DETAILED ACTION
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 .
Claims 1-20 are pending and have been examined.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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.
Claims 1-20 are directed to a method or system, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 19 as the claim that represents the claimed invention for analysis and is similar to method Claim 1 and system Claim 12.
Claim 19 recites the limitations of (Certain Methods of Organizing Human Activity):
A method, comprising:
generating a clinical trial data model from data of a clinical trial extracted from a database of clinical trials;
generating a plurality of patient data models from patient data of a respective plurality of patients extracted from a hospital database;
comparing the clinical trial data model with each patient data model of the plurality of patient data models to determine a set of patients that match the clinical trial; and
displaying the set of patients that match the clinical trial on a display device.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, highlighted in bold above, which covers performance of the limitation as a managing personal behavior and relationships between people. Generating a plurality of patient data models, comparing a clinical trial data model with each patient data model to determine a set of patients that match the clinical trial (managing relationships between people by following rules), and displaying the set of patients that match the clinical trial (managing personal behavior by teaching which set of patients match the clinical trial) is managing personal behavior and relationships between people. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a personal behavior or managing relationships between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 1 and 12 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
Claim 19 recites the limitations of (Mental Processes):
A method, comprising:
generating a clinical trial data model from data of a clinical trial extracted from a database of clinical trials;
generating a plurality of patient data models from patient data of a respective plurality of patients extracted from a hospital database;
comparing the clinical trial data model with each patient data model of the plurality of patient data models to determine a set of patients that match the clinical trial; and
displaying the set of patients that match the clinical trial on a display device.
In that the claims can be performed in the mind of a person, with pen and paper, the claims are also abstract under Mental Processes grouping of abstract ideas. A person can generate (write down with pen and paper) a clinical trial model from a database, generate patient data models from a hospital database, compare the clinical trial data model each patient model to determine a match (analyze in their mind), and display (write down) the patients that match. Further it’s noted that a generic computer can perform the abstract steps, even though a computer is not claimed. (see MPEP 2106.04(a)(2) III C).
The judicial exceptions are not integrated into a practical application. In particular, the claims only recite: Electronic health record, display device (Claim 1); processor, memory, display device (Claim 12); display device (Claim 19). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The large language model (LLM) in claim 12 is being used at a high level of generality and could be using an existing LLM (see para. [0030] and use of commercial, open source LLM’s such as OpenAI GPT). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 12, and 19 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 12, and 19 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-11, 13-18, and 20 further define the abstract idea that is present in their respective independent claims 1, 12, and 19 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Claims 8 and 20 further recites a display device at a high level of generality. Claims 13-16 further recite processor to store or display data which is using generic computer components at a high level of generality. Claims 2, 5, 17, and 18 recite large language models at a high level of generality. Therefore, the claims 2-11, 13-18, and 20 are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-3, 5-12, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2020/0381087 to Ozeran et al. in view of Wong et al. (Wong et al., “Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology,” Aug. 18, 2023, Proceedings of Machine Learning Research 219:1-18, pp. 1-24)
Regarding claim 1
A method, comprising:
generating a patient data model from data of a patient extracted from an electronic health record (EHR);
{
From Applicant’s specification on “patient data model”…
“Patient data model generation block 201 and clinical trial data model generation block 221 may both include a series of similar steps, during which raw data in various formats is extracted and expanded to generate an emiched data model including structured data to facilitate a direct comparison. In other words, patient data model generation block 201 describes a first process for extracting patient data from medical records of the patient and constructing a patient data model, and clinical trial data model generation block 221 describes a second, similar process for extracting clinical trial data from one or more lists of clinical trials and constructing a clinical trial data model.” [0038]
Therefore, the patient data model appears to be structured patient data.
}
Ozeran et al. teaches:
Fig. 1, ref. 126 and patient records (patient data model)…
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Extraction of data within patient records…
“The present disclosure relates to systems and methods for facilitating the extraction and analysis of data embedded within clinical trial information and patient records. More particularly, the present disclosure relates to systems and methods for matching patients with clinical trials and validating clinical trial site capabilities.” [0003]
Standardize (generate) patient health information (patient data model)…
“At 4908, the process 4900 can determine data elements in the patient health information. In some embodiments, the patient health information can be unstructured and/or include free-text. The process 4900 can determine the data elements in order to standardize the patient health information. In some embodiments, the data elements can include at least a portion of the features and/or other data elements in the patient data store 3202.” [0322]
determining a disease state of the patient, based on the patient data model and clinical guidelines;
Capturing (determining) a disease/condition (state) data….
