DETAILED ACTION
Status
This Final Office Action is in response to the communication filed on 23 September 2025. Claims 3 and 14 have been cancelled, claims 1, 4-6, 9, 11-12, 15-17 and 20 have been amended, and no new claims have been added. Therefore, claims 1-2, 4-13, and 15-20 are pending and presented for examination.
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
A summary of the Examiner’s Response to Applicant’s amendment:
Based on Applicant’s amendment and response, the Examiner withdraws the 112 rejection(s). Please see the Claim Interpretation below, the amendment necessitating new grounds of rejection for claim 11.
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner places new grounds of rejection.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Claim Interpretation
Applicant’s amendment and response indicates that a “clinical signature” is literally merely the clinical features and their statistical significance. Therefore, the phrase “clinical signature” is interpreted as this. However, this – by definition – offers no limitation to the claims or the data, it merely assigns the label “clinical signature” to the already existent data of what clinical features are present and what the significance is of those features.
Further, the use of a G-LLM is, based on the amending, now considered to be a breadth issue rather than indefiniteness – the claimed use includes any use of any form of G-LLM, for any purpose as long as the summary of the patient journey includes at least one of the pieces of data indicated (i.e., a “patient history evaluation, conducting physical examination, differential diagnosis, targeted clinical investigations, diagnosis, evaluation of diagnosis, defining a plan and monitoring the patient”).
Applicant claims 1 and 12 recite “the disease classifier is trained based on one or more gold diagnosis labels”, where the only description related to what a “gold diagnosis label” would constitute, appears to be Applicant ¶ 0157 (as submitted, 0231 as published) indicating “In an exemplary embodiment, to obtain the training set, in the training stage, three clinicians are first requested to create gold diagnosis labels for patients with consensus”. However, the term “clinician” is not really defined, but Applicant ¶ 0139 (as submitted, 0213 as published indicates that “the term ‘clinician inputs’ refer to treatment methods and suggestions given by at least one of a clinician, plurality of clinicians specialized in different medical care, one or more clinicians, nurse, doctor, specialist, medical expert, based on their expertise and knowledge. The clinician input may be provided via a user interface.” Therefore, a “clinician” is interpreted, based on the light of the specification, as anyone employed at a clinic that inputs information (via an interface) into a/the computer; however, a clinician then may include, but is not necessarily, and may be distinct from a nurse, doctor, specialist, or medical expert. It is also noted that the above indication of three clinicians is merely an example embodiment
However, it also appears that a diagnosis from a particular specialist, expert, or institution may also be considered a “gold diagnosis label” – when the Examiner was diagnosed with a particular, and apparently, rare disease (after several indeterminate pathology results), it was several times expressed to me that since the diagnosis came from the Mayo Clinic at Rochester, MN, the diagnosis was considered a “gold standard” that others would not question or debate.
Further, it appears that there are other general standards sometimes used for indicating a label as a “gold” label – see, e.g., Jacquin, Kennedy, Asiedu, and Viddam at the pertinent prior art not relied upon below.
Since there is no firm definition provided, and there is variation regarding what is, and/or is not, a “gold diagnosis label”, but that this also appears to be an issue of breadth rather than actual indefiniteness. Therefore, a “gold diagnosis label” is interpreted as any reliable, repeatable, or agreed upon diagnosis label.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a system (claims 1-11) and a method (claims 12-20), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a system for simulating healthcare journey of one or more patients, wherein the system comprises: a database configured to store a patient data related to the one or more patients; a processor configured to: receive a patient data stored in the database and/or from an external source; create a simulation model of the one or more patients using the received patient data by employing machine learning; execute the simulation model to predict one or more health variables; generate one or more treatment variables in response to the generated one or more health variables; and provide the predicted one or more health variables, the one or more treatment variables and one or more clinician inputs to the simulation model for continuous learning of the simulation model, wherein to create the simulation model, the processor is configured to: receive a raw data related to a plurality of diseases, from the database prepare an unstructured textual data and a structured data from the raw data extract the one or more health variables from the unstructured textual data, using a pre-trained algorithm: combine the one or more health variables from the unstructured textual data with the structured data to obtain aggregated features and feed the aggregated features and the one or more clinician inputs to the machine learning to build a disease classifier, wherein the disease classifier is trained based on one or more gold diagnosis labels and feedback from the one or more clinician inputs, and wherein the simulation model is configured to apply the disease classifier to the patient data of one or more patients to simulate the healthcare journey of the one or more patients.
