Detailed Notice
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 Claims
Claims 1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 are currently pending.
Claims 1-2, 8, 10, 17, and 29 are amended.
Claims 3, 6, 9, 11-13, 15-16, and 21-28 are canceled.
Claims 31-32 are new.
Claims 1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 are rejected.
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 1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7-11, 14, 18-20, 29-30 of Application No. 17/935,386.
Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 of the present application are similar to claims 1-5, 7-11, 14, 18-20, 29-30 of Application No. 17/935,386 as shown in the Table below:
Application No. 19/347,102
Application No. 17/935,386
1. (Currently Amended) A method for monitoring and treating a subject for reducing maternal mortality, comprising the steps of
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated; (e)notifying, via said user interface, said subject about the abnormality alert; and
(f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
1. A method for monitoring a subject for reducing maternal mortality, comprising the steps of
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) receiving, via said user interface of said application executing on one or more computer processors, a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database;
(d) receiving, via a user interface of an application executing on one or more computer processors, a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database;
(e) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(f) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated;
(g) notifying, via said user interface, said subject about the abnormality alert; and
(h) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
2. (Currently Amended) The method of Claim 1, wherein said user interface is accessed by said subject on a mobile device or a wearable device.
2. The method of claim 1, wherein said user interface is accessed by said subject on a mobile device.
3. The method of claim 1, wherein said user interface is accessed by said subject on a wearable device.
4. (Original) The method of Claim 1, wherein said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers.
4. The method of claim 1, wherein said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers.
5. (Original) The method of Claim 4, wherein said health information messaging is triggered automatically in response to trends in said personal information of said subject that indicates increased maternal mortality risk.
5. The method of claim 4, wherein said health information messaging is triggered automatically in response to trends in said personal information of said subject that indicates increased maternal mortality risk.
7. (Original) The method of Claim 1, wherein said health information messaging is displayed on said user interface as a representative symbol to said subject, wherein said symbol alerts said subject to availability of health information that is accessible via said user interface.
7. The method of claim 1, wherein said health information messaging is displayed on said user interface as a representative symbol to said subject, wherein said symbol alerts said subject to availability of health information that is accessible via said user interface.
8. (Currently Amended) The method of Claim 1, wherein the method further comprises the step of: (g) receiving, via a user interface of an application executing on one or more computer processors, messages indicating that said subject is within a predefined distance of a healthcare provider and providing information regarding how said subject can travel to said healthcare provider and wherein said messages include geo-location information.
8. The method of claim 1, wherein the method further comprises the step of:
(i) receiving, via a user interface of an application executing on one or more computer processors, messages indicating that said subject is within a predefined distance of a healthcare provider and providing information regarding how said subject can travel to said healthcare provider.
9. The method of claim 8, wherein said messages include geo-location information for said healthcare provider
10. (Currently Amended) The method of Claim 1, wherein said self-surveillance module further comprises inputs from the subject for personal information for social support selected from the types of information comprising sleep deprivation, absence of assistance with baby or other children, and mood-related symptoms, wherein said subject inputs measurements of personal stress on a predefined measurement scale presented to said subject via said user interface and wherein said health information messaging comprises stress management strategies that respond to the trends in personal information for social support of said subject.
10. The method of claim 1, wherein said self-surveillance module further comprises inputs from the subject for personal information for social support selected from the types of information comprising sleep deprivation, absence of assistance with baby or other children, and mood-related symptoms, wherein said subject inputs measurements of personal stress on a predefined measurement scale presented to said subject via said user interface.
11. The method of claim 10, said health information messaging comprises stress management strategies that respond to the trends in personal information for social support of said subject.
14. (Original) The method of Claim 1, further comprising the step of providing a system comprising:
(i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection, and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors, and
(ii) a plurality of databases, including a personal information database and a health information messaging database, wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media.
14. The method of claim 1, further comprising the step of providing a system comprising:
(i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection, and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors, and
(ii) a plurality of databases, including a personal information database and a health information messaging database, wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media.
17. (Currently Amended) A system for monitoring and reducing maternal mortality in a subject, comprising: one or more computer processors; and one or more tangible computer readable media accessible by the one or more computer processors, wherein the one or more tangible computer readable media comprise instructions that, when executed by the one or more processors, cause the one or more processors to perform:
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated;
(e) notifying, via said user interface, said subject about the abnormality alert; and
(f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
17. A system for monitoring and reducing maternal mortality in a subject, comprising:
one or more computer processors; and
one or more tangible computer readable media accessible by the one or more computer processors, wherein the one or more tangible computer readable media comprise instructions that, when executed by the one or more processors, cause the one or more processors to perform:
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) receiving, via said user interface of said application executing on one or more computer processors, a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database;
(d) receiving, via a user interface of an application executing on one or more computer processors, a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database;
(e) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(f) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated;
(g) notifying, via said user interface, said subject about the abnormality alert; and
(h) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
18. (Original) The system of Claim 17, wherein said user interface is accessed by said subject on a mobile device.
18. The system of claim 17, wherein said user interface is accessed by said subject on a mobile device.
19. (Original) The system of Claim 18, further comprising the step of tracking over time, via the one or more computer processors and the total abnormality point values, the relative improvement or lack of improvement over time with respect to maternal mortality risk by said subject.
19. The system of claim 18, further comprising the step of tracking over time, via the one or more computer processors and the total abnormality point values, the relative improvement or lack of improvement over time with respect to maternal mortality risk by said subject.
20. (Original) The system of Claim 17, wherein said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers.
20. The system of claim 17, wherein said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers.
29. (Currently Amended) A tangible non-transitory computer readable storage medium, comprising instructions that, when executed by a computer processor, cause the processor to:
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated;
(e) notifying, via said user interface, said subject about the abnormality alert; and
(f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
29. A tangible non-transitory computer readable storage medium, comprising instructions that, when executed by a computer processor, cause the processor to:
(a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression;
(b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application;
(c) receiving, via said user interface of said application executing on one or more computer processors, a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database;
(d) receiving, via a user interface of an application executing on one or more computer processors, a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database;
(e) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module;
(f) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated;
(g) notifying, via said user interface, said subject about the abnormality alert; and
(h) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject.
