DETAILED ACTION
Status of Claims
This is the first office action on the merits in response to the application filed on 27 March 2025.
Claim(s) 1-16 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application claims priority of JP Application No. 2024-080597 filed on 17 May 2024. Applicant’s claim for the benefit of this prior filed application is acknowledged.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims not listed below are rejected for dependency.
Claim 15 recites “wherein the graph indicating the relationship between the patients.” However, claim 15 does not identify or provide antecedent basis for either a relationship or a set of patients. The lack of antecedent basis makes the meaning of the claim unclear, rendering the claim indefinite. Claim 16 is similarly rejected.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, which is representative of claims 15 and 16, recites
acquire data regarding a treatment of a patient;
complement missing data in the data regarding the treatment among data used for selecting a patient to be clinically tested using a graph indicating a relationship between patients generated based on the data regarding the treatment of the patient;
select a patient to be clinically tested based on the complemented data; and
output information about the selected patient to be clinically tested,
wherein the graph indicating the relationship between the patients is a graph in which nodes indicating respective patients are connected by edges connecting similar patients, the graph indicating the relationship between the patients is generated using a graph generation model, and the graph generation model is generated .
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of analyzing patient information using graph data to select and output patients to be clinically tested. This concept describes a mental process that an investigator should follow to select patients for a clinical, similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the methods of organizing human activity sub-grouping. As such, these limitation set forth a method of organizing human activity. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 recites the additional element of a device comprising at least one memory and at least one processor. Claim 16 recites the additional element of a non-transitory, recording medium. These additional elements are recited at an extremely high level of generality and are interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of performing deep learning using a neural network. At this level of generality, this additional element amounts to instructions to implement the abstract idea with a generic computing device. As previously noted, such additional elements do not integrate an abstract idea into a practical application. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the additional elements only amount to instructions to implement the abstract idea with a computing device. As such, the combination of additional elements does not integrate the abstract idea into a practical application. As the additional elements, either individually or as a combination, do not integrate the claims into a practical application the claims are determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements and a combination of additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element, and the combination of additional elements, does not amount to significantly more. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2-14 further narrow the abstract idea set forth by the claim, but these claims are determined to continue to recite abstract ideas, albeit narrowed ones. Dependent claims 2-14 recite no further additional elements. At Prong Two, the previously identified additional elements, individually and as a combination amount to instructions to implement the narrowed abstract ideas with generic computing devices. As such, dependent claims 2-14 are determined to be directed to an abstract idea. At Step 2B, the previously identified additional elements, individually and as a combination amount to instructions to implement the narrowed abstract ideas with generic computing devices. As such, the additional elements of claims 2-14, individually and as a combination, do not amount to significantly more than the abstract idea. Because the dependent claims remain directed to an abstract idea without reciting significantly more, the dependent claims are not patent eligible
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8 and 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 2022/0084633 A1) in view of Malone et al. (US 2019/0325995 A1).
Regarding Claim 1, 15, and 16: Das discloses a clinical trial support device comprising: at least one memory storing instructions (See at least [0102]); and at least one processor (See at least [0102]) configured to access the at least one memory and execute the instructions to:
acquire data regarding a treatment of a patient (In step 501, clinical variable data is acquired from a first plurality of candidate patients for the clinical trial. See at least [0088]).
select a patient to be clinically tested based on the data (Step 507 involves selecting or identifying one or more candidate patients from the first plurality of candidate patients. See at least [0008]); and
output information about the selected patient to be clinically tested (Step 508 involves the notification or communication with a user of the identification or selection of a candidate patient for enrollment in the clinical trial. See at least [0008]).
Das does not disclose complement missing data in the data regarding the treatment among data used for selecting a patient to be clinically tested using a graph indicating a relationship between patients generated based on the data regarding the treatment of the patient or wherein the graph indicating the relationship between the patients is a graph in which nodes indicating respective patients are connected by edges connecting similar patients, the graph indicating the relationship between the patients is generated using a graph generation model, and the graph generation model is generated by performing deep learning using a neural network based on a relationship between data regarding a treatment of each patient and a graph indicating a relationship between patients.