Claim 12 is analyzed in the same manner as claim 1, being directed to a method performing the same activities as at claim 1. The Examiner notes that claim 20 depends from claim 12 (see MPEP § 608.01(n)(III)) but includes “A non-transitory computer-readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method of claim 12.
Dependent claims 2, 4-11, 13, and 15-19 appear to be encompassed by the abstract idea of the independent claims since they merely indicate what data is used (claims 2 and 13), what data is considered unstructured and/or structured (claims 4-5 and 15-16), what the health variables include (e.g., demographics, adverse events, disease outcome, treatment response, etc.) (claims 6 and 17), and/or what determination is made based on health variables (e.g., trial/treatment eligibility, disease risk, etc.) (claims 7 and 18) to determine clinician input (claims 8 and 19).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of storing or retrieving patient data to create and update a simulation model of a patient; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) based on the modeling by employing machine learning and the use of that model – described as a neural network and/or a deep learning algorithm (see Applicant ¶ 0001, 0150, and 0152 as submitted, 0001, 0224, and 0226 as published);
Certain methods of organizing human activity (e.g. … commercial or legal interactions such as … business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions) based on predicting the healthcare journey, variables, and treatment of patients (see Applicant ¶ 0136 as submitted, 0210 as published); and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) based on the observing and evaluating of patient data, for example to predict a health variable and/or determine treatment, risk, and/or outcomes (see, e.g., claims 6-8 and 17-19).
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are a system that comprises: a database configured to store … data and a processor configured to perform the activities indicated at the claims (at claim 1), and a computer-readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method activities (at claim 20). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The only description(s) that appear(s) to describe the computers, medium, instructions, etc. to perform or implement the claimed activities merely indicates a processor (of various types) as “a computational element that is operable to respond to and processes instructions that drive the system” (see Applicant ¶ 0132 as submitted, 0206 as published).
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-10, 12-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dasi et al. (U.S. Patent Application Publication No. 2022/0392642, hereinafter Dasi) in view of Aravamudan et al. (U.S. Patent Application Publication No. 2018/0082197, hereinafter Aravamudan) in further view of Tran et al. (U.S. Patent Application Publication No. 2023/0089026, hereinafter Tran) .
Claim 1: Dasi discloses a system for simulating healthcare journey of one or more patients, wherein the system comprises:
a database configured to store a patient data related to the one or more patients (see Dasi at least at, e.g., ¶ 0008, “retrieving from a patients database, a patient's updated electronic medical record (EMR) including at least two or more of: patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, X-ray images or computer generated tomography images, and medical insurance information”; citation hereafter by number only);
a processor (0005-0007, processor in a server, or of a machine) configured to:
receive a patient data stored in the database and/or from an external source 0008, “retrieving from a patients database, a patient's updated electronic medical record (EMR) including at least two or more of: patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, X-ray images or computer generated tomography images, and medical insurance information”;
create a simulation model of the one or more patients using the received patient data by employing machine learning (0013, “taking into account patient specific information that may be predictive of known risks for a treatment options available to the patient. The advent of predictive models using some combination of artificial intelligence, machine learning, big-data, and computational simulations brings the possibility of being able to predict with some certainty (or uncertainty) the time course of what may happen to a patient when and if these models begin taking into account patient's specific genetic, biological, anatomical, and/or physiological characteristics”);
execute the simulation model to predict one or more health variables (0019, “starting with 3D model generation from medical images of the patient followed by subjecting the 3D models to simulation of TAV using computational frameworks that can predict the structural response as well as the hemodynamic performance and other flow parameters and flow patterns”);
generate one or more treatment variables in response to the generated one or more health variables (0019, “starting with 3D model generation from medical images of the patient followed by subjecting the 3D models to simulation of TAV using computational frameworks that can predict the structural response as well as the hemodynamic performance and other flow parameters and flow patterns”, 0021, “This type of holistic view maps out the early decisions from the doctors or by the heart team group to the outcomes for both the patient and the hospital. As an example, the decision to treat a younger low risk patient (based on STS score alone) with TVR as opposed to open heart surgery could mean the certainty of future interventions such as additional transcatheter or open heart surgeries in the future. The analogy is the game of chess, where the player who can account for future moves and counter moves has a better probability of winning. Similarly, a medical decision support system that lays out all the implications from each decision point in a quantitative manner in order to select the best decision path for the best outcome is critical. Such an optimal decision cannot be made without a systems level planning and intelligence driven guideline system. As described above, such a system collects nearly real time data from all the centers, which updates population based predictive models with ever increasing precision to include genetic, anatomic, and other physiological parameters into the data collection. The system also consists of simulation capability to simulate the biomechanical interaction of devices within the patient while considering all possible treatment options (current and predicted interventions in the future) and then calculate a precise risk score for each treatment path”); and
