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 . This non-final office action on merits is in response to the Patent Application filed on 12/20/2024.
Status of claims
Claims 1-13 are pending and considered below. This application is a 371 of PCT/JP2023/022600 filed on 06/19/2023, claiming priority to foreign application JP2022-099352 filed on 06/21/2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 1/15/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because Figure 4 submitted on 12/20/2024 includes lettering that is too small and rendered too lightly, such that all words are not clearly visible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-5, and 11-13 are drawn to a method, claims 6-10 are drawn to a system. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 4 recites the limitation of extracting patients who have undergone the standard treatment in the past as targets; and selecting eligible persons from the targets. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind or by using a pen and paper. But for the “searching a database with search words” language, the claim encompasses a user simply reviewing patient records and evaluating eligibility criteria in their mind or by using a pen and paper. The mere nominal recitation of searching a database with search words does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Claim 6 recites the limitation of searching words converted from eligibility conditions and exclusion conditions of clinical trial information, the accumulated patient information is searched with the search words, and a matching patient can be extracted. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind or by using a pen and paper. But for the “a clinical trial database, wherein the patient information input from the electronic health record system and the terminal at each diagnosis and treatment department is accumulated in the server computer” language, the claim encompasses a user simply reviewing patient charts, identifying relevant diagnoses and treatments, and determining whether patient satisfy eligibility and exclusion criteria in their mind or by using a pen and paper. The mere nominal recitation of a clinical trial database or the server computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process which is an abstract idea.
Independent claim 1 recites identical or nearly identical steps with respect to claim 4 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this claim is therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per method claim 4, include:
searching a database with search words;
extracting patients who have undergone the standard treatment in the past as targets; and
selecting eligible persons from the targets.
The claimed limitations, as per method claim 6, include:
a server computer in which patient information is accumulated;
an electronic health record system including a data warehouse;
a terminal to which the patient information is input from each diagnosis and treatment department; and
a clinical trial database, wherein the patient information input from the electronic health record system and the terminal at each diagnosis and treatment department is accumulated in the server computer, search words converted from eligibility conditions and exclusion conditions of clinical trial information are accumulated in the clinical trial database, the accumulated patient information is searched with the search words, and a matching patient can be extracted.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 4 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of screening and selecting clinical trial candidates in a computer environment. The claimed computer components (i.e., searching a database with search words) are recited at a high level of generality and are merely invoked as tools to perform an existing process of reviewing patient information and determining eligibility. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The judicial exception expressed in claim 6 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of searching patient information using eligibility and exclusion criteria in a computer environment. The claimed computer components (i.e. a server computer in which patient information is accumulated; an electronic health record system including a data warehouse; a clinical trial database, wherein the patient information input from the electronic health record system and the terminal at each diagnosis and treatment department is accumulated in the server computer) are recited at a high level of generality and are merely invoked as tools to perform an existing process of identifying patients who match clinical trial criteria. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 6 is not integrated into a practical application. The claim recites the additional element of a terminal to which the patient information is input from each diagnosis and treatment department. This limitation is recited at a high level of generality (i.e., as a general means of collecting patient data for subsequent screening), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 4 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of screening and selecting clinical trial candidates in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is not patent eligible.
Claim 6 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of searching patient information using eligibility and exclusion criteria in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 6, under step 2B, the additional element of the terminal to which patient information is input from each diagnosis and treatment department has been evaluated. The terminal performs a general function of receiving patient data for subsequent processing, which represents a well-understood, routine, and conventional activity in the field of clinical data management. The specification discloses that the terminal is used in its ordinary capacity as a data input device and does not describe any improvement to the terminal itself or to the functioning of the overall computer system (see [0038]). Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the terminal is no more than collecting information before the evaluation and selection of clinical trial candidates and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claims 2-3, 5, and 7-12 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claim 1 recites the additional element of the clinical trial database. However, this additional element amounts to implementing an abstract idea on a generic computing device. As such, this additional element, when considered individually or in combination with the prior devices, does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Kahn et al. (U.S. Patent Publication 2014/0249845A1), referred to hereinafter as Kahn.