“Thus, what is needed is a system that is capable of efficiently capturing all relevant clinical trial and patient data, including disease/condition data, trial eligibility criteria, trial site features and constraints, and/or clinical trial status (recruiting, active, closed, etc.). Further, what is needed is a system capable of structuring that data to optimally drive different system activities including one or more of efficiently matching patients to clinical trials, activating new sites for an existing clinical trial, and updating site information, among other things. In addition, the system should be highly and rapidly adaptable so that it can be modified to absorb new data types and new clinical trial information, as well as to enable development of new user applications and interfaces optimized to specific user activities.” [0022]
Example of disease state with patient diagnosed (patient data) and stage 1 (clinical guidelines)…
“In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll. A structured data field corresponding to “stage/grade” may then be populated with “stage I,” and a structured data field corresponding to “disease type” may then be populated with “breast” or “breast cancer.” The ability of the system to create structured clinical trial data can aid in the matching of patients to an appropriate clinical trial. In particular, a patient's structured health data can be mapped to the structured clinical trial data to determine which clinical trials may be optimal for the specific patient.” [0116]
See Clinical Guideline below.
generating a clinical trial data model from data of a clinical trial extracted from a database of clinical trials;
Utilizes database of clinical trials…
“The present disclosure is described in the context of a system that utilizes an established database of clinical trials (e.g., clinicaltrials.gov, as provided by the U.S. National Library of Medicine). Nevertheless, it should be appreciated that the present disclosure is intended to teach concepts, features, and aspects that can be useful with any information source relating to clinical trials, including, for example, independently documented clinical trials, internally/privately developed clinical trials, a plurality of clinical trial databases, and the like.” [0004]
Capture, ingest (extract), structure (generating a clinical trial model) clinical trial information…
“In one example, the present disclosure includes a system, other class of device, and/or method to help a medical provider make clinical decisions based on a combination of molecular and clinical data, which may include comparing the molecular and clinical data of a patient to an aggregated data set of molecular and/or clinical data from multiple patients, a knowledge database (KDB) of clinico-genomic data, and/or a database of clinical trial information. Additionally, the present disclosure may be used to capture, ingest, cleanse, structure, and combine robust clinical data, detailed molecular data, and clinical trial information to determine the significance of correlations, to generate reports for physicians, recommend or discourage specific treatments for a patient (including clinical trial participation), bolster clinical research efforts, expand indications of use for treatments currently in market and clinical trials, and/or expedite federal or regulatory body approval of treatment compounds.” [0114]
comparing the patient data model with the clinical trial data model to determine whether the patient is a match for the clinical trial, based on the disease state, and inclusion and exclusion criteria of the clinical trial; and
Matching patients with clinical trials…
“The present disclosure relates to systems and methods for facilitating the extraction and analysis of data embedded within clinical trial information and patient records. More particularly, the present disclosure relates to systems and methods for matching patients with clinical trials and validating clinical trial site capabilities.” [0003]
Inclusion and exclusion criteria…
“In some embodiments of the present disclosure, the system can compare individual patient data to clinical trial data, and subsequently generate a report of recommended clinical trials that the patient may be eligible for. The patient's physician may review the report and use the information to enroll the patient in a well-suited clinical trial. Accordingly, physicians and/or patients do not need to manually sort and review all clinical trials within a database. Rather, a customized list of clinical trials is efficiently generated, based on the specific needs of the patient. In addition, the specific source of the patient data can easily be traced to each trial's inclusion and exclusion criteria to highlight the rationale for identifying that trial as well-suited. This generation can significantly decrease the time for a patient to find and enroll in a clinical trial, thus improving treatment outcomes for certain diseases and conditions.” [0117]
in response to determining that the patient is a match for the clinical trial, displaying one of the matching patient or the matching clinical trial on a display device.
[No Patentable Weight is given to non-functional descriptive claim language of “…displaying one of the matching patient or the matching clinical trial…”]
GUI (displaying) patient selected for (matching) clinical trial…
“FIG. 42 is shown to include a graphical user interface (GUI) 4200. In some embodiments, GUI 4200 can be implemented by the system 100 in FIG. 1. In some embodiments, the GUI 4200 can include a patient report 4202 corresponding to a patient selected and/or applying for a clinical trial.” [0311]
Clinical Guideline
Ozeran et al. teaches clinical trials and patients. They do not specifically teach clinical guideline.
