DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 8 August 2025.
Claim(s) 1-20 are currently pending and have been examined.
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 .
Priority
This application claims priority of US Provisional Application No. 63/705415 filed on 9 October 2024. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Claim Rejections - 35 USC § 112(b)
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 3 and 9 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. Claims not listed below are rejected for dependency.
Claim 3 recites “generating non-pathway data.” The specification describes this term saying the “system also generates non-pathway data using the LLM, supplementing the EHR with additional relevant medical information beyond the structured pathway” [13]. Thus “non-pathway data” refers to “relevant” medical data but which is “beyond” the ed pathway.” Thus the meaning of this term is based on multiple terms of degree. The specification provides no standards for these terms of degree. It would not be clear to one of ordinary skill in the art what data was relevant to the pathway and what data was relevant but beyond the pathway. As such, one of ordinary skill in the art would not be able to determine the boundaries of the term, rendering the claim indefinite.
Claim 3 recites “providing the EHR the non-pathway data.” The claim provides antecedent basis for an electronic health record (EHR) system, but does not reference any one electronic health record. It would be ambiguous to one of ordinary skill in the art whether the identified limitation requires providing the non-pathway data to the EHR system, or whether it requires providing the non-pathway data to a EHR of the EHR system. As such, the meaning of the claim would be unclear to one of ordinary skill in the art, rendering the claim indefinite.
Claim 9 recites “refining the care recommendation based on clinician interactions with the system implementing a feedback loop mechanism to continuously.” The limitation terminates before articulate what is continuously done, and as such one of ordinary skill in the art would not be able to determine the meaning of the claim, rendering the claim indefinite.
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.
Claim 14, which is representative of claims 1 and 20, recites a method comprising:
receiving clinical guidance in
generating a pathway from the clinical guidance using a into logical steps, including one or more of treatment goals, management strategies, and complication prevention measures; and
producing a patient chart in an
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of generating a patient treatment plan based on clinical guidance that a physician should follow similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of the managing personal behavior or relationships or interactions between people sub-grouping of methods of organizing human activity. As such, these limitations are determined to recite a method of organizing human activity. Alternatively, this concept is analogous to the examples of “observation”, “evaluation”, “judgement”, and “opinion” given in MPEP 2106.04(a)(2)(III). Further, this concept as claimed does not require a scale of data beyond the mental faculties of a human being and the operations of the abstract idea can be practically performed in the human mind. As such, these limitations are determined to recite a mental process. Therefore the claim is determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claims 14 and 20 recite the additional element of at least one device including a hardware processor. Claim 1 recites the additional element of a non-transitory computer-readable medium. These additional elements are recited at an extremely high level of generality, and are interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of a large language model. This additional element amounts to instructions to implement the abstract idea on a computer or to use a computer as a tool to perform the abstract idea. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional elements of digital data and electronic records. These additional elements reflect no improvement to technology, no particular machine, no transformation of an article, and no meaningful limitation. Instead, these additional elements only generally link the abstract idea to a technological environment of a computing device. As such, these additional elements do not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Because the additional elements, either individually or as a combination, do not integrate the abstract idea into a practical application the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not amount to significantly more than the abstract idea.
As previously noted, the claims recite an additional element of a large language model. Zorn et al. (US 2023/0418815 A1) demonstrates (“Conventional large language models are deep neural networks that have on the order of billions (and fast approaching trillions) of parameters that may each be adjusted as the model is trained on textual training data” [0001]) that large language models were conventional before the priority date of the claimed invention. As such, this additional element does not amount to significantly more than the abstract idea.
As previously noted, the claims recite the additional elements of digital data and electronic records. These additional elements reflect no improvement to technology, no particular machine, no transformation of an article, and no meaningful limitation. Instead, these additional elements only generally link the abstract idea to a technological environment of a computing device. As such, this additional element does not amount to significantly more than the abstract idea.