Claim 1: Kahn teaches a clinical trial candidate screening method for screening candidates for a clinical trial, the clinical trial candidate screening method comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”):
searching a database with search words; and extracting eligible persons (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”).
Claim 2: Kahn teaches the clinical trial candidate screening method according to claim 1, wherein data is stored in the database as structured data (Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.”).
Claim 3: Kahn teaches the clinical trial candidate screening method according to claim 2, wherein clinical trial information is explained to the selected candidates, and consent matters are electronically recorded (Kahn [0102] “FIG. 20 illustrates the ManagementTask object 1816 (FIG. 18), “Give Arm A Paclataxel Treatment”. Similarly, FIG. 21 illustrates the ManagementTask object 1820, “Submit Form C-116”. The kinds of data management tasks which can be included in an iCP according to the clinical trial protocol meta-model include, for example, tasks calling for clinical personnel to submit a particular form, and a task calling for clinical personnel to obtain informed consent.” and Kahn [0107] “FIG. 3 is a screen shot of the Management_Diagram class object for the iCP, illustrating the workflow diagram for the clinical trial protocol of FIG. 2. The workflow diagram sets forth the clinical algorithm, that is, the sequence of steps, decisions and actions that the protocol specification requires to take place during the course of treating a patient under the particular protocol. The algorithm is maintained as sets of tasks organized as a graph 310, illustrated in the left-hand pane of the screen shot of FIG. 3. The protocol author adds steps and/or decision objects to the graph by selecting the desired type of object from the palate 312 in the right-hand pane of the screen shot of FIG. 3, and instantiating them at the desired position in the graph 310. Buried beneath each object in the graph 310 are fields which the protocol designer completes in order to provide the required details about each step, decision or action. The user interface of the authoring tool allows the designer to drill down below each object in the graph 310 by double-clicking on the desired object. The Management— Diagram object for the iCP also specifies a First Step (field 344), pointing to Consent & Enroll step 314, and a Last Step (field 346), which is blank.”).
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.
Claims 4-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kahn et al. (U.S. Patent Publication 2014/0249845A1), referred to hereinafter as Kahn, in view of Graiver et al. (U.S. Patent Publication US20180046780A1), referred to hereinafter as Graiver.
Regarding claim 4, Kahn teaches a target screening method for screening patients for which a clinical trial is to be performed, the target screening method comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.” and Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.”);
searching a database with search words (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”);
selecting eligible persons from the targets (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”). and Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.”).
Kahn fails to explicitly teach a synthetic control arm (SCA) and patients who have undergone standard treatment for a new medicine; and extracting patients who have undergone the standard treatment in the past as targets.
Graiver teaches a synthetic control arm (SCA) and patients who have undergone standard treatment for a new medicine (Graiver [0334] “A system to check the eligibility criteria is developed in order to detect errors, contradictions and redundancy and to validate the eligibility criteria, resolve contradictions and remove redundancy. If all the conditions are satisfied, the system does not return any result, otherwise the system identifies the criteria that violate the conditions.” and Graiver [0335] “In particular, statistical models of the likelihood of (co-)occurrence of various findings, diseases, treatments, etc. are used to detect eligibility criteria that are very unlikely to be satisfiable—and therefore highlight likely bugs. Simple logical inconsistencies in answers are also used.”);
extracting patients who have undergone the standard treatment in the past as targets (Graiver [0334] “A system to check the eligibility criteria is developed in order to detect errors, contradictions and redundancy and to validate the eligibility criteria, resolve contradictions and remove redundancy. If all the conditions are satisfied, the system does not return any result, otherwise the system identifies the criteria that violate the conditions.” and Graiver [0335] “In particular, statistical models of the likelihood of (co-)occurrence of various findings, diseases, treatments, etc. are used to detect eligibility criteria that are very unlikely to be satisfiable—and therefore highlight likely bugs. Simple logical inconsistencies in answers are also used.” and Graiver [0502] “Furthermore, the system may also update or correct patient's electronic health records. Electronic health records tend to focus on medical information, for example drugs, disease, or treatment. Other attributes that might be relevant to a clinical trial such as for example life style questions (Do you smoke a lot at the moment?, are you overweight?, is a carer accompanying you?) might not be recorded in electronic health records.”);
It would have been obvious to a POSITA to combine the teachings of Kahn and Graiver to arrive at the claimed synthetic control arm (SCA) target screening method. Kahn teaches querying patient databases using search terms derived from eligibility criteria to identify patients who satisfy a specific protocol requirements. Graiver teaches evaluating and filtering patient data based on medical history (drug use, treatments received, and disease characteristics) by applying statistical models to determine whether the patient satisfies specified conditions. A POSITA would have recognized that these well-known techniques could be used not only for identifying candidates for enrollment, but for retrospectively identifying patients who previously received standard treatments (such as those required for a synthetic control arm).
