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 1-15 are currently pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are as follows:
“Dropout prediction engine” recited in Claims 1-8; and
“Engagement recommendation engine” recited in Claims 1-8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 1-15 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.
Regarding Claims 1 and 9, Claims 1 and 9 recite “outcome feedback regarding the effectiveness of the recommended engagement action.” There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Dependent Claims 2-8 and 10-15 are also rejected under 35 U.S.C. 112(b) due to their dependence from independent Claims 1 and 9.
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 1-15 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 1-15 are within the four statutory categories. Claims 1-8 are drawn to a method for predicting patient dropout risk, which is within the four statutory categories (i.e. process). Claims 9-15 are drawn to a system for predicting patient dropout risk, which is 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 predicting dropout risk for a patient under remote monitoring using a dropout prediction system, the method comprising:
obtaining, from an electronic patient records database, a plurality of medical records for a patient under remote monitoring by a care provider;
extracting, from the plurality of medical records for the patient under remote monitoring by the care provider, a plurality of dropout prediction features for the patient;
generating, using a dropout prediction engine, a dropout risk score for the patient based on the plurality of dropout prediction features;
determining, using an engagement recommendation engine, a potential dropout cause based on at least the plurality of dropout prediction features;
determining, using the engagement recommendation engine, a recommended engagement action, wherein the recommended engagement action is intended to prevent dropout of the patient from remote monitoring by the care provider;
accessing, by the dropout prediction system, a dropout prediction database comprising a repository of root dropout causes and a library of engagement actions, wherein the determining of the potential dropout cause and the recommended engagement action is performed based on information stored in the dropout prediction database;
receiving, by the dropout prediction system, outcome feedback regarding the effectiveness of the recommended engagement action; and
updating, by the dropout prediction system, at least one of the repository of root dropout causes or the library of engagement actions in the dropout prediction database based on the outcome feedback, such that the dropout prediction database is adaptively updated over time;
presenting, via a care provider interface, the recommended engagement action to a care team member of the care provider; and
transmitting, by the dropout prediction system, an instruction corresponding to the recommended engagement action to a patient-side monitoring device associated with the patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite 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 obtaining medical records, extracting dropout prediction features from the medical records, generating a dropout risk score based on the extracted dropout prediction features, determining a potential dropout cause based on the dropout prediction features, determining a recommended engagement action, accessing root dropout causes and engagement actions, receiving feedback regarding the effectiveness of the recommended engagement action, updating the root dropout causes and/or the engagement actions based on the feedback, presenting the recommended engagement action, and transmitting an instruction corresponding to the recommended engagement action are reasonably interpreted as following rules or instructions in order to provide a care provider and a patient with recommended actions), 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 Claim 9 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 9 is that Claim 1 recites a method, whereas Claim 9 recites a system and its associated structural limitations (i.e. processors), which, as will be further explained below, are interpreted as additional elements.
Dependent Claims 2-8 and 10-15 include other limitations, for example Claims 2 and 10 recite utilizing a trained machine learning algorithm to determine the dropout risk score, Claims 3 and 11 recite utilizing a Shapley values algorithm to determine the potential dropout cause, Claims 4-5 and 12-13 recite types of medical records, Claims 6-7 and 15 further define the dropout risk score, and Claims 8 and 14 recite determining a phenotype for the patient based on the dropout prediction features, wherein the recommended engagement action is based on the phenotype, 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, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-8 and 10-15 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 9.
Prong 2 of Step 2A
Claims 1 and 9 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, electronic patient records database, the dropout prediction engine, the engagement recommendation engine, the care provider interface, the one or more processors) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the electronic patient records database, the dropout prediction engine, the engagement recommendation engine, the care provider interface, the one or more processors, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see pg. 18, line 12 through pg. 19, line 22 of the as-filed Specification, and see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language reciting that the extracted features from the patient records are dropout prediction features, and the claim language reciting presenting the recommended engagement action to a care provider, which amount to limiting the abstract idea to the field of healthcare, e.g. see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of presenting the recommended engagement action to a care provider and transmitting the instruction corresponding to the recommended engagement action to a patient, which amounts to an insignificant application, e.g. see MPEP 2106.05(g).