There are no further additional elements. When considered as a combination, the additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not amount to significantly more than the abstract idea. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2-13 and 15-19 further describes the abstract ideas set forth by the claim, but these claims are determined to continue to recite an abstract idea. Dependent claims 2-7, 9-13, and 15-19 do not recite any further additional elements. The previously identified additional elements, individually and as a combination, fail to integrate the narrowed abstract idea into a practical application for the same reasons provided above. Therefore dependent claims 2-7, 9-13, and 15-19 remain directed to an abstract idea. At Step 2B, the previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for the same reasons provided above. Therefore dependent claims 2-7, 9-13, and 15-19 do not recite significantly more than the abstract idea. Dependent claim 8 recites the additional element of a user interface. This additional element does not constitute an improvement to technology, does not require any particular machine, does not effect a transformation of an article, and does not meaningfully limit the implementation of the abstract idea. Instead, this additional element only generally links the abstract idea to a technological environment of a computing device. As such, this additional element does not integrate the abstract idea into a practical application. When considered in combination with the prior identified additional elements, the combination of additional elements only generally link the abstract idea to a technological environment of a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Therefore dependent claim 8 is determined to be directed to an abstract idea. At Step 2B, Manto et al. (US 2004/0196306 A1) demonstrates (“the content in a file may be displayed in a conventional manner with a graphical user interface” [0027]) the conventionality of displaying information with a user interface. As such, this additional element does not amount to significantly more. When considered in combination with the prior identified additional elements, the combination of additional elements only generally link the abstract idea to a technological environment of a computing device. As such the combination of additional elements does not amount to significantly more. Therefore dependent claim 8 does not recite significantly more than the abstract idea. Thus as the dependent claims remain directed to a judicial exception, and as the additional elements of the dependent claims do not amount to significantly more, the dependent claims are not patent eligible.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5-9, 14-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A)
Regarding Claims 1, 14, and 20: Park discloses a non-transitory computer readable medium comprising instructions (See at least Column 23, Lines 20-32) which, when executed by one or more hardware processors, causes performance of operations comprising:
receiving clinical guidance in digital text form (guidelines associated with medical care of the patient associated with the electronic patient identifier are extracted via a guideline interface. For example, if the patient is suffering from lung cancer, then the system queries the guidelines database for guidelines associated with lung cancer. The system then receives a response from the guidelines database and extracts guidelines from the response. See at least Column 12, Lines 32-39).
generating a pathway from the clinical guidance using a model, wherein the generated pathway is a structured summary for a specific disease state organized into logical steps, including one or more of treatment goals, management strategies, and complication prevention measures (At 210, a clinical pathway is generated. The clinical pathway recommends a future clinical service reconciled with actual costs (e.g., time, financial, etc.) extracted from the cost information. For example, the system may recommend a future clinical service by interacting with the navigable guideline model to select the future clinical service as a node in the navigable guideline, as discussed herein. Further, the system can perform compliance monitoring of the patient by tracking progress of the patient relative to the generated clinical pathway. See at least Column 13, Lines 12-21. Also: the model component 118 can be used to generate a clinical pathway based on information gathered through the extensible API component 106, where the clinical pathway recommends a future clinical service. In some embodiments, the recommended future clinical service is reconciled with actual costs (e.g., based on a default tuning parameter or a tuning parameter chosen by the user). In several embodiments, the model component 118 can utilize the navigable guidelines 112, e.g., guidelines set by medical professionals (e.g., the NCCN guidelines, ESMO guidelines, ASCO guidelines, payer guidelines, combinations thereof). See at least Column 6, Lines 23-33).
producing a patient chart in an electronic health record (EHR) system with information derived from the pathway (By way of illustration, FIG. 9 may thus illustrate an example detail of a node in a model of a guideline (e.g., NCCN breast cancer guideline) where the best practice recommended by the NCCN is reconciled with cost of care information. In other embodiments, FIG. 9 may be implemented as a page within a clinician portal. See at least Column 20, Lines 59-64 and Fig. 9).
Park does not expressly disclose using a large language model. However, Tang teaches generating a pathway from the clinical guidance using a large language model (As shown in FIG. 1, the large language model analysis system for optimizing clinical path formulation according to the present invention comprises a natural language processing unit, a deep learning and data analysis unit, a large data processing unit, a user interface, a data security unit and a privacy protection unit; the natural language processing unit is used for understanding complex medical terms and concepts and extracting valuable information from a large amount of texts such as medical documents and medical record reports; the deep learning and data analysis unit is used for identifying and understanding the complex mode in the clinical data; the large data processing unit is used for processing the large data set and integrating the data from different sources and formats so as to identify the most effective treatment method and process; the method based on the data is used for making the clinical path so as to ensure that the made clinical path reflects the latest medical research and practice. Page 7).
Park provides a system which uses clinical guidelines and an artificial intelligence model to generate a clinical pathway, which differs from the claimed invention by the substitution of Park’s generic artificial intelligence model for a large language model. Tang demonstrates that the prior art already knew of the idea of using a large language model to generate clinical pathways. One of ordinary skill in the art could have substituted Tang’s large language model capable of generating clinical pathways into the system of Park, and the substitution would have predictably resulted in Park’s system using a large language model to generate the clinical pathways. As such, the identified substitution and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang.
