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.
Information Disclosure Statement
The information disclosure statements submitted on December 19, 2023, and April 12, 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by Examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-5, 7-8, 11-12, and 14 of the present application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-2 and 4-5 of copending Application No. 18/542,971 (“the ‘971 application”) in view of Garcia (US 2021/0134431).
Regarding Claim 1 of the present application, Claim 1 of the ‘971 application recites substantially the same limitations as Claim 1 of the present application. However, Claim 1 of the present application recites “generating a chronological snapshot of the patient, wherein the chronological snapshot includes a plurality of dropout prediction features,” “generating a likelihood of dropout for the patient by comparing the chronological snapshot of the patient with at least one of: a clinically-validated care pathway, an earlier chronological snapshot of the patient; and one or more similar patient snapshots,” “predicting a root dropout cause,” and “determining one or more recommended interventions tailored to the patient based on the generated likelihood of dropout and the predicted root dropout cause,” whereas Claim 1 of the ‘971 application does not explicitly recite generating the chronological snapshot, the various data for the comparison of the snapshot to generate the likelihood of dropout, and further does not recite the basis for the “recommended engagement action” (i.e. interpreted as equivalent to the “recommended interventions”). Additionally, Examiner notes that Claim 1 of the ‘971 application recites a “potential dropout cause,” which, given the broadest reasonable interpretation, is equivalent to a “root dropout cause.” Regarding these deficiencies of Claim 1 of the ‘971 application, Garcia teaches utilizing a segment of patient and treatment data (i.e. a chronological snapshot), e.g. see Garcia [0132], [0174]-[0175], and [0180], determining a likelihood that the patient may stop or reduce treatments (i.e. a likelihood of dropout) as a concern score based on a comparison of identified patients that stopped treatment (i.e. one or more similar patient snapshots), the history of treatments for the patient (i.e. an earlier chronological snapshot of the patient), e.g. see Garcia [0172]-[0174], and additionally teaches determining guidance that corresponds to the concern score (i.e. the likelihood of dropout) and the crucial parameters or contributing factors (i.e. the predicted root dropout cause), e.g. see Garcia [0209]-[0210], Fig. 19. Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Claim 1 of the ‘971 application to incorporate the aforementioned features as taught by Garcia in order to improve a patient’s treatment compliance/adherence, e.g. see Garcia [0004] and [0008].
Regarding Claim 2 of the present application, Claim 1 of the ‘971 application does not teach determining a second likelihood of dropout corresponding to a second future time, but Garcia teaches generating a prediction that the patient will stop treatment for a period of at least five to seven days in advance, and up to 21 to 30 days in advance, e.g. see Garcia [0173]. Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Claim 1 of the ‘971 application to incorporate the aforementioned features as taught by Garcia in order to improve a patient’s treatment compliance/adherence, e.g. see Garcia [0004] and [0008].
Regarding Claim 3 of the present application, Claim 1 of the ‘971 application does not teach a particular number of days for the first and second future times, but Garcia teaches that the prediction that the patient will stop treatment is made for a period of at least five to seven days in advance (i.e. between 1 and 14 days), and up to 21 to 30 days in advance (i.e. between 1 and 6 months), e.g. see Garcia [0173]. Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Claim 1 of the ‘971 application to incorporate the aforementioned features as taught by Garcia in order to improve a patient’s treatment compliance/adherence, e.g. see Garcia [0004] and [0008].
Regarding Claims 4-5 of the present application, Claims 4-5 of the ‘971 application teach substantially similar claim limitations.
Regarding Claim 7 of the present application, Claim 1 of the ‘971 application does not teach particular types of dropout features, but Garcia teaches that the crucial parameters include escalating messages/alarms/alerts between treatments (i.e. which may be interpreted as a history of challenges involving use of a virtual care solution by the patient and/or user interaction data for the patient), e.g. see Garcia [0187]. Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Claim 1 of the ‘971 application to incorporate the aforementioned features as taught by Garcia in order to improve a patient’s treatment compliance/adherence, e.g. see Garcia [0004] and [0008].
Regarding Claim 8 of the present application, Claim 1 of the ‘971 application does not teach utilizing an intervention component to predict the dropout prediction features, but Garcia teaches that the concern score is calculated based on at least three to five (or more) parameters (i.e. dropout prediction features), wherein the calculation is performed utilizing one or more AI models (i.e. an intervention component), e.g. see Garcia [0172]-[0174]. Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Claim 1 of the ‘971 application to incorporate the aforementioned features as taught by Garcia in order to improve a patient’s treatment compliance/adherence, e.g. see Garcia [0004] and [0008].