30. (Original) The tangible non-transitory computer readable storage medium of Claim 29, further comprising instructions that, when executed by a computer processor, cause the processor to: (i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection, and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors, and
(ii) a plurality of databases, including a personal information database and a health information messaging database, wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media.
30. The tangible non-transitory computer readable storage medium of claim 29, further comprising instructions that, when executed by a computer processor, cause the processor to:
(i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection, and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors, and
(ii) a plurality of databases, including a personal information database and a health information messaging database, wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media.
31. (New) The method of Claim 1, further comprising displaying, via said user interface of said application executing on one or more computer processors: a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database.
1. A method for monitoring a subject for reducing maternal mortality, comprising the steps of… (c) receiving, via said user interface of said application executing on one or more computer processors, a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database;
32. (New) The method of Claims 1, further comprising on said user interface of said application executing on one or more computer processors: a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database.
1. A method for monitoring a subject for reducing maternal mortality, comprising the steps of… (d) receiving, via a user interface of an application executing on one or more computer processors, a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database;
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 1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 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:
In the instant case, claims 1-2, 4-5, 7-8, 10, 14, and 31-32 are directed toward a method (i.e. a process), claims 17-20 are directed toward a system (i.e., machine), and claims 29-30 are directed toward a tangible non-transitory computer readable storage medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 17, and 29 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A method for monitoring and treating a subject for reducing maternal mortality, comprising the steps of (a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression; (b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application; (c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module; (d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated; (e) notifying, via said user interface, said subject about the abnormality alert; and (f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject”.
The limitations of (a) receiving, inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression; (b) storing, said personal information of said subject, and accessible by said subject… of said application; (c) assigning, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored… and an abnormality point matrix, the abnormality point matrix being generated and continually updated; (d) determining, a total abnormality point value of said trends for said subject, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated; (e) notifying, , said subject about the abnormality alert; and (f) providing, , health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject, 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 aforementioned steps recite a process of receiving, storing, assigning, determining, notifying, and providing , which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, 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.
Further, the abstract idea of claims 17 and 29 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Dependent claims 2, 4-5, 7-8, 10, 14, 18-20, and 30-32 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 17, and 29. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims1-2, 4-5, 7-8, 10, 14, 17-20, and 29-32 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “interface”, “application”, “computer processors”, “self-surveillance module”, “database”, “memory”, “machine learning module”, “system”, and “tangible non-transitory computer readable storage medium”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1-4, [006], [0013], [0017]-[0019], and [0022]-[0025], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises”, “in a database accessible by said application”, “in said database”, “stored on a memory device accessible by the one or more computer processors”, “by a machine learning module”, “via the one or more computer processors”, and “via said user interface”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2, 4-5, 7-8, 10, 14, 18-20, and 30-32 include other limitations, for example claims 2-3 and 18 recite “mobile device” and/or “wearable device”, but as stated above, the limitations recited by these claims do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “mobile device”, “wearable device” “interface”, “application”, “computer processors”, “self-surveillance module”, “database”, “memory”, “machine learning module”, “system”, and “tangible non-transitory computer readable storage medium”) that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2, 4-5, 7-8, 10, 14, 18-20, and 30-32 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 17, and 29, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
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 1-2, 4-5, 7-10, 14, 17-20, and 29-32 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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(s) 1-2, 4-5, 7, 14, 17-18, 20, and 29-32 are rejected under 35 U.S.C. 103 as being unpatentable over Francois (US 20170262604 A1), in view of Conward et al. (US 20240079145 A1), hereinafter Conward.
Regarding claim 1 Francois teaches a method for monitoring and treating a subject for reducing maternal mortality, comprising the steps of (a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information (Francois, [0008]: “patients may use a health tracking module to assess their condition, in accordance with their physicians' instructions, by participating in evaluations of symptoms conducted via a computer or communication device that comprises, accesses, or is controlled by a computer program product of the invention…algorithms linking symptom data and physiological data to particular patient instructions are reviewed and approved by a health care provider, health care facility, or health care organization that uses the system” and [0457]: “a system or computer program product may interact with users (e.g., via a standard graphical user interface (GUI), analyze health data, and/or assemble health data into one or more modules. As noted above, a system may comprise multiple components. For example, one or more components may receive and/or transmit health data and/or other information to or from users; one or more components may analyze health data and/or other information; one or more components may add information to a database; one or more components may generate output to be transmitted or presented to users; one or more components may extract information from a database in response to a request or query. One or more components may analyze extracted data and/or convert the data into an appropriate format for transmission or display. One or more components may receive and/or transmit information between other component(s) of the system or external to the system”); (b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application (Francois, [0018]: “selecting a set of one or more data collection modules that are relevant to the condition based on input that identifies a condition and a subset of patient characteristics selected from a set of patient characteristics deemed significant in the context of the condition; (ii) associating identifiers of the set of one or more data collection modules with an identifier of a patient based on input that identifies the patient; and (iii) associating an identifier of the T-plan and an identifier of the condition with the set of data collection modules, optionally wherein the configuration module adds, removes, or customizes one or more of the data collection modules based on input that specifies such addition, removal, or customization”); (c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”) and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module ([0187]: “a health evaluation or an SST may utilize any of a variety of different algorithms to determine a patient's health status with respect to a condition and/or to determine directions for management of the condition. It should be understood that the invention is in no way limited in this respect. Algorithms may rely entirely on embedded medical knowledge that maps each particular combination of inputs to an SST to a particular output, may employ any of the various tools of artificial intelligence. In some embodiments, an algorithm may be embodied as a decision tree. In some embodiments, an algorithm may be embodied as a set of rules and an inference engine. In some embodiments, an algorithm may make use of fuzzy logic, probabilistic methods (e.g., Bayesian networks, Hidden Markov models) neural networks, or other approaches”); (d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated (Francois, [0183]: “A trigger for prompting a patient to perform a health evaluation, e.g., to run a smart symptom tracker, may be based on data elements of any one or more types. In some embodiments, data elements of different types may be assigned weights according to their diagnostic value in terms of determining whether the patient is at sufficient risk of experiencing a deterioration to warrant performing a health evaluation. The diagnostic value of a particular type of data element may vary depending on the particular condition(s) and the patient characteristics that the patient has. A combination of the values of the data elements and their weights may be used to determine a score, which may be used to determine whether to perform a health evaluation, whether to run one or more SSTs and, in some embodiments, the order in which to run them. The values of the data elements may be absolute values, deviation from normal value (which may be adjusted for demographic characteristics such as age and sex), change from patient's baseline, rate of change from patient's baseline, or a combination thereof” and [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”); (e) notifying, via said user interface, said subject about the abnormality alert (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”); and (f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject (Francois, [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”).