Wong et al. also in the business of clinical trials and patients teaches:
Standard of care (clinical guideline)…
“Note: For participants to be eligible for enrollment they must have failed at least one line of standard of care systemic therapy (ie, not treatment naïve), with the exception of CRC participants who must have failed at least 2 lines of standard of care systemic therapy, as per CRC specific eligibility criteria. Participants must not have melanoma or NSCLC.” (pg. 9, Fig. 4)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of Ozeran et al. the ability to determine a standard of care (clinical guideline) as taught by Wong et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wong et al. who teaches the benefits of evaluating patients based on standards of care for trial enrollment.
Regarding claim 2
The method of claim 1, wherein generating the patient data model further comprises using a first large language model (LLM) to extract entities, make assertions about entities, and recognize relationships between entities in unstructured patient data extracted from the EHR.
Ozeran et al. teaches:
Reviewing (extracting) unstructured data with disease type of breast cancer (extract entities) and patient diagnosed with breast cancer (make assertion cancer is present), and stage 1 breast cancer (recognize relationship) …
“In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll. A structured data field corresponding to “stage/grade” may then be populated with “stage I,” and a structured data field corresponding to “disease type” may then be populated with “breast” or “breast cancer.” The ability of the system to create structured clinical trial data can aid in the matching of patients to an appropriate clinical trial. In particular, a patient's structured health data can be mapped to the structured clinical trial data to determine which clinical trials may be optimal for the specific patient.” [0116]
Using AI…
“In some embodiments, the AI classification system 3280 can include at least one trained model that can receive inclusion criteria and/or exclusion criteria in the inclusion and exclusion criteria module 3272 and features in the patient data store 3202, and output at least one indication of whether or not at least one criteria is met or not met. In some embodiments, the trained model can be a neural network or other appropriate machine learning model trained on a training data set. For a data-criteria concept mapping classifier, an exemplary training data set may include patient information (e.g., features that may be included in the patient data store 3202), clinical trial information including inclusion and exclusion criteria (e.g., criteria that may be included in the inclusion and exclusion criteria module 3272), and resulting line-by-line classification results for whether the inclusion or exclusion criteria were met (e.g., ground truths).” [0276]
LLM
The combined references teach unstructured data. They also teach extraction, assertions and relationships. They do not teach large language models.
Wong et al. also in the business of unstructured data teaches:
Using large language models…
“To the best of our knowledge, we are the first to explore using the emergent in-context learning capability of LLMs (Brown et al., 2020) for clinical trial matching. Out of box and with no more than three examples, cutting-edge LLMs, such as GPT-4, can already structure trial eligibility criteria with reasonable performance, outperforming strong baselines from prior systems such as Criteria2Query. We also show preliminary results in applying LLMs to end-to-end clinical trial matching.” (pg. 5, para. 1)
PubMedBERT (first large language model) for patients…
“Structured Patient Information To facilitate simulated end-to-end matching evaluation, we leverage the component for structuring patient information in the expert system at our collaborating health network (see Section 3.3). Briefly, this component system uses the Read OCR model in Azure Form Recognizer7 to convert any scanned images and PDF documents into free text, and then appends such text to digitized medical records. It then applies a series of state-of-the-art biomedical NLP models to extract relevant patient attributes from the medical records. For example, it uses self-supervised PubMedBERT models (Preston et al., 2023) to extract the tumor site, histology, and staging information, as well as additional information extraction modules to extract other attributes such as health status, PD-L1 IHC result, and medications. Information available in structured EMRs is added directly, such as date of birth and gender. Other structured data fields such as lab tests and medications are normalized to the same units and NCI Thesaurus, respectively.” (pg. 10, para. 2)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use large language models as taught by Wong et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wong et al. who teaches the benefits of using large language models for unstructured data analysis of both patients and clinical trial information.
Regarding claim 3
The method of claim 2, wherein the unstructured patient data includes textual descriptions of one or more of a diagnosis, a disease state, written results of tests, labs, imaging studies, a treatment, and a care provider comment.