Regarding Claim 11 of the present application, Claim 11 of the present application substantially mirrors the limitations of Claim 1 of the present application with the exception that Claim 1 of the present application recites a method whereas Claim 11 of the present application recites a system. Similarly, Claim 11 of the ‘971 application substantially mirrors Claim 1 of the ‘971 application with the exception that Claim 1 of the ‘971 application recites a method whereas Claim 11 of the ‘971 application recites a system. Therefore, Claim 11 of the present application is similarly rejected under non-statutory, obviousness type double patenting in view of Claim 11 of the ‘971 application in view of Garcia for the same reasons as those disclosed above for Claim 1 of the present application.
Regarding Claim 12 of the present application, the limitations of Claim 12 of the present application are substantially similar to those claimed in Claim 2 of the present application, with the sole difference being that Claim 2 of the present application recites a method, whereas Claim 12 of the present application recites a system and its associated structural limitations. Hence the grounds of rejection provided above for Claim 2 of the present application are similarly applied to Claim 12 of the present application.
Regarding Claim 14 of the present application, Claim 2 of the ‘971 application teaches substantially similar claim limitations.
Claims 6 and 13 of the present application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the combination of Claim 1 of the ‘971 application and Garcia in view of Sobol (US 2019/0209022).
Regarding Claim 6 of the present application, the combination of Claim 1 of the ‘971 application and Garcia does not teach utilizing an unsupervised hierarchical clustering technique to perform the snapshot comparison, but Sobol teaches analyzing acquired data from patients with similar health demographics utilizing an unsupervised clustering model, e.g. see Sobol [0235]. 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 Claim 1 of the ‘971 application and Garcia to incorporate the aforementioned features as taught by Sobol because it is particularly good at segmenting the data into several different groups, e.g. see Sobol [0235].
Regarding Claim 13 of the present application, the limitations of Claim 13 of the present application are substantially similar to those claimed in Claim 6 of the present application, with the sole difference being that Claim 6 of the present application recites a method, whereas Claim 13 of the present application recites a system and its associated structural limitations. Hence the grounds of rejection provided above for Claim 6 of the present application are similarly applied to Claim 13 of the present application.
Claims 9 and 15 of the present application are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the combination of Claim 1 of the ‘971 application and Garcia in view of Perkins (US 2020/0227172).
Regarding Claim 9 of the present application, the combination of Claim 1 of the ‘971 application and Garcia does not teach recording the implementation of the recommended interventions, determining an impact of the recommended interventions, and updating the intervention component based on the impact, but Perkins teaches collecting data over time including data about actions that individuals take with respect to recommendations, evaluating the actions taken are for their correlative and/or causative properties (i.e. impact) with respect to an individual’s health indicators, and updating one or more neural network models to reflect the increasing or decreasing amounts of correlation and/or causation for the recommendations and the individual’s health indicators, e.g. see Perkins [0099]. 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 Claim 1 of the ‘971 application and Garcia to incorporate the aforementioned features as taught by Perkins in order to improve the accuracy of the health recommendations, e.g. see Perkins [0114].
Regarding Claim 15 of the present application, the limitations of Claim 15 of the present application are substantially similar to those claimed in Claim 9 of the present application, with the sole difference being that Claim 9 of the present application recites a method, whereas Claim 15 of the present application recites a system and its associated structural limitations. Hence the grounds of rejection provided above for Claim 9 of the present application are similarly applied to Claim 15 of the present application.
Claim 10 of the present application is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the combination of Claim 1 of the ‘971 application and Garcia in view of Haun (US 2022/0031955).
Regarding Claim 10 of the present application, the combination of Claim 1 of the ‘971 application and Garcia does not teach determining a level of accuracy for the root dropout cause, performing the patient outreach to determine the true dropout cause, recording the patient response, and updating the dropout prediction engine based on the patient response, but Haun teaches calculating a confidence score, reaching out to the patient for confirmation when the confidence score is below a threshold, wherein the patient may indicate a reason for the low confidence score, e.g. see Haun [0421], storing responses to queries, e.g. see Haun [0026], and incorporating the historical response data into the calculation, e.g. see Haun [0029]. 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 Claim 1 of the ‘971 application and Garcia to incorporate the aforementioned features as taught by Haun in order to improve the reliability of the calculations, e.g. see Haun [0029].
The aforementioned rejections are provisional nonstatutory double patenting rejections.
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 limitation(s) uses 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:
“Intervention component” recited in Claim 8;
“Patient interface” recited in Claims 4 and 10;
“Dropout prediction engine” recited in Claims 10-15; and
“Care provider interface” recited in Claims 1-15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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.
