claims 1,3-8,11,13-18,20 are ineligible.
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Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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 use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624
February 10th, 2026
1 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 at slides 16-17,20-21, which cites MPEP 2111.04, thus the expression “so as to…reconfigure” appears to be an example of intended use or intended result, which per USPTO’s training above and MPEP 2111.04 could also be argued to carry limited to no patentable weight
2 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
3 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
4 USPTO’s training entitled Focus on Computer/Software-related Claims dated May 2015 at slides 16-17,20-21, which cites MPEP 2111.04, thus the expression “so as to…reconfigure” appears to be an example of intended use or intended result, which per USPTO’s training above and MPEP 2111.04 could also be argued to carry limited to no patentable weight
5 Minton v. Nat’l Ass’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003))
6 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
7 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
8 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
9 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);