provide the predicted one or more health variables, the one or more treatment variables and one or more clinician inputs to the simulation model for continuous learning of the simulation model (0021, “such a system collects nearly real time data from all the centers, which updates population based predictive models with ever increasing precision to include genetic, anatomic, and other physiological parameters into the data collection”),
wherein to create the simulation model, the processor is further configured to:
receive a raw data related to a plurality of diseases, from the database (0008, “retrieving from a patients database, a patient's updated electronic medical record (EMR) including at least two or more of … diseases records”;
feed the aggregated features and the one or more clinician inputs to the machine learning to build a disease classifier (0009, “The prescribing of the optimized medical intervention incudes mapping the diagnosis of the patient to a medical treatment database to select a plurality of likely medical intervention choices based on a score exceeding a defined threshold score”, 0010, “The prescribing of the optimized medical intervention includes determining a rank order of the selected plurality of medical intervention choices by comparing simulation outcomes for each choice executed by the medical predictive algorithm”),
wherein the disease classifier is trained based on … feedback from the one or more clinician inputs, and wherein the simulation model is configured to apply the disease classifier to the patient data of one or more patients to simulate the healthcare journey of the one or more patients (0022, “In this disclosure, machine learning and/or artificial neural network or deep learning algorithms are trained and updated at regular intervals of time to track patient healthcare parameters and outcomes such as length of stay, cost of care, itemized list of supplies used during care (sticks, saline bags, accessories etc.), valve durability, life expectancy, and probability of complications at national, regional, down to center/hospital specific level. For example, the machine learning algorithm will be able to predict length of stay, costs, and projected lifespan for a patient who underwent TAVR and resulted in the placement of a permanent pacemaker. These algorithms may be trained on retrospective data from all medical data available for a patient who underwent structural heart procedures such as open heart surgery or trans-catheter procedures”, 0008-0010 as above – the medical records indicating feedback such as notes and comments);
apply the disease classifier to the patient data of one or more patients (0009, “The prescribing of the optimized medical intervention incudes mapping the diagnosis of the patient to a medical treatment database to select a plurality of likely medical intervention choices based on a score exceeding a defined threshold score”, 0010, “The prescribing of the optimized medical intervention includes determining a rank order of the selected plurality of medical intervention choices by comparing simulation outcomes for each choice executed by the medical predictive algorithm”).
Dasi, however, does not appear to explicitly disclose prepare an unstructured textual data and a structured data from the raw data; extract the one or more health variables from the unstructured textual data, using a pre-trained algorithm; combine the one or more health variables from the unstructured textual data with the structured data to obtain aggregated features. Aravamudan, though, teaches that “Input data to the system can be structured data 101, semi-structured data 117, and/or unstructured data 102” (Aravamudan at 0167), “when them [sic, “there” at original filing] is latent information that can be extracted from structured data 101, a specialized encoder 104 can be used to generate unstructured data from the structured data 101. The specialized encoder 104 can send (104a) the generated unstructured data to the unstructured data extraction classifier 105, which can in turn send the output through the unstructured data extraction pathway 105a. In some embodiments, the generated unstructured data is m the form of unstructured text. For example, if the structured data is “disease=cancer; indication=weight loss; drug=methotrexate; side_effect=dizziness,” the specialized encoder 104 can generate unstructured data in the form of “disease cancer indication weight loss drug methotrexate side_effect dizziness.” In this example, latent information in the structured data can be that cancer can be associated with weight loss and methotrexate and that the patient suffers dizziness. Thus, such latent information cars be extracted and leveraged by using the unstructured data extraction classifier 105 on the structured data 101 that has been processed by specialized encoder 104” (Aravamudan at 0173 – indicating that there is structured and unstructured data prepared, extraction of unstructured data, and combining to obtain aggregated features). Therefore, the base system and/or methods of simulation as in Dasi would be predictably improved or modified by the data processing techniques indicated in Aravamudan so as to yield the predictable result of the use of aggregated structured and unstructured data so as to form a more complete and personalized “prediction with individualized simulation to predict specific adverse outcomes (of current and future interventions) will yield more accurate risk calculation at an individual level to optimize the decision making” (Dasi at 0019). As such, the Examiner understands and finds that to prepare and use unstructured and structured data, extract unstructured data, and obtain aggregate features by combining the data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to achieve a more complete and individualized simulation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the simulation of Dasi with the data processing of Aravamudan in order to prepare and use unstructured and structured data, extract unstructured data, and obtain aggregate features by combining the data so as to achieve a more complete and individualized simulation.