Additionally, dependent Claims 2-8 and 10-15 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the trained machine learning model recited in dependent Claims 2 and 10), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of patient records recited in dependent Claims 4-5 and 12-13), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 9, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-15 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 9 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, electronic patient records database, the dropout prediction engine, the engagement recommendation engine, the care provider interface, the one or more processors), 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 additional elements comprise 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:
Pg. 18, line 12 through pg. 19, line 22 of the as-filed Specification discloses that the additional elements (i.e. the electronic patient records database, the dropout prediction engine, the engagement recommendation engine, the care provider interface, the one or more processors) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following 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 receives patient data from an electronic records database, and transmit the dropout risk score and the instruction corresponding to the recommended engagement action to a provider and/or a patient over a network, e.g. see pg. 12, line 30 through pg. 13, line 4 of the as-filed Specification;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of patient records on a database;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing patient records in a database, and retrieving the data from the patient records in order to ultimately produce and present the recommended engagement action;
Dependent Claims 2-8 and 10-15 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 amount to mere instructions to apply the exception (e.g. the trained machine learning model recited in dependent Claims 2 and 10), generally link the abstract idea to a particular technological environment or field of use (e.g. the types of patient records recited in dependent Claims 4-5 and 12-13), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1 and 9, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-15 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 1-15 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claims 1-2, 4-5, 8-10, 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia (US 2021/0134431) in view of Docherty (US 2017/0337330).
Regarding Claim 1, Garcia teaches the following: A method for predicting dropout risk for a patient under remote monitoring using a dropout prediction system (The patient treatment is monitored at the patient’s home, e.g. see [0011] and [0067].), the method comprising:
obtaining, from an electronic patient records database, a plurality of medical records for a patient under remote monitoring by a care provider (The system includes a plurality of clinics and hospitals that each maintain electronic medical databases (i.e. any of which may be interpreted as a “electronic patient records database”), e.g. see Garcia [0086], wherein the databases contain patient specific treatment and prescription data obtained as log files from therapy machines, e.g. see Garcia [0087] and [0090], and wherein the patient data may be transmitted to a system hub and subsequently to a clinician device upon request, e.g. see Garcia [0089] and [0124].);
extracting, from the plurality of medical records for the patient under remote monitoring by the care provider, a plurality of dropout prediction features for the patient (The system utilizes a segment of the patient and treatment data to compare to a training dataset, e.g. see Garcia [0174]-[0175] and [0180], wherein the patient and treatment data includes parameters, at least three to five or more of which are compared to training data in order to determine a predicted probability that the patient will stop treatment, e.g. see Garcia [0174]-[0175]..);
generating, using a dropout prediction engine, a dropout risk score for the patient based on the plurality of dropout prediction features (The system determines a likelihood that the patient may stop or reduce treatments as a concern score (i.e. a dropout risk score), wherein the determination utilizes an AI model trained using training data set including at least three to five (or more) parameters (i.e. the dropout prediction features), e.g. see Garcia [0172]-[0174].);
determining, using an engagement recommendation engine, a potential dropout cause based on at least the plurality of dropout prediction features (The system identifies crucial parameters or contributing factors that cause a high concern score, e.g. see Garcia [0177], Fig. 17.);
determining, using the engagement recommendation engine, a recommended engagement action (The system includes a guidance processor that determines guidance that corresponds to the concern score and the crucial parameters or contributing factors, e.g. see Garcia [0209]-[0210], Fig. 19.), wherein the recommended engagement action is intended to prevent dropout of the patient from remote monitoring by the care provider (The guidance is intended to help improve the patient’s adherence (i.e. prevent dropout) to a prescribed therapy, e.g. see Garcia [0210].);
presenting, via a care provider interface, the recommended engagement action to a care team member of the care provider (The guidance is displayed to a user, for example a clinician, e.g. see Garcia [0210].); and
But Garcia does not teach and Docherty teaches the following:
accessing, by the dropout prediction system, a dropout prediction database comprising a repository of root dropout causes and a library of engagement actions, wherein the determining of the potential dropout cause and the recommended engagement action is performed based on information stored in the dropout prediction database (The system prompts patients for reasons for noncompliance (i.e. dropout causes), e.g. see Docherty [0050]-[0051], Figs. 5A-5E and 6, wherein the patient inputs for the reasons are stored in a memory and/or database, e.g. see Docherty [0052]. Furthermore, the system utilizes data stored in the database to identify trends, e.g. see Docherty [0059], wherein the trends may be used by an analytics module to determine a reason for noncompliance, for example the system may determine that the user is noncompliant because a trend indicates that they are often noncompliant on a particular day of the week, e.g. see Docherty [0065]. Additionally, the trends may be used by an action module to select an action for the user to remedial communication and literature, or issuing a reminder to a patient to take a medication, e.g. see Docherty [0066]-[0069].);
receiving, by the dropout prediction system, outcome feedback regarding the effectiveness of the recommended engagement action (The system utilizes responses from the patient to identify trends and actions to take based on the trends, e.g. see Docherty [0059], [0065], and [0067], wherein the actions the system takes may include reminding the patient to take a medication, e.g. see Docherty [0067]. Furthermore, the system may subsequently query the patient regarding their noncompliance despite the issuance of the reminder and the patient may respond that they did not comply even after receiving a reminder, e.g. see Docherty Figs. 5A-5E. That is, the reminder is interpreted as “the recommended engagement action,” and the patient response indicating noncompliance despite the receiving of a reminder is interpreted as “outcome feedback regarding the effectiveness of the recommended engagement action.”); and
updating, by the dropout prediction system, at least one of the repository of root dropout causes or the library of engagement actions in the dropout prediction database based on the outcome feedback, such that the dropout prediction database is adaptively updated over time (The system records the patient responses in the database over time, e.g. see Docherty [0059], and may do so for as many iterations as desirable/necessary, e.g. see Docherty [0089].);
transmitting, by the dropout prediction system, an instruction corresponding to the recommended engagement action to a patient-side monitoring device associated with the patient (The system transmits a remedial communication to a patient in response to the patient’s responses, for example a reminder to refill the patient’s medication, e.g. see Docherty [0067].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Garcia to incorporate storing the patient responses in a database and using the stored data to perform analysis to determine actions to take as taught by Docherty in order to reliably monitor and assure patient compliance with a treatment, e.g. see Docherty [0002]-[0003].
Regarding Claim 2, the combination of Garcia and Docherty teaches the limitations of Claim 1, and Garcia further teaches the following:
The method of claim 1, wherein the dropout prediction engine comprises a trained dropout prediction model (The concern score is calculated using a trained AI model, e.g. see Garcia [0172]-[0178].), the trained dropout prediction model being trained by a machine learning algorithm on a training dataset that comprises a plurality of medical records for a plurality of historical patients (The AI model is trained using a training data set, e.g. see Garcia [0173].).
Regarding Claim 4, the combination of Garcia and Docherty teaches the limitations of Claim 1, and Garcia further teaches the following:
The method of claim 1, wherein the plurality of medical records for the patient under remote monitoring include at least one of: identification information for the patient; socioeconomic information for the patient; medical history for the patient; treatment history for the patient; medical directives for the patient; and one or more physiological measurements taken from the patient (The patient data includes patient-specific treatment and prescription data (i.e. medical directives for the patient), e.g. see Garcia [0087], and patient data may be determined from treatment data, wherein the patient data includes prescription history (i.e. treatment history), and a patient identifier (i.e. identification information), e.g. see Garcia [0065].).
Regarding Claim 5, the combination of Garcia and Docherty teaches the limitations of Claim 4, and Docherty further teaches the following:
The method of claim 4, wherein the plurality of medical records for the patient under remote monitoring further includes at least one of: virtual care solution usage information; a technology affinity measured for the patient; and feedback information from one or more historical or concurrent patients (The system utilizes response data (i.e. feedback information) from other users of the medications (i.e. one or more historical or concurrent patients), e.g. see Docherty [0065].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Garcia to incorporate utilizing data from other patients also taking the medications as taught by Docherty in order to reliably monitor and assure patient compliance with a treatment, e.g. see Docherty [0002]-[0003].