Francois does not teach comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression.
However, Conward teaches comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression (Conward, [0016], [0020], and [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois to incorporate the teachings of Conward and account for modify the input data received in Francois to include a mental health survey results, appointments, and patient expressions as taught by Conward in order to evaluate and monitor patient health (Conward, [0002]).
Regarding claim 2 Francois further teaches said user interface is accessed by said subject on a mobile device or a wearable device (Francois, [0012]: “a system of the present invention is implemented at least in part through a combination of an application for use by physicians and an application for use by patients. In some embodiments the system is implemented at least in part through a combination of a web application for use by physicians and an application for a mobile device for use by patients” and [0130]: “In some embodiments a mobile device may be wearable, e.g., as a wristwatch, armband, computer with a head mounted display (such as Google Glass) optionally attached to or in the form of glasses, etc. In some embodiments, a smartphone or other mobile device comprises a touch screen and is primarily operated by touching the screen, although of course it may also have one or more controls such as buttons, switches, etc., that are distinct from the screen, e.g., on the top, side, bottom, etc., of the device”).
Regarding claim 4 Francois further teaches said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers (Francois, [0010]: “the invention provides at least some of the following features for patients: (1) View consolidated data on compliance, conditions, and hospitalizations, as well as physiological data and educational modules; (2) Receive periodic updates/information on conditions; (3) Prompting to perform periodic evaluation of conditions; (4) Option to perform evaluation of conditions at any time; (5) Receive schedule of upcoming procedures, appointments with health care providers; and (6) Receive medication reminders. In some embodiments, caregivers of the patient may have permission to access the program on the patient's behalf. Caregivers may be allowed to view at least some of the patient information, may receive instructions in addition to or instead of the patient, or both”).
Regarding claim 5 Francois further teaches said health information messaging is triggered automatically in response to trends in said personal information of said subject that indicates increased maternal mortality risk (Francois, [0058]: ““Symptom data” refers to data pertaining to symptoms. In general, the term “symptom” refers to those features of a condition that are noticed by a patient or would ordinarily be noticeable by a patient. Symptoms encompass any of the myriad departures from normal function or feeling that a patient with a condition may experience. Symptoms are often features about which a patient may tell a physician when seeking health care and/or about which a physician may question a patient when evaluating a patient for purposes of diagnosis, prognosis, and/or treatment. For example, a patient with COPD may complain about shortness of breath (dyspnea), cough, and sputum production. A physician suspecting that a patient may have COPD or deciding whether a patient with COPD would benefit from a change in medication may ask a patient about the presence or severity of these symptoms. In general, symptoms are features that are or can be perceived or observed without the use of devices or other equipment. Many symptoms are subjective, i.e., they relate to the way the patient feels and may not be directly measurable, although their presence or severity may correlate with one or more measurable parameters. For example, shortness of breath is a feeling experienced by a patient and is not directly measurable but may correlate with an abnormally high respiratory rate. Symptom data may include any information about a symptom, such as data indicating the presence, absence, intensity, or frequency of a symptom, or a change over time in any of these”).
Regarding claim 7 Francois further teaches said health information messaging is displayed on said user interface as a representative symbol to said subject, wherein said symbol alerts said subject to availability of health information that is accessible via said user interface (Francois, FIG. 3A and [0358]: “A dashboard may represent data in a variety of ways. Any of a variety of information graphics, i.e., graphic visual representations of data or knowledge may be used. For example, images that resemble the meters and indicators typically found on a dashboard or instrument panel in a vehicle, such as a speedometer, fuel gauge, etc., may be used. Plots, charts (e.g., line charts, pie charts, bar charts), may be used. The particular type of graphic used for a given data element may be selected as appropriate for that item. It will be understood that certain data elements may be appropriately represented in a variety of ways. In some embodiments, a dashboard provides one or more links that allow a user to obtain more detailed information regarding the data presented in a particular graphic on the dashboard, i.e., to “drill down” into one or more of the graphics. In certain embodiments, a graphic may represent progress towards (or away from) a particular goal. For example, a graphic pertaining to patient who has been advised to lose weight may show a target weight and a timeline for reaching the target weight. In some embodiments, the graphical display for at least a portion of a dashboard may utilize D3.js (sometimes called D3 for Data-Driven Documents), a JavaScript library of use to drive the creation and control of dynamic and interactive graphical forms that run in web browsers from digital data”).
