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 .
Response to Amendment
In the amendment filed 09/09/2026, the following has occurred: claims 1, 5-6, 14, and 18-19 have been amended and claims 4 and 17 have been canceled. Now, claims 1-3, 5-16, and 18-20 are pending.
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 1-3 and 5-13 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 “each clinical code from the plurality of clinical codes supports the decision made…at a corresponding decision node.” The claim also recites “the decisions made at the plurality of decision nodes” and “the decision made at the corresponding decision node.” The claim includes several previous recitations of “grouper decision path” as well as “each grouper decision path comprises a plurality of decision nodes.” However, there is no previous recitation of “a decision made” or “decisions made.” Therefore, these recitations lack sufficient antecedent basis in the claims.
Claim 3 also recites “the decision made at the decision node. This recitation lacks sufficient antecedent basis in the claims for the reasons given above.
Claims 2-3 and 5-13 are rejected based on their dependencies on claim 1.
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 1-3, 5-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claims 1 and 14 (claim 1 representative) recite receive an electronic medical record associated with a patient; provide the electronic medical record, wherein each historical electronic medical record is paired with a grouper decision path corresponding to a previously assigned grouper code; determine a plurality of grouper decision paths based on the electronic medical record and predetermined grouper guidelines, wherein each grouper decision path comprises a plurality of decision nodes, wherein the plurality of decision nodes is arranged in a node sequence in each grouper decision path; determine a plurality of grouper codes corresponding to the plurality of grouper decision paths, wherein each grouper code from the plurality of grouper codes is assigned to a corresponding grouper decision path from the plurality of grouper decision paths; determine a plurality of clinical codes for each grouper decision path, such that each grouper decision path is supported by the respective plurality of clinical codes, wherein each clinical code from the plurality of clinical codes supports the decision made according to the predetermined grouper guidelines at a corresponding decision node from the plurality of decision nodes of the respective grouper decision path, and wherein each clinical code comprises a plurality of characters; generate an output data comprising a plurality of outputs for the plurality of grouper decision paths, wherein each output for the respective grouper decision path comprises a plurality of symbols and a plurality of confidence scores corresponding to the plurality of symbols, wherein each symbol from the plurality of symbols has a corresponding confidence score from the plurality of confidence scores, the plurality of symbols of each output comprising: a grouper code symbol representing the grouper code of the respective grouper decision path, such that the plurality of outputs comprises a plurality of grouper code symbols having respective confidence scores; a plurality of decision symbols representing the decisions made at the plurality of decision nodes of the respective grouper decision path, wherein each decision symbol from the plurality of decision symbols represents the decision made at the corresponding decision node from the plurality of decision nodes; and a plurality of clinical code symbols representing the plurality of clinical codes of the respective grouper decision path, wherein each clinical code symbol from the plurality of clinical code symbols represents at least one character from the plurality of characters of the corresponding clinical code, and wherein the plurality of decision symbols and the plurality of clinical code symbols are together arranged in an output sequence that corresponds to the node sequence of the plurality of decision nodes; select one grouper code from the plurality of grouper codes based on the respective confidence scores of the plurality of grouper code symbols; select one or more clinical codes from the plurality of clinical codes of the grouper decision path corresponding to the selected grouper code based on the confidence scores of the plurality of clinical code symbols, wherein each clinical code from the selected one more clinical codes is at least a partial clinical code; determine if the confidence score of the grouper code symbol of the selected grouper code and respective code confidence scores of the selected one or more clinical codes exceed corresponding confidence score thresholds specified by a user, wherein the code confidence score of each clinical code is a function of the confidence scores of the plurality of clinical code symbols of the clinical code; and provide the selected grouper code and the selected one or more clinical codes.
These limitations, as drafted, given the broadest reasonable interpretation, encompass managing personal behavior interactions between people, including following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Note that certain activity between a person and a computer may fall within the Certain Methods of Organizing Human Activity grouping. For example, the claims encompass a user receiving medical records that are paired with grouper decision paths (applying rules), determining grouper decision paths and grouper codes by applying rules, determining clinical codes (e.g. ICD, DRG, etc.) for grouper decision paths by applying rules in the form of grouper guidelines, outputting symbols and corresponding confidence scores for grouper decision paths (this could be accomplished by applying rules in the act of a user generating a report regarding clinical decisions with textual data representing grouper codes, decisions, and clinical codes), selecting codes and scores from the generated symbols, determining if the selected score exceeds a threshold and providing a user with the selected grouper code and clinical code. Such manual steps encompass Certain Methods of Organizing Human Activity.