Combining Kahn and Graiver applies these known patient filtering and eligibility assessment techniques to a well-recognized example in clinical research, which is creating historical comparison groups using patients who previously underwent standard therapy. Because synthetic control arm patients require identifying known treatment histories and evaluating whether those patients satisfy the eligibility characteristics, the combination of Kahn’s database searching and Graiver’s treatment and medical history filtering represents the predictable use of prior art elements according to their established functions. A POSITA would have been motivated to combine these references to streamline identification of appropriate patients and improve clinical trial planning efficiency.
Regarding claim 5, Kahn and Graiver teach the invention in claim 4, as discussed above, and further teach wherein data is stored in the database as structured data (Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to store the patient data used for SCA screening as structured data because Kahn describes organizing patient medical information into defined fields to facilitate automated eligibility screening and database querying. Using structured data to support searching, filtering, and identifying appropriate patients represents no more than the predictable use of prior art for data organization techniques according to their established functions, specifically in systems for clinical trial analysis.
Regarding claim 6, Kahn teaches a clinical trial candidate screening system that screens candidates for a clinical trial, the clinical trial candidate screening system comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”):
a server computer in which patient information is accumulated (Kahn [0139] “In step 120, the central authority “distributes” the iCPs from the iCP database library 118 to clinical sites which are authorized to receive them. Authorization typically involves the site being part of the central authority's network of clinical sites, and also authorization by the sponsor of each study. In one embodiment, “distribution” involves merely making the appropriate iCP databases available to the appropriate clinical sites. In another embodiment, “distribution” involves downloading the appropriate iCP databases from the iCP database library 118, into a site-local database of authorized iCPs. In yet another embodiment, the entire library 118 is downloaded to all of the member clinical sites, but keys are provided to each site only for the protocols for which that site is authorized access. Preferably, the central authority maintains the iCP databases only on the central server and makes them available using a central application service provider (ASP) and thin-client model that supports multiple user devices including work stations, laptop computers and hand held devices. The availability of hand held devices allows the deployment of “intelligent” point of care data capture devices in which all protocol-specific, visit-specific and patient-specific required data elements, and their associated data validation rules, can be automatically created using information contained within the iCP. For example, an iCP can specify that if a patient exhibits evidence of an adverse event, additional data collection elements are required. Intelligent point of care data capture can detect the existence of an adverse event and add new required data elements to completely describe the event.”);
an electronic health record system including a data warehouse (Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies.”);
a clinical trial database, wherein the patient information input from the electronic health record system is accumulated in the server computer ((Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies.”, and Kahn [0139] “In step 120, the central authority “distributes” the iCPs from the iCP database library 118 to clinical sites which are authorized to receive them. Authorization typically involves the site being part of the central authority's network of clinical sites, and also authorization by the sponsor of each study. In one embodiment, “distribution” involves merely making the appropriate iCP databases available to the appropriate clinical sites. In another embodiment, “distribution” involves downloading the appropriate iCP databases from the iCP database library 118, into a site-local database of authorized iCPs. In yet another embodiment, the entire library 118 is downloaded to all of the member clinical sites, but keys are provided to each site only for the protocols for which that site is authorized access. Preferably, the central authority maintains the iCP databases only on the central server and makes them available using a central application service provider (ASP) and thin-client model that supports multiple user devices including work stations, laptop computers and hand held devices. The availability of hand held devices allows the deployment of “intelligent” point of care data capture devices in which all protocol-specific, visit-specific and patient-specific required data elements, and their associated data validation rules, can be automatically created using information contained within the iCP. For example, an iCP can specify that if a patient exhibits evidence of an adverse event, additional data collection elements are required. Intelligent point of care data capture can detect the existence of an adverse event and add new required data elements to completely describe the event.”);