Regarding claim 14 Francois further teaches the step of providing a system comprising: (i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection (Francois, [0473]: “The cloud computing environment may include a resource manager. The resource manager may be connected to the resource providers and the computing devices over the computer network. In some implementations, the resource manager may facilitate the provision of computing resources by one or more resource providers to one or more computing devices. The resource manager may receive a request for a computing resource from a computing device. The resource manager may identify one or more resource providers capable of providing the computing resource requested by the computing device. The resource manager may select a resource provider to provide the computing resource. The resource manager may facilitate a connection between the resource provider and the computing device. In some implementations, the resource manager may establish a connection between a resource provider and a computing device. In some implementations, the resource manager may redirect a computing device to a resource provider with the requested computing resource”), and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors (Francois, [0126]: “As will be appreciated by one of ordinary skill in the art, the technology disclosed herein or any one or more aspects thereof may be used to implement, for example, as a system, apparatus, method, or computer program product. Accordingly, one of ordinary skill in the art could implement hardware, software, or embodiments combining software and hardware aspects based on this disclosure. Such implementations, made up of hardware and/or software, may all generally be referred to herein as “systems”, which may comprise one or more “components”. Similarly, one of ordinary skill in the art could implement technology based on the present disclosure in the form of a computer program product embodied in any tangible medium (e.g., a non-transitory storage medium) having computer usable program instructions embodied in the medium. Such a computer program product may comprise multiple distinct computer program products that may interface with each other. Such computer program products, sometimes termed “applications”, may provide functions for use by different types of users, e.g., patients, health care providers”), and (ii) a plurality of databases (Francois, [0018]: “(a) a library of modules comprising one or more data collection modules each comprising computer-executable instructions for performing a method comprising: obtaining symptom data, physiological data, behavioral data, environmental data, health care event data, other health or health-related data, or a combination thereof regarding or relating to a patient, wherein at least some of the data collection modules are configured to be made accessible to a patient through an application on a mobile device or through an electronic communication network”), including a personal information database (Francois, [0081]: “The term “patient record” refers to a collection of information regarding or relating to a patient, stored in association with an identifier of the patient. The information may be stored in one or more databases or data stores, which may be physically located in different places. It is considered to constitute a patient record as long as it is stored in a way that allows the system to determine which patient the information pertains to or that the information belongs to a particular patient. The information in a patient record may include any type of health data described herein, patient characteristics, patient's name, contact information (e.g., email address, phone number(s)), demographic information, patient's health care provider(s), patient's caregivers, family members, and any other information obtained or generated by a system of the invention regarding or relating to the patient. It should be noted that a patient record may be de-identified in that it lacks personally identifiable information. Thus, a patient identifier need not personally identify the patient but may simply serve to identify particular items of information as pertaining to the same patient”) and a health information messaging database (Francois, [0164]: “at least some of the directions may be customized. Triage directions may be customized by, for example, specifying the name of a particular health care provider, physician practice, or health care facility; providing contact information such as phone number and/or email address for a particular health care provider, physician practice, health care facility, and the like. Treatment directions may be customized by, for example, specifying a particular medication and/or dose within the context of a generalized direction. For example, a generalized direction might be “double the dose of your diuretic” for the next 24 hours. A customized treatment direction might specify the name of a particular diuretic that has been prescribed to the patient. The particular diuretic may have been specified by the health care provider who prescribed the T-plan to the patient, who may also have prescribed the diuretic. Information required for customizing directions to the patient will typically have been stored in a database as part of the patient's T-plan for that condition”), wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media (Francois, [0018]: “(i) receiving and/or monitoring receipt of data relating to a patient; and (ii) performing one or more scheduling or notification functions based on said receiving and/or monitoring and the specified frequency and time window. In some embodiments, one or more of the scheduling and notification functions is selected from (i) determining when the next data element of that type is due, (ii) determining whether a data element is current (not expired), (iii) determining whether a data request or notification should be issued; (iv) determining when a data request or notification should be issued, and (v) issuing a data request or notification. In some embodiments, at least some of the data collection modules, themselves or together with one or more associated modules, collectively comprise computer-executable instructions for: (1) obtaining data comprising symptom data, physiological data, behavioral data, and/or environmental data of one or more types that are relevant to the condition identified in step (a); and (2) determining directions for management of the condition by a patient or caregiver based on the data and providing the directions to a patient or caregiver; (3) determining a compliance score based on the data, (4) determining a health score based on the data, (5) determining whether the patient has experienced a significant deterioration in health within a selected time period or is at risk of experiencing a significant adverse change in health within a selected time period based on the data, (6) determining whether a change in the type of data to be collected and/or in the timing of data collection is warranted based on the data; (7) determining whether a notification to a caregiver or health care provider should be issued based on the data; (8) determining, based on the location of a patient's mobile device, that the patient likely experienced a hospitalization event, and, optionally, (A) adjusting one or more modules of a T-plan assigned to the patient to a heightened sensitivity mode or post-discharge use mode, further optionally switching the module out of heightened sensitivity mode or post-discharge use mode after a predetermined time period; and/or (B) notifying a health care provider or caregiver of the patient of the likely hospitalization event, or (9) any combination of (1)-(8)”).
