Notice of Pre-AIA or AIA Status
This action is made in response to the amendments/remarks filed on 09/03/2025. This action is made FINAL.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment files 09/03/2025 has been entered. Claims 1-8, 11-12, 14-18, and 20 remain pending in the application. Claims 9, 10, 13, and 19 have been cancelled.
Claim Objections
Claims 1, 12, and 20 are objected to because of the following informality: “wherein the at least one description in the first data set relates to at least one following fields” should read “wherein the at least one description in the first data set relates to at least one of: operation…”. Appropriate correction is required.
Claim 8 is objected to because of the following informality: “the first data set relates to at least one following fields” should read “the first data set relates to at least one of the following fields” or “the first data set relates to at least one of: EGFR…”. Appropriate correction is required.
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 1-8, 11-12, 14-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims
Step 1 analysis:
Claim 1 is drawn to a method (i.e., process), Claim 12 is drawn to a device (i.e., machine), and Claim 20 is drawn to a non-transitory computer storage medium (i.e., manufacture), which are all within the four statutory categories. (Step 1 – Yes, the claim falls into one of the statutory categories).
Step 2A analysis – Prong One:
Claim 1 recites:
A method of matching clinical trials, comprising:
obtaining a first data set, including pathological feature data and demographic data, from a pathology report, wherein at least one description in the first data set it obtained by performing a sequence tagging task, by a pre-trained model, on the pathology report, wherein the at least one description in the first data set relates to at least one following fields: operation, histology, tumor size, stage, or PDL1;
obtaining a second data set, including pathological feature data and demographic data of each clinical trial;
determining whether the first data set and the second data set are matched with respect to a first set of fields;
determining a relevance value between the first data set and the second data set with respect to a second set of fields when the first data set and the second data set are matched with respect to the first set of fields, wherein the relevance value is associated with the total number of the clinical trials and the number of clinical trials including one or more keywords for each field of the second set of fields; and
determining the clinical trial as recommended when the relevance value exceeds a threshold.
The series of steps as recited above describes managing personal behavior or relationships or interactions between people including following rules or instructions, and therefore fall within the scope of certain methods of organizing human activity. Fundamentally, the method is that of a person gathering patient data and clinical trial data, then following a series of steps to recommend a clinical trial to the patient through different matchmaking procedures. Accordingly, the claim recites an abstract idea of managing interactions between people.
The series of steps as recited above also falls within the “mental processes” grouping of abstract ideas, and describes concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion. The limitations of “performing a sequence tagging task”, “determining whether the first data set and the second data set are matched with respect to a first set of fields”, “determining a relevance value between the first data set and the second data set…”, and “determining the clinical trial as recommended when the relevance value exceeds a threshold” are all concepts that can be performed in the human mind through observation, evaluation, judgement, and opinion, and therefore, the claim recites an abstract idea of a mental process.
Claims 12 and 20 recite/describe nearly identical steps as claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Step 2A analysis – Prong 2:
This judicial exception is not integrated into a practical application. Specifically, independent claims 1, 12, and 20 recite the following additional elements beyond the abstract idea: a pre-trained model, a processor, a memory, and a non-transitory computer storage medium. These limitations are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components (see specification paragraphs [0079]-[0081]). The limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the limitations “obtaining a first data set, including pathological feature data and demographic data” and “obtaining a second data set, including pathological feature data and demographic data of each clinical trial” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). In addition, In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. 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 20 are directed to an abstract idea without practical application. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
Step 2B analysis:
As discussed above in “Step 2A analysis – Prong 2”, the identified additional elements in Independent Claims 1, 11, and 20 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The applicant’s specification discloses in paragraphs [0079] through [0081]: The computing device may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, or a smartphone. The computing device 710 comprises processor 711 (specification [0080]).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the steps for matching clinical trials amount to no more than using computer related devices to implement the abstract idea.
The additional elements of “obtaining a first data set, including pathological feature data and demographic data” and “obtaining a second data set, including pathological feature data and demographic data of each clinical trial” were both found to be insignificant extra-solution activity in Step 2A – Prong 2, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the recitations of “obtaining a first data set, including pathological feature data and demographic data” and “obtaining a second data set, including pathological feature data and demographic data of each clinical trial” are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible. The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above (Step 2B: Independent claims - NO).
