DETAILED ACTION
Notices to Applicant
This communication is a Final Office Action on the merits. Claims 1-20 as filed 11/05/2025, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application is a continuation of U.S. Patent Application No. 16/742,152, filed 01/14/2020, which claims priority to and the benefit of U.S. Provisional Patent Application No. 62/792,572, filed 01/15/2019.
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., an abstract idea) without significantly more.
Claims 1-13 are drawn to a system for exchanging phenotype data, which is within the four statutory categories (i.e. machine).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
1. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform;
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database;
wherein the report database includes health data;
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor;
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes, and a user attribute inference module;
wherein the user attribute inference module includes an artificial intelligence module operable to identify that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value; and
wherein the health data exchange platform automatically generates a phenotype network for the at least one user.
The above claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations such as “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” and “a user attribute interface module,” including “an artificial intelligence module,” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, identifying that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value and generating a phenotype network for the at least one user, wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes in the context of this claim encompasses the observation, evaluation, judgment and/or opinion of health data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” and “a user attribute interface module,” including “an artificial intelligence module,” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., a user device such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00187], [00188], [00195])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool, or invoking machinery as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Further, the additional element of using health data “collected from at least one body sensor and at least one environmental sensor,” amounts to are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” and “a user attribute interface module,” including “an artificial intelligence module,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component or machinery used in its normal function (i.e., a user device such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00187], [00188], [00195])). Mere instructions to apply an exception using a generic computer component or machinery in its normal function cannot provide an inventive concept. See MPEP 2106.05(f)(2). Further, the additional element of health data collected from at least one body sensor and at least one environmental sensor amounts to are mere data gathering and output receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Dependent claims 2-13 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the health data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the observation, evaluation, judgment, and/or opinion of health data. Claim 2 recites the additional element of “a plurality of distributed servers,” claim 5 recites “a user profile database,” claim 7 recites “a geographic database,” claim 8 recites “a medical community database,” claim 9 recites “a plurality of prediction modules,” claim 11 recites “a candidate connection generator, a conversion predation engine, a value computation engine, an expected value scoring engine,” however, each of these limitations are recited at a high level of generality such that they amount to using generic computer components as a tool to perform the abstract idea. See Application Specification [0098] [00187], [00188], [00195]; MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claims 14-19 are drawn to a system for exchanging phenotype data, which is within the four statutory categories (i.e. machine).
Independent Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 14 recites:
14. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform;
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database;
wherein the report database includes health data;
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor;
wherein the at least one user device is operable to authorize sharing of selected health data with a third-party device via a graphical user interface (GUI), thereby creating selected permissible data;
wherein the health data exchange platform is operable to exchange the selected permissible data with the third-party device;
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes, and a user attribute inference module;
wherein the user attribute inference module includes an artificial intelligence module operable to identify that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value;
wherein the phenotype network is operable to automatically suggest a connection with another user, at least one phenotype, at least one medical entity, and/or at least one health status;
wherein the health data exchange platform automatically generates a phenotype network for the at least one user.
The above claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations such as “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI), and “a user attribute interface module,” including “an artificial intelligence module,” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, authorize sharing of selected data thereby making permissible data, identifying that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value and generating a phenotype network for the at least one user, wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes in the context of this claim encompasses the observation, evaluation, judgment and/or opinion of health data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI), and “a user attribute interface module,” including “an artificial intelligence module,” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., a user device/third party such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, a graphical user interface for accessing data, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00184] [00187], [00188], [00195])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool or invoking machinery as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Further, the additional element of using health data “collected from at least one body sensor and at least one environmental sensor,” amounts to are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI), and “a user attribute interface module,” including “an artificial intelligence module,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component or machinery used in its normal function (i.e., a user device/third party such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, a graphical user interface for accessing data, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00184] [00187], [00188], [00195])). Mere instructions to apply an exception using a generic computer component or machinery used in its normal function cannot provide an inventive concept. See MPEP 2106.05(f)(2). Further, the additional element of health data collected from at least one body sensor and at least one environmental sensor amounts to are mere data gathering and output receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
Dependent claims 15-19 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the health data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the observation, evaluation, judgment, and/or opinion of health data. Claim 18 recites the additional element of “an Ethereum blockchain or a Bitcoin blockchain,” and claim 19 recites “the permissions database stores links to one or more locations where the selected permissible data is stored on a distributed ledger,” however, each of these limitations are recited at a high level of generality such that they amount to using generic computer components as a tool to perform the abstract idea. See Application Specification [0039], [00132], [00187], [00188], [00195]; MPEP 2106.05(f). Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore the dependent claims are rejected under 35 U.S.C. § 101.