Regarding Claim 8, the combination of Garcia and Docherty teaches the limitations of Claim 1, and Garcia further teaches the following: The method of claim 1, further comprising:
determining a phenotype for the patient under remote monitoring based on one or more of the dropout prediction features, the dropout risk score generated for the patient, and the potential dropout cause determined for the patient (Given the broadest reasonable interpretation in light of the as-filed Specification, a “phenotype” may be interpreted as “a classification or subgroup that the patient may be sorted into based on one or more similar characteristics,” e.g. see pg. 8, lines 18-19 of the as-filed Specification. The system determines a likelihood that the patient may stop or reduce treatments as a concern score (i.e. a dropout risk score), wherein the determination utilizes an AI model trained using training data set including at least three to five (or more) parameters (i.e. the dropout prediction features), e.g. see Garcia [0172]-[0174], wherein the system may further determine that the patient belongs to a group of patients whose concern score exceeds a threshold (i.e. a phenotype), and in response creates an alert and/or a document for the patient including the concern score and a top number of contributing critical parameters, e.g. see Garcia [0204] and [0214].), wherein the phenotype for the patient indicates a subgroup of similar historical patients (The patient concern score is based on a comparison of similar historical patients, e.g. see Garcia [0174], and hence the concern score exceeding a threshold (i.e. the phenotype) is indicative of the similarity of the patient to at least some of the historical patients.);
wherein the recommended engagement action is determined at least in part based on the phenotype for the patient (The system provides recommendations for addressing critical parameters based on the score exceeding the threshold, e.g. see Garcia [0208]-[0210] and [0214]-[0215].).
Regarding Claim 9, the limitations of Claim 9 are substantially similar to those claimed in Claim 1, with the sole difference being that Claim 1 recites a method whereas Claim 9 recites a system. Specifically pertaining to Claim 9, Examiner notes that Garcia teaches both a method and a system including a processor that performs the aforementioned functions, e.g. see Garcia [0088], and hence the grounds of rejection provided above for Claim 1 are similarly applied to Claim 9.
Regarding Claims 10 and 12-14, the limitations of Claims 10 and 12-14 are substantially similar to those claimed in Claims 2, 4-5, and 8 respectively, with the sole difference being that Claims 2, 4-5, and 8 recite a method whereas Claims 10 and 12-14 recite a system. Specifically pertaining to Claims 10 and 12-14, Examiner notes that Garcia teaches both a method and a system including a processor that performs the aforementioned functions, e.g. see Garcia [0088], and hence the grounds of rejection provided above for Claims 2, 4-5, and 8 are similarly applied to Claims 10 and 12-14.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Garcia and Docherty in view of Appelbaum (US 2022/0051773).
Regarding Claim 3, the combination of Garcia and Docherty teaches the limitations of Claim 1, but does not teach and Appelbaum teaches the following:
The method of claim 1, wherein the potential dropout cause is determined by evaluating a feature value contribution for one or more dropout prediction features, the feature value contributions being determined using a Shapley values algorithm (The system utilizes a Tree Shapley Additive Explanation (SHAP) algorithm to generate more interpretable predictions, wherein the SHAP algorithm assigns each explanatory variable an importance value for each prediction, such that it determines which explanatory variables drove a particular prediction, e.g. see Appelbaum [0127].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Garcia and Docherty to incorporate the SHAP algorithm as taught by Appelbaum in order to generate more interpretable predictions, e.g. see Appelbaum [0127].
Regarding Claim 11, the limitations of Claim 11 are substantially similar to those claimed in Claim 3, with the sole difference being that Claim 3 recites a method whereas Claim 11 recites a system. Specifically pertaining to Claim 11, Examiner notes that Garcia teaches both a method and a system including a processor that performs the aforementioned functions, e.g. see Garcia [0088], and hence the grounds of rejection provided above for Claim 3 are similarly applied to Claim 11.
Claims 6-7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia in view of Baym (US 2015/0112728).
Regarding Claim 6, the combination of Garcia and Docherty teaches the limitations of Claim 1, but does not teach and Baym teaches the following:
The method of claim 1, wherein the dropout risk score generated for the patient is a likelihood that the patient will fail to meet a participation threshold set by a third party (The system includes required conditions (i.e. a threshold) specified by an insurer (i.e. a third party) in order for an insurer to provide coverage, wherein the conditions include that a patient must engage in one or more specified activities such as making all treatment appointments, e.g. see Baym [0097]-[0098], wherein the system further calculates a probability than an insurer will deny some or all coverage (i.e. by virtue of failing to meet the requirements), e.g. see Baym [0058] and [0124].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Garcia and Docherty to incorporate the insurance coverage limitations as taught by Baym in order to enable a patient to manage their risk, e.g. see Baym [0054].
Regarding Claim 7, the combination of Garcia, Docherty, and Baym teaches the limitations of Claim 6, and Baym further teaches the following:
The method of claim 6, wherein the third party is an insurance company and the participation threshold determines whether the remote monitoring of the patient is covered under an insurance plan associated with the patient (The insurer requirements (i.e. the participation threshold) determine whether the insurer will provide coverage, e.g. see Baym [0058], [0097]-[0098], and [0124].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Garcia and Docherty to incorporate the insurance coverage limitations as taught by Baym in order to enable a patient to manage their risk, e.g. see Baym [0054].