Claims 2-3, 5-13, 15-16, and 18-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 2 and 15 further expand on determining a destination of the codes based on the threshold determinations, which a user could perform by following rules. Claims 3, 5-8, 13, 16, and 18-20 further define the groupers, decisions, and clinical codes, encompassing letters and numbers. For example, as described above, this information encompasses text and clinical coding (e.g. ICD, DRG, etc.). Claim 9 further expands on the clinical coding and thresholds to validate part of the clinical code. Claims 10-11 further recite communicating numerical representations of the medical record and providing a weighted average. As explained above, these manual steps encompass Certain Methods of Organizing Human Activity.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with adding elements similar to adding the words “apply it” to the abstract idea, and generally linking the abstract idea to a particular technological environment, along with insignificant, extra-solution data gathering activity.
Claims 1-3 and 5-13, directly or indirectly, recite the following additional elements at a high level of generality and merely utilized as tools to implement the abstract idea:
Claims 1:
one or more computer processors; and at least one non-transitory computer-readable storage, communicatively coupled to the one or more computer processors, having stored thereon a machine learning model and instructions that when executed by the one or more computer processors cause the one or more computer processors.
The written description discloses that the recited computer components encompass generic components including “the term “processor” or “computer processor” refers any device that performs logic operations. A computer processor may include a general processor, a central processing unit, an application specific integrated circuit (ASIC), a digital signal processor, a field programmable gate array (FPGA), a digital circuit, an analog circuit, a controller, a microcontroller, any other type of processor, or any combination thereof” (see page 7, lines 25-29). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-3, 5-16, and 18-20, directly or indirectly, recite the following additional elements at a high level of generality, involving no more that extra-solution data gathering and transmitting activity:
Claims 1 and 14:
Provide…to at least one of an automatic processing application and a user interface.
Claims 2 and 15:
Provide…to the automatic processing application and the user interface.
Claim 9:
Receive, via the user interface, one or more user inputs.
These additional elements are recited at a high degree of generality and are merely involved in insignificant extra solution data gathering and transmitting of data over a generic computer network. As set forth in MPEP 2106.05(g) insignificant, extra-solution activity, such as insignificant acquisition and data transmission, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-3, 5-16, and 18-20, recite the following additional elements at a high level of generality, generally linking the abstract idea to a particular technological environment:
Claims 1 and 14:
provide the electronic medical record to the machine learning model, wherein the machine learning model is trained on a collection of historical electronic medical records...
determine, via the machine learning model…
generate, via the machine learning model…
Claim 10:
the machine learning model comprises: an encoder configured…and a decoder configured…
Claim 11:
the machine learning model further comprises an attention module coupling the encoder to the decoder, and wherein the attention module is configured to pass a weighted average of the plurality of numerical representations from the encoder to the decoder.
Claim 12:
the automatic processing application is a billing application.
The “machine learning model is recited at a high degree of generality and is only recited in the form of being applied to the abstract idea (e.g. no more than performing “determine” and “generate” operations). Additionally, the recitation of “the machine learning model is trained” only specifies electronic data on which it is trained without limitation on the data or any specificity on the training. The processing application being defined as a “billing application” simply labels recited software as “billing application.” Finally, the “encoder” and decoder” are broadly recited and only involved in receiving, transferring, and outputting data. The specification describes a “machine learning model” as including simple algorithms such as “a decision tree” and “a linear regression model” (see page 8, lines 17-21). These broadly recited elements do not provide any type of technical improvement or an improvement to another technical field. Merely reciting the words "apply it" (or an equivalent) with the judicial exception and generally linking the use of a judicial exception to a particular technological environment or field of use do not integrate the abstract idea into a practical application (MPEP 2106.04(d)I).
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Insignificant, extra solution, data gathering activity (e.g. transmitting and receiving data over a computer network) has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
Generally linking the abstract idea to a particular technological environment (e.g. a trained machine learning model) does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. These additional elements merely carry out the abstract idea through data collection, data processing, data communication, and data storage. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Distinguishing Subject Matter
Claims 1-3, 5-16, and 18-20 distinguish over the prior art. The following is a statement of reasons for the indication of distinguishing subject matter: The primary reason that claims 1-20 distinguish over the prior art is the combination of grouper decision paths, grouper codes, grouper guidelines, decision nodes, clinical codes, confidence scores, all processed and defined in terms of characters and symbols used in combination, along with confidence score thresholds, to select and provide grouper codes and clinical codes from different paths and nodes. The closest prior art (de Traversay, US Patent Application Publication No. 2015/0039334, Delaney, US Patent Application Publication No. 2016/0092641, and Ramarajan, US Patent Application Publication No. 2012/0016690) discloses extracting clinical codes from patient information, identifying coding sets, grouping codes, scoring groups of codes, comparing scores to thresholds, and outputting groups of codes. While various aspects of the claims are disclosed in the references, when considered in combination, there is not teaching or suggestion to combine the various disparate limitations in the manner recited in the claims.