search words converted from eligibility conditions and exclusion conditions of clinical trial information are accumulated in the clinical trial database, the accumulated patient information is searched with the search words, and a matching patient can be extracted (Kahn [0054] “Some recent Web-based services aim to match sponsors and sites, based on a database of trials by sponsor and of sites' patient demographics. A related approach is to identify trials that a specific patient may be eligible for, based on matching patient characteristics against a database of eligibility criteria for active trials. This latter functionality is often embedded in a disease-specific healthcare portal such as cancerfacts.com.” and Kahn [0077] “The meta-models also include lists, again appropriate to the particular disease category, within which a protocol designer can define preliminary criteria for the eligibility of patients for a particular study. These preliminary eligibility criteria lists do not preclude a protocol designer from building further eligibility criteria into any particular clinical trial protocol. As set forth in more detail below, the options available in the lists of preliminary eligibility criteria are intentionally limited in number so as to facilitate the building of a large database of potential patients for studies within the particular disease area. At the same time, however, it is also desirable that the options be numerous or narrow enough in order to provide a good first cut of eligible patients. In order to best satisfy these two competing goals, it is desirable that an expert or a team of experts knowledgeable about the particular disease category of a particular meta-model be heavily involved in the development of the preliminary eligibility criteria lists for the particular meta-model. In addition, because of the difficulty and length of time required to develop a large database of potential patients, it is further desirable that once the eligibility criteria options are established for a particular meta-model, they do not change except as absolutely necessary. Such changes might be mandated as a result of improved understanding of a disease, for example, and are rigorously managed throughout the overall system of FIG. 1.”., Kahn [0099] “FIG. 15 illustrates the instance of the EligibilityCriteriaSet class which appears in the CALGB 9840 iCP. It can be seen that the object contains a list of inclusion criteria and a list of exclusion criteria, each criterion of which is an instance of the ElgibilityCriterion class 1126. One of such instances 1510 is illustrated in FIG. 16. Only the short description 1610 and the long description 1612 have been entered by the protocol author.”, and Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”).
Kahn fails to explicitly teach a terminal to which the patient information is input from each diagnosis and treatment department.
Graiver teaches a terminal to which the patient information is input from each diagnosis and treatment department (Graiver [0477] “We refine the hyperparameters of our trial suitability model by optimizing our system against a metric that reflects the extent to which our matching engine is effective in facilitating patient participation in trials. Since information extracted from a patient's EHR may be incomplete or uncertain, a further useful innovation is to augment the information extracted from the EHR with information provided directly by the patient (or his or her doctor).”);
It would have been obvious to a POSITA to combine the teachings of Kahn and Graiver to arrive at the claimed clinical trial candidate screening system. Kahn teaches a computer infrastructure for clinical trial protocol management and patient eligibility identification. Graiver teaches supplementing medical data and using conventional doctor interfaces to provide additional patient information for use in automated eligibility and matching processes. A POSITA would have found it obvious to incorporate this terminal into the Kahn system to allow patient information from each department to be captured and put into the screening database. This is because medical organizations rely on department workflows, and integrating these inputs into a centralized screening system would have been a predictable choice yielding improved patient data. Additionally, both references address the problem of improving accuracy and efficiency in matching patients to clinical trial criteria using databases. Combining Kahn’s eligibility screening with Graiver’s patient information input mechanisms represents the predictable use of prior art elements according to their established functions.