Dependent Claims
Dependent Claims 2-8, 11, and 14-18 are directed towards elements used to describe the relevance process and the data sets for the matching process. These elements include the relevance value, an individual assigned weight for the relevance value, a set of fields including: ALK, ROS1, KRAS, BRAF, RET, NTRK, MET, P53, Her2, tumor size, tumor maximum diameter, programmed death-ligand 1 (PD-L1 ), nodal metastases, distant metastases, CNS metastases, bone metastases, wild type, anti-angiogenesis, platinum, EGFR TKIs, ALK inhibitors, PD-1/PD-L1 inhibitors, CTLA-4 inhibitor, radiotherapy, cisplatin/ carboplatin, chemotherapy, systemic therapy, disease status, or eastern cooperative oncology group performance status (ECOG PS); another set of fields including estimated glomerular filtration rate (EFGR), surgical operation, histology, pathologic staging, age, gender, or smoking; and performing a classification task to obtain state values. All of these limitations amount to a form of managing interactions between people and mental processes, and therefore fall within the same abstract idea identified in claims 1 and 12, i.e., method for organizing human activity and mental processes.
Dependent claims 2-4 and 14-15 further describe the relevance value and recites “the relevance value is a sum of an individual relevance value of each of the second set of fields” and “the individual relevance value is associated with a respective inverse document frequency (IDF)”. Each of these limitations falls within the “mathematical concepts” grouping of abstract ideas in addition to the abstract ideas set forth in independent claims 1 and 12. The concept of a sum of values to give the relevance value and using inverse document frequency recites mathematical operations and calculations, therefore, claims 2-4 and 14-15 recite an abstract idea of mathematical concepts. (Step 2A – Prong 1: Yes, the dependent claims are abstract).
Dependent claims 7, 8, 11, and 18 recite the additional element of a pre-trained model. This limitation is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using generic computer components. The limitation does not impose any meaningful limits on practicing the abstract idea, and therefore does not integrate the abstract idea into a practical application (See MPEP 2106.05(f)). Dependent claims 2-6 and 14-17 do not include any additional elements. (Step 2A – Prong 2: No, the additional elements are not integrated into a practical application).
As discussed above, the identified additional elements in dependent claims 7, 8, 11, and 18 are equivalent to adding the words “apply it” on a generic computer. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Generic computer components recited as performing generic computer functions that are well- understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system.
The dependent claims as a whole do not amount to significantly more than the judicial exception itself. The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the dependent claims are not patent eligible.
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. (Step 2B - NO).
Therefore, Claims 1-20 are not eligible subject matter under 35 USC 101.
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)(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 12-13, 16-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ozeran et al. (US 2020/0381087) (hereinafter Ozeran).
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.
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 1-8, 12, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ozeran et al. (US 2020/0381087) (hereinafter Ozeran), in view of Wu et al. (WO 2018/060838) (hereinafter Wu).
Regarding Claim 1, Ozeran discloses the following:
A method of matching clinical trials (Ozeran [0173], [0174] discloses appropriately matching patients with available clinical trials), comprising:
obtaining a first data set, including pathological feature data and demographic data, from a pathology report (Ozeran [0235]-[0236] discloses patient data which may include demographic information, clinical, molecular, or genetic features. The patient data store 3202 can include the molecular data features 3204. In some embodiments, the molecular data features 3204 can be derived from RNA and/or DNA sequencing (e.g., RNA sequencing features 3206 and/or DNA sequencing features 3208), a pathologist review of stained H&E and/or IHC slides (e.g., slide features 3210), and/or further derivative features obtained from the analysis of the individual and combined results.), wherein at least one description in the first data set is obtained by performing a sequence tagging task, by a pre-trained model (Ozeran [0162], [0176], [0276], [0277], [0280]: a machine learning tool that can review the trial description 603, as well as the criteria listed within the description, and make a determination about what structured data fields could be appropriate to include in the trial details. The combination of attributes shown for the patient can be provided using similar methods as the above-described "trial metadata" data abstraction. 