Regarding claim 17 Francois teaches a system for monitoring and reducing maternal mortality in a subject, comprising: one or more computer processors; and one or more tangible computer readable media accessible by the one or more computer processors, wherein the one or more tangible computer readable media comprise instructions that, when executed by the one or more processors, cause the one or more processors to perform: (a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information (Francois, [0008]: “patients may use a health tracking module to assess their condition, in accordance with their physicians' instructions, by participating in evaluations of symptoms conducted via a computer or communication device that comprises, accesses, or is controlled by a computer program product of the invention…algorithms linking symptom data and physiological data to particular patient instructions are reviewed and approved by a health care provider, health care facility, or health care organization that uses the system” and [0457]: “a system or computer program product may interact with users (e.g., via a standard graphical user interface (GUI), analyze health data, and/or assemble health data into one or more modules. As noted above, a system may comprise multiple components. For example, one or more components may receive and/or transmit health data and/or other information to or from users; one or more components may analyze health data and/or other information; one or more components may add information to a database; one or more components may generate output to be transmitted or presented to users; one or more components may extract information from a database in response to a request or query. One or more components may analyze extracted data and/or convert the data into an appropriate format for transmission or display. One or more components may receive and/or transmit information between other component(s) of the system or external to the system”); (b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application (Francois, [0018]: “selecting a set of one or more data collection modules that are relevant to the condition based on input that identifies a condition and a subset of patient characteristics selected from a set of patient characteristics deemed significant in the context of the condition; (ii) associating identifiers of the set of one or more data collection modules with an identifier of a patient based on input that identifies the patient; and (iii) associating an identifier of the T-plan and an identifier of the condition with the set of data collection modules, optionally wherein the configuration module adds, removes, or customizes one or more of the data collection modules based on input that specifies such addition, removal, or customization”); (c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”) and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module ([0187]: “a health evaluation or an SST may utilize any of a variety of different algorithms to determine a patient's health status with respect to a condition and/or to determine directions for management of the condition. It should be understood that the invention is in no way limited in this respect. Algorithms may rely entirely on embedded medical knowledge that maps each particular combination of inputs to an SST to a particular output, may employ any of the various tools of artificial intelligence. In some embodiments, an algorithm may be embodied as a decision tree. In some embodiments, an algorithm may be embodied as a set of rules and an inference engine. In some embodiments, an algorithm may make use of fuzzy logic, probabilistic methods (e.g., Bayesian networks, Hidden Markov models) neural networks, or other approaches”); (d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated (Francois, [0183]: “A trigger for prompting a patient to perform a health evaluation, e.g., to run a smart symptom tracker, may be based on data elements of any one or more types. In some embodiments, data elements of different types may be assigned weights according to their diagnostic value in terms of determining whether the patient is at sufficient risk of experiencing a deterioration to warrant performing a health evaluation. The diagnostic value of a particular type of data element may vary depending on the particular condition(s) and the patient characteristics that the patient has. A combination of the values of the data elements and their weights may be used to determine a score, which may be used to determine whether to perform a health evaluation, whether to run one or more SSTs and, in some embodiments, the order in which to run them. The values of the data elements may be absolute values, deviation from normal value (which may be adjusted for demographic characteristics such as age and sex), change from patient's baseline, rate of change from patient's baseline, or a combination thereof” and [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”); (e) notifying, via said user interface, said subject about the abnormality alert (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”); and (f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject (Francois, [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”).
Francois does not teach comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression.
However, Conward teaches comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression (Conward, [0016], [0020], and [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois to incorporate the teachings of Conward and account for modify the input data received in Francois to include a mental health survey results, appointments, and patient expressions as taught by Conward in order to evaluate and monitor patient health (Conward, [0002]).
Regarding claim 18 Francois further teaches said user interface is accessed by said subject on a mobile device (Francois, [0012]: “a system of the present invention is implemented at least in part through a combination of an application for use by physicians and an application for use by patients. In some embodiments the system is implemented at least in part through a combination of a web application for use by physicians and an application for a mobile device for use by patients” and [0130]: “Often a mobile electronic device may weigh under about 1-2 pounds, e.g., between about 2-3 ounces and about 1.5 pounds. For example, a smartphone or PDA may weigh between about 3 ounces and about 6 ounces and height and width dimensions in the range of less than about 7×5 inches and depth less than about 0.5-1.0 inch, though smaller or larger weight and/or dimensioned devices may be used. Exemplary portable electronic devices include, e.g., a PDA such as an iTouch (Apple, Inc.), a smartphone such as an iPhone (Apple, Inc.) or Galaxy phone (Samsung) or Windows phone (Microsoft), or a tablet computer such as the iPad or iPad mini (Apple, Inc.), Galaxy tablet, Windows Surface, Fire (Amazon), etc. In some embodiments a mobile device may be wearable, e.g., as a wristwatch, armband, computer with a head mounted display (such as Google Glass) optionally attached to or in the form of glasses, etc. In some embodiments, a smartphone or other mobile device comprises a touch screen and is primarily operated by touching the screen, although of course it may also have one or more controls such as buttons, switches, etc., that are distinct from the screen, e.g., on the top, side, bottom, etc., of the device”).
Regarding claim 20 Francois further teaches said health information messaging to said subject comprises one or more type of messaging including self-tracking reminders, doctor appointment reminders, alerts to notify a healthcare provider and de-stress providers (Francois, [0010]: “the invention provides at least some of the following features for patients: (1) View consolidated data on compliance, conditions, and hospitalizations, as well as physiological data and educational modules; (2) Receive periodic updates/information on conditions; (3) Prompting to perform periodic evaluation of conditions; (4) Option to perform evaluation of conditions at any time; (5) Receive schedule of upcoming procedures, appointments with health care providers; and (6) Receive medication reminders. In some embodiments, caregivers of the patient may have permission to access the program on the patient's behalf. Caregivers may be allowed to view at least some of the patient information, may receive instructions in addition to or instead of the patient, or both”).