Ozeran et al. teaches:
Example of unstructured data and breast cancer (disease state)…
“In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll. A structured data field corresponding to “stage/grade” may then be populated with “stage I,” and a structured data field corresponding to “disease type” may then be populated with “breast” or “breast cancer.” The ability of the system to create structured clinical trial data can aid in the matching of patients to an appropriate clinical trial. In particular, a patient's structured health data can be mapped to the structured clinical trial data to determine which clinical trials may be optimal for the specific patient.” [0116]
Regarding claim 5
The method of claim 1, wherein generating the clinical trial data model further comprises using a second large language model (LLM) to extract entities from unstructured clinical trial data and identify beginnings and endings of inclusion and exclusion sections of eligibly criteria in the unstructured clinical trial data.
Ozeran et al. teaches:
Inclusion and exclusion criteria…
“As shown, outputs from analytics module 136 can be provided to display device 116 via communication network 118. Further, provider 112 can input additional data via display device 116, and the data can be transmitted to server 120. In some embodiments, provider 112 can input clinical trial information via display device 116, and the data can be transmitted to server 120. The clinical trial information can include inclusion and exclusion criteria, site information, trial status (e.g., recruiting, active, closed, etc.), among other things.” [0135]
Example of free-text (unstructured data)…
“As an example, the first element shown within the inclusion criteria 511 is “histologically confirmed newly diagnosed stage I-II HER2/neu positive breast cancer.” Accordingly, within the trial details 504, “newly diagnosed” may be selected (e.g., checked), the disease criteria 513 may be selected (or otherwise input) as “breast,” and the stage/grade criteria may include “stage II, stage I, stage IIA, IIB, IA, IB.” Using GUI 500, the free-text within the inclusion criteria 511 may be mapped/associated with existing structured data fields. In some aspects, the existing structured data fields (e.g., disease criteria 513, etc.) can align with the structured data fields that may be used to capture patient data. In some situations, it may be desirable to have very granular information. Therefore, the various matching criteria fields may be fairly granular. The specificity of the matching criteria fields can enable accurate comparisons between patient data and clinical trial eligibility data, for example.” [0155]
Example of inclusion/exclusion criteria and greater than (beginning) criteria (Table 7) and less than (ending) criteria (Table 8)…
“Example data elements or fields that an abstractor may find in a respective template may be mapped to respective inclusion/exclusion criteria according the below tables.” [0286]
“TABLE-US-00008 TABLE 7 Inclusion Criteria Mapping Total bilirubin >= 1.5 × institutional upper limit of normal (ULN) Bilirubin Count (bCnt) Greater than equal to (GTET) 1.5 Institution ID (HD) Physician ID (pID) Institutional ULN (iULN) Physician ULN (pULN) Inclusion Expression(s): Binary (T/F) = bCnt >= 1.5 × (iULN(iID)) Binary (T/F) = bCnt >= 1.5 × (pULN(pID)) Binary (T/F) = bCnt >= 1.5 × (iULN(iID,pID))” (Table 7)
“TABLE-US-00009 TABLE 8 Inclusion Criteria Mapping Aspartate Aminotransferase (AST)/Serum Glutamic-Oxaloacetic Transaminase (SGOT) >= 1.5 × institutional upper limit of normal (ULN) AST Count (astCnt) SGOT Count (sgotCnt) Less than equal to (LTET) 2.5 Institution ID (HD) Physician ID (pID) Institutional ULN (iULN) Physician ULN (pULN) Inclusion Expression(s): Binary (T/F) = astCnt <= 2.5 × (iULN(iID)) Binary (T/F) = astCnt <= 2.5 × (pULN(pID)) Binary (T/F) = astCnt <= 2.5 × (iULN(iID,pID))” (Table 8)
LLM
The combined references teach unstructured data. They also teach extraction, assertions and relationships. They do not teach large language models.