Regarding Claim 2 and 15: Park in view of Tang makes obvious the above limitations. Park further discloses wherein the operations further comprise: providing the large language model with the clinical guidance and a context including one or more of pathway templates, pathway examples and pathway standards (In various embodiments, a clinician may decide to use the model component 118 to organize patient care such that patient care follows a pathway defined by a guideline (i.e., strictly follow the guideline), and may even follow a static, pre-defined pathway, such as where the model component 118 is based on guidelines that do not change. In other embodiments, the clinician may decide to use the model component 118 such that patient care follows a pathway determined at least in part, by patient factors and/or other external factors outside recommended guidelines. Yet further, the clinician may decide to use the model component 118 such that patient care can account for transitioning a patient out of or otherwise away from a guideline recommendation (e.g., to purse a pathway along the lines of a different guideline or other treatment plan-such as may be necessary due to changing medical condition, environmental factors, updated guidelines or best practices, etc.). In this regard, the clinician may decide to use the model component 118 such that patient care follows a pathway dependent upon one or more guidelines, independent of one or more guidelines, based upon feedback from patients that do not match up to guidelines, based upon feedback from patients that match to one or more guidelines, any other factor that the clinician determines to be informative to determining the patient's plan of care, combinations thereof, etc. See at least Column 6, Lines 34-58).
Regarding Claim 3 and 16: Park in view of Tang makes obvious the above limitations. Park further discloses generating non-pathway data and providing the EHR the non-pathway data (the system generates an output for the user interface that characterizes the clinical pathway. In some embodiments, the output includes indica of a total predicted cost and how much of that total predicted cost has been used. In various embodiments, the output includes attributions of the total cost. For example, if a total cost is predicted to be $10,000 and two services have been performed for $2000 and $3000, then the output may include an indication that 50% of the costs have been used, and the first service used 20% of those costs and the second service used 30% of those costs. The predicted costs (including future costs) are based on the clinical pathway generated from the guidelines. Further, the output may include a prediction of future costs based upon detected changes representing a diversion in a treatment regimen defined by the generated clinical pathway. See at least Column 13, Lines 22-36). As previously noted, Tang teaches using the large language model. The motivation to combine Park and Tang is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 5 and 18: Park in view of Tang makes obvious the above limitations. Park further discloses creating a graph representation from the pathway, wherein the graph representation is provided to the EHR system for the patient chart (The example navigable graph 600 includes a legend 602 that identifies example node types (e.g., Group, Activity, Guideline, Header, Reference, Reference Link, and Result). Each has a unique shading matched to associated nodes in a node graph view 604 so that node types are visually represented in the graph within the node graph view 604. In practical applications, the graph can utilize at least one of color, size, shape, etc., to represent each node in the graphical representation. The legend 602 can also include other relevant data to assist in the usability of the visualization, such as node type counts, references to the guideline visualized, sort and filter options to show or hide certain nodes or relationships, etc. Of course, the exact medical condition will ultimately affect the node types/node definitions. See at least Column 17, Line 66 through Column 18 Line 12.
Regarding Claim 6 and 19: Park in view of Tang makes obvious the above limitations. Park further discloses wherein the operations further comprise: receiving patient information from the patient chart in the EHR system; parsing the patient information using the large language model; and generating a care recommendation based on a comparison of the parsed patient (medical records regarding a patient can be extracted and mapped to a guideline along with cost information, thus pulling from multiple add-on applications to perform a clinical pathway process that is tuned to a value-based care algorithm. See at least Column 6, Lines 6-10. Also: The system then receives a response from the electronic medical records database and extracts electronic medical information from the response. For example, values for some of the variables of the model may be extracted from the response. See at least Column 12, Lines 27-31. Also: A fifth section 916 provides a regimen section. In this example configuration, the interface provides a regimen, e.g., according to the navigable guideline (e.g., as optionally tweaked by the performance tuning). Moreover, in some embodiments, the interface can provide alternatives. This allows the clinician to select a regimen based upon a number of factors. In the illustrated example, the regimen screen includes cycles, frequency, risks, and cost. This allows the clinician to tune the patient care based upon a complete vison of risk, cost, burden, and other factors. See at least Column 21, Lines 31-40).
Regarding Claim 7: Park in view of Tang makes obvious the above limitations. Park further discloses wherein generating the care recommendation comprises one or more of: suggesting alternative medications considering one or more of condition, diagnosis, and known allergies, optimizing treatment options based on factors such as cost or side effects, and identifying potential gaps in documentation crucial for accurate diagnosis and proper reimbursement (A fifth section 916 provides a regimen section. In this example configuration, the interface provides a regimen, e.g., according to the navigable guideline (e.g., as optionally tweaked by the performance tuning). Moreover, in some embodiments, the interface can provide alternatives. This allows the clinician to select a regimen based upon a number of factors. In the illustrated example, the regimen screen includes cycles, frequency, risks, and cost. This allows the clinician to tune the patient care based upon a complete vison of risk, cost, burden, and other factors. See at least Column 21, Lines 31-40).
