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 .
Status of the Claims
Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding Claims 1, 8, and 15, Claims 1, 8, and 15 recite “automatically normalizing, by the processor, the retrieved data into a unified data format without human intervention.” [0045] of the as-filed Specification discloses utilizing APIs to automatically receive data from electronic data sources without requiring any human intervention. That is, the Specification discloses automatically receiving the data, but does not recite any normalizing or conversion of the received data into a unified data format, and hence this language represents new matter.
Additionally, Claims 1, 8, and 15 recite “transmitting…the predicted patient burden score to…a clinical trial management platform that, in response, modifies visit schedules or procedure assignments for trial participants.” [0111] of the as-filed Specification discloses that “the algorithm discussed herein may also be used to demonstrate and affirm that the more complex and burdensome the protocol, the higher the screen failure rate; and the higher the number of unplanned and unbudgeted amendments implemented to modify the protocol after it has been finalized.” That is, the Specification discloses that the burden score may be used in order to modify the protocol, but does not teach actively modifying visit schedules or procedure assignments, as is claimed in Claims 1, 8, and 15. Furthermore, Furthermore, [0134]-[0136] of the as-filed Specification disclose that the server determines the patient burden score, but there is no disclosure of any type of adjustment to a patient protocol such as a modification to a visit schedule or procedure assignment. Hence, this language represents new matter.
Dependent Claims 2-7, 9-14, and 16-20 are also rejected under 35 U.S.C. 112(a) due to their dependence from independent Claims 1, 8, and 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-7 are drawn to a method for determining patient burden for a clinical study, which is within the four statutory categories (i.e. process). Claims 8-20 are drawn to systems for determining patient burden for a clinical study, which are within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A method for generating a training dataset used to train a model to predict a burden of a clinical study for a patient using a decreased feature size, the method comprising:
in response to transmitting a clinical study questionnaire to a set of patients associated with a set of clinical studies, retrieving, by a processor via an application programming interface heterogenous patient input corresponding to different categories of patient data and operational parameter data from a set of electronic data sources, input received via the set of patients, the input corresponding to demographic data and a quantified burden associated with each clinical study;
automatically normalizing, by the processor, the retrieved data into a unified data format without human intervention;
generating, by the processor, a training dataset comprising:
each patient's demographic data,
for at least some patients of the set of patients, an indication of a therapeutic area that a disease associated with the patient belongs to;
a patient burden score for each patient generated in accordance with an algorithm evaluating each patient's input with regards to participation logistics, lifestyle factors, caregiver involvement, and procedural burden associated with each clinical study, and
a set of operational parameters associated with the set of clinical studies;
segmenting, by the processor, in accordance with the operational parameters, the training dataset into one or more procedure subgroups representing the procedure types of the clinical study associated with each patient according to a data splitting protocol based on the set of operational parameters associated with the set of clinical studies; and
training, by the processor, a computer model including a predictive model associated with each procedure group using the segmented training dataset, such that the computer model is configured to ingest data associated with a new clinical study, generate an individual burden prediction for each procedure group included in the new clinical study by executing one or more corresponding predictive models, and predict a new patient burden score based on the individual burden predictions of the one or more corresponding predictive models, wherein training the computer model comprises, with each iteration for each segment of the training dataset:
eliminating a least significant independent variable of the demographic data in each iteration of an iterative multivariate elimination regression modeling protocol for each procedure group, wherein eliminating the least significant independent variable in each iteration reduces the model’s feature set for the procedure subgroup, thereby decreasing the data dimensionality trained in subsequent iterations and allowing faster convergence and reduced computation time for training each predictive model;
generating a coefficient associated with each procedure group based on the iterative multivariate elimination regression modeling protocol; and
transmitting, by the processor, the predicted patient burden score to a graphical user interface of a clinical trial platform that, in response, modifies visit schedules or procedure assignments for trial participants.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of cover the abstract idea of mathematical concepts and a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the steps of normalizing the retrieved data, generating the training dataset, segmenting the training dataset into procedure subgroups according to a splitting protocol, training a model using a multivariate elimination regression modeling protocol, generating an individual burden prediction for each procedure group, and predicting a new patient burden score based on the individual burden predictions include at least mathematical calculations), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of transmitting the study questionnaire to patients, retrieving patient data, transmitting the burden score to a graphical user interface, and modifying visit schedules or procedure assignments for patients include following rules or instructions for organizing clinical trial participation for patients), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 8 and 15 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 8, and 15 is that Claim 1 recites a method, whereas Claim 8 recites a system including a server and a non-transitory computer-readable medium containing instructions that are executed by a processor, and Claim 15 recites a system including a server.
Dependent Claims 2-7, 9-14, and 16-20 include other limitations, for example Claim 2, 9, and 16 recite that the burden score is based on a new patient attribute, Claims 3, 10, and 17 recite using the new patient burden score to populate an interface, Claims 4, 11, and 18 recite various parameters in the training dataset, Claims 5, 12, and 19 recite identifying the strength of features in the training dataset, Claims 6, 13, and 20 recite determining which inputs have a statistically significant relationship to the output patient burden score, and Claims 7 and 14 recite utilizing a supervised training method, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-7, 9-14, and 16-20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent Claims 2-7, 9-14, and 16-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 8, and 15.