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. The model(s) can include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) and each trained model can receive at least one feature and output and indication whether the criteria is met or not met), on the pathology report wherein the at least one description in the first data set relates to at least one following fields: operation, histology, tumor size, stage, or PDL1 ([0235] the information can include a number of features for a given patient. The features can include information related to various fields of medicine. For example, the features can include diagnoses, responses to treatment regimens, genetic profiles, clinical and phenotypic characteristics, and/or other medical, geographic, demographic, clinical, molecular, or genetic features. [0245] In some embodiments, the imaging data features 3242 can include features derived from imaging data, such as a report associated with a stained slide, size of tumor, tumor size differentials over time (including treatments during the period of change), a classification and/or a score generated using a machine learning technique (e.g., machine learning techniques for classifying PDL1 status);
obtaining a second data set, including pathological feature data and demographic data, of each clinical trial (Ozeran [0144], [0145], and [0154] discloses a graphical user interface that includes trial metadata, which can be used to view, update, and sort data corresponding to clinical trials. The trial description can include inclusion criteria and exclusion criteria. Further, the trial details can include disease criteria, stage/grade criteria, genetic criteria, and biomarker criteria.);
determining whether the first data set and the second data set are matched (Ozeran [0117] discloses that the system can compare individual patient data to clinical trial data. Ozeran [0249] discloses that the flow 3200 can include matching the patient with one or more clinical trials using the patient data store) with respect to a first set of fields (Ozeran [0176]: The trial matching 1040 can include the patent demographics 1042, such as disease status, disease type, etc. The combination of attributes shown for the patient can be provided using similar methods as the above-described "trial metadata" data abstraction. Accordingly, a user can view and/or enter all of the relevant information corresponding to the patients and diseases. This can enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.)); and
determining a relevance value between the first data set and the second data set with respect to a second set of fields ([0182] The match function can determine and provide a score (e.g., the highest score listed first) of clinical trial matches. The score can be based on the disease site, the histology, the stage, molecular information, as well as the distance. [0307] In some embodiments, the search process can generate a relevance score 3818 for each clinical trial and/or rank the clinical trials by relevance score. The relevance score may be generated based on a number of factors including patient demographics) [when the first data set and the second data set are matched with respect to the first set of fields] (Examiner’s note: bracketed limitation is taught by another reference), wherein the relevance value is associated with the total number of the clinical trials (Ozeran [0307] discloses a user can select multiple check boxes corresponding to a number of clinical trials and select a compare element (e.g., a compare button). In some embodiments, the search process can generate a relevance score for each clinical trial and/or rank the clinical trials by relevance score) and the number of clinical trials including one or more keywords for each field of the second set of fields (Ozeran [0307] discloses a search process can search a clinical trials database using the data values and display search results (e.g., clinical trials) in the search results portion. In some embodiments, the search results can be filtered by a number of results filter fields such as a trial name filter field); and
determining the clinical trial as recommended when the relevance value exceeds a threshold (Ozeran [0325]: 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).
However, Ozeran does not disclose the following that is met by Wu:
determining a relevance value between the first data set and the second data set with respect to a second set of fields when the first data set and the second data set are matched with respect to the first set of fields (Wu discloses in (Pg. 9, lines 1-2, 4) that the invention uses Lucene’s practical scoring function to calculate the score of each matched document, and that the score is the relevance score of the document. Wu also discloses in (Pg. 11, lines 15-22) an example match process, where a potential match is made with a clinical trial and the system further prioritizes the matches with a second set of conditions. A similar example is disclosed in Wu (Pg. 12, lines 4-7), where a first match is done to match the patient and clinical trial based on a tumor, and the second match to further prioritize the match is based on distance to clinical trial sites.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the relevance value process as disclosed in Ozeran, with the several steps of the relevance value process of Wu because it allows for prioritization of the clinical trial that the patient is matched with, and overall, provides better sensitivity and precision for matching (Wu Pg. 1, lines 11-12; Pg. 10, lines 4-5).
