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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 17, 2025, has been entered.
Response to Amendment
Claims 1, 3-8, 11, 13-18 and 20 have been amended. Claims 1-20 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant's arguments filed November 17, 2025, have been fully considered but they are not persuasive. A response is provided below in bold where appropriate.
Applicant argues 35 USC §112 Rejection, starting pg. 2 of Remarks:
Response to Rejections under 35 U.S.C. § 112
As discussed during the interview and without acquiescing to the interpretation of the written description support for the allegedly unsupported claim language, Applicant hereby amends claims 1-20 to remove the conditional determination of machine learning models by the rules engine and rule sets. Withdrawal of the 112 rejections is respectfully requested.
Withdrawn based on the claim amendments. However, based on the amendments, a new rejection is provided.
Applicant argues 35 USC §101 Rejection, starting pg. 2 of Remarks:
Response to Rejections under 35 U.S.C. § 101
Applicant’s claims are not directed to an abstract idea that amounts to nothing more than a mental process executed on generic computing components. Applicant’s claims are directed to technical improvements to a computer system that manages automated processing of prescriptions and, more specifically, to a specific topology of machine learning processors and interleaved Boolean logic that operate in a specific ordered configuration based on rule sets and data transformations to automatically select the next best action with the highest likelihood of reducing processing time and computing resources used by the system. More specifically, the specification identifies the size and complexity of the general machine learning model, particularly when trained and operated for the full set of next best action service calls. The addition of the Boolean logic rules and special resolution models improve the operation of the artificial intelligence system by reducing both the frequency of use of the more resource intensive general machine learning model (i.e., reducing the conditions under which the general resolution model is used, see Spec. para. 0034) and the size and complexity of that model when it is needed (i.e., reduce the set of next best actions that the general machine learning model is trained for, see Spec. para. 0033). These are both technical improvements that reduce the total memory and processing requirements of the artificial intelligence system and are, therefore, technical improvements to the operation of the computer systems themselves.
From Applicant’s specification cited above:
“However, using machine learning models to automate a complex decision-making process may benefit from a general resolution machine learning model being supported by a logical rules engine (based on Boolean logic, rather than machine learning algorithms) configured with one or more rule sets to reduce the edge cases with known next best actions and/or validate that the next best action determinations made by machine learning models comply with logical, regulatory, and/or third-party provider conditions. For example, a rules engine may pre-filter for certain rejection codes and/or specific next best actions that have either a direct logical outcome based on the rejection code and one or more prescription parameter values and/or require manual intervention. The rules engine may also run rules following determination of a next best action by the machine learning models that can override the models' outputs and generate alternative next best actions. The pre and/or post-processing by the rules engine may reduce the set of next best actions that the general resolution machine learning model needs to be trained to and support production processing of. In some implementations, machine learning models for each next best action may be integrated and tested with pre-model and postmodel rule sets to increase the efficiency and accuracy of the automated selection of next best action service calls.” [0033]
“Further, performance of the general resolution machine learning model may be
improved by using one or more special resolution machine learning models trained to evaluate specific rejection codes and/or next best actions and further reduce the conditions under which the general resolution machine learning model is used. In some implementations, specialized machine learning models may also be integrated that generate parameter changes for use in implementing the next best action. These specialized resolution models, including parameter change models, may improve both processing efficiency of the combined models in the artificial intelligence system and the accuracy of the next best action predictions.” [0034]
From Applicant’s arguments and the above specification, the rules engine and Boolean logic act as a filter to select the next best action with the highest likelihood of reducing processing time and computing resources used. However, there is no claimed highest likelihood of reducing processing time claimed.
From Clam 1…
“A computer-implemented method comprising:
receiving prescription information comprising a prescription for a patient;
identifying, based on the prescription information, a parameter set for determining a next best action service call from a set of next best action services;
processing the parameter set using a rules engine and a first rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a first special resolution machine learning model, from a plurality of special resolution machine learning models and trained for a specific rejection code from a plurality of rejection codes, the parameter set to determine a first confidence value;
From above, the above first processing step is processing the parameter set to conditionally determine the next best action service call.
The next processing step is processing using a first special resolution machine learning model the parameter set to determine a first confidence value.