Regarding claim 7, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach wherein the patient information is accumulated in the server computer as structured patient information (Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.” And Kahn [0139] “In step 120, the central authority “distributes” the iCPs from the iCP database library 118 to clinical sites which are authorized to receive them. Authorization typically involves the site being part of the central authority's network of clinical sites, and also authorization by the sponsor of each study. In one embodiment, “distribution” involves merely making the appropriate iCP databases available to the appropriate clinical sites. In another embodiment, “distribution” involves downloading the appropriate iCP databases from the iCP database library 118, into a site-local database of authorized iCPs. In yet another embodiment, the entire library 118 is downloaded to all of the member clinical sites, but keys are provided to each site only for the protocols for which that site is authorized access. Preferably, the central authority maintains the iCP databases only on the central server and makes them available using a central application service provider (ASP) and thin-client model that supports multiple user devices including work stations, laptop computers and hand held devices. The availability of hand held devices allows the deployment of “intelligent” point of care data capture devices in which all protocol-specific, visit-specific and patient-specific required data elements, and their associated data validation rules, can be automatically created using information contained within the iCP. For example, an iCP can specify that if a patient exhibits evidence of an adverse event, additional data collection elements are required. Intelligent point of care data capture can detect the existence of an adverse event and add new required data elements to completely describe the event.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to store the accumulated patient information on the server as structured patient information, as Kahn teaches storing patient data in standardized fields to support automated eligibility screening and data consistency across different sites. Using structured data to facilitate querying and screening is a well-known and predictable choice in clinical trial informatics.
Regarding claim 8, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach wherein personal information of a patient is stored in a server storage unit along with a patient ID and an anonymized ID (Kahn [0139] “In step 120, the central authority “distributes” the iCPs from the iCP database library 118 to clinical sites which are authorized to receive them. Authorization typically involves the site being part of the central authority's network of clinical sites, and also authorization by the sponsor of each study. In one embodiment, “distribution” involves merely making the appropriate iCP databases available to the appropriate clinical sites. In another embodiment, “distribution” involves downloading the appropriate iCP databases from the iCP database library 118, into a site-local database of authorized iCPs. In yet another embodiment, the entire library 118 is downloaded to all of the member clinical sites, but keys are provided to each site only for the protocols for which that site is authorized access. Preferably, the central authority maintains the iCP databases only on the central server and makes them available using a central application service provider (ASP) and thin-client model that supports multiple user devices including work stations, laptop computers and hand held devices” and Kahn [0143] “FIG. 26 is a flow chart detail of step 122 (FIG. 1). The steps in FIG. 1 typically use or contribute to a site-private patient information database 2610, which contains a number of different kinds of patient information. Because this information is maintained in conjunction with the identity of the patient, these databases 2610 are typically confidential to the clinical site or SMO, and not made available to anyone else, including study sponsors and the central authority. In one embodiment, the patient information database 2610 is located physically at the clinical site. In another embodiment, storage of the database 2610 is provided by the central authority as a service to clinical sites. In the latter embodiment, cryptographic or other security measures may be taken to ensure that no entity but the individual clinical site can view any confidential patient information.” and Kahn [0144] “As shown in FIG. 1, the central authority also maintains its own “operational” database 124, containing patient-anonymized patient information. The operational database 124 can be separate from the confidential patient information database(s) 2610 in which case a patient anonymized version of the patient information database 2610, or at least portions of database 2610, are transferred periodically for inclusion in an operational database 124 (FIG. 1). Alternatively, the two databases can be integrated together into one, with the central authority being denied access to sensitive patient-confidential information cryptographically.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to store personal information together with both a patient ID and an anonymized ID, because Kahn teaches maintaining confidential patient identifiers at the site level while generating anonymized versions for use by external systems such as the central authority. Using these paired identifiers to separate personally identifiable information from anonymized operational data reflects a routine security architecture in clinical systems.
