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 .
DETAILED ACTION
Status of Claims
This action is a Non-Final action on the merits in response to communications filed on 02/102026.
Claims 1, 13 and 20 have been amended. Claim 12 has been cancelled. Claims 1 11 and 13-20 are currently pending and have been examined in this application.
Response to Amendment
Applicant’s amendment has been considered.
Response to Arguments
Applicant’s arguments have been considered.
In the remarks Applicant argues, “ Applicant respectfully submits that claim 1 does not recite an abstract idea.” (pg. 8)
Examiner respectfully disagrees. The claims encompass Mental Processes related to observation and evaluation of data. For example, utilizing category specific models to assess business functions and product validation reports involves analyzing data and producing a result (e.g. observation and evaluation). Accordingly, the claim recites an abstract idea of Mental Processes.
Additionally, the claim encompasses Certain Methods of Organizing Human Activity related to assessing business functions which may include potential risks.
Applicant argues, “ …claim 1 as a whole integrates the recited judicial exception into a practical application of the alleged abstract idea such that claim I is not, in effect, a patent to the abstract idea itself.” (pgs. 8-9)
Examiner respectfully disagrees. The judicial exceptions are not integrated into a practical application. The claims recite the additional elements of one or more processors, a memory, a non-transitory computer-readable medium and one or more processors of a computing system. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0105, general purpose processor; see also Fig 2).
For instance, the steps of generating a plurality of validation reports assessing a procedure associated with an organization using a plurality of category specific models, each category specific model is associated with a business function and training via machine learning to produce a validation report is analyzing data including is using complex mathematics. The step of generating a combined validation report that is a written assessment as a natural language text summary involves data analysis and producing a result. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Applicant argues, “Thus, the claimed machine learning architecture in amended claim 1 provides an improvement in the functioning of a computer, and an improvement in both the training of category-specific models and the accuracy of validation reports generated by the category specific models” (pgs. 9-11)
Examiner notes that there is no support in the Specification of the claims for an improvement in the functioning of the computer. In reference to Applicant argument that the parameter size of a category specific model may be smaller than an omnibus model which may take up less memory than an omnibus model and may have faster inference speeds, using a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept). The computer is operating as it normally would.
In reference to an improvement in both training category specific models and accuracy of validation reports, Examiner notes that this appears to be an improved business process for producing a written assessment. Further, as previously stated there is no indication of an improvement in a technology or a technical field.
Applicant argues, “ As described above, similar to Ex Parte Desjardins, the claimed subject matter of amended claim 1 are also directed to improvements in the machine learning technology itself. As such, the amended claims amount to significantly more than the judicial exception..” (pg. 11)
In Desjardins it was determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems (PTAB September 26, 2025, Appeals Review Panel Decision).
Unlike Desjardins the instant claims are directed to generating validation reports using a plurality of category specific models that are trained using a specific dataset corresponding to a business function and generating a combined written assessment validation report. There is no indication in the claims or Specification in how the models operate or a specific manner of training that produces an improvement in the operation of the model.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of one or more processors, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The remainder of Applicant’s arguments are moot in view of new grounds of rejection as necessitated by amendment.
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 1-11 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more. Claim 1 recites:
generating, … and using a plurality of category-specific models, a plurality of validation reports assessing a procedure associated with an organization, wherein each category-specific model of the plurality of category-specific models is associated with a corresponding business function of the organization and is trained via machine learning on a corresponding training dataset specific to the corresponding business function to generate a corresponding validation report that is a natural language written assessment of whether the procedure is satisfactory for the corresponding business function; and
generating, … and based on the plurality of validation reports, a combined validation report that is a written assessment of whether the procedure is satisfactory for the organization wherein the written assessment of whether the procedure is satisfactory for the corresponding business function is a natural language text summary that indicates whether the procedure is satisfactory for implementation by the organization.
The limitation under its broadest reasonable interpretation covers Mental Processes related to observation and evaluation of data but for the recitation of generic computer components (e.g. a processor). For example, utilizing category specific models to assess business functions and product validation reports involves analyzing data and producing a result (e.g. observation and evaluation). Accordingly, the claim recites an abstract idea of Mental Processes.
Additionally, the claim encompasses Certain Methods of Organizing Human Activity related to assessing business functions which may include potential risks.
Independent Claims 13 and 20 substantially recite the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract idea. For instance, Claim 2 is directed to a classification of the procedure (analyzing data); Claim 3 is directed to selecting models; Claim 4 is directed to setting weights for validation reports; Claim 5 is directed to an order of operations for assessing validation reports (analyzing data); Claim 6 is directed to scoring the models (analyzing data utilizing mathematics); Claim 7 is directed to training a combiner model (analyzing data utilizing complex mathematics) and Claims 8-11 is directed to training models. Claims 14-19 substantially recite the subject matter of Claims 1-5, 7 and 8 and encompass the same abstract idea.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of one or more processors. Claim 13 recites the additional elements of a memory and one or more processors. Claim 20 recites the additional elements of a non-transitory computer-readable medium and one or more processors of a computing system. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0105, general purpose processor; see also Fig 2).
