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
Response to Amendment
In the Amendment dated 06 August 2025, the following occurred:
Claims 1, 4-6, 11, 14, 15, and 20 were amended.
Claims 2, 3, and 12 were canceled.
Claims 1, 4-11, and 13-20 are pending.
Subject Matter Free of Art
Claims 1-20 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claims 1, 11, and 20. In particular, the cited prior art fails to expressly teach or suggest the combination of:
generating a base code embedding in a first vector space for the specified entity using at least one first machine learning model based on a set of codes corresponding to the specified entity from a claims datastore, wherein the at least one first machine learning model is configured to generate the base code embeddings such that semantically similar codes are closer in the first vector space, the set of codes includes more than 10,000 codes, and the set of codes is associated with at least one of International Statistical Classification of Diseases tenth revision (ICD-10) codes or Current Procedural Terminology (CPT) codes;
generating an event embedding in a second vector space for the specified entity using a second machine learning model based on a set of historical events corresponding to the specified entity and the base code embedding, wherein the second machine learning model is configured to generate the event embedding such that codes commonly found in a single event are closer in the second vector space;
generating a time embedding in a third vector space for the specified entity using a third machine learning model based on times between consecutive ones of the set of historical events and the base code embedding, wherein the third machine learning model is configured to generate the time embedding such that sets of similarly spaced events are closer in the third vector space;
generating an aggregated embedding for the specified entity based on the event embedding and the time embedding;
and in response to a query designating the specified entity, generating the prediction by supplying the aggregated embedding to a fourth machine learning model.
The closest prior art Soltani Bidgoli et al. (U.S. 2023/0178199) teaches generating a base code embedding and generating an event embedding. However, Soltani fails to teach generating a time embedding.
The prior art Tillman et al. (U.S. 2025/0068903) teaches generating a time embedding. However, Tillman fails to teach generating an aggregated embedding and generating the prediction.
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, 4-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 11, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method for generating a prediction for a specified entity, and therefore meet step 1.
Step 2A1
The limitations of (Claim 1 being representative) generating a base code embedding in a first vector space for the specified entity… based on a set of codes corresponding to the specified entity from a claims datastore… such that semantically similar codes are closer in the first vector space, the set of codes includes more than 10,000 codes, and the set of codes is associated with at least one of International Statistical Classification of Diseases tenth revision (ICD-10) codes or Current Procedural Terminology (CPT) codes; generating an event embedding in a second vector space for the specified entity… based on a set of historical events corresponding to the specified entity and the base code embedding… such that codes commonly found in a single event are closer in the second vector space; generating a time embedding in a third vector space for the specified entity… based on times between consecutive ones of the set of historical events and the base code embedding… such that sets of similarly spaced events are closer in the third vector space; generating an aggregated embedding for the specified entity based on the event embedding and the time embedding; and in response to a query designating the specified entity, generating the prediction…, as drafted, is a process that, under the broadest reasonable interpretation, falls in the grouping of certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions).
That is, other than reciting a system implemented by memory hardware and processor hardware, the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Alternately, the claimed invention encompasses a mathematical concept in that it generates and aggregates vectors. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of memory hardware and processor hardware that implement the identified abstract idea. The processor/memory is not exclusively described by the applicant and is recited at a high-level of generality (i.e., generic computer components) such that it amounts no more than mere instructions to apply the exception using a generic computer or components thereof. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim further recites the additional elements of using first, second, third, and fourth machine learning models to generate a prediction for a specified entity. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The Examiner notes that the machine learning models are described in the Specification at Para. 0093 as encompassing recurrent neural networks, transformer-based models, and/or large language models. Alternatively, or in addition, the implementation of the machine learning models to the claims data merely confines the use of the abstract idea (i.e., the trained models) to a particular technological environment or field of use (the noted types of ML) and thus fails to add an inventive concept to the claims. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using memory hardware and processor hardware to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of first, second, third, and fourth machine learning models were determined to represent “apply it” on a generic computer. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(I)(A) indicates that merely saying “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such the claim is not patent eligible.
Claims 4-10 and 13-19 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 13 merely describes data manipulations to generate the base code. Claim 13 further recites fifth, sixth, and seventh machine learning models, which are considered to “apply it” under both the practical application and the significantly more analysis in the same manner as the machine learning models of the independent claims.
Claims 5 and 14 merely describe generating the event embedding. Claims 6 and 15 merely describe obtaining the aggregated embedding. Claims 7 and 16 merely describe transforming the prediction and displaying the transformed prediction. Claims 7 and 16 further recite a user device, which is considered to “generally link” under both the practical application and the significantly more analysis.
Claims 8-10 and 17-19 merely describe the prediction.
Response to Arguments
Interview Request
Examiner acknowledges Applicant's request for an interview; however, given the strength of the subject matter eligibility rejection, the Examiner declines the interview at this time.
Claim Objections
Regarding the objections to Claims 5, 6, 14, and 15, the Applicant has amended the claims to overcome the bases of objection.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claim 20, the Applicant has amended the claim to overcome the basis of rejection.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-20, the Examiner has considered the arguments but they are not persuasive. Applicant argues:
This coordinated multi-embedding approach provides a concrete technological improvement by enhancing prediction accuracy for outcomes such as cost, utilization, and disease progression… The invention thus represents a specific advancement in computer-based data modeling and learning—improving how computers process and interpret large-scale, heterogenous claims data—rather than merely automating mental analysis or performing generic data processing.
Regarding (a), the Examiner respectfully disagrees. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves the functioning of a computer. See also MPEP 2106.05(a)(I). The technological environment of Applicant’s claim is a general-purpose computer configured to execute one or more particular functions embodied in computer programs (see Spec. Para. 0153). Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the computer that results from the implementation of Applicant’s claim. There is no indication that the computer is made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know. Because there is no improvement to the function of the computer, a practical application is not present.
As recognized in Ex parte Desjardins, which was just designated as precedential on November 4, 2025, improvements that enhance how a machine-learning model operates—such as reducing system complexity, improving efficiency, and maintaining model performance across tasks—constitute meaningful technical advancements… Consistent with that reasoning, the claimed invention achieves a similar improvement by optimizing the generation and aggregation of embeddings from a large set of ICD-10 and CPT codes, enhancing model efficiency and predictive accuracy while reducing the need for redundant or less relevant inputs.
Regarding (b), the Examiner respectfully notes that Ex parte Desjardins currently does not represent a substantive change in subject matter eligibility analysis at the Examiner level. The Examiner is required to follow the MPEP and the current rejection reflects this. As discussed above, there is no improvement to the computer within the meaning of MPEP 2106. Further, the Examiner has reviewed the as-filed disclosure and can find no disclosure stating that the claimed invention provides for “enhancing model efficiency and predictive accuracy while reducing the need for redundant or less relevant inputs.”
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Adaboina et al. (U.S. 2024/0419702) which discloses methods and systems for automatic appeal authorization using a machine learning algorithm.
Subramanian (U.S. 2023/0162831) which discloses a method for automated entity field correction.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMRYN B LEWIS whose telephone number is (703)756-1807. The examiner can normally be reached Monday - Friday, 11:00 am - 8:00 pm EST.
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, Robert W Morgan can be reached on 571-272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAMRYN B LEWIS/
Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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