Hence Claims 1-20 recite the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1, 8, and 15 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the computer of the computer model, the application programming interface, and the step of transmitting the clinical study questionnaire) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a processor and a computer of the computer model, and the API, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0004], [0012], [0015], and [0054] of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the types of data of the patient inputs, which amounts to limiting the abstract idea to the field of clinical trials, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of transmitting the clinical study questionnaire to a set of patients that causes the retrieval of the input, which amounts to mere data gathering, see MPEP 2106.05(g).
Additionally, dependent Claims 2-7, 9-14, and 16-20 include other limitations, but these limitations also amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 4, 11, and 18), and/or adding insignificant extra-solution activity to the abstract idea (e.g. populating a graphical user interface recited in Claims 3, 10, and 17), and/or do not include any additional elements beyond those already recited in independent Claims 1, 8, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-20 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1, 8, and 15 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the computer of the computer model, and the steps of transmitting the clinical study questionnaire and retrieving the patient input), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
paragraphs [0004], [0015], and [0054] of the Specification disclose that the additional elements (i.e. the processor and the computer of the computer model) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention transmits a clinical study questionnaire to a set of patients and in response receives input data over a network, for example the Internet, e.g. see [0044] of the present Specification;
Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life – similarly, the current invention performs basic calculations (i.e. calculating an individual burden prediction for each procedure group, calculating a patient burden score based on the individual burden prediction, performing multiple iterations of training and removing the least significant independent variable with each iteration) and does not impose meaningful limits on the scope of the claims;
Dependent Claims 2-7, 9-14, and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly generally link the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 4, 11, and 18), represent no more than generic structural elements performing generic functions (e.g. populating a graphical user interface recited in Claims 3, 10, and 17), and/or do not recite any additional elements not already recited in independent Claims 1, 8, and 15, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1-20 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 112 and 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Walpole (US 2019/0206521) teaches transmitting surveys to patients to obtain various patient data, generating a training dataset for a machine learning system, training the machine learning system, and calculating a patient burden index. However, Walpole does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Walpole does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Neumann (US 2021/0004715) teaches training datasets including an area of expertise for an advisor. However, Neumann does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Neumann does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Clark (US 2020/0258599) teaches labeling training data with various labels, and utilizing at least a subset of the labeled training data to train separate machine learning models. However, Clark does not teach that the training dataset includes a therapeutic area for the patient disease, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Clark does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Lash (US 2001/0020229) teaches calculating a likelihood of a patient becoming a high user of healthcare resources based on existing coefficients for claims. Additionally, Lash teaches utilizing iterative multivariate logistic regression that eliminates the least predictive variable with each iteration until all remaining variables are determined to be significant. However, Lash does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, and generating an individual burden score for each procedure subgroup.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claim 1-20 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed January 20, 2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant alleges that the present invention is patent eligible because it is not directed towards an abstract idea, specifically because it recites a training process for a computer model that reduces data dimensionality, and analogizing these features to Example 39 of the USPTO-issued Examples, e.g. see pgs. 9-12 of Remarks – Examiner disagrees.
Initially Examiner notes that, as shown above, the presently-amended Claims are not characterized as reciting a mental process, and hence any arguments pertaining to a mental process are moot.
Regarding Example 39, Examiner asserts that unlike the invention of Example 39, the claimed invention is more properly analogized to the invention of Claim 2 of Example 47 of the UPSTO-issued Examples in that it recites at least a mathematical concept (i.e. multivariate elimination regression modeling). Furthermore, similar to Claim 2 of Example 47, the claimed invention merely recites computing structure (i.e. a server, a processor) at a high level of generality (i.e. as a generic computer performing generic computer functions).
Applicant further alleges that the claimed invention is patent eligible because it integrates any abstract idea into a practical application in that they provide a technical improvement, specifically because it standardizes data into a unified format which improves the efficiency and reliability of the computer system, e.g. see pgs. 12-14 of Remarks – Examiner disagrees.
Even assuming, arguendo, that the present invention achieves the improvements alleged by Applicant, the improvements of, for example, “a more accurate patient burden score” or a “model that is more effective for determining a patient burden score,” these represent improvements to the abstract idea of a mathematical calculation. Furthermore, the improvement of “[providing] immediate, automated adjustments to trial protocols…enhancing the efficiency of trial operations” represents an improvement to the abstract idea of a certain method of organizing human activities in that they provide a more accurate patient burden score resulting in an improved patient visit schedule. Additionally, an improvement to an abstract idea itself is not an improvement to technology, e.g. see MPEP 2106.05(a)(II).
Examiner further notes that, for the reasons disclosed above with regards to 35 U.S.C. 112(a), the Specification does not disclose the limitations upon which Applicants rely for showing technical improvements. Namely, the Specification does not disclose the limitation of normalizing heterogenous data into a unified data format without human intervention and/or the limitation of modifying visit schedules or procedure assignments, and hence there is not an adequate nexus between the claimed limitations and the alleged improvements.
For the aforementioned reasons, Claims 1-20 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed January 26, 2026, regarding the rejections of Claims 1-20 under 35 U.S.C. 103 have been considered and, in combination with the amendments, are persuasive for the reasons disclosed above. The previous rejections of Claims 1-20 under 35 U.S.C. 103 have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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/JOHN P GO/Primary Examiner, Art Unit 3681