The rationale for combining in this manner is that to prepare and use unstructured and structured data, extract unstructured data, and obtain aggregate features by combining the data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to achieve a more complete and individualized simulation as explained above.
Dasi in view of Aravamudan, however, does not appear to explicitly disclose training as based on one or more gold diagnosis labels. Tran, though, teaches training a deep learning classifier in radiology with gold standard labeling including statistical analysis of diagnoses and consensus of three radiologists (Tran at 0542-0543) where “This may advantageously increase the accuracy of the prediction due to the model training benefitting both from high granularity of the findings in the training data as well as high confidence training data for findings at lower granularity levels” (Tran at 0036). Therefore, the Examiner understands and finds that to train a model using gold labeled data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to increase prediction accuracy based on the training data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the simulation of Dasi in view of Aravamudan with the gold label training data of Tran in order to train a model using gold labeled data so as to increase prediction accuracy based on the training data.
The rationale for combining in this manner is that to train a model using gold labeled data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to increase prediction accuracy based on the training data as explained above.
Claim 2: Dasi in view Aravamudan in further view of Tran discloses the system of claim 1, wherein patient data comprises at least one of: imaging data, genomics, clinical notes, a disease symptoms data and a plurality of lab test reports of the patient (Dasi at 0008, symptoms, X-ray or tomography images, 0020, CT scan images, 0039, medical images).
Claim 4: Dasi in view Aravamudan in further view of Tran discloses the system and method of claim 1, wherein the unstructured textual data comprises at least one of: nursing notes, prescriptions, insurance claims, clinical notes, discharge summaries, radiology reports (Dasi at 0008, medical insurance information, 0009, “out-patient rehabilitation cost, … and reimbursable cost from insurance carrier”).
Claim 5: Dasi in view Aravamudan in further view of Tran discloses the system and method of claim 1, wherein the structured data comprises at least one of: blood test results, genomic testing, laboratory data, demographics, temperature, heart rate (Dasi 0008, “a patient's updated electronic medical record (EMR) including at least two or more of: patient's demographic data, morbid symptoms, vital signs, … genetic data, laboratory test data, diseases records, allergies, … and medical insurance information”).
Claim 6: Dasi in view Aravamudan in further view of Tran discloses the system and method of claim 1, wherein the one or more health variables includes phenotypic features and patient information that comprises at least one of: demographics, adverse events, disease outcome, response to treatments, social habits, family history, treatment, biomarkers, therapy and medications, extracted from the unstructured textual data (Dasi at 0008, “a patient's updated electronic medical record (EMR) including … patient's demographic data, morbid symptoms, … medications, surgery history, family medical history, … diseases records, allergies, … and medical insurance information”, 0026, “the model may include relevant information including genetic, race, gender, geographic location, and relevant vital functional status of kidneys, lungs, liver, blood cholesterol etc. Family history and all past diagnosis and their treatment status”).
