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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/17/21 has been entered.
Application Status
This Office Action is in response to RCE received 07/01/2025.
Claim 52 was amended in response received.
The other claims remain as previously presented.
Claims 1 and 45-63 are pending and have been examined.
This action is non-final.
Claim Objections
Independent claims 1, 52, and 60 remain objected to for a minor issue pertaining to inconsistent terminology.
Claim Rejections
Claims 1 and 45-631 remain rejected under 35 U.S.C. § 101 for being directed to an abstract idea without significantly more.
Acknowledgement of Issues Raised by Applicant
Applicant arguments drawn to the 35 U.S.C. §101 rejections have been fully considered but are not persuasive – see “Response to Arguments” section below.
Response to Arguments
With respect to the 101 rejections, examiner notes Applicant Remarks assert the claims are patent eligible under 35 U.S.C. § 101 and Alice/Mayo analysis per the claims not reciting an abstract idea under step 2A Prong I of Alice/Mayo analysis. Applicant further provides a supporting argument drawn to comparisons between Example 47 and the instant claims. The Examiner respectfully disagrees with Applicant’s assertions and conclusions reached (analysis continues below).
With respect to Applicant’s arguments drawn to rationales that the claims are patent eligible per not reciting an abstract idea under step 2A prong I of Alice/Mayo analysis and being similar in fact-pattern to that of Example 472, the Examiner respectfully fails to find the arguments convincing and disagrees for the following reasons:
The Examiner does not contend that artificial intelligence (AI) algorithms, or it’s corresponding steps of training, and inputting are abstract3. However, the mere inclusion of claim limitations drawn to the additional elements does not necessarily preclude the same aforementioned claims from being considered to recite an abstract idea under step 2A prong I of Alice/Mayo analysis – see Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015): ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This fact is evident in light of the sequence of steps performed during the revised Alice/Mayo test – see MPEP § 2106.04(II)(A) showing a visual summary of revised step 2A of the Alice/Mayo test.
The examiner respectfully maintains that the claims do not merely involve an exception, but rather include positive recitations of fundamental economic practices, of which includes steps of “…receiving a first[/second] set of data associated with a first[/second] insurance claim denied by the first[/second] payer…”, “…determining a first percentage likelihood that an appeal of an insurance claim to a first payer will result in a paid insurance claim…”, and “determining a second percentage likelihood that an appeal of an insurance claim to a second payer will result in a paid insurance claim”. See MPEP § 2106.04(a)(1), emphasis added: “Examiners should accordingly be careful to distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis”.
The examiner respectfully maintains that the claims positively recite fundamental economic practices of mitigating risk, not just mere involvement of the abstract idea (see above item). The examiner respectfully submits one of ordinary skill in the art appreciates that assessing the likelihood of appeals of claim denials from insurance providers reduces the uncertainty of payment from payers (e.g., insurance providers) to healthcare provider(s) for denied claims (i.e., a component of risk mitigation). The outcome of such an assessment is directly tied to financial outcomes extremely pertinent to those healthcare providers (e.g., outcome of receiving payment from insurance providers for accepting potentially appealed insurance claim or not, i.e., revenue-impacting outcomes). This is further evidenced by the fact that the methods are “…of prioritizing denied insurance claims for appeal to … payer[s]” – prioritizing denied insurance claims based on the likelihood of recouping funds from payers is a method of organizing human activity rooted in mitigating financial risk. Additionally, the ‘percentage likelihood determination’ steps carried out are not peripheral aspects of the claimed methods, they are one of the explicit purposes of the subject matter claimed, as indicated in Applicant’s specification – see ¶7 of Applicant’s published specification, underline emphasis added:
¶7 of Applicant’s published specification: “It would be advantageous for providers to have a predictor of claim success prior to filing a claim,[...] The present disclosure is directed to solving these and other problems”.