Malone teaches complementing missing data in data regarding a treatment among data used using a graph indicating a relationship between patients generated based on the data regarding the treatment of the patient and wherein the graph indicating the relationship between the patients is a graph in which nodes indicating respective patients are connected by edges connecting similar patients, the graph indicating the relationship between the patients is generated using a graph generation model, and the graph generation model is generated based on a relationship between data regarding a treatment of each patient and a graph indicating a relationship between patients (the present invention provides a method for predicting a patient outcome from a caretaker episode. The method includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities using a respective one of the submodels. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph which connects the current episode snapshot to other historical episode snapshots based on a similarity measure. The first and second embeddings are combined to obtain a complete embedding for the current episode snapshot. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots. See at least [0021]. Also: FIG. 2 schematically shows an example of an episode snapshot graph 40 in which the nodes represent distinct episode snapshots 42 of different patients each having one or more available data modalities 44 represented by the icons beside each of the nodes. See at least [0094]. Also: edges in a graph connect instances deemed similar in some way. See at least [0015]. Also: expert knowledge is used to define a similarity among episode snapshots based on the raw data. For example, the status of the patient at the time of admission could be used to calculate a similarity. In a specific embodiment, the similarity can be determined in accordance with Equation 6 below. The episode snapshot graph is then created by connecting the most similar episode snapshots. See at least [0093]).
Das provides a system which evaluates patent data to select a patient for a clinical trial, upon which the claimed invention’s determination of unavailable data based on a graph can be seen as an improvement. However, Malone demonstrates that the prior art already knew of using graphs linking similar patients in the determination of unavailable data. One of ordinary skill in the art could have easily applied the techniques of Malone to the system of Das in order to generate additional data for Das to consider, which one of ordinary skill in the art would recognize as allowing Das to make superior patient selection decisions. As such, the application of Malone would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Das and the teachings of Malone.
Malone does not expressly disclose that the graph is generated by performing deep learning using a neural network. However, Das separately discloses performing deep learning using a neural network (Machine (and deep) learning comes in three types: supervised, unsupervised, and reinforcement. … unsupervised machine learning techniques generally look for similarities between input data (e.g. attempting to split the data into multiple clusters, where each data point in the cluster is similar to the others). See at least [0053]. Also: Deep learning in a neural network environment includes numerous interconnected nodes referred to as neurons. Input neurons, activated by input data, circumstantially activate other neurons based on connections to those other neurons which are governed by the machine parameters. A neural network behaves in a certain manner based on its own parameters. Learning refines the network parameters, and, by extension, the connections or weight factors associated with the connections between neurons in the network, such that the neural network behaves in a desired manner, such as by producing accurate predictions of drug response in a candidate patient. See at least [0056]. Also: Deep learning operates on the understanding that higher level insights can be derived from many datasets based on lower level input features. While examining an image, for example, rather than looking for an object, a deep learning algorithm can learn to look for edges which form motifs, which form parts, which form the object being sought by using only the pixel location and pixel color and inputs. These hierarchies of features can be found in many different forms of data such as speech and text, etc. See at least [0057]).
Das and Malone suggests a system which evaluates patient data based on a graph identifying similar patients, upon which the claimed invention’s generation of the graph using deep learning can be seen as an improvement. However, Das demonstrates that the prior art already knew of using deep learning and neural networks to identify similarities between data points. One of ordinary skill in the art could have easily applied the deep learning of Das to the graph generation of Malone to generate Malone’s patient similarity graph. Further, one of ordinary skill in the art would have recognized that such an application of Das would have resulted in an improved system which would require less expert intervention. As such, the application of Das, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Das and the teachings of Malone.
