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 .
Status of the Claims
Claims 1, 4-9, 11, 14-18, and 21-25 are currently pending. Claims 24-25 are newly added in the Claims filed on November 24, 2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 23, 2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-9, 11, 14-18, and 21-25 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claims 1, 11, and 18, Claims 1, 11, and 18 recite “adjusting one or more model parameters using supervised learning to reduce a difference between a predicted length of stay and the observed length of stay.” The underlined language represents new matter because it is not disclosed by the Specification. For example, [0100]-[0101] of the as-filed Specification disclose that the ML model is trained “to predict length of stays for patients at different geographic locations,” wherein “these predictions may be more accurate than using a single ML model.” Hence, at most, the as-filed Specification discloses that a ML model may be made “more accurate,” but does not explicitly state that this is accomplished by, for example, “reducing a difference between a predicted length of stay and the observed length of stay” for the patients of the respective group of patients. Appropriate correction is required.
Dependent Claims 4-9, 14-17, and 21-25 are also rejected under 35 U.S.C. 112(a) due to their dependence from independent Claims 1, 11, and 18.
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, 4-9, 11, 14-18, and 21-25 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.
Regarding Claims 1, 11, and 18, Claims 1, 11, and 18 recite “adjusting one or more model parameters using supervised learning to reduce a difference between a predicted length of stay and the observed length of stay.” The metes and bounds of this limitation are unclear because it is incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: a step of the plurality of neural network models generating a predicted length of stay and/or retrieving an already-existing predicted length of stay for the respective group of patients. That is, it is unclear what the step of adjusting the parameters comprises because the intended purpose of the adjusting of the parameters is in order to “reduce a difference between a predicted length of stay and the observed length of stay [for each of the patients of the respective group of patients],” and there has been no previous introduction of a predicted length of stay for each of the patients of the respective group of patients. In the interest of compact prosecution, Examiner will interpret this language as reciting an additional step of, for example, “each neural network model of the plurality of neural network models including a predicted length of stay,” and “adjusting one or more model parameters to increase the accuracy of the plurality of neural network models,” in accordance with the disclosures of [0101]-[0102] of the as-filed Specification. Appropriate correction is required.
Dependent Claims 4-9, 14-17, and 21-25 are also rejected under 35 U.S.C. 112(b) due to their dependence from independent Claims 1, 11, and 18.
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, 4-9, 11, 14-18, and 21-25 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.
Step 1
Claims 1, 4-9, 11, 14-18, and 21-25 are within the four statutory categories. Claims 1, 4-9, and 23 are drawn to a method for determining revenue, which is within the four statutory categories (i.e. process). Claims 11, 14-17, and 24 are drawn to a non-transitory medium for determining revenue, which is within the four statutory categories (i.e. manufacture). Claims 18, 21-22, and 25 are drawn to a system for determining revenue, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A method, comprising:
collecting medical records of a plurality of patients previously discharged from one or more healthcare facilities;
generating training data by parsing the medical records to identify, for each patient, a plurality of diagnostic parameters and a corresponding observed length of stay;
training a plurality of neural network (NN) models, each model corresponding to a respective group of patients defined based on a factor, comprising:
using, as input features, the plurality of diagnostic parameters extracted from the medical records;
using, as a labeled output target, the observed length of stay corresponding to each patient, and
adjusting one or more model parameters using supervised learning to reduce a difference between a predicted length of stay and the observed length of stay;
receiving a medical record of a current patient;
selecting, based on a value of the factor associated with the current patient, one of the trained NN model corresponding to a matching group;
parsing, using natural language processing enabled by one or more computer processors, the medical record for the current patient to identify an input to a rate calculator and a plurality of diagnostic parameters corresponding to the current patient;
determining while parsing the medical record, a failure in the natural language processing to identify a required input to the rate calculator;
transmitting for display a graphical user interface (GUI) indicating the required input is missing, the GUI displaying a first field by which a user can provide the required input; and
receiving the required input via the GUI, wherein the GUI comprises (i) a second field that is automatically populated to include the identified input based on the success of the natural language processing, (ii) a selectable element permitting the user to provide an additional input for the rate calculator using a third field, and (iii) a prompt indicating a value must be entered into the selectable element before a reimbursement or cost rate of the patient at a healthcare facility can be calculated;
determining, using the rate calculator and after receiving the required input, a reimbursement or cost rate of the current patient at a healthcare facility based on the identified input;
predicting, using the selected NN model, a length of stay of the patient based on the plurality of diagnostic parameters corresponding to the current patient;
determining, before the patient is admitted into the healthcare facility, a revenue stream corresponding to the current patient based on the reimbursement or cost rate and the predicted length of stay; and
transmitting for display, via the GUI, at least one of the predicted length of stay, the reimbursement or cost rate, or the revenue stream corresponding to the current patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite fundamental economic practices (i.e. hedging, insurance, mitigating risk – in this case, the aforementioned underlined data processing steps are performed in order to determine a revenue stream, which recites an economic calculation), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of collecting previously discharged patient records, receiving a current patient medical record, selecting a model, parsing the medical record to identify data to input to a rate calculator and diagnostic parameters, determining while parsing a failure to identify required data for input, displaying an indication that the required data is missing, receiving the required data, determining the reimbursement or cost rate of the patient based on the identified data input, predicting a length of stay of the patient, and determining a revenue stream corresponding to the patient, and displaying at least one of a predicted length of stay, the reimbursement cost rate, or the revenue stream for the current patient recites following rules or instructions to predict a patient length of stay and determine a revenue stream for the patient), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 11 and 18 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 11, and 18 is that Claim 11 recites a non-transitory computer readable medium and Claim 18 recites a system, whereas Claim 1 recites a method.