Examiner’s above stance is even further supported by the fact that the focus of the claims is not on any particular machine learning algorithm, architecture, or technique (e.g., the additional elements addressed in step 2A Prong II). This is evidenced by ¶¶113-114 of Applicant’s published disclosure4, as it states that the hardware implementing the claimed solution may be implemented on any and all hardware/software, including a pre-programmed general-purpose computer, where there is not a specific implementation of the disclosed machine learning models contemplated by the Applicants; the algorithms are only specific insofar as to their inputs and outputs being those sought by Applicant’s disclosure (i.e., the machine learning algorithms amount to a black-box):
¶¶113-114 of Applicant’s published specification, emphasis added: “[0113] It should also be understood that the disclosure herein may be more generally implemented with any type of hardware and/or software, and may be a pre-programmed general purpose computing device. […] [0114] It should also be noted that the disclosure is illustrated and discussed herein as having a plurality of modules which perform particular functions. It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes only, and do not necessary represent specific hardware or software. ….”
With respect to the supporting argument drawn to Example 47, examiner notes the claims are examined under the revised Alice/Mayo framework. The examples, including Example 47, are not precedential, and the examples provided in Office Guidance are hypothetical and intended to be illustrative only. While some of the fact patterns in the examples draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions.
The eligibility rationale of Example 47 is not applicable to the instant claims, as the instant claims do recite an abstract idea under 35 U.S.C. §101 and Alice/Mayo analysis – the instant claims are not drawn to an application specific integrated circuit (ASIC) comprising synaptic circuits, unlike example 47. Furthermore, and more importantly, Example 47 does not recite and fundamental economic principle or practice – the same cannot be said of Applicant’s instant claims. Converse to Applicant’s position, the examiner respectfully submits that the instant claims are closer to that of claim 1 of example 48, where the claims were deemed patent ineligible, because, despite including additional elements drawn to artificial intelligence, the claims did recite an abstract idea under step 2A Prong I, were directed to the abstract idea recited5, and did not amount to significantly more.
Accordingly, the examiner respectfully disagrees with Applicant’s stance that the claims do not recite an abstract idea under Alice/Mayo analysis, and respectfully maintains the 35 U.S.C. § 101 rejections of claims 1 and 45-63.
Claim Objections
With respect to independent claims 1, 52, and 60, they objected to for a minor issue: inconsistent terminology. More specifically, the claims recite: “…a first artificial intelligence (AI) algorithm…”, yet seemingly refers to the same AI algorithm later as “…the first AI model…”. Appropriate correction is required – see MPEP § 608.01(m).
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 and 45-63 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Based upon consideration of all relevant factors with respect to the claims as a whole, the claims are determined to be directed to an abstract idea without significantly more. The rationale for the aforementioned determination of patent ineligibility under 35 USC §101 is explained further below:
The relevant test to determine patent eligibility under 35 U.S.C. §101 is the Alice/Mayo test. The following analysis provided in this section results from the instant application’s claims being examined within the scope of the Alice/Mayo test framework.
With respect Step 1 of Alice/Mayo analysis, the claims are directed to a method, which is a statutory category of invention (Step 1 of Alice/Mayo Test: YES).
When analyzed under step 1 of the Alice/Mayo test6, claims 1 and 45-63 have been determined to be directed to an abstract idea of mitigating claims denial risk. The rationales for the aforementioned determination are explained further below.
With respect Step 2A Prong I of Alice/Mayo analysis, independent claims 1, 52, and 60 recites a method of organizing human activity because the claims recite:
“1. A method of prioritizing denied insurance claims for appeal to one or more payers, the method comprising:
…[updating], a first … algorithm to determine a percent likelihood that an appeal of an insurance claim to a first payer will result in a paid insurance claim, …
…[updating], a second … model to determine a percent likelihood that an appeal of an insurance claim to a second payer will result in a paid insurance claim;
receiving a first set of data associated with a first insurance claim denied by the first payer;
receiving a second set of data associated with a second insurance claim denied by the second payer;
inputting at least a portion of the first set of data into the first … model;
using the first … model, determining a first percentage likelihood that an appeal of the first insurance claim to the first payer will result in a first paid claim;
inputting at least a portion of the second set of data into the second … model;
and using the second … model, determining a second percentage likelihood that an appeal of the second insurance claim to the second payer will result in a second paid claim.