Claim 20 are drawn to a system for exchanging health data, which is within the four statutory categories (i.e. machine).
Independent Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 20 recites:
20. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform;
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database;
wherein the report database includes health data;
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor;
wherein the at least one user device is operable to authorize sharing of selected health data with a third-party device via a graphical user interface (GUI), thereby creating selected permissible data;
wherein the health data exchange platform is operable to exchange the selected permissible data with the third-party device;
wherein the health data exchange platform is operable to process a micropayment between the at least one user device and the third-party device;
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes, and a user attribute inference module;
wherein the health data exchange platform receives a request from the at least one user device to generate a requested phenotype network based on one or more designated criteria;
wherein the health data exchange platform includes an artificial intelligence module operable to automatically generate the requested phenotype network.
The above claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the above bolded limitations such as “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI),” “a user attribute interface module,” and “an artificial intelligence module,” nothing in the claim precludes the steps from practically being performed in the mind. For example, but for the above bolded language, authorize sharing of selected data thereby making permissible data, exchange the selected permissible data, identifying that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value and generating a phenotype network for the at least one user based on a request with one or more designated criteria, wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes in the context of this claim encompasses the observation, evaluation, judgment and/or opinion of health data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Further, the limitation of “process a micropayment” falls under the “Certain Methods of Organizing Human Activity.” Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI),” “a user attribute interface module,” and “an artificial intelligence module,” to perform the claim limitations. The additional elements in each of these steps are recited at a high-level of generality (i.e., a user device/third party such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, a graphical user interface for accessing data, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00184] [00187], [00188], [00195])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer, or merely uses a computer or invoking machinery as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Further, the additional element of using health data “collected from at least one body sensor and at least one environmental sensor,” amounts to are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using for example “at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform; wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database,” “collected from at least one body sensor and at least one environmental sensor;” “a third-party device via a graphical user interface (GUI),” “a user attribute interface module,” and “an artificial intelligence module,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component or machinery used in its normal function (i.e., a user device such as computing devices, a remote server as a plurality of distributed servers, a network connection such as cloud-based network via a wireless communion, each database housing an operating system, memory, and programs, a processor, a user attribute interface module including an artificial intelligence module as software driven modules such as program modules as performed instructions/algorithms as they relate to general purpose computer components and sensors such as temperature, humidity, noise, light, motion, etc. and wearable devices sensors such as heart and respiratory sensors e.g. apple watch, Fitbit, etc. (Application Specification [0064]-[0069], [0080], [0098], [00187], [00188], [00195])). Mere instructions to apply an exception using a generic computer component or machinery used in its normal function cannot provide an inventive concept. See MPEP 2106.05(f)(2). Further, the additional element of health data collected from at least one body sensor and at least one environmental sensor amounts to are mere data gathering and output receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Pub. No. 2018/0165588 A1 (hereinafter “Saxena et al.”) in view of U.S. Patent Application Pub. No. 2019/0295703 A1 (hereinafter “Das et al.”) and U.S. Patent Application Pub. No. 2019/0209022 A1 (hereinafter “Sobol et al.).