Regarding claim 29 Francois teaches a tangible non-transitory computer readable storage medium, comprising instructions that, when executed by a computer processor, cause the processor to: (a) receiving, via a user interface of an application executing on one or more computer processors, a self-surveillance module, wherein said self-surveillance module comprises inputs from the subject for personal information for a plurality of types of information selected from the types of information (Francois, [0008]: “patients may use a health tracking module to assess their condition, in accordance with their physicians' instructions, by participating in evaluations of symptoms conducted via a computer or communication device that comprises, accesses, or is controlled by a computer program product of the invention…algorithms linking symptom data and physiological data to particular patient instructions are reviewed and approved by a health care provider, health care facility, or health care organization that uses the system” and [0457]: “a system or computer program product may interact with users (e.g., via a standard graphical user interface (GUI), analyze health data, and/or assemble health data into one or more modules. As noted above, a system may comprise multiple components. For example, one or more components may receive and/or transmit health data and/or other information to or from users; one or more components may analyze health data and/or other information; one or more components may add information to a database; one or more components may generate output to be transmitted or presented to users; one or more components may extract information from a database in response to a request or query. One or more components may analyze extracted data and/or convert the data into an appropriate format for transmission or display. One or more components may receive and/or transmit information between other component(s) of the system or external to the system”); (b) storing, via the one or more computer processors, said personal information of said subject in a database accessible by said application, and accessible by said subject via said user interface of said application (Francois, [0018]: “selecting a set of one or more data collection modules that are relevant to the condition based on input that identifies a condition and a subset of patient characteristics selected from a set of patient characteristics deemed significant in the context of the condition; (ii) associating identifiers of the set of one or more data collection modules with an identifier of a patient based on input that identifies the patient; and (iii) associating an identifier of the T-plan and an identifier of the condition with the set of data collection modules, optionally wherein the configuration module adds, removes, or customizes one or more of the data collection modules based on input that specifies such addition, removal, or customization”); (c) assigning, via the one or more computer processors, an abnormality point value for any of a plurality of deviations from trends over time in said personal information stored in said database (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”) and an abnormality point matrix stored on a memory device accessible by the one or more computer processors, the abnormality point matrix being generated and continually updated by a machine learning module ([0187]: “a health evaluation or an SST may utilize any of a variety of different algorithms to determine a patient's health status with respect to a condition and/or to determine directions for management of the condition. It should be understood that the invention is in no way limited in this respect. Algorithms may rely entirely on embedded medical knowledge that maps each particular combination of inputs to an SST to a particular output, may employ any of the various tools of artificial intelligence. In some embodiments, an algorithm may be embodied as a decision tree. In some embodiments, an algorithm may be embodied as a set of rules and an inference engine. In some embodiments, an algorithm may make use of fuzzy logic, probabilistic methods (e.g., Bayesian networks, Hidden Markov models) neural networks, or other approaches”); (d) determining, via the one or more computer processors, a total abnormality point value of said trends for said subject via the one or more computer processors, based on the sum total of abnormality point values in real-time, wherein when the total abnormality point value of said trends for said subject exceeds a pre-selected point value an abnormality alert is generated (Francois, [0183]: “A trigger for prompting a patient to perform a health evaluation, e.g., to run a smart symptom tracker, may be based on data elements of any one or more types. In some embodiments, data elements of different types may be assigned weights according to their diagnostic value in terms of determining whether the patient is at sufficient risk of experiencing a deterioration to warrant performing a health evaluation. The diagnostic value of a particular type of data element may vary depending on the particular condition(s) and the patient characteristics that the patient has. A combination of the values of the data elements and their weights may be used to determine a score, which may be used to determine whether to perform a health evaluation, whether to run one or more SSTs and, in some embodiments, the order in which to run them. The values of the data elements may be absolute values, deviation from normal value (which may be adjusted for demographic characteristics such as age and sex), change from patient's baseline, rate of change from patient's baseline, or a combination thereof” and [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”); (e) notifying, via said user interface, said subject about the abnormality alert (Francois, [0182]: “A patient may be prompted to perform a health evaluation, e.g., to run a smart symptom tracker, at various times. In some embodiments, a patient may be prompted to run a smart symptom tracker based on physiological data collected by a monitoring module indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. The particular criteria used to determine whether data are indicative a deterioration or potential deterioration or other clinically significant occurrence in the patient's health will vary depending on the condition and the types of data elements being considered. The system may store information identifying normal ranges, limits below which a value is considered to indicate a deterioration or potential deterioration in the patient's health, or limits above which a value is considered to indicate a deterioration or potential deterioration in the patient's health. Sometimes any value markedly outside the normal range may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly below the normal range or below a particular value may indicate a deterioration or potential deterioration in the patient's health. Sometimes a value markedly above the normal range or above a particular value may indicate a deterioration or potential deterioration or other clinically significant occurrence in the patient's health. For some variables, a change from a previously measured value may be indicative of a deterioration or potential deterioration or other clinically significant occurrence in the patient's health”); and (f) providing, via said user interface, health information messaging to said subject, wherein said health information messaging is tailored to undo said abnormality in said trends of said subject (Francois, [0266]: “detection of an abnormal value may, instead of or in addition to triggering the running of an SST, trigger a different action such as (i) notifying the patient of the abnormal value and, optionally, providing appropriate management direction(s); (ii) notifying a caregiver of the patient of the abnormal value and, optionally, providing appropriate management direction(s); (iii) notifying a health care provider of the patient of the abnormal value; or (iv) any combination of (i), (ii), and (iii)”).
Francois does not teach comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression.
However, Conward teaches comprising doctor appointments, physiologic parameters, mental health survey results, indicators of social instability and personally recorded expression (Conward, [0016], [0020], and [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois to incorporate the teachings of Conward and account for modify the input data received in Francois to include a mental health survey results, appointments, and patient expressions as taught by Conward in order to evaluate and monitor patient health (Conward, [0002]).
