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 1-20 are currently pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-9 are directed toward a computing system (i.e., machine) and claims 10-20 are directed toward a method (i.e. a process). 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 and 10 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity and/or mental process, but for the recitation of generic computer components.
Claim 1 recites: “A computing system for medical data processing, comprising: a plurality of member computing entities each storing and executing a member application configured to track, execute, and communicate a member's dynamic wellness plan; a health assistant computing entity coupled to the plurality of member computing entities via a network, wherein the automated health assistant computing entity includes: a processor configured to execute a machine learning and artificial intelligence application that uses model inference to apply a trained model for facilitating member wellness functions; a private database for storing member-specific data; an automated health operating system, the operating system including modules for private database management, automated health content management, communication management, and security management; and an automated health database system coupled to the automated health assistant computing entity via a private network, the database system comprising a plurality of databases storing health-related data including medical diagnosis, pharmaceutical data, mental health data, nutrition data, physical fitness data, and genetic data, wherein the health assistant computing entity is configured to: process and analyze the member-specific data using the machine learning and artificial intelligence programs; generate personalized health insights and treatment plans based on the analyzed data; facilitate communication between the member computing entities and professional computing entities to coordinate health and wellness actions; and monitor and update the member’s wellness plan in real-time based on new data inputs”.
The limitations of track, execute, and communicate a member's dynamic wellness plan; and storing health-related data including medical diagnosis, pharmaceutical data, mental health data, nutrition data, physical fitness data, and genetic data, wherein the health assistant computing entity is configured to: process and analyze the member-specific data; generate personalized health insights and treatment plans based on the analyzed data; coordinate health and wellness actions; and monitor and update the member’s wellness plan in real-time based on new data inputs, 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 track, execute, communicate, store, process, analyze, generate, coordinate, monitor, and update, which is properly interpreted as a “personal behavior”), and/or a mental process that a physician performs when tracking, monitoring, and updating a patient’s wellness plan, 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.
Additionally, claim 10 recites: “A method for personalized medical data processing, comprising: receiving, by a health assistant computing entity, member-specific health data from a plurality of member computing entities via a network; processing the received health data using machine learning and artificial intelligence algorithms executed by the health assistant computing entity to generate personalized health insights; storing the processed health data and generated insights in a private database associated with the automated health assistant computing entity; generating a dynamic wellness plan for each member based on the processed data and insights; facilitating communication between the member computing entities and professional computing entities to implement the wellness plan; continuously monitoring the member’s health data and dynamically updating the wellness plan based on new data inputs; and providing real-time feedback and recommendations to the member through the member computing entities”.
The limitations of receiving, member-specific health data; processing the received health data; generate personalized health insights; storing the processed health data and generated insights; generating a dynamic wellness plan for each member based on the processed data and insights; implement the wellness plan; continuously monitoring the member’s health data and dynamically updating the wellness plan based on new data inputs; and providing real-time feedback and recommendations to the member, 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, processing, generating, storing, implementing, monitoring, updating, and providing, which is properly interpreted as a “personal behavior”), and/or a mental process that a physician will perform to monitor, update, and provide feedback and recommendation of a patient’s wellness plan, but instead automates the process via a computer model or machine learning, 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.
Dependent claims 2-9 and 11-20 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 and 10. 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:
Claims 1-20 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 “computing entities”, “member application”, “health assistant computing entity”, “network”, “processor”, “machine learning”, “artificial intelligence application”, “model”, “private database”, “operating system”, “modules”, “private database”, “heath content management”, “communication management”, “security management”, “automated health assistant computing entity”, “health assistant computing entity”, “member computing entities”, and “professional computing entities”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014]-[0015], and [0023], 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, “a plurality of member computing entities each storing and executing a member application configured to”, “a health assistant computing entity coupled to the plurality of member computing entities via a network, wherein the automated health assistant computing entity includes: a processor configured to execute a machine learning and artificial intelligence application that uses model inference to apply a trained model for facilitating member wellness functions; a private database for storing member-specific data; an automated health operating system, the operating system including modules for private database management, automated health content management, communication management, and security management”, “an automated health database system coupled to the automated health assistant computing entity via a private network, the database system comprising a plurality of databases”, “using the machine learning and artificial intelligence programs”, “facilitate communication between the member computing entities and professional computing entities to”, “by a health assistant computing entity”, “ from a plurality of member computing entities via a network”, “using machine learning and artificial intelligence algorithms executed by the health assistant computing entity to”, “ in a private database associated with the automated health assistant computing entity”, “facilitating communication between the member computing entities and professional computing entities to”, and “through the member computing entities”, 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, by a health assistant computing entity, member-specific health data from a plurality of member computing entities via a network”, 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 2-9 and 11-20 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1 and 10, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements 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 2-9 and 11-20 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 and 10, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, by a health assistant computing entity, member-specific health data from a plurality of member computing entities via a network”), 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.”