Both steps are using the same parameter set, therefore there is no advantage to the second processing step as the parameter set is not filtered. That is, the first processing step is not used in subsequent steps.
The claims have been amended to clarify the interleaving of Boolean logic and special resolution machine learning models to reduce the conditions for selective processing by the general machine learning model and reduce the number of next best actions the general machine learning model is trained for (reducing its size, complexity, and processing/memory requirements). Even if the claims include allegedly abstract ideas, using a transformative set of machine learning models to reduce the processing requirements of a subsequent general machine learning model is a practical application that improves the operation of the computer system itself and is, therefore, not an abstract idea. Additionally, the claims as a whole are directed to the selection and initiation of service calls by a computer processor based on a cooperative set of logic and machine learning models, which is an internal function of a computing system and not something that a person can do with a pen and paper. Further, where the technology improves operation of a computer system, such as an artificial intelligence system, whether or not elements of the claims could be executed by hand outside of the computing environment is irrelevant to the subject matter eligibility of the improvement—simplicity is not a ban to practical application to improve computer systems themselves.
From Claim 1…
A computer-implemented method comprising:
receiving prescription information comprising a prescription for a patient;
identifying, based on the prescription information, a parameter set for determining a next best action service call from a set of next best action services;
processing the parameter set using a rules engine and a first rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a first special resolution machine learning model, from a plurality of special resolution machine learning models and trained for a specific rejection code from a plurality of rejection codes, the parameter set to determine a first confidence value;
selectively processing, responsive to the first confidence value meeting a first confidence threshold, the parameter set through a first parameter change machine learning model to determine a first parameter change value;
replacing a corresponding parameter value in the parameter set with the first parameter change value to determine an updated parameter set;
selectively processing the updated parameter set using the rules engine and a second rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a second special resolution machine learning model from the plurality of special resolution machine learning models and trained for a specific next best action service from the set of next best action services, the updated parameter set to determine a second confidence value;
selectively determining, in response to the second confidence value meeting a second confidence threshold, the next best action service call without using a general machine learning model for the set of next best action services;
selectively processing, using the general machine learning model, the updated parameter set to determine the next best action service call
wherein the general machine learning model is trained for a reduced set of next best action services that is less than all next best action services in the set of next best action services; and
initiating the next best action service call.
The first processing step and second selectively processing steps of determine a next best action, which are not used in subsequent steps, it is indefinite as to their purpose in the claim. The third selectively processing step determined a next best action, but without a machine learning model. The fourth selectively processing step uses the machine learning model to determine the next best action but is using the updated parameter set.
Another way to look at this, if the rules engine and Boolean logic act as filtering, nothing is done with the filtered next best action service call.
Also, the step of …
processing, using a second special resolution machine learning model from the plurality of special resolution machine learning models and trained for a specific next best action service from the set of next best action services, the updated parameter set to determine a second confidence value;
This step is using the identifying step that has a “set of next best action services” for the second confidence value, which in the next step selectively determines the next best service call.
In Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 US 208, 134 S.Ct. 2347 (2014) (“Alice”), the Supreme Court held that the following analysis framework must be followed in determining whether a claimed invention is patent-ineligible as being directed to a judicially recognized exception. First, one must determine whether the claims at issue are directed to a patent ineligible concept (e.g., an abstract idea). Next, if the claims at issue are directed to a patent ineligible concept, one must determine whether any element, or combination of elements, in the claims is sufficient to ensure that the claims amount to significantly more than the patent ineligible concept itself.
Applicant respectfully submits that the claimed invention is directed to statutory subject matter under 35 U.S.C. § 101, and, even if the claimed invention involves an allegedly abstract idea, the claims clearly integrate any abstract ideas into a practical application under Prong Two of Revised Step 2A as described in the Current Patent Eligibility Guidance. More specifically, Applicant’s claims, as a whole, describe “the how” of a specific architecture of machine learning models and Boolean logic operating together to reduce the processing resources needed for decision-making using the general machine learning model, which integrates any alleged judicial exception into a practical application. For example, where the general machine learning model is a multi-class classifier model, the number of possible next best actions directly determines the size and complexity of the classifier model and a reduced set of possible next best actions reduces the memory size and processing requirements for that general machine learning model. [see Spec. para. 0103].