Claim 4 is 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 Claim 4, Claim 4 recites “the patient interface.” There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 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 1-10 are drawn to a method for predicting patient dropout, which is within the four statutory categories (i.e. process). Claims 11-15 are drawn to a system for predicting patient dropout, 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 a likelihood of dropout and a root dropout cause for a patient under remote monitoring using a dropout prediction system, the method comprising:
obtaining, from an electronic patient records database, a plurality of records for a patient under remote monitoring by a care provider;
generating a chronological snapshot of the patient, wherein the chronological snapshot includes a plurality of dropout prediction features extracted from the plurality of records obtained from the electronic patient records database;
generating a likelihood of dropout for the patient by comparing the chronological snapshot of the patient with at least one of: a clinically-validated care pathway, an earlier chronological snapshot of the patient; and one or more similar patient snapshots;
predicting a root dropout cause for the patient based on one or more of the dropout prediction features of the chronological snapshot of the patient;
determining one or more recommended interventions tailored to the patient based on the generated likelihood of dropout and the predicted root dropout cause, wherein the one or more recommended interventions are intended to prevent dropout of the patient from remote monitoring by the care provider; and
presenting to the care provider, via a care provider interface of the dropout prediction system, the one or more recommended interventions.
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 patient records, generating a chronological snapshot including dropout prediction features based on the obtained patient records, generating a likelihood of dropout, predicting the root dropout cause, determining recommended interventions, and presenting the recommended interventions are reasonably interpreted as following rules or instructions in order to provide a care provider with recommendations), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claim 11 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 11 is that Claim 1 recites a method, whereas Claim 11 recites a system and its associated structural limitations (i.e. the dropout prediction engine, the processors, the care provider interface), which, as will be further explained below, are interpreted as additional elements.
Dependent Claims 2-10 and 12-15 include other limitations, for example Claims 2 and 12 recite that the likelihood of dropout is for a first future time, and further recites predicting a second root dropout cause and a second likelihood of dropout for a second future time, Claim 3 recites the timing of the first future time and the second future time, Claims 4-5 recite types of patient records, Claims 6 and 13 recite that the generating of the likelihood of dropout includes comparing the chronological snapshot to similar patient snapshots using an unsupervised hierarchical clustering technique, Claim 7 recites types of features extracted from the patient records, Claim 8 recites limitations pertaining to predicting the root dropout cause including an intervention component, Claims 9 and 15 recite recording the implementation of the recommended interventions, determining an impact of the implemented interventions on the dropout prediction features, and updating the intervention component based on the impact, and Claim 10 recites determining an accuracy of the prediction for the root dropout cause, and performing various steps if the accuracy is below a threshold, and Claim 14 recites utilizing a trained machine learning model to make the patient dropout prediction, 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 2-10 and 12-15 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 11.
Prong 2 of Step 2A
Claims 1 and 11 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the electronic patient records database, the care provider interface, the processors, the dropout prediction engine) 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 care provider interface, the processors, the dropout prediction engine, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see pg. 17, line 14, through pg. 18, line 25 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 interventions to a healthcare 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 interventions to a care provider, which amounts to an insignificant application, e.g. see MPEP 2106.05(g).
Additionally, dependent Claims 2-10 and 12-15 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 Claim 14), 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 the types of features recited in dependent Claim 7), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 11, 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 11 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 electronic patient records database, the care provider interface, the processors, the dropout prediction engine), 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 additional elements are well-understood, routine, and conventional in nature:
Pg. 17, line 14, through pg. 18, line 25 of the as-filed Specification discloses that the additional elements (i.e. the electronic patient records database, the care provider interface, the processors, the dropout prediction engine) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. storing data, obtaining data, analyzing the data, and displaying the results of the analysis) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
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 receives patient data from an electronic records database over a network, e.g. see pg. 12, lines 3-14 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 data in an electronic records 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 data in an electronic records database, and retrieving the patient data from storage in order to ultimately produce and present one or more recommended interventions determined from the retrieved patient data;
Dependent Claims 2-10 and 12-15 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 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 the types of features recited in dependent Claim 7), electronic recordkeeping (e.g. the storing of the record of the implementation of the interventions and the response from the patient recited in dependent Claims 8-10), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1 and 11, 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7-8, 11-12, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Garcia (US 2021/00134431).