RE: Claim 1 (Currently Amended) Saxena et al. teaches the claimed:
1. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform ((Saxena et al., [0005], [0024], [0313]) (a processor; The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server; EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database ((Saxena et al., [0152], [0160], [0170], [0313], [0320]) (the blockchain exchange 948 may be implemented with permission and identity management controls to determine the degree to which data associated with the public 912 and private 932 blockchains can be respectively accessed by the private 924 and hosted 904 cognitive platforms; universal knowledge repositor implemented as a cognitive graph; data may be stored in a relational database management system; a composite electronic health record for the patient stored in a collection of electronically-stored patient healthcare information stored in a digital format; As an example, a CILS may process a combination of healthcare-related data and blockchain-associated data to generate a healthcare-related, blockchain-associated cognitive insight 1302 for business operations 1312 associated with the provision of a particular healthcare service));
wherein the report database includes health data ((Saxena et al., [0313]) (an EHR may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information));
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes ((Saxena et al., [0049], [0151], [0278]) (a cognitive graph 228 refers to a representation of expert knowledge, associated with individuals and groups over a period of time, to depict relationships between people, places, and things using words, ideas, audio and images; nodes within the hosted or private universal graph contain one or more knowledge elements; each individual cognitive session graph that is associated with the user and stored in a repository of cognitive session graphs '1' through 'n' 1252 introduces edges that are not already present in the application cognitive graph)),
and a user attribute inference module; wherein the user attribute inference module includes an artificial intelligence module operable to identify that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute ((Saxena et al., [0032], [0165]) (Cognitive systems achieve these abilities by combining various aspects of artificial intelligence; an inferred 1008 cognitive learning style broadly refers to the use of inferred data by a CILS to perform a corresponding cognitive learning operation. In various embodiments the inferred data may include data inferred from the processing of source data. In certain embodiments, the source data may include data associated with one or more blockchains. In various embodiments, the inferred data may include concepts that are inferred from the processing of other concepts. In these embodiments, the inferred data resulting from the processing of the source data, the concepts, or a combination thereof, may result in the provision of new information that was not in the source data or other concepts)),
wherein the health data exchange platform automatically generates a phenotype network for the at least one user
Saxena et al. fails to explicitly teach, but Das et al. teaches the claimed:
and determine an inferred user attribute confidence value ((Das et al., [0041]) (There are other, more complicated methods for data imputation, including the filling of empty bins with the running average of the measurements of the relevant input, or inferring the missing value from a patient with a quantitatively similar trajectory of measurements)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the inferring of a missing value from a patient as taught by Das et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. with the motivation of deciding whether or not a drug will be effective based on underlying physiologic insult and whether or not the mechanism of action of the drug will treat that specific manifestation of the condition (Das et al. at [0002]).
Saxena et al. and Das et al. fail to explicitly teach, but Sobol et al. teaches the claimed:
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor ((Sobol et al., [0165], [0167]) (both the presently acquired data and any historical or baseline data (including those with significant temporal components as discussed herein) may be placed in memory 173B in order to provide appropriate signatures that correspond to the LEAP data for subsequent comparison or analysis purposes. the sensors 121 may be placed into three major groups for the acquisition of the other components of the LEAP data. the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like));
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the present and historically acquired environmental and physiological sensor data as taught by Sobol et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. and the inferring of a missing value from a patient as taught by Das et al. with the motivation of providing data-informed care insights for family members, nurses, doctors or other caregivers for patients suffering with adverse health conditions (Sobol et al. at [0002], [0008], [0261]).