Regarding Claim 15, the limitations of Claim 15 are substantially similar to those claimed in Claims 6-7, with the sole difference being that Claims 6-7 recite a method whereas Claim 15 recites a system performing the functions of Claims 6-7. Specifically pertaining to Claim 15, Examiner notes that Garcia teaches both a method and a system including a processor that performs the aforementioned functions, e.g. see Garcia [0088], and hence the grounds of rejection provided above for Claims 6-7 are similarly applied to Claim 15.
Response to Arguments
Applicant’s arguments, see Remarks, filed September 15, 2025, with respect to the rejections of Claims 1-15 under nonstatutory double patenting have been fully considered and, in combination with the claim amendments, are persuasive. The nonstatutory double patenting rejections of Claims 1-15 have been withdrawn, as the current claim language is sufficiently distinguished from the claim language of copending Application No. 18/544606.
Applicant’s arguments, see Remarks, filed September 15, 2025, with respect to the interpretations of Claims 1-15 under 35 U.S.C. 112(f) have been fully considered, and are partially persuasive.
As an initial matter, Claims 9-15 are no longer interpreted under 35 U.S.C. 112(f) because Claim 9 recites one or more processors configured to perform the functions of the dropout prediction engine, engagement recommendation engine, and care provider interface, and hence the one or more processors are interpreted as sufficient structure to perform these functions such that the aforementioned terms should not be interpreted under 35 U.S.C. 112(f).
Additionally, the “care provider interface” is no longer interpreted under 35 U.S.C. 112(f) for Claims 1-8 because an “interface” is not properly interpreted as a generic placeholder and/or a nonce term used as a substitute for “means.”
However, the “dropout prediction engine” and the “engagement recommendation engine” recited in Claims 1-8 are nonetheless interpreted under 35 U.S.C. 112(f) because an “engine” is properly interpreted as a generic placeholder and/or nonce term used as a substitute for “means,” is modified by functional language (i.e. generating a dropout risk score and determining a potential dropout cause respectively), and is not modified by sufficient structure (e.g. a computer processor).
For the aforementioned reasons, the “dropout prediction engine” and the “engagement recommendation engine” recited in Claims 1-8 are nonetheless interpreted under 35 U.S.C. 112(f).
Applicant’s arguments, see Remarks, filed September 15, 2025, with respect to the rejections of Claims 1-15 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claimed invention is patent eligible under 35 U.S.C. 101 because it recites a particular machine, in line with MPEP 2106.05(b), e.g. see pg. 9 of Remarks – Examiner disagrees.
The considerations evaluated when determining whether a claim integrates a judicial exception into a practical application via the application of the abstract idea by of a particular machine includes at least evaluating the particularity of generality of the elements of the machine or apparatus.
The current claim language does not particularly define the elements of the alleged machine or apparatus because it merely recites “a dropout prediction system” without any further details. Fig. 4 and its accompanying explanatory language from the as-filed Specification disclose that the “dropout prediction system” merely comprises various general purpose computer components including, for example, processors and machine-readable memory – that is, the “dropout prediction system” as described by the present Specification comprises any general purpose computer that is programmed to execute the claimed functions. Examiner notes a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine, e.g. see MPEP 2106.05(b)(I). Hence the claimed invention does not recite a particular machine that applies the judicial exception.
For the aforementioned reasons, Claims 1-15 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed September 15, 2025, with respect to the rejections of Claims 1-2, 4, 8-10, 12, and 14 under 35 U.S.C. 102(a)(1) have been fully considered, and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-2, 4, 8-10, 12, and 14 under 35 U.S.C. 102(a)(1) have been withdrawn. However, Examiner notes that Claims 1-15 are nonetheless rejected under 35 U.S.C. 103 for the reasons disclosed above.
Applicant’s arguments, see Remarks, filed September 15, 2025, regarding the rejections of Claims 1-15 under 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection. As stated above, the newly amended claim limitations of Claims 1-15 have necessitated the new grounds of rejection, and Docherty is now cited to address the newly amended claim limitations. Hence Claims 1-15 are rejected under 35 U.S.C. 103 for the reasons disclosed above.
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 PST.
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/JOHN P GO/Examiner, Art Unit 3681
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