Regarding Claim 1, Garcia discloses the following: A method for predicting a likelihood of dropout and a root dropout cause for a patient under remote monitoring (The patient treatment is monitored at the patient’s home, e.g. see [0011] and [0067].) using a dropout prediction system, the method comprising:
obtaining, from an electronic patient records database, a plurality of 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].);
generating a chronological snapshot of the patient (The system utilizes a segment of the patient and treatment data (i.e. a chronological snapshot) to compare to a training dataset, e.g. see Garcia [0174]-[0175] and [0180], wherein the patient and treatment data includes an indication of a date and/or time a treatment was administered, e.g. see Garcia [0132].), wherein the chronological snapshot includes a plurality of dropout prediction features extracted from the plurality of records obtained from the electronic patient records database (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 a likelihood of dropout for the patient by comparing the chronological snapshot of the patient with at least one of: a clinically-validated care pathway, an earlier chronological snapshot of the patient; and one or more similar patient snapshots (The system determines a likelihood that the patient may stop or reduce treatments (i.e. a likelihood of dropout) as a concern score, wherein the determination utilizes an AI model trained using training data set that comprises treatment data and/or logs for patients (i.e. the chronological snapshot), wherein the treatment data is compared to identified patients that stopped treatment (i.e. one or more similar patient snapshots), the history of treatments for the patient (i.e. an earlier chronological snapshot of the patient), e.g. see Garcia [0172]-[0174].);
predicting a root dropout cause for the patient based on one or more of the dropout prediction features of the chronological snapshot of the patient (The system identifies crucial parameters or contributing factors that cause a high concern score, e.g. see Garcia [0177], Fig. 17.);
determining one or more recommended interventions tailored to the patient based on the generated likelihood of dropout and the predicted root dropout cause (The system includes a guidance processor that determines guidance that corresponds to the concern score (i.e. the likelihood of dropout) and the crucial parameters or contributing factors (i.e. the predicted root dropout cause), e.g. see Garcia [0209]-[0210], Fig. 19.), wherein the one or more recommended interventions are 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].); and
presenting to the care provider, via a care provider interface of the dropout prediction system, the one or more recommended interventions (The guidance is displayed to a user, for example a clinician, e.g. see Garcia [0210].).
Regarding Claim 2, Garcia discloses the limitations of Claim 1, and Garcia further discloses the following:
The method of claim 1, wherein generating a likelihood of dropout for the patient includes generating a first likelihood of dropout for the patient corresponding to a first future time and generating a second likelihood of dropout for the patient corresponding to a second future time (The prediction that the patient will stop treatment is made for a period of at least five to seven days in advance, and up to 21 to 30 days in advance, e.g. see Garcia [0173].);
wherein predicting a root dropout cause for the patient includes predicting a first root dropout cause for the patient corresponding to the first future time and predicting a second root dropout cause for the patient corresponding to the second future time (The system determines critical parameters or contributing factors that cause a high concern score, e.g. see Garcia [0177].); and
wherein the one or more recommended interventions include a first recommended intervention determined based on the first likelihood of dropout generated for the patient and the first root dropout cause predicted for the patient, and a second recommended intervention determined based on the second likelihood of dropout generated for the patient and the second root dropout cause predicted for the patient (The system includes a guidance processor that determines guidance that corresponds to the concern score (i.e. the likelihood of dropout) and the crucial parameters or contributing factors (i.e. the predicted root dropout cause), e.g. see Garcia [0209]-[0210], Fig. 19.).
Regarding Claim 3, Garcia discloses the limitations of Claim 2, and Garcia further discloses the following:
The method of claim 2, wherein the first future time is between 1 and 14 days from a current time, and the second future time is between 1 and 6 months from the current time (The prediction that the patient will stop treatment is made for a period of at least five to seven days in advance (i.e. between 1 and 14 days), and up to 21 to 30 days in advance (i.e. between 1 and 6 months), e.g. see Garcia [0173].).
Regarding Claim 4, Garcia discloses the limitations of Claim 1, and Garcia further discloses the following:
The method of claim 1, wherein the plurality of records for the patient under remote monitoring by the care provider include at least one of: identification information for the patient; medical history for the patient; treatment history for the patient; medical directives for the patient; unique identifiers/fingerprints of patient's virtual trajectory through the patient interface based on abstraction of virtual movements represented in a virtual trajectory of human-machine interaction within patient interfacing computer program or application; 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 7, Garcia discloses the limitations of Claim 1, and Garcia further discloses the following:
The method of claim 1, wherein the plurality of dropout prediction features extracted from the plurality of records includes at least one of: a response time from the care provider to a patient-initiated action; a history of challenges involving use of a virtual care solution by the patient; clinical trends for the patient; user interaction data for the patient; and portal use data showing frequency of use of specific components over time (The system identifies the crucial parameters or contributing factors causing a high concern score, wherein the crucial parameters include escalating messages/alarms/alerts between treatments (i.e. which may be interpreted as a history of challenges involving use of a virtual care solution by the patient and/or user interaction data for the patient), e.g. see Garcia [0187].).