RE: Claim 2 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
2. The system of claim 1, wherein the at least one remote server includes a plurality of distributed servers ((Saxena et al., [0023], [0103]) (the network may comprise gateway computers and/or edge servers; the dynamic pipeline engine 438 manages the distribution of these various operations to a predetermined compute cluster and tracks versioning of the data as it is processed across various distributed computing resources))
RE: Claim 3 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
3. The system of claim 2, wherein the health data exchange platform includes a distributed ledger, and wherein the health data exchange platform uses a private key and a public key to encrypt data stored on the distributed ledger ((Saxena et al., [0126]) (the authentication is performed by a Registration Authority (RA) operating as a component of a Public Key Infrastructure (PKI). The resulting authentication may then be used as the basis for creating a set of digital credentials, such as a public/private key pair or digital certificate, which in turn can be used to perform various blockchain operations familiar to those of skill in the art)).
RE: Claim 4 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
4. The system of claim 1, wherein the permissions database stores information relating to what data each user has authorized to release to other entities ((Saxena et al., [0144]) (a private blockchain 932 broadly refers to a blockchain where its participants are known and are granted read and write permissions by an authority that governs the use of the blockchain. For example, the private blockchain 932 participants may belong to the same or different organizations within an industry sector. In various embodiments, these relationships may be governed by informal relationships, formal contracts, or confidentiality agreements.))
RE: Claim 5 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
5. The system of claim 1, wherein the phenotype database further includes a user profile database, and wherein the user profile database includes purchase history, social media history, web browsing history, biodata, sleep data, stress information, medications, supplements, disease profile, genotypes, workout information, diet information, social behaviors, emotional data, cognitive pursuits, data from third-party health applications, a frequency of interaction by the user with the phenotype network, hobbies, group memberships, and/or interests ((Saxena et al., [0211]) (the profile services 1140 include services related to the provision and management of cognitive personas and cognitive profiles used by a CILS when performing a cognitive learning operation. As used herein, a cognitive persona broadly refers to an archetype user model that represents a common set of attributes associated with a hypothesized group of users. In various embodiments, the common set of attributes may be described through the use of demographic, geographic, psychographic, behavioristic, and other information. As an example, the demographic information may include age brackets ( e.g., 25 to 34 years old), gender, marital status (e.g., single, married, divorced, etc.), family size, income brackets, occupational classifications, educational achievement, and so forth)).
RE: Claim 6 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
6. The system of claim 1, wherein the health data includes medical reports, sleep logs, wearable sensor data, blood test results, and/or genetic testing data ((Saxena et al., [0313]) (a composite electronic health record (EHR) for the patient. As used herein, an EHR broadly refers to the collection of electronically-stored patient healthcare information stored in a digital format. In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so. In certain embodiments, an EHR may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information)).
RE: Claim 7 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
7. The system of claim 1, further including a geographic database, wherein the geographic database includes information regarding known health issues by neighborhood, city, county, state, country, region, and/or zip code ((Saxena et al., [0320]) (In this example, the healthcare-related data may include information related to a target community or population, such as identity and contact information associated with individuals within the community or population, their respective online and physical addresses, and associated socioeconomic and demographic details)).
RE: Claim 8 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
8. The system of claim 1, further including a medical community database, wherein the medical community database includes research studies, clinical trials, and/or disease information ((Saxena et al., [0320]) (the healthcare-related data may include information related to a target community or population, such as identity and contact information associated with individuals within the community or population, their respective online and physical addresses, and associated socioeconomic and demographic details. The healthcare-related data may likewise include information associated with administration of the vaccine, such as recommended dosages, availability dates, number of dosages available, addresses of administration sites, and so forth. Likewise, the blockchain-associated data may include information related to various vaccines administered to individuals within the target group or community, the dates they were respectively administered, who they were administered by, any associated adverse reactions, and any healthcare-related issues the vaccine failed to prevent)).
RE: Claim 9 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
9. The system of claim 1, further including a plurality of prediction modules to determine an unknown, incomplete, and/or inaccurate user attribute ((Saxena et al., [0094], [0165]) (the predict 431 component is implemented to perform predictive operations to provide insight into what may next occur for a predetermined topic; the inferred data resulting from the processing of the source data, the concepts, or a combination thereof, may result in the provision of new information that was not in the source data or other concepts)).