“52. A method of prioritizing denied insurance claims for appeal to one or more payers, the method comprising:
… [updating], a first … algorithm to determine a percentage likelihood that an appeal of an insurance claim to a first payer will result in a paid insurance claim;
…[updating], a second … model to determine a percentage likelihood that an appeal of an insurance claim to a second payer will result in a paid insurance claim;
receiving a first set of data associated with a first insurance claim denied by the first payer;
receiving a second set of data associated with a second insurance claim denied by the second payer;
inputting at least a portion of the first set of data into the first … model;
using the first … model, determining a first percentage likelihood that an appeal of the first insurance claim to the first payer will result in a first paid claim;
inputting at least a portion of the second set of data into the second … model;
using the second … model, determining a second percentage likelihood that an appeal of the second insurance claim to the second payer will result in a second paid claim;
receiving, from the first payer, first payer post appeal decision data;
…[updating], based on the first payer post appeal decision data, the first … model;
receiving, from the second payer, second payer post appeal decision data; and
… [updating], based on the second payer post appeal decision data, the second … model.”
“60. A method of prioritizing denied insurance claims for appeal of a payer, the method comprising:
…[updating], a first … algorithm to determine a percentage likelihood that an appeal of an insurance claim to a first payer will result in a paid insurance claim, wherein the first … model is associated with the first payer;
receiving a first set of data associated with a first insurance claim denied by the first payer;
inputting at least a portion of the first set of data into the first … model;
using the first … model, determining a first percentage likelihood that an appeal of the first insurance claim to the first payer will result in a first paid claim;
receiving, from the first payer, first payer post appeal decision data;
further …[updating], based on the first payer post appeal decision data, the first … model.”
Under broadest reasonable interpretation, these are recitations of fundamental economic practices of mitigating claims denial risk, including steps of (A) determining a percent likelihood that an appeal of a denied insurance claim to a first / second payer will result in a paid insurance claim based on relevant portions of sets of data, and, in the cases of claims 52 and 60, (B) receiving post appeal data for making subsequent percent likelihood determinations that an appeal of a denied insurance claim to a first / second payer will result in a paid insurance claim (See MPEP §2106.04(a)(2)(II)). Thus, the independent claims recite an abstract idea (Step 2A Prong I of Alice/Mayo Test: Yes, the claims recite an abstract idea).
This judicial exception recited in independent claims 1, 52, and 60 is not integrated into a practical application because, when analyzed under prong II of revised step 2A of the Alice/Mayo test7:
The additional elements “…artificial intelligence (AI)…”, “training …first AI…”, and “training …second AI…” of claim 1, the additional elements “…artificial intelligence (AI)…”, “training …AI…”, “…further training, … first AI …” of claim 52, and the additional elements of “…training a first artificial intelligence (AI) [algorithm]…”, “…first AI model…”, and “…further training…first AI model” of claim 60 amount to no more than mere instructions to implement the abstract idea and/or merely limit the use of the abstract idea to a particular technological environment (MPEP §§ 2106.05 (f), (h)), even when considering each claim’s additional elements both separately and as an ordered combination. Stating an abstract idea while adding the words "apply it" (or an equivalent) is insufficient to impart patent eligibility under Alice. See Alice Corp. v. CLS Bank International, 573 U.S. 208, 223-24 (2014): "… Stating an abstract idea "while adding the words ‘apply it’ " is not enough for patent eligibility. … Nor is limiting the use of an abstract idea " ‘to a particular technological environment.’ … Stating an abstract idea while adding the words "apply it with a computer" simply combines those two steps, with the same deficient result”.
The claims merely invoke computers as tools to perform an abstract business process (e.g., the recited steps corresponding to mitigating claims denial risk – see MPEP §2106.05(f)(2)). This stance is supported by ¶7 of Applicant specification stating: “It would be advantageous for providers to have a predictor of claim success prior to filing a claim... The present disclosure is directed to solving these and other problems”, and ¶113-114 of Applicant’s published specification stating: “It should also be understood that the disclosure herein may be more generally implemented with any type of hardware and/or software, … It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes only, and do not necessary represent specific hardware or software.” Accordingly, even when considered as an ordered combination, the claims’ additional elements are indistinguishable from mere addition of general-purpose computers added to the abstract idea ‘after the fact’ / ‘post-hoc’, which is insufficient to indicate improvements to computer functionality8, or a particular machine9.