Wong et al. also in the business of unstructured data teaches:
Using large language models…
“To the best of our knowledge, we are the first to explore using the emergent in-context learning capability of LLMs (Brown et al., 2020) for clinical trial matching. Out of box and with no more than three examples, cutting-edge LLMs, such as GPT-4, can already structure trial eligibility criteria with reasonable performance, outperforming strong baselines from prior systems such as Criteria2Query. We also show preliminary results in applying LLMs to end-to-end clinical trial matching.” (pg. 5, para. 1)
GPT-4 (second large language model) for trial…
“To transform the trial XML into a structured representation, we employ a prompt template that guides GPT-4 (same for GPT-3.5) to extract and structure relevant criteria from each trial. Specifically, we focus on four types of criteria: trial cohort, disease state, tumor histology, and biomarkers. The prompt directs GPT-4 to output the structured representation in JSON format and provides instructions on how to handle unclear or missing information. In addition, the prompt may include few-shot example (input, output) pairs for in-context learning (Brown et al., 2020). Our prompt template can be found in Figures 6 to 9 in Appendix. For inference, we replace the placeholder {input trial} in the prompt template with the input trial text and prompt GPT-4 to output the structured representation in JSON format. As shown in Figure 9, the output organizes the criteria into five categories: disease state, histology inclusion, biomarker inclusion, histology exclusion, and biomarker exclusion. The criteria logical expression is in disjunctive normal form (DNF). Our prompt instructs GPT-4 to assign a cohort name for each clause if possible.” (pg. 11, para. 1)
Fig. 2 and example of inclusion criteria…
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Appendix A teaches Templates with inclusion and exclusion trial criteria (pp. 18-24)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use large language models as taught by Wong et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wong et al. who teaches the benefits of using large language models for inclusion and exclusion criteria.
The combined references teach inclusion and exclusion. They do not explicitly teach beginning and ending. However, one of ordinary skill in the art would recognize that inclusion and exclusion criteria could have a beginning and an ending.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s filing to modify the combined references with the knowledge available to such an artisan that inclusion and exclusion criteria set ranges for meeting criteria and could have a beginning and an ending. This would have been known work in the field of endeavor prompting variations of it in the same field based on use of inclusion and exclusion criteria and would provide predictable results.
Regarding claim 6
The method of claim 1, wherein determining the disease state of the patient based on the patient data model and the clinical guidelines further comprises comparing a longitudinal journey of the patient through various diagnosis and treatment phases to a standard clinical guideline.
Ozeran et al. teaches:
Longitudinal data and multiple points during course of treatment (various treatment phases)…
“Referring again to FIG. 1, server 120 is shown to receive data from several sources. According to some aspects, clinical trial data can be provided to server 120 from database 132. Further, patient data can be provided to server 120. As shown, patient 114 has corresponding data from multiple sources (such as lab results 126 will be furnished from a laboratory or technician, imaging data 128 will be furnished from a radiologist, etc.). For simplicity, this is representatively shown in FIG. 1 as individual patient data 122. In some aspects, individual patient data 122 includes clinical record(s) 124, lab results 126, and/or imaging data 128. In some aspects, clinical record(s) 124 can include physician notes (for example, handwritten notes). The clinical record(s) 124 may include longitudinal data, which is data collected at multiple time points during the course of the patient's treatment.” [0130]
The combined references teach longitudinal data. They don’t teach diagnosis and treatment.
Wong et al. also in the business of longitudinal data teaches:
Applying LLMs to help triage (diagnose and treat) longitudinal medical records in clinical matching…
“While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.” (Abstract)
Example of fail standard of care (standard clinical guideline)…
“Note: For participants to be eligible for enrollment they must have failed at least one line of standard of care systemic therapy (ie, not treatment naïve), with the exception of CRC participants who must have failed at least 2 lines of standard of care systemic therapy, as per CRC specific eligibility criteria. Participants must not have melanoma or NSCLC.” (pg. 9, Fig. 4)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use longitudinal data as taught by Wong et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Wong et al. who teaches the benefits of using longitudinal data with LLM models.
Regarding claim 7
The method of claim 1, wherein comparing the patient data model with the clinical trial data model to determine whether the patient is a match for the clinical trial further comprises calculating a match score based on a similarity between the patient data model and the clinical trial data model, and determine that the patient is a match for the clinical trial in response to the match score being greater than a threshold match score.
Ozeran et al. teaches:
Example of match with score…
“As shown, additional details (e.g., the clinical trial description 1558) relating to the clinical trial may be displayed upon selection. The additional details can include the score 1560 that corresponds to the specific patient being matched. In some aspects, information about the inclusion and exclusion criteria can be displayed as matched to the patient. As an example, the GUI 1500 can color code and highlight (e.g., with green and red) the inclusion criteria 1561 and exclusion criteria 1562, based on data that has been successfully matched to the criteria that the trial has defined.” [0190] Inherent with include the score is calculating the score.