Claim 7: Dasi in view Aravamudan in further view of Tran discloses the system of claim 1, wherein the one or more health variables are used to determine at least one of: eligible patients for treatment and trials (Dasi at 0010, “prescribing of the optimized medical intervention includes receiving by the patient's physician or patient's electronic medical record database, a rank order of recommended medical intervention choices including possible options and associated metrics based on an accepted level of simulated outcome”), patients at risk of specific diseases (Dasi at 0019, “the more accurate the combined model will become at predicting individualized risk for a given medical treatment or intervention”), predicted patient outcomes (Dasi at 0010, “comparing simulation outcomes for each choice executed by the medical predictive algorithm, on respective choices among each of the selected likely medical interventions”, 0013, “predict with some certainty (or uncertainty) the time course of what may happen to a patient when and if these models begin taking into account patient's specific genetic, biological, anatomical, and/or physiological characteristics”, 0018, “assessing the risk of several adverse outcomes that may happen at the time of surgery or even months after surgery”), predicted risk of recurrence and suggestions of monitoring steps.
Claim 8: Dasi in view Aravamudan in further view of Tran discloses the system of claim 7, wherein the eligible patients for treatment and trials, patients at risk of specific diseases, predicted patient outcomes, predicted risk of recurrence and suggestions of monitoring steps are used to determine the one or more clinician input (Dasi at 0020, “if a decision is being considered to implant a prosthetic valve now, then it should also be anticipated that the patient will need another valve within about 10 years. Furthermore, if the patient will require coronary interventions in the future (based on prognosis of coronary artery disease) then the access to the coronaries must also be factored into the current decision depending on how advanced coronary artery disease is in the patient as observed from the Computerized Tomography (CT) scan images”).
Claim 9: Dasi in view Aravamudan in further view of Tran discloses the system of claim 3, wherein the aggregated features are obtained by combining clinical features from the structured data with the extracted one or more health variables of the unstructured textual data (Aravamudan at 0173, as combined above and using the rationale as at the combination above).
Claim 10: Dasi in view Aravamudan in further view of Tran discloses the system of claim 9, wherein a clinical signature of a target disease is based on the clinical features and their clinical and statistical significance with respect to the target disease (Dasi at 0020, “if a decision is being considered to implant a prosthetic valve now, then it should also be anticipated that the patient will need another valve within about 10 years. Furthermore, if the patient will require coronary interventions in the future (based on prognosis of coronary artery disease) then the access to the coronaries must also be factored into the current decision depending on how advanced coronary artery disease is in the patient as observed from the Computerized Tomography (CT) scan images”).
Claims 12-13, and 15-20 are rejected on the same basis as claims 1-2 and 4-8 above since Dasi discloses a method for simulating healthcare journey of one or more patients, the method comprising the same or similar activities as at claims 1-2 and 4-8 above and a computer-readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method of claim 12 (for claim 20 – see Dasi at 0007).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Dasi in view Aravamudan in further view of Tran and in still further view of Kim (U.S. Patent Application Publication No. 2020/0243076).
Claim 11: Dasi in view Aravamudan in further view of Tran discloses the system of claim 9, but does not appear to explicitly disclose wherein Generative Large Language Models (G-LLM) are employed to assist the clinicians at each step of patient journey and to generate the summaries of the patient journey, wherein the patient journey comprises at least one of: patient history evaluation, conducting physical examination, differential diagnosis, targeted clinical investigations, diagnosis, evaluation of diagnosis, defining a plan and monitoring the patient. Kim, however, teaches that “AI text generators operate to generate natural language from structured data, such as a knowledge base or a logical form (linguistics) to produce documents that summarize or explain the contents of computer databases. These are used, for example in, generating news reports, summarizing medical records, generating technical manuals, product descriptions for e-commerce sites, etc. For instance, OpenAI (at https://openai.com) has developed a large-scale unsupervised transformer-based language model called GPT-2 that generates coherent paragraphs of text without task-specific training. A related website, talktotransformer.com offers and accessible version of OpenAI's GPT-2 (the term “transformer” refers to the type of neural network used by GPT-2 and other systems), where Texar, developed by Petuum, Inc., is an open-source toolkit focused on text generation through the use of TensorFlow language” (Kim at 0004, where at least GPT-2 is a G-LLM). Therefore, the Examiner understands and finds that to use G-LLM to assist in summarizing is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to more easily summarize patient histories, diagnosis, and other evaluations.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the simulation of Dasi in view Aravamudan in further view of Tran with the summarizing of Kim in order to use G-LLM to assist in summarizing is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to more easily summarize patient histories, diagnosis, and other evaluations.