Regarding claim 9, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach comprising a mechanism that creates and checks an electronic consent form, the mechanism confirming and recording consent matters for the candidates for the clinical trial (Kahn [0102] “FIG. 20 illustrates the ManagementTask object 1816 (FIG. 18), “Give Arm A Paclataxel Treatment”. Similarly, FIG. 21 illustrates the ManagementTask object 1820, “Submit Form C-116”. The kinds of data management tasks which can be included in an iCP according to the clinical trial protocol meta-model include, for example, tasks calling for clinical personnel to submit a particular form, and a task calling for clinical personnel to obtain informed consent.” and Kahn [0107] “FIG. 3 is a screen shot of the Management_Diagram class object for the iCP, illustrating the workflow diagram for the clinical trial protocol of FIG. 2. The workflow diagram sets forth the clinical algorithm, that is, the sequence of steps, decisions and actions that the protocol specification requires to take place during the course of treating a patient under the particular protocol. The algorithm is maintained as sets of tasks organized as a graph 310, illustrated in the left-hand pane of the screen shot of FIG. 3. The protocol author adds steps and/or decision objects to the graph by selecting the desired type of object from the palate 312 in the right-hand pane of the screen shot of FIG. 3, and instantiating them at the desired position in the graph 310. Buried beneath each object in the graph 310 are fields which the protocol designer completes in order to provide the required details about each step, decision or action. The user interface of the authoring tool allows the designer to drill down below each object in the graph 310 by double-clicking on the desired object. The Management— Diagram object for the iCP also specifies a First Step (field 344), pointing to Consent & Enroll step 314, and a Last Step (field 346), which is blank.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to incorporate a mechanism that creates, checks, and records electronic consent forms, because Kahn teaches protocol management tasks, which include obtaining informed consent and generating or submitting required forms. Integrating electronic consent workflows into a clinical trial candidate screening system represents a routine extension of protocol management functionality and provides predictable operational benefits.
Regarding claim 10, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach comprising a mechanism that performs anonym processing information processing of the patient information so that subjects can be selected while protecting personal information (Kahn [0144] “As shown in FIG. 1, the central authority also maintains its own “operational” database 124, containing patient-anonymized patient information. The operational database 124 can be separate from the confidential patient information database(s) 2610 in which case a patient anonymized version of the patient information database 2610, or at least portions of database 2610, are transferred periodically for inclusion in an operational database 124 (FIG. 1). Alternatively, the two databases can be integrated together into one, with the central authority being denied access to sensitive patient-confidential information cryptographically.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include a mechanism that performs anonymization of patient information, as Kahn teaches maintaining an operational database containing anonymized patient data and discusses techniques for ensuring that sensitive patient secure information is protected from unauthorized access. Implementing this anonymization within the screening system yields the predictable way of enabling subject selection while preserving privacy.
Regarding claim 11, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach a method using the clinical trial candidate screen system according to claim 6, comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”):
converting eligibility conditions and exclusion conditions of a clinical trial into search words (Kahn [0054] “Some recent Web-based services aim to match sponsors and sites, based on a database of trials by sponsor and of sites' patient demographics. A related approach is to identify trials that a specific patient may be eligible for, based on matching patient characteristics against a database of eligibility criteria for active trials. This latter functionality is often embedded in a disease-specific healthcare portal such as cancerfacts.com.” and Kahn [0077] “The meta-models also include lists, again appropriate to the particular disease category, within which a protocol designer can define preliminary criteria for the eligibility of patients for a particular study. These preliminary eligibility criteria lists do not preclude a protocol designer from building further eligibility criteria into any particular clinical trial protocol. As set forth in more detail below, the options available in the lists of preliminary eligibility criteria are intentionally limited in number so as to facilitate the building of a large database of potential patients for studies within the particular disease area. At the same time, however, it is also desirable that the options be numerous or narrow enough in order to provide a good first cut of eligible patients. In order to best satisfy these two competing goals, it is desirable that an expert or a team of experts knowledgeable about the particular disease category of a particular meta-model be heavily involved in the development of the preliminary eligibility criteria lists for the particular meta-model. In addition, because of the difficulty and length of time required to develop a large database of potential patients, it is further desirable that once the eligibility criteria options are established for a particular meta-model, they do not change except as absolutely necessary. Such changes might be mandated as a result of improved understanding of a disease, for example, and are rigorously managed throughout the overall system of FIG. 1.”., Kahn [0099] “FIG. 15 illustrates the instance of the EligibilityCriteriaSet class which appears in the CALGB 9840 iCP. It can be seen that the object contains a list of inclusion criteria and a list of exclusion criteria, each criterion of which is an instance of the ElgibilityCriterion class 1126. One of such instances 1510 is illustrated in FIG. 16. Only the short description 1610 and the long description 1612 have been entered by the protocol author.”);