Regarding Claim 2: Das in view of Malone makes obvious the above limitations. Das further discloses non-structural data among the data regarding the treatment (Clinical variable information can include, but is not limited to, whether past or present, one or more of patient information relating to: a patient's electronic health record (“EHR”) whether structured or unstructured. See at least [0038]). As previously noted in combination with Das, Malone teaches complement the missing data using data among the data regarding the treatment. The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 3: Das in view of Malone makes obvious the above limitations. Das further discloses generate a feature vector of each patient as data obtained (In the process of analyzing a new set of data (e.g., patient medical records), various techniques may be used to provide feature vectors to the model. … By analyzing the snippet of text surrounding words or phrases of interest, one or more features may be extracted, forming a feature vector that may be provided as input to the trained selection model. See at least [0097]) and a criterion for selecting a patient to be clinically tested (Typically, clinical trials define certain candidate patient “eligibility criteria” (or “inclusion criteria”) specifying the characteristics of candidate patients who may be eligible to participate in a specified trial, and “disqualifying criteria” (or “exclusion criteria”) specifying the characteristics of patients who are not eligible for participation in the trial. For example, the inclusion criteria may include the age of the candidate patient, the gender of the candidate patient, the candidate patient may be required to have been diagnosed with the medical condition that the experimental therapy is seeking to address, a stage of medical treatment that patients should be at, the state of disease or condition progression the candidate patient should be at, what previous treatments a patient may have received prior to entering the clinical trial, and the like. The disqualifying criteria defining the characteristics of patients who are not eligible to participate in a specified trial may include, for example, a stage of a disease beyond which a patient would be ineligible for inclusion in the trial, previous or current treatments that disqualify a patient from participating in the trial, and the like. See at least [0002]). As previously noted in combination with Das, Malone teaches complementing the missing data, based on a graph indicating a relationship between patients generated based on the data regarding the treatment of the each patient. The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 4: Das in view of Malone makes obvious the above limitations. Additionally, Malone teaches convert the graph indicating the relationship between the patients and a goodness of fit to a criterion for selecting a patient to be clinically tested in each patient into a feature vector of each patient (For the available data modalities of the new episode snapshot, the respective trained submodels of the embedding model 16 can be used to generate a first embedding in each case in a step S12. For the missing data modalities, a second embedding in each case is generated using neighbors in the episode snapshot graph 40 in a step S11. For this purpose, the new episode snapshot is located in the episode snapshot graph based on the similarity measure defined from domain knowledge, and message passing can be used to generate the embeddings for the missing data modalities in accordance with any of the embodiments discussed herein. In a step S13, the first and second embeddings are concatenated to form a complete embedding for the new episode snapshot. See at least [0109]). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 5: Das in view of Malone makes obvious the above limitations. As previously noted, Malone teaches complement missing data in the data regarding the treatment among data used for calculating goodness of fit to a criterion for selecting a patient to be clinically tested (Examiner’s note, there is no positive limitation requiring calculating a goodness of fit, and the description of the data as “among data used for calculating goodness of fit…” is considered intended use and given limited patentable weight). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 6: Das in view of Malone makes obvious the above limitations. Additionally, Das discloses select a patient to be clinically tested from among patients in which the selection criterion is met at a past time point (Step 507 involves selecting or identifying one or more candidate patients from the first plurality of candidate patients that satisfy the clinical trial inclusion criteria 504 and that are statistically likely to meet at least one of the one or more clinical trial endpoints 505. See at least [0088]) and data regarding a treatment for an implementation period of a clinical trial from a time point at which the selection criterion is met is recorded (Clinical trials in medicine are research studies that are used to test and evaluate various medical treatments, drugs, or devices under development. See at least [0002]).
Regarding Claim 7: Das in view of Malone makes obvious the above limitations. Das further discloses select the patient to be clinically tested further based on data regarding a treatment of a selected patient as the patient to be clinically tested (Step 507 involves selecting or identifying one or more candidate patients from the first plurality of candidate patients. See at least [0008]. Also: Step 508 involves the notification or communication with a user of the identification or selection of a candidate patient for enrollment in the clinical trial. See at least [0008]).
Regarding Claim 8: Das in view of Malone makes obvious the above limitations. As previously noted, Malone predict data regarding a treatment at a predetermined time point based on the data regarding the treatment (the present invention provides a method for predicting a patient outcome from a caretaker episode. The method includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities using a respective one of the submodels. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph which connects the current episode snapshot to other historical episode snapshots based on a similarity measure. The first and second embeddings are combined to obtain a complete embedding for the current episode snapshot. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots. See at least [0021]). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 11: Das in view of Malone makes obvious the above limitations. Das further discloses select a patient to be clinically tested based on the feature vector of each patient (In the process of analyzing a new set of data (e.g., patient medical records), various techniques may be used to provide feature vectors to the model (e.g., natural language processing techniques). See at least [0097]. Also: A trained model (e.g., a supervised machine learning system) may use a framework based on a set of data labels, and may be trained to generate results consistent with that set of labels. In some cases, the trained model may be provided with a set of inputs (e.g., one or more feature vectors derived from patient medical records, which may be generated as part of the procedure to train the model) and may generate as an output a score or confidence level that may be used to determine if a particular individual may be omitted from a clinical trial or whether the individual may be an appropriate candidate for the clinical trial (e.g., based on comparison of the output to a predetermined threshold level). See at least [0083]).