Dependent Claims 4-9, 14-17, and 21-25 include other limitations, for example Claims 4-6, 14-16, and 21 recite specific types of inputs, Claims 7-8, 17, and 22 recite the data used to train the NN model, and Claim 9 recites displaying a confidence score for the predicted length of stay for the patient, and Claims 23-25 recite pre-processing the medical records, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 4-9, 14-17, and 21-25 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 11, and 18.
Prong 2 of Step 2A
Claims 1, 11, and 18 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea – in this case, the computer processors, the GUI and its display, the natural language processing operation, the NN models and their training) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computer processor and memory, and the outputting of the GUI, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0031], [0042], and [0103]-[0104] of the as-filed Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language reciting a medical record, patient, and length of stay, which amounts to limiting the abstract idea to the field of healthcare, and the claim language reciting the training of the plurality of NN models and the recitation of natural language processing, which amounts to limiting the abstract idea to the field of machine learning, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of the display of the contents of the GUI, which amounts to an insignificant application, see MPEP 2106.05(g).
Additionally, dependent Claims 4-9, 14-17, and 21-25 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the recitation of the GUI limitations in dependent Claims 4, 14, and 21), generally linking the abstract idea to a particular technological environment or field of use (e.g. the limitations pertaining to the training of the NN model recited in dependent Claims 5-9, 15-17, and 22), and/or do not include any additional elements beyond those already recited in independent Claims 1, 11, and 18, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1, 11, and 18 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea – in this case, the computer processors, the GUI and its display, the natural language processing operation, the NN models and their training), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0031], [0042], and [0103]-[0104] of the as-filed Specification discloses that the additional elements (i.e. the computer, processor, the natural language processing, the neural network model) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life – similarly, the additional elements recite performing basic calculations (i.e. the steps of repeatedly training and adjusting the neural network model utilizing the recited input training data) and does not impose meaningful limits on the scope of the claims;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing medical data, and parsing/retrieving the medical data in order to determine a revenue stream;
Electronically scanning or extracting data from a physical document, e.g. see Content Extraction and Transmission, LLC v. Wells Fargo Bank – similarly, the current invention recites scanning a medical document, and/or parsing the medical data via natural language processing for input to the rate calculator from the medical document;
Determining an estimated outcome and setting a price, e.g. see OIP Techs. v. Amazon, Inc. – similarly, the current invention determines a particular revenue stream for a patient based on data parsed from the medical record;
Dependent Claims 4-9, 14-17, and 21-25 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply an exception (e.g. the recitation of the GUI limitations in dependent Claims 4, 14, and 21), generally linking the abstract idea to a particular technological environment or field of use (e.g. the limitations pertaining to the training of the NN model recited in dependent Claims 5-9, 15-17, and 22), and/or do not recite any additional elements not already recited in independent Claims 1, 11, and 18 and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1, 4-9, 11, 14-18, and 21-25 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1, 4-9, 11, 14-18, and 21-25 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Delaney (US 2016/0092641) teaches receiving patient records and parsing the patient records to extract financial data and medical data. Furthermore, Delaney teaches determining a cost for a facility based on input data, and utilizing an admissions modeling component to model patient admissions and determine a potential return on investment based on a predicted patient length of stay and a cost analysis. Additionally, Delaney teaches displaying reports including various data including an expected LOS and expected costs and revenue. However, Delaney does not teach utilizing natural language processing to parse the medical records, determining a failure in the NLP to identify a required input into a rate calculator, and displaying an indication on a GUI that the required input is missing. Additionally, Delaney does not teach the specific contents of the GUI, the specific steps of the training of the neural network model, and the selection of a particular trained neural network model corresponding to a matching group.
Blumenthal (US 2019/0304582) teaches utilizing natural language processing and identifying when the NLP fails to recognize data, and indicates that the data is missing. However, Blumenthal does not teach determining a reimbursement or cost rate for a patient at a healthcare facility, and further does not teach determining a potential revenue stream based on the reimbursement or cost rate. Additionally, Blumenthal does not teach the specific contents of the GUI, the specific steps of the training of the neural network model, and selecting a particular trained neural network model.
Denton (US 2018/0174121) teaches a GUI displaying user input fields, wherein some of the interfaces include fields that are auto-filled, and further displays a prompt indicating a missing data field. However, Denton does not teach any type of training of a neural network model, determining a reimbursement or cost rate, and determining a potential revenue stream based on the reimbursement or cost rate. Additionally, Denton does not teach the selecting a particular trained neural network model.