If the processing steps with rules engine and Boolean logic are interpreted as filtering parameter sets, the result is filtering for a next best action service call, where the next best action service calls are not used in the subsequent machine learning steps.
The amended claims recite technical elements that require specific system configurations and operations that apply any alleged abstract ideas in a meaningful way that is more than a drafting effort to monopolize the alleged abstract idea. See MPEP 2106.05(b). From claim 1, for example:
...
processing the parameter set using a rules engine and a first rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a first special resolution machine learning model from a plurality of special resolution machine learning models and trained for a specific rejection code from a plurality of rejection codes, the parameter set to determine a first confidence value;
selectively processing, responsive to the first confidence value meeting a first confidence threshold, the parameter set through a first parameter change machine learning model to determine a first parameter change value;
replacing a corresponding parameter value in the parameter set with the first parameter change value to determine an updated parameter set;
selectively processing the updated parameter set using the rules engine and a second rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a second special resolution machine learning model from the plurality of special resolution machine learning models and trained for a specific next best action service from the set of next best action services, the updated parameter set to determine a second confidence value;
selectively determining, responsive to the second confidence value meeting a second confidence threshold, the next best action service call without using a general machine learning model for the set of next best action services;
selectively processing, using the general machine learning model, the updated parameter set to determine the next best action service call, wherein the general machine learning model is trained for a reduced set of next best action services that is less than all next best action services in the set of next best action services;…
[amended claim 1, in part]
Amended claim 1 includes logic for selectively processing (based on a combination of rule sets and a specialized machine learning model selected by those rule sets) the parameter set through a particular machine learning model that generates parameter change values and allows the system to automatically replace parameter values in the parameter set prior to processing through the general machine learning model for next best action service calls. This specific configuration for transforming parameters based on a series of logical rules and machine learning models improves the efficiency and reduces the total processing time and computing resources for determining the service calls selected by the processor. As stated in MPEP 2106.05(a), improvements to the functioning of any technology are patent eligible and reducing the data processing overhead of real-time data processing is a technical improvement to artificial intelligence engines and/or the computer systems executing those artificial intelligence engines.
Even if the claims included abstract ideas the additional elements identified above integrate those allegedly abstract ideas into a practical application for improving the operation of a specific real-time data processing architecture and reducing the processing requirements of the host computer system. Applicant’s claims do not threaten to monopolize any alleged abstract idea related to determining problems or generating next best actions generally. Thus, amended claim 1 is eligible because it is not directed to the alleged judicial exception, but a practical application related to the alleged judicial exception.
While the foregoing arguments are presented in relation to amended claim 1, similar elements are present in amended claim 11 and 20. That independent claim is, therefore, subject matter eligible for similar reasons.
The rejection is respectfully maintained but modified for the claim amendments. Respectfully, the rules engine and Boolean logic are indefinite as they are not integrated into the claim to provide a benefit. Also, the later steps that determine the next step are based on the original identifying set of next steps.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a method or systems, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis and is similar to system claims 11 and 20.
Claim 1 recites the limitations of:
A computer-implemented method comprising:
receiving prescription information comprising a prescription for a patient;
identifying, based on the prescription information, a parameter set for determining a next best action service call from a set of next best action services;
processing the parameter set using a rules engine and a first rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a first special resolution machine learning model, from a plurality of special resolution machine learning models and trained for a specific rejection code from a plurality of rejection codes, the parameter set to determine a first confidence value;
selectively processing, responsive to the first confidence value meeting a first confidence threshold, the parameter set through a first parameter change machine learning model to determine a first parameter change value;
replacing a corresponding parameter value in the parameter set with the first parameter change value to determine an updated parameter set;
selectively processing the updated parameter set using the rules engine and a second rule set comprising Boolean logic configured to conditionally determine the next best action service call;
processing, using a second special resolution machine learning model from the plurality of special resolution machine learning models and trained for a specific next best action service from the set of next best action services, the updated parameter set to determine a second confidence value;
selectively determining, in response to the second confidence value meeting a second confidence threshold, the next best action service call without using a general machine learning model for the set of next best action services;
selectively processing, using the general machine learning model, the updated parameter set to determine the next best action service call
wherein the general machine learning model is trained for a reduced set of next best action services that is less than all next best action services in the set of next best action services; and