Regarding Claim 8: Park in view of Tang makes obvious the above limitations. Park further discloses wherein the operations further comprise: presenting the care recommendation through a user interface; and distinguishing between information derived from established guidelines and insights generated by the large language model (A third section 912 is a reference section that identifies the underlying source, justification, attribution, or other support references to establish the clinical pathway and clarify where in the progression, the patient is currently situated. See at least Column 21, Lines 23-26. Also: A fifth section 916 provides a regimen section. In this example configuration, the interface provides a regimen, e.g., according to the navigable guideline (e.g., as optionally tweaked by the performance tuning). Moreover, in some embodiments, the interface can provide alternatives. This allows the clinician to select a regimen based upon a number of factors. In the illustrated example, the regimen screen includes cycles, frequency, risks, and cost. This allows the clinician to tune the patient care based upon a complete vison of risk, cost, burden, and other factors. See at least Column 21, Lines 31-40).
Regarding Claim 9: Park in view of Tang makes obvious the above limitations. Additionally, Tang teaches refining the care recommendation based on clinician interactions with the system implementing a feedback loop mechanism to continuously (the analysis system for optimizing clinical path formulation further comprises a self-learning unit for learning from new data and user feedback, continuously improving algorithms and suggestions. Page 5). The motivation to combine Park and Tang is the same as explained under claim 1 above, and is incorporated herein.
Claim(s) 4 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A), and further in view of Shastri et al. (US 2012/0150498 A1).
Regarding Claim 4 and 17: Park in view of Tang makes obvious the above limitations. Park does not appear to disclose codifying the pathway using standard medical terminologies for concepts with specific codes. However, Shastri teaches codifying the pathway using standard medical terminologies for concepts with specific codes (In various embodiments of the present invention, clinical pathways are forecasted based on Standard Operating Procedures (SOPs) defined for classifications from the International Classification of Diseases (ICD). A standard operating procedure (SOP) is defined as the prescribed set of prerequisite procedures to be followed in order to treat the patient for an ICD. These procedures evolve over a period of time and are generally acceptable to many healthcare service providers. In general, a SOP acts as clinical guideline. Further, the SOP for a particular ICD consists of one or more pre-existing clinical pathways. See at least [0052]. Also: Clinical pathway repository 102 is configured to store pre-existing clinical pathways corresponding to a set of ICD codes. Pre-existing clinical pathways are the clinical pathways defined based on data collected from medical procedures followed in the treatment of diseases corresponding to the set of ICD codes. Clinical pathway repository 102 is further configured to store SOPs and corresponding pre-existing clinical pathways associated with ICD codes. See at least [0056]).
Park and Tang suggests a system generates clinical pathways, upon which the claimed invention’s codifying of the pathways can be seen as an improvement. However, Shastri demonstrates that the prior art already knew of codifying clinical pathways with ICD codes. One of ordinary skill in the art could have trivially applied the techniques of Shastri to the system of Park and Tang. Further, one of ordinary skill in the art would have recognized that such an application of Shastri would have resulted in an improved system which would make it easier to find clinical pathways. As such, the application of Shastri and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang and Shastri.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A), and further in view of Amarasingham et al. (US 2015/0106123 A1).
Regarding Claim 10: Park in view of Tang makes obvious the above limitations. Park does not appear to disclose analyzing unstructured text in clinical notes of the patient chart using the large language model to extract relevant clinical concepts. However, Amarasingham teaches analyzing unstructured text in clinical notes of the patient chart to extract relevant clinical concepts (The allergy information is extracted from the patient's Electronic Medical Record (EMR) as well as from clues found in unstructured text such as physician's notes or patient input/comments. This widget is preferably defined to be accessible from clinical, social, and patient views. See at least [00563]. Also: The demographic information is extracted from the patient's Electronic Medical Record (EMR) as well as from clues found in unstructured text such as physician's notes or patient input/comments. This widget is preferably defined to be accessible from the clinical, social, and patient views. See at least [0055]).
Park and Tang suggests a system which utilizes patient electronic medical records, upon which the claimed invention’s extraction of clinical note information can be seen as an improvement. However, Amarasingham demonstrates that the prior art already knew of extracting and using physician notes. One of ordinary skill in the art could have easily applied the techniques of Amarasingham to the system of Park and Tang. Further, one of ordinary skill in the art would have recognized that such an application of Amarasingham would have resulted in an improved system which would have more patient related medical data to consider. As such, the application of Amarasingham and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang and Amarasingham.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A), and further in view of Gupta et al. (US 2024/0127933 A1).