Regarding Claim 2, the combination of Ozeran and Wu teaches the limitations of Claim 1, and Wu further discloses the following:
The method of claim 1, wherein the relevance value is a sum of an individual relevance value of each of the second set of fields (Wu (Pg. 9, line 5) discloses the summation part [of the formula on Pg. 9, line 3] calculates the sum of the weights for each term t in the query q for document d; Wu (Pg. 7, lines 21-22): Real world query usually involves multiple factors, including, e.g., disease name, gene, mutation type, gender, age, cancer stage, and tumor grade).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Ozeran and Wu to incorporate the summation of the relevance value as taught by Wu with the relevance value as taught by Ozeran since the invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Including the summation into the calculation of the relevance value (as taught by Wu) would perform the same function of assigning a relevance score to a matched clinical trial as disclosed in Ozeran, therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 3, the combination of Ozeran and Wu discloses the limitations of Claim 2, and Wu further discloses the following:
The method of claim 2, wherein the individual relevance value is associated with an individual assigned weight (Wq) (See Wu Pg. 9, lines 11-12 and 20-22: the relevance score depends on the weight of each query term that appears in the document. Weights are assigned for each field thus, when calculating score, a term that occurs in a field with weight 2 will get twice the score than the same term that occurs in a field with weight 1, i.e., a field with weight two is twice as important as the field with weight one).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Ozeran and Wu to incorporate the associated weight as taught by Wu because it tunes the relevancy score to represent more important fields of the document, giving a higher weight to the more important terms in the report (See Wu Pg. 9, lines 17-19).
Regarding Claim 4, the combination of Ozeran and Wu discloses the limitations of Claim 2, and Wu further discloses the following:
The method of claim 2, wherein the individual relevance value is associated with a respective inverse document frequency (IDF) for a respective keyword (See Wu Pg. 9, line 8: idf(t) is the inverse document frequency for term t. (Pg. 9, lines 12-14) Term frequency, inverse document frequency, and field-length norm are used together to calculate the weight of a single term in a particular document).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Ozeran and Wu to incorporate the associated inverse document frequency as taught by Wu because it provides a better solution for matching patients with clinical trials (Wu Pg. 2, lines 22-23).
Regarding Claim 5, the combination of Ozeran and Wu discloses the limitations of Claim 1, and Ozeran further discloses the following:
The method of claim 1, wherein the first set of fields include one or more of: estimated glomerular filtration rate (EFGR), surgical operation, histology, pathologic staging, age, gender, or smoking ([0176] enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.); [0238]-[0239] the flow can include generating and/or receiving a number of clinical data features associated with the patient. Clinical features can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history, or family medical history, clinical diagnoses such as date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, treatments and outcomes such as line of therapy, therapy groups, clinical trials, medications prescribed or taken, surgeries...).
Regarding Claim 6, the combination of Ozeran and Wu discloses the limitations of Claim 1, and Ozeran further discloses the following:
The method of claim 1, wherein the second set of fields include one or more of: ALK, ROS1, KRAS, BRAF, RET, NTRK, MET, P53, Her2, tumor size, tumor maximum diameter, programmed death-ligand 1 (PD-L1), nodal metastases, distant metastases, CNS metastases, bone metastases, wild type, anti-angiogenesis, platinum, EGFR TKIs, ALK inhibitors, PD-1/PD-L1 inhibitors, CTLA-4 inhibitor, radiotherapy, cisplatin/carboplatin, chemotherapy, systemic therapy, disease status, or eastern cooperative oncology group performance status (ECOG PS) ([0155] 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."; [0160] selection menu 618 can include several known biomarker names (e.g., "ALK," "BRAF," etc.); [0237] the slide features 3210 can include tumor infiltration, Programmed death-ligand 1 (PD-L1) Status, human leukocyte antigen (HLA) Status, and/or other immunology features can be generated based on H&E staining and/or IHC staining; [0245] the flow 3200 can include generating and/or receiving a number of imaging data features associated with the patient. The imaging data features can include features derived from imaging data, such as a report associated with a stained slide, size of tumor, tumor size differentials over time (including treatments during the period of change), a classification and/or a score generated using a machine learning technique (e.g., machine learning techniques for classifying PDL1 status).
Regarding Claim 7, the combination of Ozeran and Wu discloses the limitations of Claim 1, and Ozeran further discloses the following:
The method of claim 1, wherein the obtaining the first data set comprises: performing a classification task ([0258] In some embodiments, the data-criteria concept matching module 3274 can include a classification code system 3276, a dictionary based classification system 3278, and/or an artificial intelligence (AI) classification system 3280.), by the pre-trained model ([0280] each trained model can receive at least one feature and output and indication whether the criteria is met or not met.), on the pathology report such that at least one state value in the first data set is obtained (Table 3, [0261], [0276]: DNA/RNA Molecular features may have a classification table for genetic mutations, variants, transcriptomes, cell lines, methods of evaluating expression (TPM, FPKM), a lab which provided the results, etc. At least a portion of the classification table can include the codes in Table 3. Table 3 shows classification codes to express the state value of DNA/RNA molecular features (e.g., overexpressed or underexpressed). 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).