Patient meet threshold amount…
“At 4920, the process 4900 can determine the eligibility of the patient for each of the at least one clinical trial. In some embodiments, the process 4900 can determine that the patient is eligible for each trial for which the patient does not meet any of the exclusion criteria and does meet at least a portion of the inclusion criteria. In some embodiments, the process may require that the patient meets at least a threshold amount (e.g., 60%) of the inclusion criteria to be eligible for a given clinical trial. The process 4900 can then determine any number of the at least one clinical trial for which the patient is eligible. The trials the patient is eligible for can be referred to as the at least one eligible clinical trial.” [0325]
Regarding claim 8
The method of claim 7, wherein displaying the matching clinical trial on the display device further comprises displaying a plurality of matching clinical trials on the display device, the plurality of matching clinical trials ranked based on the match score of each clinical trial of the plurality of matching clinical trials.
Ozeran et al. teaches:
“FIG. 43 is shown to include a graphical user interface (GUI) 4300. In some embodiments, GUI 4300 can be implemented by the system 100 in FIG. 1. In some embodiments, the GUI 4300 can include a patient report 4302 corresponding to a patient selected and/or applying for a clinical trial. The patient report 4302 can include a number of therapies 4304 (e.g., drug therapies) that have been matched to the patient based on DNA and/or RNA data. The patient report 4302 can include a number of clinical trials 4306 that have been matched to the patient.” [0312]
GUI (displaying) and final group of top four (ranked) scoring trials…
“FIG. 44 is shown to include a graphical user interface (GUI) 4400. In some embodiments, GUI 4400 can be implemented by the system 100 in FIG. 1. In some embodiments, the GUI 4400 can include a clinical trial report 4402 including information about a selected clinical trial from the search results. In some embodiments, the selected clinical trial can be included in a final group (e.g., the top four scoring clinical trials) from the search results. The clinical trial report 4402 can include information about the clinical trial, such as a molecular match 4404 (e.g., a copy number gain required by the clinical trial that the patient possesses).” [0313]
Regarding claim 9
The method of claim 8, further comprising displaying the plurality of matching clinical trials in a graphical user interface (GUI), wherein the GUI includes a display of an explanation of how a criterion of a clinical trial matches with the patient data, the explanation referencing a first textual description of a patient condition extracted from the data of the patient, and a second textual description of the criterion, and explaining how the first textual description was mapped to the second textual description.
[No Patentable Weight is given to non-functional descriptive claim language of “displaying the plurality of matching clinical trials in a graphical user interface (GUI), wherein the GUI includes a display of an explanation of how a criterion of a clinical trial matches with the patient data, the explanation referencing a first textual description of a patient condition extracted from the data of the patient, and a second textual description of the criterion, and explaining how the first textual description was mapped to the second textual description.]
Ozeran et al. teaches:
Matching (mapping) text-based patient criteria to clinical trial and including a match indication…
“One implementation of the present disclosure is a method of matching a patient to a clinical trial. The method includes receiving text-based criteria for the clinical trial, including a molecular marker, associating at least a portion of the text-based criteria to one or more pre-defined data fields containing molecular marker information, comparing a molecular marker of the patient to the one or more pre-defined data fields, and generating a report for a provider, the report based on the comparison and including a match indication of the patient to the clinical trial.” [0023]
Mapping patient data to clinical trials…
“In some embodiments of the present disclosure, the system can create structure around clinical trial data. This can include reviewing free text (i.e., unstructured data), determining relevant information, and populating corresponding structured data field with the information. As an example, a clinical trial description may specify that only patients diagnosed with stage I breast cancer may enroll. A structured data field corresponding to “stage/grade” may then be populated with “stage I,” and a structured data field corresponding to “disease type” may then be populated with “breast” or “breast cancer.” The ability of the system to create structured clinical trial data can aid in the matching of patients to an appropriate clinical trial. In particular, a patient's structured health data can be mapped to the structured clinical trial data to determine which clinical trials may be optimal for the specific patient.” [0116]
Regarding claim 10
The method of claim 1, wherein the method is applied to generate a list of clinical trials for which a selected patient is eligible.
Ozeran et al. teaches:
List of clinical trials that match patient’s specific data…
“As shown, the table 1044 can include a list of clinical trials that match the patient's specific data (as indicated on the left side of GUI 1000). System 100 can be configured to analyze and compare patient data to the clinical trial data. Further, system 100 can provide the table 1044 based on clinical trials that substantially align with patient data. Each clinical trial within the table 1044 can include a trial selector 1045, a trial name, a disease site, histology data, disease stage, DNA data, RNA data, distance 1046 (e.g., from the physician's zip code), and/or a “score” 1047. In some aspects, the table 1044 can be sorted based on user-specified criteria (e.g., by distance, by score, etc.).” [0178]
Regarding claim 11
The method of claim 1, wherein the method is applied to generate a list of patients that may be eligible for a selected clinical trial.