extracting patients who match the conditions using the clinical trial candidate screening system (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”); and
determining whether the eligibility conditions and the exclusion conditions of the clinical trial are appropriate by grasping the number of patients who match the conditions (Kahn [0077] “The meta-models also include lists, again appropriate to the particular disease category, within which a protocol designer can define preliminary criteria for the eligibility of patients for a particular study. These preliminary eligibility criteria lists do not preclude a protocol designer from building further eligibility criteria into any particular clinical trial protocol. As set forth in more detail below, the options available in the lists of preliminary eligibility criteria are intentionally limited in number so as to facilitate the building of a large database of potential patients for studies within the particular disease area. At the same time, however, it is also desirable that the options be numerous or narrow enough in order to provide a good first cut of eligible patients. In order to best satisfy these two competing goals, it is desirable that an expert or a team of experts knowledgeable about the particular disease category of a particular meta-model be heavily involved in the development of the preliminary eligibility criteria lists for the particular meta-model. In addition, because of the difficulty and length of time required to develop a large database of potential patients, it is further desirable that once the eligibility criteria options are established for a particular meta-model, they do not change except as absolutely necessary. Such changes might be mandated as a result of improved understanding of a disease, for example, and are rigorously managed throughout the overall system of FIG. 1.”.).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to convert clinical trial eligibility and exclusion criteria into searchable terms, extract matching patients using the screening system, and determine appropriateness of the criteria based on how many patients match, because Kahn teaches using eligibility criteria in structured list form, using the structured criteria to query patient databases, and evaluating whether the criteria are too restrictive or too broad in order to facilitate trial feasibility. Kahn further teaches using eligibility criteria as searchable elements for automated patient matching. Applying these known steps represents a predictable use of existing clinical trial feasibility tools and query matching techniques.
Regarding claim 12, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach an adverse event analysis method for analyzing adverse events by a medicine using the clinical trial candidate screen system according to claim 6, the adverse event analysis method comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.” and Kahn [0139] “For example, an iCP can specify that if a patient exhibits evidence of an adverse event, additional data collection elements are required. Intelligent point of care data capture can detect the existence of an adverse event and add new required data elements to completely describe the event.”):
extracting patients who have used a medicine for which analysis is to be performed using the clinical trial candidate screening system (Graiver [0334] “A system to check the eligibility criteria is developed in order to detect errors, contradictions and redundancy and to validate the eligibility criteria, resolve contradictions and remove redundancy. If all the conditions are satisfied, the system does not return any result, otherwise the system identifies the criteria that violate the conditions.” and Graiver [0335] “In particular, statistical models of the likelihood of (co-)occurrence of various findings, diseases, treatments, etc. are used to detect eligibility criteria that are very unlikely to be satisfiable—and therefore highlight likely bugs. Simple logical inconsistencies in answers are also used.” and Graiver [0502] “Furthermore, the system may also update or correct patient's electronic health records. Electronic health records tend to focus on medical information, for example drugs, disease, or treatment. Other attributes that might be relevant to a clinical trial such as for example life style questions (Do you smoke a lot at the moment?, are you overweight?, is a carer accompanying you?) might not be recorded in electronic health records.”); and
analyzing examination values, observation and symptoms at the time of use of the medicine (Graiver [0326] “Some additional complexity arises due to the existence of qualified eligibility criteria, i.e. criteria that express constraints on other criteria. For example, disease treated by drug, or drug given with dosage, or symptoms presented within a time period. It is important to note that qualified criteria are not the same as conjunctions of criteria (logic-ands). To see why, consider the eligibility criterion lung cancer treated by radiotherapy (expressed using our grammar as _disease(lung cancer) _treated_by_procedure(radiotherapy)). A patient who (i) has lung cancer and (ii) has received past treatment by radiotherapy would not satisfy this criterion if the radiotherapy had been used to treat a different cancer. Instead, qualified criteria give rise to symbolic references in the logic proposition, e.g. lung cancer x and x treated by radiotherapy. When attempting to determine whether a patient satisfies a qualified criteria, our system must first generate a question about the root criterion and then generates a question (or questions) about the qualifier(s). E.g. Have you had lung cancer? And (if yes), Has your lung cancer been treated by radiotherapy? In this way, both the root criterion (lung cancer) and the qualifiers (lung cancer treated by radiotherapy) may be shared between several of the trials in the corpus. It is noteworthy that the notion of qualification is not very well expressed by EMR coding schemes, and a significant benefit of our question-based matching system is that we can capture this important nuance.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to extract patients who previously used a particular medicine and analyze their examination values, observations, and symptoms, because Kahn teaches identifying patients with specific treatment histories and monitoring adverse events, and Graiver teaches extracting subsets of patients based on treatment attributes and evaluating associated clinical findings. Combining these teachings to analyze adverse events for a specific medicine follows a routine and predictable data analysis method in clinical systems.