RE: Claim 10 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
10. The system of claim 9, wherein the unknown, incomplete, and/or inaccurate user attribute is determined using at least one decision tree, a random forest classifier, a binary classifier, a multiclass classifier, a linear classifier, a Naive Bayesian classifier, a neural network, a Hidden Markov model, or a support vector machine ((Das et al., [0055]) (Multiple learning techniques can be tested when developing the disclosed companion algorithms and the one that produces the best area under the receiver operating characteristic curve can be utilized. Simple techniques such as linear regression can be used, which attempts to find the best equation for a linear regression to fit to the data. Also tested are more complicated techniques, such as gradient boosted trees. Gradient boosted trees utilize multiple weak prediction models, in this case, decision trees. Decision trees are rule-based models which assign what is in effect a score based on an established set of rules. When combining many decision trees through gradient boosting, very robust predictions are often seen. Further, deep learning methods such as neural nets can be used to train the algorithm)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the inferring of a missing value from a patient using multiple techniques such as decision trees, linear regression, neural nets, etc. as taught by Das et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. with the motivation of deciding whether or not a drug will be effective based on underlying physiologic insult and whether or not the mechanism of action of the drug will treat that specific manifestation of the condition (Das et al. at [0002]).
RE: Claim 11 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
11. The system of claim 1, further including a candidate connection generator, a conversion prediction engine, a value computation engine, an expected value scoring engine, and expected value rankings, and wherein the candidate connection generator, the conversion prediction engine, the value computation engine, the expected value scoring engine, and the expected value rankings are operable to automatically suggest a connection with another user ((Saxena et al., [0039]) (Collaborative filtering 206, as used herein, broadly refers to the process of filtering for information or patterns through the collaborative involvement of multiple agents, viewpoints, data sources, and so forth. The application of such collaborative filtering 206 processes typically involves very large and different kinds of data sets, including sensing and monitoring data, financial data, and user data of various kinds. Collaborative filtering 206 may also refer to the process of making automatic predictions associated with predetermined interests of a user by collecting preferences or other information from many users; if person 'A' has the same opinion as a person 'B' for a given issue 'x', then an assertion can be made that person 'A' is more likely to have the same opinion as person 'B' opinion on a different issue 'y' than to have the same opinion on issue 'y' as a randomly chosen person)).
RE: Claim 12 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
12. The system of claim 1, wherein the phenotype network system is operable to dynamically make recommendations based on social interactions, location data, biometric data, a digital footprint, and/or usage of the at least one user device ((Saxena et al., [0053], [0188], [0211], [0254]) (Such use results in the provision of information to the CILS 118. In response, the CILS 118 processes that information, in the context of what it knows about the user, and provides additional information to the user, such as a recommendation; a contextual recommendation broadly refers to a recommendation made to a user based upon a particular context; contextual information broadly refers to information associated with a location, a point in time, a user role, an activity, a circumstance, an interest, a desire, a perception, an objective, or a combination thereof; attributes may be described through the use of demographic, geographic, psychographic, behavioristic, and other information. As an example, the demographic information may include age brackets ( e.g., 25 to 34 years old), gender, marital status (e.g., single, married, divorced, etc.), family size, income brackets, occupational classifications, educational achievement, and so forth)).
RE: Claim 13 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
13. The system of claim 1, wherein the health data exchange platform includes an action log, and wherein the action log records a timestamp of when an action occurred, an identification of a user, an action type, an identification of where the action was directed, and content associated with the action ((Saxena et al., [0129], [0134]) (every transaction in a blockchain is time-stamped, which is useful for tracking interactions between participants and verifying various information contained in, or related to, a blockchain; The transaction record may likewise include additional data and metadata, such as a transaction identifier 812, a transaction payload 814, and a transaction timestamp)).