Response to Arguments
In the remarks filed 09/09/2026, Applicant argues (1) the amendments to the claims specifying “each decision node corresponds to a decision made according to the predetermined grouper guidelines” overcomes the rejections under 35 U.S.C. 112(b); (2) the claims do not recite an abstract idea because the steps involve the use of a machine learning model; (3) the claims integrate the abstract idea into a practical application by addressing the technical challenge of using an EMR that is lacking assigned clinical codes or contains incomplete or inaccurate clinical codes with a technical solution that applies machine learning to output a data structure with clinical codes that follow symbols for associated decision nodes and associated with confidence based selection and routing operations; (4) the ordered combination of limitations is not well-understood, routine, and conventional, as evidenced by the finding that the combination or limitations distinguish over the prior art..
In response to argument (1), while this amendment has been made to claim 14, it has not been made to claim1. Accordingly, the 112(b) rejection of claims 14-16 and 18-20 have been withdrawn, but the 112(b) rejections of 1-3 and 5-13 have been maintained.
In response to argument (2), the examiner agrees that the machine learning model is not part of the abstract idea. However, it is analyzed and treated as an additional element that is simply applied to the abstract idea. The examiner respectfully maintains that the steps identified as part of the abstract idea in the rejections encompasses human interactions by managing personal interactions following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Applicant highlights certain functions as not being capable of being carried out by users following rules or in the mind of a user at pages 11-12 of the response. With regard to generating path-specific outputs with different categories of symbols and confidence scores arranged in a sequence corresponding to a node sequence of a grouper decision path, a user could write this with pen and paper, ordered and categorized sequentially, including symbolic codes. For example Figure 4 shows an example of such an arrangement of alpha-numeric symbols and could be generated with pen and paper. Encoding and decoding operations could likewise be achieved in a similar manner. Additionally, while the claimed invention may involve large amounts of data in practice, the claims are not limited to a particular amount of data and simply being a burden to perform the steps manually is not a consideration for whether they encompass an abstract idea. Therefore, the examiner respectfully maintains that the claims recite and abstract idea as set forth in the above rejections.
In response to argument (3), it is initially noted that the challenge described in the specification is not a technical challenge, but rather a challenge faced when a medical record lacks certain data or contains inaccurate data. The provided example is a situation in which an EMR does not include any clinical codes, has partial codes, or inaccurate codes. This is a challenge related to incomplete records, but not a technical challenge. With regard to the argued solution, the examiner respectfully maintains that the “machine learning model” is broadly recited and only involved in certain steps at a high level equivalent to adding the words “apply it” to the abstract idea. Specifically, some of the steps are recited as being carried out “via the machine learning model” with nothing further. There is no indication that combining the machine learning model with the other recitations provide a technical improvement to the operation of the model or to any other technical field. Additionally, the selection and routing of information is part of the abstract idea itself. Accordingly, the examiner respectfully maintains that abstract idea is not integrated into a practical application.
In response to argument (4), the question is not whether the entirety of the claim is well-understood, routine, and conventional. Rather, the question is whether any of the additional elements have been identified as well-understood, routine, and conventional. In this case, none of the additional elements have been identified as well-understood, routine, and conventional. Applicant acknowledges that novelty of the claims does not equate to subject matter eligibility. Identifying the fact that the claims, as a whole, distinguish over the prior art, is not determinate over whether the claims recite significantly more than the abstract idea. Notably, in the identification of subject matter that distinguishes over the prior art, none of the additional elements are identified as a primary reason for the distinguishing subject matter. Therefore, the examiner respectfully maintains that the claims, considered as an ordered combination, do not amount to significantly more than the abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bouman, US Patent Application Publication No. 2019/0034593, discloses medical episode data mapped to grouping schemes and sequencing coding symbols such as ICD coding.
Conag et al., Multiple Convolutional Neural Networks with Stratified K Fold Strategy for ICD Code Classification, discloses applying machine learning to medical coding to derive sequenced coding symbols.
THIS ACTION IS MADE FINAL. 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 C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 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, Robert Morgan can be reached at 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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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683