In light of the above rationales provided for step 2A Prong II analysis, the Examiner respectfully submits the focus of the claims is not on an improvement in computers as tools, but rather on an abstract idea that uses computers as tools. Considered both separately and as an ordered combination, the additional elements of the independent claims do not integrate the abstract idea into a practical application, as they do no more than represent computers performing functions that correspond to (,i.e., implement,) the acts of the abstract mitigating claims denial risk within a particular technological environment, and do not provide details such that one of ordinary skill in the art would recognize the claims as reflecting an improvement to the functioning of a computer or any other technology or technical field. (Step 2A Prong II of Alice/Mayo Test: NO, the additional elements do not integrate the judicial exception into a practical application). Accordingly, claims 1, 52, and 60 are determined to be directed to an abstract idea.
To further support the stance that the additional elements (i.e., machine learning algorithms) are merely applied, Examiner notes the following:
The following claim limitations:
training a first[/second] machine learning algorithm to determine a percent likelihood
inputting at least a portion of the first[/second] set of data into the first/second machine learning algorithm;
using the first[/second] machine learning algorithm, determining a first[/second] percentage likelihood, (i.e., determining an output responsive to inputs)
further training, …, the first/second AI model.
are merely describing the generic machine learning algorithm being used in a manner customary to the technological environment of machine learning, and merely attempt to limit the use of the abstract idea to a particular technological environment (computer-implemented machine learning), which is insufficient to impart patentability under Alice - see Alice Corp. v. CLS Bank International, 573 U.S. 208, 223-24 (2014): "Neither stating an abstract idea "while adding the words 'apply it,' [...], nor limiting the use of an abstract idea "'to a particular technological environment'", [...] is enough for patent eligibility. Stating an abstract idea while adding the words "apply it with a computer" simply combines those two steps, with the same deficient result."). See also Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). Examiner respectfully submits that the claimed machine learning algorithm functionalities of 1) training, 2) receiving inputted data sets, 3), determining a percent likelihood as output, responsive to input received, and 4) retraining based on new data, are functionalities indistinguishable from typical functionality of generic, commercially available-off-the-shelf (COTS) machine learning algorithms being implemented at a high level of generality.
To further support this stance, Examiner notes the following indicating or otherwise suggesting the aforementioned features of the machine learning algorithms as well-understood, routine and conventional activity in the field of machine learning10:
¶37 of Applicant’s Specification: Generally, machine learning algorithms require training data to identify the features of interest that they are designed to detect and predict an outcome. […]
US 20010011259 A1 (Howard), disclosing it is known that neural network classifiers (e.g., machine learning algorithm) can provide outputs (i.e., classifications) as probabilities (¶92).
US 20080005108 A1 (Ozzie), disclosing machine learning algorithms have inputs, and produce outputs, of which the outputs are typically a probability (¶49).
US 20100082506 A (Avinash), disclosing or otherwise suggesting machine learning typically involves re-training (¶56).
Applicant’s disclosure specifies that the hardware implementing the claimed solution may be a pre-programmed general-purpose computer, and that there is no specific software implementing the claimed method, beyond the result it logically provides (see ¶¶113, 114 of published specification). Examiner respectfully takes the stance this is indicative of the claims using the machine learning algorithms merely as a tool (MPEP §2105.05(f)), rather than improving computer capabilities in a technological environment (MPEP §2106.05(a)), or claiming a particular machine (MPEP §2106.05(b)3). See MPEP §2106.05(f)(1): When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
As evidenced by the above, the additional elements generally corresponding to machine learning, including the limitations drawn to training and retraining, are not indicative of improvements to the functioning of a computer or to any other technology or technical field under either step 2A Prong II or step 2B, and instead are indicative of the machine learning being merely applied (MPEP 2106.05(f)). Even though claims involve machine learning, nothing in the claims indicate specific steps undertaken computer components being used at a high degree of generality, excepting the abstract idea they are merely used as a tool for. Accordingly, the claimed computer implementation itself is wholly generic, when considered in light of the technological environment of computers and the technical field of machine learning.