Regarding claim 30 Francois further teaches (i) one or more computing devices in data communication with each other, each device having one or more computer processors, a data communication connection (Francois, [0473]: “The cloud computing environment may include a resource manager. The resource manager may be connected to the resource providers and the computing devices over the computer network. In some implementations, the resource manager may facilitate the provision of computing resources by one or more resource providers to one or more computing devices. The resource manager may receive a request for a computing resource from a computing device. The resource manager may identify one or more resource providers capable of providing the computing resource requested by the computing device. The resource manager may select a resource provider to provide the computing resource. The resource manager may facilitate a connection between the resource provider and the computing device. In some implementations, the resource manager may establish a connection between a resource provider and a computing device. In some implementations, the resource manager may redirect a computing device to a resource provider with the requested computing resource”), and one or more tangible non-transitory computer-readable media accessible by the one or more computer processors (Francois, [0126]: “As will be appreciated by one of ordinary skill in the art, the technology disclosed herein or any one or more aspects thereof may be used to implement, for example, as a system, apparatus, method, or computer program product. Accordingly, one of ordinary skill in the art could implement hardware, software, or embodiments combining software and hardware aspects based on this disclosure. Such implementations, made up of hardware and/or software, may all generally be referred to herein as “systems”, which may comprise one or more “components”. Similarly, one of ordinary skill in the art could implement technology based on the present disclosure in the form of a computer program product embodied in any tangible medium (e.g., a non-transitory storage medium) having computer usable program instructions embodied in the medium. Such a computer program product may comprise multiple distinct computer program products that may interface with each other. Such computer program products, sometimes termed “applications”, may provide functions for use by different types of users, e.g., patients, health care providers”), and (ii) a plurality of databases (Francois, [0018]: “(a) a library of modules comprising one or more data collection modules each comprising computer-executable instructions for performing a method comprising: obtaining symptom data, physiological data, behavioral data, environmental data, health care event data, other health or health-related data, or a combination thereof regarding or relating to a patient, wherein at least some of the data collection modules are configured to be made accessible to a patient through an application on a mobile device or through an electronic communication network”), including a personal information database (Francois, [0081]: “The term “patient record” refers to a collection of information regarding or relating to a patient, stored in association with an identifier of the patient. The information may be stored in one or more databases or data stores, which may be physically located in different places. It is considered to constitute a patient record as long as it is stored in a way that allows the system to determine which patient the information pertains to or that the information belongs to a particular patient. The information in a patient record may include any type of health data described herein, patient characteristics, patient's name, contact information (e.g., email address, phone number(s)), demographic information, patient's health care provider(s), patient's caregivers, family members, and any other information obtained or generated by a system of the invention regarding or relating to the patient. It should be noted that a patient record may be de-identified in that it lacks personally identifiable information. Thus, a patient identifier need not personally identify the patient but may simply serve to identify particular items of information as pertaining to the same patient”) and a health information messaging database (Francois, [0164]: “at least some of the directions may be customized. Triage directions may be customized by, for example, specifying the name of a particular health care provider, physician practice, or health care facility; providing contact information such as phone number and/or email address for a particular health care provider, physician practice, health care facility, and the like. Treatment directions may be customized by, for example, specifying a particular medication and/or dose within the context of a generalized direction. For example, a generalized direction might be “double the dose of your diuretic” for the next 24 hours. A customized treatment direction might specify the name of a particular diuretic that has been prescribed to the patient. The particular diuretic may have been specified by the health care provider who prescribed the T-plan to the patient, who may also have prescribed the diuretic. Information required for customizing directions to the patient will typically have been stored in a database as part of the patient's T-plan for that condition”), wherein a personal information database and a health information messaging database are each stored in the one or more tangible non-transitory computer-readable media (Francois, [0018]: “(i) receiving and/or monitoring receipt of data relating to a patient; and (ii) performing one or more scheduling or notification functions based on said receiving and/or monitoring and the specified frequency and time window. In some embodiments, one or more of the scheduling and notification functions is selected from (i) determining when the next data element of that type is due, (ii) determining whether a data element is current (not expired), (iii) determining whether a data request or notification should be issued; (iv) determining when a data request or notification should be issued, and (v) issuing a data request or notification. In some embodiments, at least some of the data collection modules, themselves or together with one or more associated modules, collectively comprise computer-executable instructions for: (1) obtaining data comprising symptom data, physiological data, behavioral data, and/or environmental data of one or more types that are relevant to the condition identified in step (a); and (2) determining directions for management of the condition by a patient or caregiver based on the data and providing the directions to a patient or caregiver; (3) determining a compliance score based on the data, (4) determining a health score based on the data, (5) determining whether the patient has experienced a significant deterioration in health within a selected time period or is at risk of experiencing a significant adverse change in health within a selected time period based on the data, (6) determining whether a change in the type of data to be collected and/or in the timing of data collection is warranted based on the data; (7) determining whether a notification to a caregiver or health care provider should be issued based on the data; (8) determining, based on the location of a patient's mobile device, that the patient likely experienced a hospitalization event, and, optionally, (A) adjusting one or more modules of a T-plan assigned to the patient to a heightened sensitivity mode or post-discharge use mode, further optionally switching the module out of heightened sensitivity mode or post-discharge use mode after a predetermined time period; and/or (B) notifying a health care provider or caregiver of the patient of the likely hospitalization event, or (9) any combination of (1)-(8)”).
Regarding claim 31 Francois further teaches a data visualization dashboard, wherein said dashboard displays to said subject trends over time in said personal information stored in said database (Francois, [0355]: “the REVON system provides one or more dashboards for use by health care providers to review patient health data obtained pursuant to T-plans. For purposes of the present disclosure, a dashboard is a user interface that shows an at least partly graphical or schematic presentation of the current status and/or trends of one or more parameters or indicators. An indicator may, for example, be an indicator of the health or compliance of a particular patient or group of patients, an indicator of use of a system of the present invention or a component thereof by a particular user or group of users, symptom data, physiological data, or any information or statistic derived therefrom. A dashboard may be presented on a single screen, which may be the first screen that the health care provider is presented with upon selecting a particular patient from the patient list”).
Regarding claim 32 Francois further teaches a goal-setting module, wherein said goal-setting module comprises inputs from the subject regarding personal performance targets that are desired to be achieved in connection with trends in those types of personal information stored in said database (Francois, [0358]: “In certain embodiments, a graphic may represent progress towards (or away from) a particular goal. For example, a graphic pertaining to patient who has been advised to lose weight may show a target weight and a timeline for reaching the target weight. In some embodiments, the graphical display for at least a portion of a dashboard may utilize D3.js (sometimes called D3 for Data-Driven Documents), a JavaScript library of use to drive the creation and control of dynamic and interactive graphical forms that run in web browsers from digital data” and [0422]: “initiating and tracking recalls of medications or medical devices, tracking treatment outcomes (e.g., comparing outcomes achieved through different therapeutic approaches, comparing the efficacy of different medications or surgical procedures, determining whether certain elective surgeries result in clinical benefit), identifying patients who may be eligible for clinical trials or otherwise eligible to receive experimental therapies, comparing treatment outcomes achieved by different health care providers or health care facilities, analyzing the effect of particular follow-up or discharge procedures on readmission rates, identifying patient characteristics that are associated with an increased likelihood of readmission, identifying patients who are poorly compliant with treatment recommendations”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Francois and Conward, in view of Zides et al. (US 20100235295 A1), hereinafter Zides.
Regarding claim 8 Francois and Conward do not teach (g) receiving, via a user interface of an application executing on one or more computer processors, messages indicating that said subject is within a predefined distance of a healthcare provider and providing information regarding how said subject can travel to said healthcare provider and wherein said messages include geo-location information.