For instance, the steps of generating a plurality of validation reports assessing a procedure associated with an organization using a plurality of category specific models, each category specific model is associated with a business function and training via machine learning to produce a validation report is analyzing data including is using complex mathematics. The step of generating a combined validation report that is a written assessment as a natural language text summary involves data analysis and producing a result. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of one or more processors, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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(a) 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 nonobviousness.
Claims 1-4, 6, 13-16 and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chirochangu et al. (US 2023/0116345) in view of Wellmann et al. (WO 2023/168055).
Claim 1:
Chirochangu discloses:
A method comprising: generating, by one or more processors and using a plurality of category-specific models, a plurality of validation reports assessing a procedure associated with an organization, (see at least ¶0013, determining risk assessment scores based obtained data and risk models; see also ¶0029, risk scores (validation report) determined for each category; see also ¶0057-¶0058, cpu)
wherein each category-specific model of the plurality of category-specific models is associated with a corresponding business function of the organization and is [trained via machine learning on a corresponding training dataset specific to the corresponding business function to generate a corresponding validation report that is a natural language reasoned written assessment of whether the procedure is satisfactory for the corresponding business function;] and (see at least ¶0016, each risk model may be specific to a particular risk category; see also ¶0022, training sets; see also ¶0037, risk models may be configured to provide an assessment of future risk such as by using AI or ML; see also ¶0027, risk model types; see also ¶0053, aggregate risk scores are sent to stakeholders for immediate access)
generating, by the one or more processors and based on the plurality of validation reports, a combined validation report that is a written assessment of whether the procedure is satisfactory for the organization, … (see at least ¶0053, aggregate risk scores are sent to stakeholders for immediate access; see also ¶0042, aggregate risk score is the total risk score for each risk category)
While Chirochangu discloses the above limitations, Chirochangu does not explicitly disclose the following limitation; however, Wellmann does disclose:
wherein each category-specific model of the plurality of category-specific models is associated with a corresponding business function of the organization and is trained via machine learning on a corresponding training dataset specific to the corresponding business function to generate a corresponding validation report that is a natural language reasoned written assessment of whether the procedure is satisfactory for the corresponding business function; (see at least ¶0052-¶0053, machine learning models trained based on specific data and empowers users to generate written reports using natural language narratives; see also ¶0097, summary information; see Figure 3 and associated text)
generating, by the one or more processors and based on the plurality of validation reports, a combined validation report that is a written assessment of whether the procedure is satisfactory for the organization, wherein the written assessment of whether the procedure is satisfactory for the corresponding business function is a natural language text summary that indicates whether the procedure is satisfactory for implementation by the organization. (see at least ¶0052-¶0053, machine learning models trained based on specific data and empowers users to generate written reports using natural language narratives; see also ¶0097, summary information; see Figure 3 and associated text)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu with the machine learning models for generating assessments in written format of Wellman to provide regulatory insight analysis (see ¶0002)
Claim 2:
Chirochangu and Wellman discloses claim 1. Chirochangu further discloses:
wherein generating the plurality of validation reports further comprises: determining, by the one or more processors, a classification of the procedure; and (see at least ¶0013, categorizing various risk categories; see also ¶0029)
generating, by the one or more processors and using the plurality of category-specific models, the plurality of validation reports assessing the procedure based on the classification of the procedure. (see at least ¶0016, each risk model may be specific to a particular risk category; see also ¶0029-¶0030)
Claim 3:
Chirochangu and Wellmann discloses claim 2. Chirochangu further discloses:
selecting, by the one or more processors and based on the classification of the procedure, a subset of a second plurality of category-specific models as the plurality of category-specific models. (see at least ¶0013, risk assessment engine obtains data and models to perform risk assessment of various risk categories; see also ¶0027, obtains at least one risk model from the risk model datastore where each risk model is specific to a risk source item, risk category)
Claim 4:
Chirochangu and Wellmann discloses claim 2. Chirochangu further discloses:
wherein generating the combined validation report further comprises: determining, by the one or more processors and based on the classification of the procedure, a set of weights for weighing the plurality of validation reports; and (see at least ¶0030, model scoring engine determines a risk score and determines a priority level for each risk item, where the priority level is considered a weight; see also ¶0031, priority level is calculated)
weighing, by the one or more processors, the plurality of validation reports using the set of weights to generate the combined validation report. (see at least ¶0032, score aggregation engine provides a cumulative risk scores and sums the priority levels generating a cumulative operations risk score)
Claim 6:
Chirochangu and Wellmann discloses claim 1. Chirochangu further discloses:
wherein generating the combined validation report further comprises: determining, by one or more processors, a corresponding score for each category-specific model of the plurality of category-specific models; and (see at least ¶0029-¶0030, model scoring engine determines a risk score for a risk item within a risk category)
generating, by the one or more processors, the combined validation report as a scorecard based on the corresponding score for each category-specific model of the plurality of category-specific models. (see at least ¶0032, score aggregation engine determines cumulative risk scores for each risk source category)
Claims 13-16 for a system (see Chirochangu, Figure 5) substantially recites the subject matter of Claims 1-4 for a method and are rejected based on the same rationale. Claim 20 for a crm (Chirochangu ¶0063) substantially recites the subject matter of Claims 1 and 13 and is rejected based on the same rationale.