When analyzed under step 2B11, claims 1, 52, and 60 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1, 52, and 60, each when viewed as a whole, do not include additional elements amounting to significantly more, as their additional elements, each viewed both individually and as an ordered combination, amount to no more than mere instructions to implement the abstract steps corresponding to the mitigating claims denial risk concept within a particular technological environment – see MPEP §§ 2106.05 (f), (h) and Alice Corp. v. CLS Bank International, 573 U.S. 208, 223-24 (2014). Accordingly, when considered both separately and as an ordered combination, none of the elements of the independent claims add significantly more to the abstract idea itself (i.e., an inventive concept), as merely employing computers as tools to automate and/or implement the abstract idea cannot provide significantly more than the judicial exception itself – see BSG Tech LLC vs. BuySeasons, Inc., 899 F.3d 1281, 1290 (Fed. Cir. 2018): “It has been clear since Alice that a Claimed invention’s use of the ineligible concept to which it is directed cannot supply the inventive concept that renders the invention ‘significantly more’ than that ineligible concept”.
With respect to the dependent claims, they have each been given the full Alice/Mayo analysis, including analyzing the additional elements both individually and as an ordered combination (if any). The dependent claims are also held patent ineligible under 35 U.S.C. § 101 because of the same reasoning as above, and because the claim limitations of the dependent claims fail to establish that the claims are integrated into a practical application or amount to significantly more. The rationales for the aforementioned determinations are explained further below.
With respect to dependent claims 45, 47, 53, 57, and 62, their limitations each fail to provide any further additional elements outside the abstract idea, and only further specify the abstract concept. Furthermore, their limitations do not indicate that the previously mentioned additional elements of their respective parent claims successfully integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself, either individually or as an ordered combination. Accordingly, claims 45, 47, 49, 53, 57, and 62, do not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, dependent claims 45, 47, 49, 53, 57, and 62, are also not patent eligible.
With respect to dependent claims 46 and 54, they recite further details of the abstract idea, except for the limitation “…on a display device…”. The additional element “…on a display device…” does no more than represent the use of computers as tools to perform the abstract idea and/or merely limit the use of the abstract idea to a particular technological environment (MPEP §§ 2106.05(f) & 2106.05(h)). Accordingly, when considered as a whole, these claims do not improve the functioning of a computer, or to any other technology or technical field, do not integrate the judicial exception into a practical application, and do not amount to significantly more. Therefore, dependent claims 46 and 54 are also not patent eligible.
With respect to dependent claims 48, 50, 51, 58, 59, 61-63, they recite further details of the abstract idea, except for the limitations drawn to AI (e.g, “…AI…”, “…further training…the first AI model…”, “…training (data)…”). The aforementioned additional elements do no more than represent the use of computers as tools to perform the abstract idea and/or merely limit the use of the abstract idea to a particular technological environment (MPEP §§ 2106.05(f) & 2106.05(h)). Accordingly, when considered as a whole, these claims do not improve the functioning of a computer, or to any other technology or technical field, do not integrate the judicial exception into a practical application, and do not amount to significantly more. Therefore, dependent claims 48, 50, 51, 58, 59, 61-63, are also not patent eligible.
No Prior Art Rejection
The closest prior art of record is the same as the prior art referenced in the 01/02/2025 Final Rejection (i.e., Rao, Ligon, Chintamaneni, and Sohr).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK A MALKOWSKI whose telephone number is (313)446-6624. The examiner can normally be reached Monday - Friday, 9:00AM - 5:00PM ET.
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/M.A.M./Examiner, Art Unit 3695
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
1 I.e., the subject matter claimed.
2 See pages 1-2 of Remarks received 07/01/2025.
3 See the 101 rejections below.
4 United States Patent Application Publication No. US-2023/0084146-A1.
5 See 101 Rejections and ¶7 of Applicant’s specification.
6 See MPEP §§ 2106.04 I, II, (d) I.
8 See MPEP §§ 2106.05(f)(2) & 2106.05(a) I.
9 See MPEP §2106.05(b).
10 See MPEP §2106.05(f): “For example, because this [Mere Instructions To Apply An Exception] consideration often overlaps with the improvement consideration (see MPEP § 2106.05(a)), the particular machine and particular transformation considerations (see MPEP § 2106.05(b) and (c), respectively), and the well-understood, routine, conventional consideration (see MPEP § 2106.05(d)), evaluation of those other considerations may assist examiners in making a determination of whether an element (or combination of elements) is more than mere instructions to apply an exception”.
11 See MPEP § 2106.05.