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claim(s) 11-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Knox (US-20200364588-A1).
Regarding claims 11-20 Knox teaches:
11. A method for personalized medical data processing, comprising: receiving, by a health assistant computing entity, member-specific health data from a plurality of member computing entities via a network (Knox, [0070]: “Computing device 26 or a plurality of computing device 26 can send and receive input from users and transmit and receive data via network 14 using protocols that may comprise any combination of local area and/or wide area networks, using wired and/or wireless communication systems”); processing the received health data using machine learning and artificial intelligence algorithms executed by the health assistant computing entity to generate personalized health insights (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data”); storing the processed health data and generated insights in a private database associated with the automated health assistant computing entity (Knox, [0072]-[0073]); generating a dynamic wellness plan for each member based on the processed data and insights (Knox, [0113]-[0114]);
facilitating communication between the member computing entities and professional computing entities to implement the wellness plan (Knox, [0113]-[0114]); continuously monitoring the member’s health data and dynamically updating the wellness plan based on new data inputs (Knox, [0051]: “Deep learning applications increase insight data with continuous interactions and improve recommendation quality for all parties regarding optimal engagement conditions, timing, best practices, contingencies for unforeseen circumstances and the like”); and providing real-time feedback and recommendations to the member through the member computing entities (Knox, [0010], [0012], and [0019]).
12. The method of claim 11, further comprising the step of validating the generated health insights and recommendations through a professional computing entity before presenting them to the member (Knox, [0113]-[0114]).
13. The method of claim 11, wherein the health data received from the member computing entities includes data from wearable health monitoring devices (Knox, [0047], [0056], [0062], and [0070]).
14. The method of claim 11, further comprising the step of performing health risk assessment by comparing the member-specific health data against population-level health data stored in the automated health database system (Knox, [0088]: “rated and ranked recommendations based on aggregated data from related local, national, anonymized and/or demographic statistical data”, [0104], [0105], and [0112]).
15. The method of claim 11, wherein the real-time feedback provided to the member includes alerts for potential health risks detected by the machine learning algorithms (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data” and [0078]).
16. The method of claim 11, wherein the dynamic wellness plan includes personalized recommendations for physical fitness, nutrition, mental health, and medical treatments (Knox, [0114]).
17. The method of claim 11, further comprising aggregating health data from multiple members to improve the accuracy of the machine learning model used by the automated health assistant computing entity (Knox, [0088]: “rated and ranked recommendations based on aggregated data from related local, national, anonymized and/or demographic statistical data”, [0104], [0105], and [0112]).
18. The method of claim 11, wherein continuously monitoring the member’s health data includes detecting changes in the member's biometrics and adjusting the wellness plan accordingly (Knox, [0061], [0078], and [0114]).
19. The method of claim 11, wherein the automated health assistant computing entity generates wellness plan compliance reports for the member based on tracked adherence to the recommendations (Knox, [0114]).
20. The method of claim 11, wherein the machine learning algorithms used for processing the health data are trained on anonymized historical health data stored in the automated health database system (Knox, [0068], [0077], and [0088]).
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) 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Knox (US-20200364588-A1), in view of Neumann (US-20200321121-A1).
Regarding claim Knox teaches computing system for medical data processing, comprising: a plurality of member computing entities each storing and executing a member application configured to track, execute, and communicate a member's dynamic wellness plan (Knox, [0105]: “A platform that can conveniently map user habits with correlated preferences enables individuals and groups to gain visibility and clarity of optimal conditions and schedules will improve their ability to better anticipate, plan, and efficiently execute customized interactions between users and on behalf of other users”); a health assistant computing entity coupled to the plurality of member computing entities via a network, wherein the automated health assistant computing entity includes (Knox, [0016]-[0017], [0044], and [0047]): a processor configured to execute a machine learning and artificial intelligence application that uses model inference to apply a trained model for facilitating member wellness functions (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data”); a private database for storing member-specific data (Knox, [0068]: “In some aspects, blockchain module 22 may include a private blockchain, public blockchain, blockchain database, blockchain processor, data anonymizer and crypto wallet” and [0079]); an automated health operating system, the operating system including modules for private database management, automated health content management, communication management, and security management (Knox, [0061], [0064]-[0065], and [0076]); and an automated health database system coupled to the automated health assistant computing entity via a private network (Knox, [0016]-[0017], [0044], [0047], and [0064]-[0065]), process and analyze the member-specific data using the machine learning and artificial intelligence programs (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data”); generate personalized health insights and treatment plans based on the analyzed data (Knox, [0113]: “provides personal health care and related information to one or more users 516, 518. In some aspects, health care program 530 is a subscription or account-based third-party program service for collecting, viewing, sharing, scheduling, and analyzing personal health care data and information generated from user experiences therein. In the present example, administrator 542 may create a user group 544 on behalf of a subject 516 to utilize health care program 530 for generating, analyzing, and sharing personal health care related information including real-time health status, interpersonal communications, care request notifications, patient conditions including ubicomp data 514 feedback information originating from respective health care program 530 user experiences. In the present example, ubicomp data 514 feedback information may include preference data, analytic data, predictive data, recommendation data” and [0114]); facilitate communication between the member computing entities and professional computing entities to coordinate health and wellness actions (Knox, [0113]-[0114]); and monitor and update the member’s wellness plan in real-time based on new data inputs (Knox, [0113]-[0114]).