However, Zides teaches (g) receiving, via a user interface of an application executing on one or more computer processors, messages indicating that said subject is within a predefined distance of a healthcare provider and providing information regarding how said subject can travel to said healthcare provider and wherein said messages include geo-location information (Zides, [0029]: “The health portal may identify and display healthcare providers based on characteristics of healthcare providers (e.g. location, aggregate cost, health insurance accepted, reputation, and success rate) a user finds most important. A user may view additional information (e.g. success rate, treatment cost, and reputation) about each identified healthcare provider to select an appropriate healthcare provider. The aggregate treatment cost for each healthcare provider may include the healthcare provider's estimated treatment costs including insurance deductibles, medication, and rehabilitation costs and the estimated travel costs including work downtime, meals, and housing during treatment. In addition, information, such as healthcare provider reviews shared between users of the health portal may be utilized by a user to select an appropriate healthcare provider. As a result, a user may select a healthcare provider outside of the user's hometown who has a lower aggregate cost and better user reviews over a healthcare provider located in the user's hometown”)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois and Conward to incorporate the teachings of Zides and account for identifying and presenting one or more healthcare providers to a user (Zides, Abstract and [0029]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Francois and Conward, in view of Lee et al. (US 20190083003 A1), hereinafter Lee.
Regarding claim 10 Francois does not teach said self-surveillance module further comprises inputs from the subject for personal information for social support selected from the types of information comprising sleep deprivation, and mood-related symptoms, wherein said subject inputs measurements of personal stress on a predefined measurement scale presented to said subject via said user interface and wherein said health information messaging comprises stress management strategies that respond to the trends in personal information for social support of said subject.
However, Conward teaches said self-surveillance module further comprises inputs from the subject for personal information for social support selected from the types of information comprising sleep deprivation (Conward, [0057-[0060]: “the models described herein recognize and utilize associations between modifiable SDoH variables, race, and sleep which can lead to early actionable clinician recommendations for sleep improvement and subsequently mitigate risk of pregnancy morbidity, particularly for at-risk racial and ethnic groups. Sleep health contributes to physical, mental, and emotional well-being, including gestational hypertension, preeclampsia, gestational diabetes mellitus, mood, attention, and memory. To date, it is unclear how distinct sleep variables such as sleep quantity (fewer than 7 hours or more than 7 hours of sleep per night), sleep quality (the degree to which one has felt refreshed upon waking in the prior 4 months), and sleep-disordered breathing are impacted”), and mood-related symptoms (Conward, [0097]: “the user-generated content for each individual comprises survey data comprising one or more of a mood log or a symptom log”), wherein said subject inputs measurements of personal stress on a predefined measurement scale presented to said subject via said user interface (Conward, FIG. 6, [0020]: “Certain embodiments also relate to HIPAA- and HITRUST-compliant patient mobile applications to allow for individuals (e.g., patients such as individuals in a pregnancy-related period) to regularly log their mood, complete health risk assessment tests (e.g., Edinburgh Postnatal Depression Scale (EPDS), Patient Health Questionair-9 (PHQ-9), questions or Generalized Anxiety Test Questionnaire (GAD-7) questions), track their symptoms, and capture and monitor relevant biometric data using wearable devices or contactless sensors”, [0022], and [0054]) and wherein said health information messaging comprises stress management strategies that respond to the trends in personal information for social support of said subject (Conward, [0045]: “the data analysis engine 114 may generate recommendations for the patient based on the associations made between health indicators and biometric data in order to treat or mitigate symptoms and their underlying causes. For example, such recommendations may include nutritional recommendations, medical procedure or examination recommendations, pharmacological recommendations, complementary or alternative medicine recommendations, exercise recommendations (e.g., breathing exercises), or sleep recommendations (e.g., one or more recommendations/suggestions for improving sleep habits and overall sleep health). Recommendations may be in the form of affirmations, task alerts, clinician matching, or feedback from a health screening tool (e.g., a mood assessment tool). Recommendations may also include product recommendations that are tailored to the particular needs of the patient. In some embodiments, the data analysis engine 114 may track the patient's appointments with clinicians, track the outcomes of such appointments, and provide notifications or reminders of upcoming appointments”, [0058], [0069], ).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois to incorporate the teachings of Conward and account for modify the input data received in Francois to include a mental health survey results, appointments, and patient expressions as taught by Conward in order to evaluate and monitor patient health (Conward, [0002]).
Francois and Conward do not teach absence of assistance with baby or other children. However, Lee teaches absence of assistance with baby or other children (Lee, [0042], [0057], [0066], and [0079]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois and Conward to incorporate the teachings of Lee and account for health care system for a caregiver to monitor and manage sleep metric, patterns and quality (Lee, Abstract and [0013]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Francois and Conward, in view of Gupta et al. (US 20200005946 A1), hereinafter Gupta.
Regarding claim 19 Francois and Conward does not teach the step of tracking over time, via the one or more computer processors and the total abnormality point values, the relative improvement or lack of improvement over time with respect to maternal mortality risk by said subject.
However, Gupta teaches the step of tracking over time, via the one or more computer processors and the total abnormality point values, the relative improvement or lack of improvement over time with respect to maternal mortality risk by said subject (Gupta, [0013]: “identification of social determinants of adverse health outcomes may be utilized to stratify particular demographics of people that may be afflicted with particular adverse health outcomes, demographics of a community, environment, e.g., for those people, etc. As described and recited herein, a stratification, i.e., multivariable algorithm utilizing weighted factors, may be established to identify expectant mothers who may be prone to risk of complications that are related to pregnancy or child-delivery and lead to adverse health outcomes such as maternal mortality, maternal morbidity, infant mortality, and low birth weight for a child. As a result, one of multiple benefits may include the identification of high-risk expectant mothers whose pregnancies may be monitored and/or managed to reduce or even eliminate risks that lead to such adverse health outcomes for mother and/or child”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Francois and Conward to incorporate the teachings of Gupta and account for monitoring an improvement in the risk of maternal mortality as taught by Gupta in order to reduce the risk of adverse health outcomes (Gupta [0013]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 7 AM - 7 PM (EST).
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
