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
Claims 1-20 are pending.
This action is in response to the application filed on June24, 2025.
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. Application No. 17/937,906, entitled "INTELLIGENT AUTOMATIC ORCHESTRATION OF MACHINE-LEARNING BASED PROCESSING PIPELINE," filed October 4, 2022, the contents of which are incorporated herein by reference in their entireties
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “…. (i) a predictive data verification sub-routine configured to populate the input data object profile with one or more input data object profile parameters,(ii) a robotic data augmentation sub-routine configured to generate a predicted value for the member, or (iii) a predictive data augmentation sub-routine configured to generate one or more inferences based at least in part on the input data object profile…. “ in claims 2 and 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-12 and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Markson et al (US 20210255880 A1).
With respect to claims 1, 12 and 18, Markson et al teaches
receiving, by one or more processors, an input data object profile for a member associated with one or more health insurance payers for a medical claim and an investigative process associated with the one or more health insurance payers ([0114] FIG. 4, user metric determination device 132 receives input parameters and accesses member data 120 and claims data 122 from the storage device 110 to calculate a per-user metric of a user, depending on the input parameters. [0122] trained model that predicts future. the intake represents margin or resources received from a user for an event for the user and/or another payer, such as a health plan);
generating, by the one or more processors and using a machine learning model, an investigative score for the member based at least in part on the input data object profile, wherein the investigative score identifies an investigative potential of the medical claim ([0154] trained machine learning model that predicts an increase in the number of claims the pharmacy may be expected to receive from the user for any given year in the future. the expected intake module 412 may receive at least one of the order data 118, member data 120, claims data 122, as well as additional input parameters, and use the trained machine learning model to calculate an expected number of additional claims the pharmacy may be expected to receive from the user for any given year in the future. The expected intake module 412 may include the expected number of additional claims as an additional input for the intake model to calculate an updated predicted future intake of the user); and
initiating, by the one or more processors and based at least in part on the investigative score, a processing orchestration action for the member, the processing orchestration action comprising at least one of:
(i) providing the input data object profile to a predictive data analysis sub-routine of a plurality of predictive data analysis sub-routines ([0006] machine learning model may be trained using parallel processing of records from the data store. The parallel processing may include assigning analysis of the indexed event data of each of a subset of the first set of identifiers),
(ii) providing the input data object profile to a processing representative ([0154] predicts an increase in the number of claims the pharmacy may be expected to receive from the user for any given year in the future. expected intake module 412 may receive at least one of the order data 118, member data 120, claims data 122, as well as additional input parameters, and use the trained machine learning model to calculate an expected number of additional claims the pharmacy expected to receive from the user for any given year in the future) or
(iii) removing the input data object profile from the investigative process ([0082] metric(s) may cause the user interface of the data analyst device to be updated, such as by removing a user interface element or modifying).
With respect to claims 4 and 15, Markson et al teaches input data object profile is removed from the investigative process by generating a non-investigative action responsive to the investigative score not achieving a threshold investigative score ([0082] metric(s) may cause the user interface of the data analyst device to be updated, such as by removing a user interface element or modifying).
With respect to claims 5 and 16, Markson et al teaches receiving an investigative outcome from the processing representative ([0038] to anticipate the expected population retention, a likelihood of retention is calculated based on historical retention rates. Similarly, the expected output and intake are calculated based on historical outputs and intakes).
With respect to claims 6 and 17, Markson et al teaches processing representative is selected using a machine-learning based predictive placement model ([0018] machine learning model may be trained using parallel processing of records from the data store. The parallel processing may include assigning analysis of the index event data of each of a subset).
With respect to claim 7, Markson et al teaches machine-learning based predictive placement model is previously trained using a historical optimization data object indicative of an efficiency of processing one or more previously selected input data objects ([0018] machine learning model may be trained using parallel processing of records from the data store. The parallel processing may include assigning analysis of the index event data of each of a subset).
With respect to claim 8, Markson et al teaches machine-learning based predictive placement model is retrained based at least in part on the investigative outcome from the processing representative ([0018] machine learning model may be trained using parallel processing of records from the data store. The parallel processing may include assigning analysis of the index event data of each of a subset).
With respect to claims 9 and 19, Markson et al teaches investigative process comprises a coordination of benefits (COB) process ([0041] FIG. 1 is a block diagram of an example implementation of a system 100 for a high-volume pharmacy. While the system 100 is generally described as being deployed in a high-volume pharmacy or a fulfillment center (for example, a mail order pharmacy, a direct delivery pharmacy, etc.), the system 100 and/or components of the system 100 may otherwise be deployed (for example, in a lower-volume pharmacy).
With respect to claims 10 and 20, Markson et al teaches existing medical claim or a prospective medical claim, and the input data object profile is received based at least in part on a creation of the existing medical claim or a probability of the prospective medical claim ([0041] FIG. 1 is a block diagram of an example implementation of a system 100 for a high-volume pharmacy. While the system 100 is generally described as being deployed in a high-volume pharmacy or a fulfillment center (for example, a mail order pharmacy, a direct delivery pharmacy, etc.), the system 100 and/or components of the system 100 may otherwise be deployed (for example, in a lower-volume pharmacy).
Allowable Subject Matter
Claims 2-3 and 13-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
COATES (US 20240412309 A1) Machine-Learning To Predict Claim Outcomes.
Considered for teachings related generally Computing systems and methods, and non-transitory storage media, are provided for a machine learning or artificial intelligence model to output one or more predicted probabilities of potential claim outcomes in an open claim file that potentially results in a nuclear verdict during civil litigation, along with estimated pecuniary consequences of a potential verdict or settlement recorded.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached at 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/ Primary Examiner, Art Unit 2163
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