initiating the next best action service call.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. The claim recites elements, in bold above, which covers performance of the limitation that can be concepts performed in the mind of a person or with pen and paper. A person in their mind and with pen and paper can receive prescription information for a patient, identify a parameter set for determining a next best action service call, process the parameter set using rules and Boolean logic to conditionally determine the next best action service call (follow rules mentally and solve logic problem with pen and paper), process the parameter set to determine a first confidence value, selectively process responsive to first confidence value meeting a first threshold to determine a first parameter change value, replace a corresponding parameter value in the parameter set with the first parameter change value to determine an updated parameter set, selectively process the updated parameter set using rules and second rule set comprising Boolean logic, process the updated parameter set to determine a second confidence value, selectively determine in response to the second confidence value meeting a second threshold, the next best action service call, selectively processing the updated parameter to determine the next best action service call, and initiating the next best action service call. See also MPEP 2106.04(a)(2) III C where using a generic computer for a judicial exception has been shown to be non-statutory. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a mental process, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 11 and 20 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Processing Boolean logic is processing a mathematical algorithm, which is abstract as a Mathematical Concept.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: computer, machine (Claim 1); processor, processor, memory, machine (Claim 11); processor, memory, machine (Claim 20). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The machine is a generic machine. The special resolution machine learning model and the generic machine learning model are recited and applied at a high level of generality. See Applicant’s specification para. [0120] about general-purpose computer and para. [0106] about various types of machine learning models and para. [0103] of a general-purpose machine learning model. Also, para. [0070] where machine learning and para. [0158] where the special resolution model can be a decision tree, mapped to a decision tree, or regression. A person in their mind with pen and paper can solve for a decision tree or regression. Also, the special resolution machine learning models are analogous to special purpose computer, where using a computer to execute an algorithm or software was not enough (see MPEP 2106 I with respect to Alappat). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 11, and 20 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving are steps that are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1, 11, and 20 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-10 and 12-18 further define the abstract idea that is present in their respective independent claims 1 and 11 and thus correspond to Mental Processes and Mathematical Concepts and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 3, 5-8, 13, and 15-18 further recite machine learning models at a high level of generality. The special resolution machine learning models in claims 3, 5-7, 13, and 15-17 are analogous to special purpose computer, where using a computer to execute an algorithm or software was not enough (see MPEP 2106 I with respect to Alappat). The other claims further limit the abstract idea or are abstract themselves. Claims 2, 4, 9, 10, 12, and 14 related to insurance, where insurance is a fundamental economic practice, therefore, these claims are abstract under Certain Methods of Organizing Human Activity. Therefore, the claims 2-10 and 12-18 directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “processing the parameter set using a rules engine and a first rule set comprising Boolean logic configured to conditionally determine the next best action service call;…” and “selectively processing the updated parameter set using the rules engine and a second rule set comprising Boolean logic configured to conditionally determine the next best action service call;…” where nothing is done in the claim with the next best action service call using the rules engine and Boolean logic. For examination purposes, the limitations are considered but interpreted as not required in the claim, and similar to filtering a parameter set to determine a next best action service call. Claims 11 and 20 have a similar problem.
Claim 1 recites “selectively determining, in response to the second confidence value meeting a second confidence threshold, the next best action service call without using a general machine learning model for the set of next best action services;
selectively processing, using the general machine learning model, the updated parameter set to determine the next best action service call
wherein the general machine learning model is trained for a reduced set of next best action services that is less than all next best action services in the set of next best action services; and…” where it is indefinite as to: 1) why determine the next best action services when the next best service call was already determined twice before; and 2) the general machine learning model trained for a reduced set of next best action services as training and with the model and reduced set of next best actions never determined.
Claims 2-10 and 12-19 are further rejected as they depend from their respective independent claim.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
.
Claim Rejections - 35 USC § 103
A prior art search was conducted but does not result in a prior art rejection at this time. The best prior art found to date is Pub. No. US 2020/0005919 to Hill, SR. et al., however, Hill fails to teach the combination of claimed elements. In particular, the machine learning with training and the combination of a confidence value for a resolution condition, the confidence value meeting a first threshold, and replacing parameter value to determine a parameter change set.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH BARTLEY whose telephone number is (571)272-5230. The examiner can normally be reached Mon-Fri: 7:30 - 4:00 EST.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684