Regarding Claim 8, Garcia discloses the limitations of Claim 1, and Garcia further discloses the following:
The method of claim 1, wherein the root dropout cause for the patient is predicted based on one or more of the dropout prediction features of the chronological snapshot of the patient using an intervention component (The concern score is calculated based on at least three to five (or more) parameters (i.e. dropout prediction features), wherein the calculation is performed utilizing one or more AI models (i.e. an intervention component), e.g. see Garcia [0172]-[0174].).
Regarding Claims 11-12, the limitations of Claims 11-12 are substantially similar to those claimed in Claims 1-2, with the sole difference being that Claims 1-2 recites a method, whereas Claims 11-12 recites a system and its associated structural limitations. Specifically pertaining to Claims 11-12, Examiner notes that Garcia teaches 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 1-2 are similarly applied to Claims 11-12.
Regarding Claim 14, Garcia discloses the limitations of Claim 11, and Garcia further discloses the following:
The dropout prediction system of claim 11, wherein the dropout prediction engine is configured to predict the root dropout cause for the patient based on one or more of the dropout prediction features of the chronological snapshot of the patient using a trained dropout prediction model (The concern score is calculated using a trained AI model, e.g. see Garcia [0172]-[0178].), the dropout prediction model being trained on a training dataset by a machine learning algorithm (The AI model is trained using a training data set, e.g. see Garcia [0173].).
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Garcia in view of Sadler (US 2009/0240525).
Regarding Claim 5, Garcia discloses the limitations of Claim 4, but does not teach and Sadler teaches the following:
The method of claim 4, wherein the plurality of records for the patient under remote monitoring by the care provider 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 includes patient medical records which include recorded telemedicine virtual visit sessions (i.e. virtual care solution usage information), e.g. see Sadler [0022]-[0023].).
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 a record of patient telemedicine virtual visit sessions as taught by Sadler in order to reduce the number of patient errors, fraud, and improve overall efficiency, e.g. see Sadler [0026].
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia in view of Sobol (US 2019/0209022).
Regarding Claim 6, Garcia discloses the limitations of Claim 1, but does not teach and Sobol teaches the following:
The method of claim 1, wherein generating a likelihood of dropout for the patient by comparing the chronological snapshot of the patient with one or more similar patient snapshots includes comparing the chronological snapshot of the patient with one or more similar patient snapshots using an unsupervised hierarchical clustering technique (The system analyzes acquired data from patients with similar health demographics utilizing an unsupervised clustering model, e.g. see Sobol [0235].).
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 the unsupervised clustering to analyze the similar patient data as taught by Sobol because it is particularly good at segmenting the data into several different groups, e.g. see Sobol [0235].
Regarding Claim 13, the limitations of Claim 13 are substantially similar to those claimed in Claim 6, with the sole difference being that Claim 6 recites a method, whereas Claim 13 recites a system and its associated structural limitations. Specifically pertaining to Claim 13, Examiner notes that Garcia teaches 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 6 are similarly applied to Claim 13.
Claims 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Garcia in view of Perkins (US 2020/0227172).
Regarding Claim 9, Garcia discloses the limitations of Claim 8, but does not teach and Perkins teaches the following: The method of claim 8, further comprising:
recording, in the electronic patient records database, the implementation of one or more of the recommended interventions by the care provider (The system collects data over time including data about actions that individuals take with respect to recommendations, e.g. see Perkins [0099].);
determining an impact of the one or more recommended interventions implemented by the care provider on the plurality of dropout prediction features extracted from the plurality of records (The actions taken are evaluated for their correlative and/or causative properties (i.e. impact) with respect to an individual’s health indicators, e.g. see Perkins [0099].); and
updating the intervention component based on the impact determined for the one or more recommended interventions implemented by the care provider (The system includes one or more models based on neural networks, e.g. see Perkins [0096], wherein the models are updated to reflect the increasing or decreasing amounts of correlation and/or causation for the recommendations and the individual’s health indicators, e.g. see Perkins [0099].).
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 evaluating the impact of the actions taken and updating the model based on the impact as taught by Perkins in order to improve the accuracy of the health recommendations, e.g. see Perkins [0114].
Regarding Claim 15, the limitations of Claim 15 are substantially similar to those claimed in Claim 9, with the sole difference being that Claim 9 recites a method, whereas Claim 15 recites a system and its associated structural limitations. Specifically pertaining to Claim 15, Examiner notes that Garcia teaches 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