Regarding claim 13, Kahn and Graiver teach the invention in claim 6, as discussed above, and further teach a search method for searching a clinical trial that matches a patient using the clinical trial candidate screen system according to claim 6, the search method comprising (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.”):
referring to data that is structured data of patient information of the patient using search words converted so as to enable search, from clinical trial information accumulated in the clinical trial database for each clinical trial (Kahn [0146] “After the clinical site has decided to proceed with a study, then it can use either a “Find-Me Patients” tool (step 2614) or a “QuickScreen” tool (step 2616) to identify enrollment candidates. The “Find-Me Patients” tool is either the same or different from the local accrual simulation tool, and it operates to develop a list of patients from its patient information database 2610 who are likely to satisfy the eligibility criteria for a particular protocol. Again, this local “Find-Me Patients” tool makes appropriate queries to the patient information database 2610 for patients who are believed to satisfy the preliminary eligibility criteria for the subject protocol.” Kahn [0129] “In one embodiment, the accrual simulation database includes one or more externally provided patient-anonymized electronic medical records databases. In another embodiment, it includes patient-anonymized data collected from various clinical sites which have participated in past studies. In the latter case the patient-anonymized data typically includes data collected by the site during either preliminary eligibility screening, further eligibility screening, or both. Preferably the database includes information about a large number of anonymous patients, including such information as the patient's current stage of several different diseases (including the possibility in each case that the patient does not have the disease); what type of prior chemotherapy the patient has undergone, if any; what type of prior radiation therapy the patient has undergone; whether the patient has undergone surgery; whether the patient has had prior hormonal therapy; metastases; and the presence of cancer in local lymph nodes. Not all fields will contain data for all patients. Preferably, the fields and values in the accrual simulation database 116 are defined according to the same CMT 112 used in the protocol meta-models and preliminary and further eligibility criteria. Such consistency of data greatly facilitates automation of the accrual simulation step 1012. Note that since the patients included in the accrual simulation database may be different from and may not accurately represent the universe of patients from which the various clinical sites executing the study will draw, some statistical correction of the numbers returned by the accrual simulation tool may be required to more accurately predict accrual.”).);
extracting a matching clinical trial (Kahn [0054] “Some recent Web-based services aim to match sponsors and sites, based on a database of trials by sponsor and of sites' patient demographics. A related approach is to identify trials that a specific patient may be eligible for, based on matching patient characteristics against a database of eligibility criteria for active trials. This latter functionality is often embedded in a disease-specific healthcare portal such as cancerfacts.com.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use structured patient data and search terms to query clinical trial information and extract matching trials, because Kahn teaches organizing patient and trial eligibility data in structured form, converting eligibility criteria into standardized searchable representations, and identifying matching trials based on comparison of patient attributes to trial eligibility criteria. Employing this structured data and searchable terms to retrieve matching trials represents a routine database search operation that is widely used in clinical trial matching systems.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mao et al. (U.S. Patent Publication 2020/0234801 A1) teaches a method for recruiting a clinical trial patient that standardizes eligibility criteria from multiple tools, stores them in a database, receives patient specific data, queries the standardized criteria to determine which trials the patient satisfies, and outputs a report identifying the matching trial.
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/K.R.L./Examiner, Art Unit 3685
/Bion A Shelden/Primary Examiner, Art Unit 3685 2025-12-11
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