Claims 5 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chirochangu et al. (US 2023/0116345) in view of Wellmann et al. (WO 2023/168055) further in view of Rieth et al. (US 2017/0308958).
Claim 5:
While Chirochangu and Wellmann disclose claim 2, neither explicitly disclose the following limitations, however, Reith does disclose:
wherein generating the combined validation report further comprises: determining, by the one or more processors and based on the classification of the procedure, an order of operations for assessing the plurality of validation reports; and (see at least ¶0031, The outputs of these models 352 may then be collected by a model aggregation on and data fusion element 360 (e.g., that might assign results from different models 352 different weights depending on a type of electronic record, a likelihood value received from each model 352, etc.).
assessing, by the one or more processors, the plurality of validation reports according to the order of operations to generate the combined validation report. (see at least ¶0031, Moreover, the model aggregation on and data fusion component 360 might assign a first priority value to a first advanced analytics decision making model and a second priority value to a second advanced analytics decision making model, and the combining of outputs from those models might be based at least in party on the first and second priority values)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu and the machine learning models for generating assessments in written format of Wellman with the model aggregation based on priority of Rieth in order to determine if a supplemental review process should be performed for that particular record (see ¶0001).
Claim 17 for a system substantially recites the subject matter of Claim 5 for a method and is rejected based on the same rationale.
Claims 7-11, 18 and 19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Chirochangu et al. (US 2023/0116345) in view of Wellmann et al. (WO 2023/168055) further in view of Scheibelhut et al. (US 2025/0139137)
Claim 7:
While Chirochangu and Wellmann disclose claim 1, neither explicitly discloses the following limitation; however, Scheibelhut does disclose:
wherein a combiner model is a large language model (LLM), further comprising: training, by the one or more processors and using machine learning, the combiner model to generate, based on the plurality of validation reports, the combined validation report that is the written assessment of whether the procedure is satisfactory for the organization. (see at least Abstract, aggregating output by aggregating feedback responses; see also ¶0080, generate an aggregated output of a quality assessment; see also ¶0035, trained LLM)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu and the machine learning models for generating assessments in written format of Wellman with the aggregating output using LLM of Scheibelhut to o perform various tasks and synthesize and formulate output responses based on information extracted from the training data (see ¶0036).
Claim 8:
While Chirochangu and Wellmann disclose claim 1, neither explicitly discloses the following limitation; however, Scheibelhut does disclose:
wherein each category-specific model of the plurality of category-specific models is a large language model (LLM), further comprising: training, by the one or more processors and using machine learning, each category-specific model of the plurality of category-specific models to produce the corresponding validation report that is the written assessment of whether the procedure is satisfactory for the corresponding business function. (see at least ¶0035, LLM trained to generate outputs; see also ¶0079, LLM generates feedback response)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu and the machine learning models for generating assessments in written format of Wellman with the aggregating output using LLM of Scheibelhut to o perform various tasks and synthesize and formulate output responses based on information extracted from the training data (see ¶0036).
Claim 9:
While Chirochangu, Wellmann and Scheibelhut disclose claim 8, neither Chirochangu nor Scheibelhut explicitly disclose the following limitation; however Wellmann does disclose:
wherein each category-specific model of the plurality of category-specific models is trained using corresponding training data that includes sets of an example procedure, an example classification of the example procedure, and an example validation report for the example procedure to learn to generate validation reports for procedures. (see at least ¶0053 custom machine learning models trained on specific data and a NLP component that generates written reports; see also ¶0057-¶0058, generating sample results)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu and the machine learning models for generating assessments in written format of Wellman with the aggregating output using LLM of Scheibelhut to o perform various tasks and synthesize and formulate output responses based on information extracted from the training data (see ¶0036).
Claim 10:
Chirochangu, Wellmann, Scheibelhut disclose claim 8. Chirochangu further discloses:
wherein each category-specific model of the plurality of category-specific models is trained using corresponding training data that includes documents associated with the corresponding business function. (see at least ¶0012-¶0015, internal and external data includes company data, regulatory data, other external data which reasonably could include documents)
Claim 11:
While Chirochangu , Wellmann and Scheibelhut disclose claim 8, neither Chirochangu nor Wellmann discloses the following limitation; however, Scheibelhut does disclose:
wherein each category-specific model of the plurality of category-specific models is trained to ask one or more questions about the procedure and to generate an answer to the one or more questions as the corresponding validation report. (see at least ¶0031, trained using questions)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine evaluating risk items using risk models specific to risk impact categories of Chirochangu and the machine learning models for generating assessments in written format of Wellman with the aggregating output using LLM of Scheibelhut to o perform various tasks and synthesize and formulate output responses based on information extracted from the training data (see ¶0036).
Claims 18 and 19 for a system substantially recites the subject matter of Claims 7 and 8 for a method and are rejected based on the same rationale.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Heckman et al. (US 11431740) discloses providing an assessment of risk management and maturity for a cybersecurity/privacy program by computing risk factors.
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 Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625
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