Regarding Claim 8, the combination of Ozeran and Wu discloses the limitations of Claim 7, and Ozeran further discloses the following:
The method of claim 7, wherein the at least one state value in the first data set relates to at least one following fields: EGFR, ALK, ROS1, KRAS, BRAF, RET, NTRK, MET, P53, or Her2 ((Table 3, [0261], [0263], Table 5: DNA/RNA Molecular features may have a classification table for genetic mutations, variants, transcriptomes, cell lines, methods of evaluating expression (TPM, FPKM), a lab which provided the results, etc. At least a portion of the classification table can include the codes in Table 3. Table 3 shows classification codes to express the state value of DNA/RNA molecular features (e.g., overexpressed or underexpressed); inclusion and exclusion criteria may be mapped according to the same classification conventions above…an inclusion criteria "Histologically or cytologically confirmed diagnosis of locally advanced or metastatic solid tumor that harbors an NTRK1/2/3, ROS1, or ALK gene rearrangement" may touch Limn the following classification codes in Table 5).
Regarding Claim 12, Ozeran discloses the following:
A device of matching clinical trials ([0029] a clinical trial matching system), comprising:
a processor ([0029] a clinical trial matching system including at least one processor); and
a memory coupled with the processor ([0127] the system can include at least one processor coupled to and in communication with at least one memory),
wherein the processor executes computer-readable instructions stored in the memory to perform operations, and the operations comprise ([0320] In some embodiments, the process can be implemented as executable computer-readable instructions and stored on a non-transitory medium such as a memory):
obtaining a first data set, including pathological feature data and demographic data, from a pathology report (Ozeran [0236] discloses receiving a number of molecular data features for a patient. The patient data store 3202 can include the molecular data features 3204. In some embodiments, the molecular data features 3204 can be derived from RNA and/or DNA sequencing (e.g., RNA sequencing features 3206 and/or DNA sequencing features 3208), a pathologist review of stained H&E and/or IHC slides (e.g., slide features 3210), and/or further derivative features obtained from the analysis of the individual and combined results.), wherein at least one description in the first data set is obtained by performing a sequence tagging task, by a pre-trained model (Ozeran [0162], [0176], [0276], [0277], [0280]: a machine learning tool that can review the trial description 603, as well as the criteria listed within the description, and make a determination about what structured data fields could be appropriate to include in the trial details. The combination of attributes shown for the patient can be provided using similar methods as the above-described "trial metadata" data abstraction. 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. The model(s) can include supervised algorithms (such as algorithms where the features/classifications in the data set are annotated) and each trained model can receive at least one feature and output and indication whether the criteria is met or not met), on the pathology report, wherein the at least one description in the first data set is related to at least one following fields: operation histology, tumor size, stage, or PDL1 ([0235] the information can include a number of features for a given patient. The features can include information related to various fields of medicine. For example, the features can include diagnoses, responses to treatment regimens, genetic profiles, clinical and phenotypic characteristics, and/or other medical, geographic, demographic, clinical, molecular, or genetic features. [0245] In some embodiments, the imaging data features 3242 can include features derived from imaging data, such as a report associated with a stained slide, size of tumor, tumor size differentials over time (including treatments during the period of change), a classification and/or a score generated using a machine learning technique (e.g., machine learning techniques for classifying PDL1 status);
obtaining a second data set, including pathological feature data and demographic data, of a clinical trial (Ozeran [0144], [0145], and [0154] discloses a graphical user interface that includes trial metadata, which can be used to view, update, and sort data corresponding to clinical trials. The trial description can include inclusion criteria and exclusion criteria. Further, the trial details can include disease criteria, stage/grade criteria, genetic criteria, and biomarker criteria.);
determining whether the first data set and the second data set are matched (Ozeran [0117] discloses that the system can compare individual patient data to clinical trial data. Ozeran [0249] discloses that the flow 3200 can include matching the patient with one or more clinical trials using the patient data store) with respect to a first set of fields (Ozeran [0176]: The trial matching 1040 can include the patent demographics 1042, such as disease status, disease type, etc. The combination of attributes shown for the patient can be provided using similar methods as the above-described "trial metadata" data abstraction. Accordingly, a user can view and/or enter all of the relevant information corresponding to the patients and diseases. This can enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.));
determining a relevance value between the first data set and the second data set with respect to a second set of fields ([0182] The match function can determine and provide a score (e.g., the highest score listed first) of clinical trial matches. The score can be based on the disease site, the histology, the stage, molecular information, as well as the distance; [0307] In some embodiments, the search process can generate a relevance score 3818 for each clinical trial and/or rank the clinical trials by relevance score. The relevance score may be generated based on a number of factors including patient demographics) [when the first data set and the second data set are matched with respect to the first set of fields], wherein the relevance value is associated with the total number of the clinical trials (Ozeran [0307] discloses a user can select multiple check boxes corresponding to a number of clinical trials and select a compare element (e.g., a compare button). In some embodiments, the search process can generate a relevance score for each clinical trial and/or rank the clinical trials by relevance score) and the number of clinical trials including one or more keywords for each field of the second set of fields (Ozeran [0307] discloses a search process can search a clinical trials database using the data values and display search results (e.g., clinical trials) in the search results portion. In some embodiments, the search results can be filtered by a number of results filter fields such as a trial name filter field); and
determining the clinical trial is recommended when the relevance value exceeds a threshold (Ozeran [0325]: 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).