RE: Claim 14 (Currently Amended) Saxena et al. teaches the claimed:
14. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform ((Saxena et al., [0005], [0024], [0313]) (a processor; The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server; EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database ((Saxena et al., [0152], [0160], [0170], [0313], [0320]) (the blockchain exchange 948 may be implemented with permission and identity management controls to determine the degree to which data associated with the public 912 and private 932 blockchains can be respectively accessed by the private 924 and hosted 904 cognitive platforms; universal knowledge repositor implemented as a cognitive graph; data may be stored in a relational database management system; a composite electronic health record for the patient stored in a collection of electronically-stored patient healthcare information stored in a digital format; As an example, a CILS may process a combination of healthcare-related data and blockchain-associated data to generate a healthcare-related, blockchain-associated cognitive insight 1302 for business operations 1312 associated with the provision of a particular healthcare service));
wherein the report database includes health data ((Saxena et al., [0313]) (an EHR may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information));
wherein the at least one user device is operable to authorize sharing of selected health data with a third-party device via a graphical user interface (GUI), thereby creating selected permissible data ((Saxena et al., [0060], [0313]) (various embodiments, the application accelerators 306 include widgets, user interface (UI) components, reports, charts, and back-end integration components familiar to those of skill in the art; In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the health data exchange platform is operable to exchange the selected permissible data with the third-party device ((Saxena et al., [0146], [0313]) (permission controls typically associated with private blockchains can provide dynamic control over who can connect, send, receive and enact individual transactions, based upon any number of parameters that may not be available or implementable in public blockchains; In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes ((Saxena et al., [0049], [0151], [0278]) (a cognitive graph 228 refers to a representation of expert knowledge, associated with individuals and groups over a period of time, to depict relationships between people, places, and things using words, ideas, audio and images; nodes within the hosted or private universal graph contain one or more knowledge elements; each individual cognitive session graph that is associated with the user and stored in a repository of cognitive session graphs '1' through 'n' 1252 introduces edges that are not already present in the application cognitive graph)),
and a user attribute inference module; wherein the user attribute inference module includes an artificial intelligence module operable to identify that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute ((Saxena et al., [0032], [0165]) (Cognitive systems achieve these abilities by combining various aspects of artificial intelligence; an inferred 1008 cognitive learning style broadly refers to the use of inferred data by a CILS to perform a corresponding cognitive learning operation. In various embodiments the inferred data may include data inferred from the processing of source data. In certain embodiments, the source data may include data associated with one or more blockchains. In various embodiments, the inferred data may include concepts that are inferred from the processing of other concepts. In these embodiments, the inferred data resulting from the processing of the source data, the concepts, or a combination thereof, may result in the provision of new information that was not in the source data or other concepts)),
wherein the phenotype network is operable to automatically suggest a connection with another user, at least one phenotype, at least one medical entity, and/or at least one health status ((Saxena et al., [0039]) (Collaborative filtering 206, as used herein, broadly refers to the process of filtering for information or patterns through the collaborative involvement of multiple agents, viewpoints, data sources, and so forth. The application of such collaborative filtering 206 processes typically involves very large and different kinds of data sets, including sensing and monitoring data, financial data, and user data of various kinds. Collaborative filtering 206 may also refer to the process of making automatic predictions associated with predetermined interests of a user by collecting preferences or other information from many users; if person 'A' has the same opinion as a person 'B' for a given issue 'x', then an assertion can be made that person 'A' is more likely to have the same opinion as person 'B' opinion on a different issue 'y' than to have the same opinion on issue 'y' as a randomly chosen person));
wherein the health data exchange platform automatically generates a phenotype network for the at least one user ((Saxena et al., [0093], [0094]) (the insight/learning engine 330 is implemented to encapsulate a predetermined algorithm, which is then applied to a target cognitive graph to generate a result, such as a recommendation, a cognitive insight, a blockchain-associated cognitive insight, or some combination thereof. In certain embodiments, one or more such algorithms may contribute to answering a specific question and provide additional cognitive insights or recommendations. In these and other embodiments, the insight/learning engine 330 is implemented to perform insight/learning operations; the discover/visibility 430 component is implemented to provide detailed information related to a predetermined topic, such as a subject or an event, along with associated historical information; the historical information may be related to a particular industry sector, process, or operation, such as healthcare. In certain embodiments, the predict 431 component is implemented to perform predictive operations to provide insight into what may next occur for a predetermined topic. In various embodiments, the rank/recommend 432 component is implemented to perform ranking and recommendation operations to provide a user prioritized recommendations associated with a provided cognitive insight)).