Knox does not teach the database system comprising a plurality of databases storing health-related data including medical diagnosis, pharmaceutical data, mental health data, nutrition data, physical fitness data, and genetic data, wherein the health assistant computing entity is configured to.
However, Neumann teaches the database system comprising a plurality of databases storing health-related data (Neumann, [0063]-[0066]) including medical diagnosis (Neumann, [0060], [0102], and [0131]), pharmaceutical data (Neumann, [0069] and [0077]), mental health data (Neumann, [0138] and [0189]), nutrition data (Neumann, [0057], [0069], and [0077]), physical fitness data (Neumann, [0081]), and genetic data (Neumann, [0043], [0056], and [0065]), wherein the health assistant computing entity is configured to (Neumann, [0196]-[0200]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Knox to incorporate the teachings of Neumann and account for a system for an artificial intelligence support network for informed advisor guidance includes a diagnostic engine operating on a computing device and configured to receive a biological extraction related to a user, said biological extraction comprising a self-assessment of the user, and generate a diagnostic output as a function of the self-assessment of the user The system includes an advisor module (Neumann, Abstract and [0003]).
Regarding claim 2 Knox further teaches the private network connecting the automated health assistant computing entity to the automated health database system is a virtual private network (VPN) (Knox, [0068] and [0099]).
Regarding claim 3 Knox further teaches a plurality of professional computing entities each storing and executing an automated professional application configured to interact with the automated health assistant computing entity and member computing entities (Knox, [0061], [0064]-[0065], and [0076]).
Regarding claim 4 Knox further teaches the machine learning and artificial intelligence programs include modules for predictive analysis, treatment recommendation, and health risk assessment (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data”).
Regarding claim 5 Knox further teaches the automated health operating system further includes a module for interoperability of data, enabling the integration of external health data sources into the automated health database system (Knox, [0050]: “ efficiently aid machine learning applications that perform predictive analytics and offer customized recommendations for each unique individual”, [0059]: “This information is customized based on a unique subject's lifestyle habits, routine and experiential preferences, and with machine learning becomes increasingly refined as empirical data is generated to improve insight analytic”, and [0066]: “recommendation engine 18 manages artificial intelligence, machine learning, deep learning and other neural network applications to process, analyze, convert and interpret ubicomp 12 information into human activity and behavioral data for use by system 10 components and inference intelligence 20. In some aspects, recommendation engine 18 correlates inference intelligence 20 with human activity and behavioral data”).
Regarding claim 6 Knox further teaches the member computing entities are selected from the group consisting of smartphones, tablets, laptops, and wearable devices (Knox, [0009], [0018], and [0047]).
Regarding claim 7 Knox further teaches the automated health assistant computing entity is further configured to perform behavioral health monitoring through voice and text analysis (Knox, [0011]-[0013], [0020]-[0021], [0081], and [0083]).
Regarding claim 8 Knox does not teach the automated health database system includes a genome database for storing and processing genetic data related to the member.
However, Neumann teaches the automated health database system includes a genome database for storing and processing genetic data related to the member (Neumann, [0043] and [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Knox to incorporate the teachings of Neumann and account for a system for an artificial intelligence support network for informed advisor guidance includes a diagnostic engine operating on a computing device and configured to receive a biological extraction related to a user, said biological extraction comprising a self-assessment of the user, and generate a diagnostic output as a function of the self-assessment of the user The system includes an advisor module (Neumann, Abstract and [0003]).
Regarding claim 9 Knox further teaches the automated health assistant computing entity is further configured to interface with external health information exchanges (HIEs) for retrieving additional medical data (Knox, [0069], [0073], [0079], and [0083]).
Regarding claim 10 Knox further teaches a data communication module configured to encrypt all communications between the member computing entities, professional computing entities, and the automated health assistant computing entity (Knox, [0113]-[0114]).
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 9 AM - 5 PM (EST).
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/R.S.S./Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681