Regarding Claim 12: Das in view of Malone makes obvious the above limitations. Additionally, Malone complement missing data in the predicted data regarding the treatment (the present invention provides a method for predicting a patient outcome from a caretaker episode. The method includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities using a respective one of the submodels. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph which connects the current episode snapshot to other historical episode snapshots based on a similarity measure. The first and second embeddings are combined to obtain a complete embedding for the current episode snapshot. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots. See at least [0021]). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 13: Das in view of Malone makes obvious the above limitations. Additionally, Malone predict data regarding a treatment at the predetermined time point for the selected patient (the present invention provides a method for predicting a patient outcome from a caretaker episode. The method includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities using a respective one of the submodels. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph which connects the current episode snapshot to other historical episode snapshots based on a similarity measure. The first and second embeddings are combined to obtain a complete embedding for the current episode snapshot. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots. See at least [0021]). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Regarding Claim 14: Das in view of Malone makes obvious the above limitations. Additionally, Malone the predetermined time point is a start time point of a clinical trial or a time point during an implementation period (the present invention provides a method for predicting a patient outcome from a caretaker episode. The method includes receiving a current episode snapshot of the caretaker episode comprising multi-modal data of the patient from an electronic health records (EHR) system, the multi-modal data including one or more available data modalities and one or more missing data modalities. The multi-modal data is applied as input to an embedding model having a submodel for each of the data modalities. A first embedding is generated for each of the available data modalities using a respective one of the submodels. A second embedding is generated for each of the missing data modalities using corresponding embeddings of neighbors in an episode snapshot graph which connects the current episode snapshot to other historical episode snapshots based on a similarity measure. The first and second embeddings are combined to obtain a complete embedding for the current episode snapshot. The patient outcome is predicted based on the complete embedding for the current episode snapshot using a machine learning component which has been trained using patient outcomes of the historical episode snapshots. See at least [0021]). The motivation to combine Das and Malone is the same as explained under claim 1 above, and is incorporated herein.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (US 2022/0084633 A1) in view of Malone et al. (US 2019/0325995 A1), and further in view of Baldauf-Lenschen et al. (US 2025/0201386 A1) [hereafter referenced as Baldauf].
Regarding Claim 9: Das in view of Malone makes obvious the above limitations. Das does not expressly disclose select at least some of patients in a control group based on data of selected patients in a clinical trial group as the patients to be clinically tested. However, Baldauf teaches select at least some of patients in a control group based on data of selected patients in a clinical trial group as the patients to be clinically tested (the model-generated prognosis data 635 is generated via medical outcome prognostication system and/or the medical outcome prognosis score 636 is utilized by prognosis-based trial arm assignment system 508 to assign a corresponding patient to cither a control group or investigational in a corresponding clinical trial. See at least [0246]).
Das and Malone suggests a system which identifies patients for a clinical trial, upon which the claimed invention’s assigning the patients to trial groups can be seen as an improvement. However, Baldauf demonstrates that the prior art already knew of assigning patients to control and experimental groups based on their prognoses. One of ordinary skill in the art could have trivially applied the techniques of Baldauf to the system of Das and Malone. Further, one of ordinary skill in the art would have recognized that such an application of Baldauf would have resulted in an improved system more likely to produce positive results for the clinical trial. As such, the application of Baldauf, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Das and the teachings of Malone and Baldauf.
Regarding Claim 10: Das in view of Malone makes obvious the above limitations. Das does not expressly disclose select a patient in a clinical trial group based on data of a selected patient in a clinical trial group as the patient to be clinically tested. However, Baldauf teaches select a patient in a clinical trial group based on data of a selected patient in a clinical trial group as the patient to be clinically tested (the model-generated prognosis data 635 is generated via medical outcome prognostication system and/or the medical outcome prognosis score 636 is utilized by prognosis-based trial arm assignment system 508 to assign a corresponding patient to cither a control group or investigational in a corresponding clinical trial. See at least [0246]).
Das and Malone suggests a system which identifies patients for a clinical trial, upon which the claimed invention’s assigning the patients to trial groups can be seen as an improvement. However, Baldauf demonstrates that the prior art already knew of assigning patients to control and experimental groups based on their prognoses. One of ordinary skill in the art could have trivially applied the techniques of Baldauf to the system of Das and Malone. Further, one of ordinary skill in the art would have recognized that such an application of Baldauf would have resulted in an improved system more likely to produce positive results for the clinical trial. As such, the application of Baldauf, and the claimed invention, would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention in view of the disclosures of Das and the teachings of Malone and Baldauf.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Fernandes et al. (US 2025/0201360 A1) describes imputing values associated with eligibility criterion of clinical trials (See at least [0015]).
Colin et al. (US 2025/0372224 A1) describes imputing missing values in patient data and using that data to predict treatment outcomes.
Will et al. (US 2020/0234800 A1) describes analyzing patient data including considering similar patients to predict a trial treatment outcome (See at least [0057]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bion A Shelden/Primary Examiner, Art Unit 3685 2026-06-13
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