Shriberg (US 2019/0385711) teaches training a model incorporating a plurality of patient data, and selecting an appropriate model for a particular patient based on the patient data. However, Shriberg does not teach the specifics of the training process itself, including the particular type of training data used as inputs. Additionally, although Shriberg teaches enabling the prediction of patient costs, and tracking of hospitalizations, Shriberg does not specifically teach tracking reimbursements and/or utilizing a predicted length of stay for a hospitalization for the determination of a revenue stream.
The aforementioned references are understood to be the closest prior art. Various aspects of the claimed invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claims 1, 4-9, 11, 14-18, and 21-25 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicants’ arguments, see Remarks, filed November 24, 2025, with respect to the rejections of Claims 7-9 and 17 under 35 U.S.C. 112(b) have been fully considered and, in combination with the claim amendments, are persuasive. The previous rejections of Claims 7-9 and 17 under 35 U.S.C. 112(b) have been withdrawn. However, for the reasons shown above, Claims 1, 4-9, 11, 14-18, and 21-25 are presently rejected under 35 U.S.C. 112(a) and (b) due to the newly amended claim limitations.
Applicants’ arguments, see Remarks, filed November 24, 2025, with respect to the rejections of Claims 1, 4-9, 11, 14-18, and 21-25 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claims are patent eligible in view of Step 2A of the 2-step Alice analysis, specifically because the newly amended steps of the collecting of the medical records, the generating of the training data, and the training of the plurality of neural network models represent limitations that improve the functioning of a computer or improve another technology or technical field, e.g. see pgs. 12-15 of Remarks – Examiner disagrees.
As an initial matter, Examiner notes that although Applicants allege that the claimed invention is not directed towards any abstract idea, Applicants do not provide any rationale explaining why and/or how Examiner’s grounds of rejection presented above in view of prong 1 of step 2A of the two-step analysis is incorrect. Hence, for the reasons presented above, the claimed invention is directed towards the identified abstract idea.
Furthermore, Applicants cite [0002]-[0003] of the as-filed Specification, stating that they disclose that the claimed invention improves healthcare facility planning and a reimbursement prediction system because patients often move between providers and further because conventional current payment calculators do not indicate a total amount of revenue associated with a patient. However, even assuming, arguendo, that the aforementioned improvements are achieved by the claimed invention, these are not technological improvements because they merely automate a process that was previously performed manually (i.e. the automated input of patient information), and/or because they represent business/economic improvements (i.e. increased revenue and/or providing more information regarding revenue) rather than technological improvements. That is, the improvements achieved by the claimed invention merely recite improvements to the overall process of determining revenue streams, which represents an improvement to the abstract idea of a certain method of organizing human activity, and an improvement to the abstract idea itself is not a technological improvement, e.g. see MPEP 2106.05(a)(II). Additionally, the problems addressed by the claimed invention are not technological problems because the problems of inconveniences from patient recordkeeping between multiple entities and time-consuming processes involved with payment calculations are problems that have existed since long before the advent of any type of computer technology rather than problems arising specifically in the realm of computer technology.
Applicants further allege that the claimed invention is patent eligible because it is properly analogized to a patent eligible invention disclosed in the USPTO-Issued 2025 August Memorandum, e.g. see pgs. 15- of Remarks – Examiner disagrees.
Regarding the August Memorandum, Examiner notes that the August Memorandum re-emphasizes that the two-step subject matter eligibility analysis is performed in accordance with MPEP 2106, which Examiner has cited in support of the grounds of rejection under 35 U.S.C. 101 shown above.
Additionally, regarding the rationale disclosed in the Desjardins decision, as Applicants note, the invention of Desjardins was deemed patent eligible because it recited an improvement to the training of a machine learning model. In contrast, as stated above, the claimed invention does not improve the training of a machine learning model, but instead at most recites a particular input (i.e. the plurality of diagnostic parameters extracted from the medical records) into a machine learning model, setting a desired output (i.e. the observed length of stay), and performing some unspecified “adjustments” to “parameters” of the model in order to produce a predicted length of stay that is close to the observed length of stay. That is, the present claim language recites the type of data input into the model and an intended goal of the model, without reciting any specifics regarding how the goal is actually achieved. Furthermore, the improvements achieved by the machine learning model recited in Desjardins included reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks. In contrast, the claimed limitations at most make the determinations regarding the revenue stream output by the system more accurate, via use of the machine learning model, rather than specifically producing a unique/improved machine learning model itself. Hence, the claimed invention is not properly analogized to the invention of Desjardins.
For the aforementioned reasons, Claims 1, 4-9, 11, 14-18, and 21-25 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed November 24, 2025, with respect to the rejections of Claims 1, 4-9, 11, 14-18, and 21-25 under 35 U.S.C. 103 have been fully considered and, in combination with the claim amendments, are persuasive for the reasons disclosed above. The rejections of Claims 1, 4-9, 11, 14-18, and 21-25 under 35 U.S.C. 103 have been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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/JOHN P GO/Primary Examiner, Art Unit 3681