However, Ozeran does not disclose the following that is met by Wu:
determining a relevance value between the first data set and the second data set with respect to a second set of fields when the first data set and the second data set are matched with respect to the first set of fields (Wu discloses in (Pg. 9, lines 1-2, 4) that the invention uses Lucene’s practical scoring function to calculate the score of each matched document, and that the score is the relevance score of the document. Wu also discloses in (Pg. 11, lines 15-22) an example match process, where a potential match is made with a clinical trial and the system further prioritizes the matches with a second set of conditions. A similar example is disclosed in Wu (Pg. 12, lines 4-7), where a first match is done to match the patient and clinical trial based on a tumor, and the second match to further prioritize the match is based on distance to clinical trial sites.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the relevance value process as disclosed in Ozeran, with the several steps of the relevance value process of Wu because it allows for prioritization of the clinical trial that the patient is matched with, and overall, provides better sensitivity and precision for matching (Wu Pg. 1, lines 11-12; Pg. 10, lines 4-5).
Regarding Claim 14, Ozeran discloses the following:
The device of claim 12, wherein the relevance value ([0307] the search process can generate a relevance score) [is a sum of an individual relevance value of each of the first set of fields] (Examiner’s note: bracketed limitation is taught by another reference as discussed below).
However, Ozeran does not disclose the following that is met by Wu:
wherein the relevance value is a sum of an individual relevance value of each of the first set of fields (Wu (Pg. 9, line 5) discloses the summation part [of the formula on Pg. 9, line 3] calculates the sum of the weights for each term t in the query q for document d; Wu (Pg. 7, lines 21-22): Real world query usually involves multiple factors, including, e.g., disease name, gene, mutation type, gender, age, cancer stage, and tumor grade).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have combined the teachings of Ozeran and Wu to incorporate the summation of the relevance value as taught by Wu with the relevance value as taught by Ozeran since the invention is only a combination of these well-known elements which would have performed the same function in combination as each did separately. Including the summation into the calculation of the relevance value (as taught by Wu) would perform the same function of assigning a relevance score to a matched clinical trial as disclosed in Ozeran, therefore, the results would have been predictable to one of ordinary skill in the art (MPEP 2143).
Regarding Claim 15, the combination of Ozeran and Wu discloses the limitations of Claim 14, and Wu further discloses the following:
The device of claim 14, wherein the individual relevance value is associated with a respective inverse document frequency (IDF) for a respective keyword (See Wu Pg. 9, line 8: idf(t) is the inverse document frequency for term t. (Pg. 9, lines 12-14) Term frequency, inverse document frequency, and field-length norm are used together to calculate the weight of a single term in a particular document).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the combination of Ozeran and Wu to incorporate the associated inverse document frequency as taught by Wu because it provides a better solution for matching patients with clinical trials (Wu Pg. 2, lines 22-23).