Regarding Claim 11: Park in view of Tang makes obvious the above limitations. Park does not appear to disclose generating an immunization forecast by validating existing vaccine doses and suggesting missing vaccinations based on standard schedules or specific requirements. However, Gupta teaches generating an immunization forecast by validating existing vaccine doses and suggesting missing vaccinations based on standard schedules or specific requirements (The immunization management system may provide recommendations of missing vaccinations as well as alerts if vaccinations have been received that do not appear to be appropriate or necessary. As an example, if the user is fifty years old, the immunization management system may provide a recommendation that the user have a shingles vaccine. In another example, if the user is under fifty years old and indicates that they have received the shingles vaccine, the immunization management system may send a message or an alert for a healthcare provider for more information as to why the vaccine was received before the age of fifty. See at least [0023]).
Park and Tang suggests a system generates clinical pathways. Gupta provides a vaccine management system. Between these references the prior art includes each element claimed, with the only difference between the claimed invention and the prior art being the lack of actual combination in a single reference. One of ordinary skill in the art could easily have combined the elements as claimed by known methods, and in combination each element merely performs the same function as it does separately. Further, one of ordinary skill in the art would have recognized that such a combination would have predictably resulted in a system which could both generate clinical pathways and manage vaccines. As such, the identified combination and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang and Gupta.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A), and further in view of Green, III et al. (US 2011/0202370 A1) [hereafter referenced as “Green”].
Regarding Claim 12: Park in view of Tang makes obvious the above limitations. Park does not appear to disclose analyzing the patient chart in real-time to identify potential documentation gaps; and prompting clinicians to address these gaps during a patient visit. However, Green teaches analyzing patient chart in real-time to identify potential documentation gaps; and prompting clinicians to address these gaps during a patient visit (As illustrated in FIG. 30, the review of systems section is populated with an inventory of body systems 1700 that is defined based on a series of questions the clinician asks the patient to identify symptoms and/or modifiers 1800 corresponding to the specific illness that the patient may be experiencing or has experienced. Some of those questions may have been asked during the patient check-in process, and the answers will automatically flow to the review of systems section of the progress note from the registration component 420. Some answers may also flow from a triage note. The clinician will be prompted by pup-up boxes to ask any questions not already answered during his or her physical examination of the patient. In FIG. 30, the clinician is being prompted to ask the patient about any feelings of chest pain. See at least [0272]).
Park and Tang suggests a system generates clinical pathways. Green provides a system which manages a doctor’s examination. Between these references the prior art includes each element claimed, with the only difference between the claimed invention and the prior art being the lack of actual combination in a single reference. One of ordinary skill in the art could easily have combined the elements as claimed by known methods, and in combination each element merely performs the same function as it does separately. Further, one of ordinary skill in the art would have recognized that such a combination would have predictably resulted in a system which could both generate clinical pathways and manage a doctor’s examination. As such, the identified combination and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang and Green.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (US 12148528 B1) in view of Tang (CN 118366593 A), and further in view of Paul, Jr. et al. (US 12243646 B1) [hereafter referenced as “Paul”].
Regarding Claim 13: Park in view of Tang makes obvious the above limitations. Park does not appear to disclose providing a suggested differential diagnosis. However, Paul teaches providing a suggested differential diagnosis (the language learning model (LLM) disclosed herein can include a differential diagnosis generator (DDx) module 200 to generate an accurate or improved differential diagnosis for a patient. The DDx module 200 can be used by clinicians to expand their differential diagnosis for a patient's presentation. See at least Column 11, Lines 21-26).
Park and Tang suggests a system generates clinical pathways, upon which the claimed invention’s use of a differential diagnosis can be seen as an improvement. However, Paul demonstrates that the prior art already knew of using LLMs to generate differential diagnoses. One of ordinary skill in the art could have easily applied the techniques of Paul to the system of Park and Tang. And one of ordinary skill in the art would have recognized it as an improvement because the new system would be more likely to suggest medically helpful interventions. As such, the application of Paul and the claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Park and the teachings of Tang and Paul.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Ober et al. (US 2007/0185739 A1) describes processing clinical guidelines to generate clinical protocols.
Vesto (US 2013/0197922 A1) describes improving existing clinical pathways.
Dadlani Mahtani et al. (US 2015/0006193 A1) and Yazdavar et al. (US 2024/0242802 A1) describe personalizing clinical pathways.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-07-11
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