The rationale for combining in this manner is that to use G-LLM to assist in summarizing is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to more easily summarize patient histories, diagnosis, and other evaluations as explained above.
Response to Arguments
Applicant's arguments filed 23 September 2025 have been fully considered but they are not persuasive.
Applicant first argues the 112 rejections (Remarks at 7-12); however, these rejections have been withdrawn based on the amendment. Therefore, the arguments are considered moot and not persuasive.
Applicant then argues the 101 rejections (Id. at 12-20), alleging analogy to McRO in that “The primary technical issue revolves around the constraint of inaccurate disease classification in healthcare systems due to reliance on imperfect ICD codes and lack of clinician expertise integration” (Id. at 14). However, the “solution” at the claims is merely to use what Applicant calls “gold diagnosis labels” that are merely reputed to be better labels (since there may be, but also may not be, per the light of the specification and the plain/ordinary meaning of the term, some consensus on a diagnosis). The Examiner notes that these same gold labels can be used by persons either mentally or as one of certain methods of human activity. As such, merely using “gold diagnosis labels” is considered to be part of the abstract idea – as merely limiting the data that is gathered and used – and does not rescue the claims.
Applicant then argues an improved system over allegedly inaccurate systems (Id. at 15); however, an improved abstract idea is still an abstract idea and “the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting” per SAP v. Investpic, slip op at p. 2, line 22 – p. 3, line 13, 898 F.3d 1161, 1162 (Fed. Cir. 2018), see also Id., slip op. at p. 10, lines 18-24, 898 F.3d 1161, 1167.
Applicant then argues that the above alleged solution and/or improvement indicates a practical application at Prong 2 of the eligibility analysis (Remarks at 16-17). However, as indicated above, this is part of the abstract idea and only elements additional to the abstract idea may transform to a practical application.
Applicant then alleges that the above alleged solution and/or improvement indicates significantly more at Step 2B of the eligibility analysis (Id. at 17-20), citing data indicating “The experimental results demonstrate the technical superiority of this approach” (Id. at 19). However, again, improved results appears to merely point to, or be caused by, an improved abstract idea and/or more accurate or better data that is gathered and used. However, SAP v. Investpic indicates that even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting (SAP v. Investpic, slip op at p. 2, line 22 – p. 3, line 13, 898 F.3d 1161, 1162 (Fed. Cir. 2018).
Applicant then argues the prior art rejections as based on the amendment indicating using gold diagnosis labels (Remarks at 20-24). The amending necessitates new grounds of rejection – please see the current rejections above.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marr, Bernard, What’s The Difference Between Structured, Semi-Structured And Unstructured Data?, dated 18 October 2019, downloaded from https://www.forbes.com/sites/bernardmarr/2019/10/18/whats-the-difference-between-structured-semi-structured-and-unstructured-data/, illustrating structured vs. unstructured data.
Jacquin et al. (U.S. Patent Application Publication No. 2011/0038515, hereinafter Jacquin) indicates that “Classifiers are built by forming a training dataset, where each subject is assigned a "label," namely a diagnostic class based on information provided by doctors with the help of state-of-the-art diagnostic systems, such as CT scan, MRI, etc. (these labels are usually referred to as "gold standard" labels)” (Jacquin at 0004).
Kennedy et al. (U.S. Patent No. 9,495,515, hereinafter Kennedy) indicates “Pathology labels are the gold-standard used to characterize a given sample” (Kennedy at column 29, lines 2-3).
Asiedu et al. (U.S. Patent Application Publication No. 2021/0374953, hereinafter Asiedu) indicates “to develop a series of feature extraction and machine algorithms … this method uses pathology gold standard labels for training and does not require a health provider to pre-select an area of concern” (Asiedu at 0046, see also 0047).
Viddam et al. (U.S. Patent Application Publication No. 2023/0049642, hereinafter Viddam) indicates “The system can perform the classification using a classifier such as a machine learning system. A machine learning system may be trained on a large training data set (e.g., hundreds of thousands of subjects) labeled with “gold standard” techniques such as PSG data labeled by clinicians” (Viddam at 0007), where PSG refers to “polysomnography (PSG)” (Viddam at 0003)>
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685