Ozeran et al. teaches:
List of patients eligible for trial…
“In some embodiments of the present disclosure, the system can compare an individual clinical trial data to patient data at an organization, and subsequently generate a report of patients that may be eligible for that particular clinical trial. A physician, principal investigator, or clinical research administrator may review the report and use the information to enroll patients into that specific clinical trial. Accordingly, physicians and/or patients do not need to manually sort and review all patients' data to assess eligibility for a specific trial. Rather, a customized list of patients eligible for that trial is efficiently generated, based on the specific needs of the trial. This generation can significantly decrease the time for a physician, principal investigator, clinical research administrator, or other similar stakeholder to identify patients for a specific clinical trial, in part, due to the ability to reference individual source documentation for each patient's eligibility for each inclusion and exclusion criteria of the trial. Overall, the system allows for healthcare providers to track patient-level management of pre-screening, notification, consent, and enrollment into their clinical trials. Ultimately, this generation is intended to find and enroll patients in a clinical trial, thus improving treatment outcomes for certain diseases and conditions.” [0118]
Regarding claim 12
A clinical trial matching system, comprising:
one or more large language models (LLM);
See LLM below.
a processor, and a memory storing instructions that when executed, cause the processor to:
Ozeran et al. teaches:
Processor and memory with programming (instructions)…
“Furthermore, the disclosed subject matter may be implemented as a system, method, apparatus, or article of manufacture using programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer or processor based device to implement aspects detailed herein. The term “article of manufacture” (or alternatively, “computer program product”) as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (such as hard disk, floppy disk, magnetic strips), optical disks (such as compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (such as card, stick). Additionally it should be appreciated that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Transitory computer-readable media (carrier wave and signal based) should be considered separately from non-transitory computer-readable media such as those described above. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.” [0112]
determine whether a clinical trial is a match for a patient by comparing a first patient data model of the patient with a second clinical trial data model of the clinical trial, the first patient data model generated from patient data using a first LLM of the one or more LLMs, the second clinical trial data model generated from clinical trial data retrieved from a clinical trials database using a second LLM of the one or more LLMs; and
Standardize (generate) patient health information (patient data model)…
“At 4908, the process 4900 can determine data elements in the patient health information. In some embodiments, the patient health information can be unstructured and/or include free-text. The process 4900 can determine the data elements in order to standardize the patient health information. In some embodiments, the data elements can include at least a portion of the features and/or other data elements in the patient data store 3202.” [0322]
Utilizes database of clinical trials…
“The present disclosure is described in the context of a system that utilizes an established database of clinical trials (e.g., clinicaltrials.gov, as provided by the U.S. National Library of Medicine). Nevertheless, it should be appreciated that the present disclosure is intended to teach concepts, features, and aspects that can be useful with any information source relating to clinical trials, including, for example, independently documented clinical trials, internally/privately developed clinical trials, a plurality of clinical trial databases, and the like.” [0004]
Capture, ingest (extract), structure (generating a clinical trial model) clinical trial information…
“In one example, the present disclosure includes a system, other class of device, and/or method to help a medical provider make clinical decisions based on a combination of molecular and clinical data, which may include comparing the molecular and clinical data of a patient to an aggregated data set of molecular and/or clinical data from multiple patients, a knowledge database (KDB) of clinico-genomic data, and/or a database of clinical trial information. Additionally, the present disclosure may be used to capture, ingest, cleanse, structure, and combine robust clinical data, detailed molecular data, and clinical trial information to determine the significance of correlations, to generate reports for physicians, recommend or discourage specific treatments for a patient (including clinical trial participation), bolster clinical research efforts, expand indications of use for treatments currently in market and clinical trials, and/or expedite federal or regulatory body approval of treatment compounds.” [0114]
Matching patients with clinical trials…
“The present disclosure relates to systems and methods for facilitating the extraction and analysis of data embedded within clinical trial information and patient records. More particularly, the present disclosure relates to systems and methods for matching patients with clinical trials and validating clinical trial site capabilities.” [0003]
in response to determining that the clinical trial is a match for the patient:
indicate that the clinical trial is a match for the patient on a display device; and
[No Patentable Weight is given to non-functional descriptive claim language of “indicate that the clinical trial is a match for the patient on a display device”]
GUI (displaying) patient sel