Regarding Claim 16, Ozeran discloses the following:
The device of claim 12, wherein the first set of fields include one or more of: estimated glomerular filtration rate (EFGR), surgical operation, histology, pathologic staging, age, gender, or smoking ([0176] enable system 100 to correctly match clinical trial elements with patient data (e.g., histology, stage/grade, disease type, etc.); [0238]-[0239] the flow can include generating and/or receiving a number of clinical data features associated with the patient. Clinical features can include features such as diagnosis, symptoms, therapies, outcomes, patient demographics such as patient name, date of birth, gender, ethnicity, date of death, address, smoking status, diagnosis dates for cancer, illness, disease, diabetes, depression, and/or other physical or mental maladies, personal medical history, or family medical history, clinical diagnoses such as date of initial diagnosis, date of metastatic diagnosis, cancer staging, tumor characterization, tissue of origin, treatments and outcomes such as line of therapy, therapy groups, clinical trials, medications prescribed or taken, surgeries...).
Regarding Claim 17, Ozeran discloses the following:
The device of claim 12, wherein the second set of fields include one or more of: ALK, ROS1, KRAS, BRAF, RET, NTRK, MET, P53, Her2, tumor size, tumor maximum diameter, programmed death-ligand 1 (PD-L1), nodal metastases, Distant metastases, CNS metastases, bone metastases, wild type, Anti-angiogenesis, Platinum, EGFR TKIs, ALK inhibitors, PD-1/PD-L1 inhibitors, CTLA-4 inhibitor, Radiotherapy, cisplatin/carboplatin, Chemotherapy, systemic therapy, Disease status, or Eastern Cooperative Oncology Group Performance Status (ECOG PS) ([0155] As an example, the element shown within the inclusion criteria 511 is "histologically confirmed newly diagnosed stage I-II HER2/neu positive breast cancer."; [0160] selection menu 618 can include several known biomarker names (e.g., "ALK," "BRAF," etc.); [0237] the slide features 3210 can include tumor infiltration, Programmed death-ligand 1 (PD-L1) Status, human leukocyte antigen (HLA) Status, and/or other immunology features can be generated based on H&E staining and/or IHC staining; [0245] the flow 3200 can include generating and/or receiving a number of imaging data features associated with the patient. The imaging data features can include features derived from imaging data, such as a report associated with a stained slide, size of tumor, tumor size differentials over time (including treatments during the period of change), a classification and/or a score generated using a machine learning technique (e.g., machine learning techniques for classifying PDL1 status).
Regarding Claim 18, Ozeran discloses the following:
The method of claim 1, wherein the obtaining the first data set comprises: performing a classification task ([0258] In some embodiments, the data-criteria concept matching module 3274 can include a classification code system 3276, a dictionary based classification system 3278, and/or an artificial intelligence (AI) classification system 3280.), by the pre-trained model ([0280] each trained model can receive at least one feature and output and indication whether the criteria is met or not met.), on the pathology report such that at least one state value in the first data set is obtained (Table 3, [0261], [0276]: DNA/RNA Molecular features may have a classification table for genetic mutations, variants, transcriptomes, cell lines, methods of evaluating expression (TPM, FPKM), a lab which provided the results, etc. At least a portion of the classification table can include the codes in Table 3. Table 3 shows classification codes to express the state value of DNA/RNA molecular features (e.g., overexpressed or underexpressed). 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).
Regarding Claim 20, Ozeran discloses the following:
A non-transitory computer storage medium having stored thereon program instructions that, upon execution by a processor, cause the processor to perform operations, comprising ([0320] In some embodiments, the process 4900 can be implemented as executable computer-readable instructions and stored on a non-transitory medium such as a memory):
obtaining a first data set, including pathological feature data and demographic data, from a pathology report (Ozeran [0236] discloses receiving a number of molecular data features for a patient. The patient data store 3202 can include the molecular data features 3204. In some embodiments, the molecular data features 3204 can be derived from RNA and/or DNA sequencing (e.g., RNA sequencing features 3206 and/or DNA sequencing features 3208), a pathologist review of stained H&E and/or IHC slides (e.g., slide features 3210), and/or further derivative features obtained from the analysis of the individual and combined results.), wherein at least one description in the first data set is obtained by performing a sequence tagging task, by a pre-trained model (Ozeran [0162], [0176], [0276], [0277], [0280]: a machine learning tool that can review the trial description 603, as well as the criteria listed within the description, and make a determination about what structured data fields could be appropriate to include in the trial details. The combination of attributes shown for the patient can be provided using similar methods as the above-described "trial metadata" data abstracti