Saxena et al. fails to explicitly teach, but Das et al. teaches the claimed:
and determine an inferred user attribute confidence value ((Das et al., [0041]) (There are other, more complicated methods for data imputation, including the filling of empty bins with the running average of the measurements of the relevant input, or inferring the missing value from a patient with a quantitatively similar trajectory of measurements)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the inferring of a missing value from a patient as taught by Das et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. with the motivation of deciding whether or not a drug will be effective based on underlying physiologic insult and whether or not the mechanism of action of the drug will treat that specific manifestation of the condition (Das et al. at [0002]).
Saxena et al. and Das et al. fail to explicitly teach, but Sobol et al. teaches the claimed:
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor ((Sobol et al., [0165], [0167]) (both the presently acquired data and any historical or baseline data (including those with significant temporal components as discussed herein) may be placed in memory 173B in order to provide appropriate signatures that correspond to the LEAP data for subsequent comparison or analysis purposes. the sensors 121 may be placed into three major groups for the acquisition of the other components of the LEAP data. the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like));
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the present and historically acquired environmental and physiological sensor data as taught by Sobol et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. and the inferring of a missing value from a patient as taught by Das et al. with the motivation of providing data-informed care insights for family members, nurses, doctors or other caregivers for patients suffering with adverse health conditions (Sobol et al. at [0002], [0008], [0261]).
RE: Claim 15 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
15. The system of claim 14, wherein the health data exchange platform includes at least one distributed ledger ((Saxena et al., [0126]) (the authentication is performed by a Registration Authority (RA) operating as a component of a Public Key Infrastructure (PKI). The resulting authentication may then be used as the basis for creating a set of digital credentials, such as a public/private key pair or digital certificate, which in turn can be used to perform various blockchain operations familiar to those of skill in the art)).
RE: Claim 16 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
16. The system of claim 15, wherein the health data is not stored on the at least one distributed ledger ((Saxena et al., [0313]) (an EHR broadly refers to the collection of electronically-stored patient healthcare information stored in a digital format. In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so i.e. health data is stored and accessed on enterprise-wide information systems)).
RE: Claim 17 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
17. The system of claim 14, wherein the health data exchange platform is operable to process a micropayment between the at least one user device and the third-party device ((Saxena et al., [0135]) (the transaction record may also contain a list of validated digital assets and instruction statements, such as transactions made, their associated financial amounts, and the addresses of the parties to those transactions)).
RE: Claim 19 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
19. The system of claim 14, wherein the permissions database stores links to one or more locations where the selected permissible data is stored on a distributed ledger ((Saxena et al., [0092]) (the bridging agent interprets a translated query generated by the query 426 component within a predetermined user context, and then maps it to predetermined nodes and links within a target cognitive graph)).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Pub. No. 2018/0165588 A1 (hereinafter “Saxena et al.”) in view of U.S. Patent Application Pub. No. 2019/0295703 A1 (hereinafter “Das et al.”), and U.S. Patent Application Pub. No. 2019/0209022 A1 (hereinafter “Sobol et al.), and further in view of U.S. Patent Application Pub. No. 2016/0028552 A1 (hereinafter “Spanos et al.).
RE: Claim 18 (Original) Saxena et al., Das et al., and Sobol et al. teach the claimed:
18. The system of claim 14,
Saxena et al., Das et al., and Sobol et al. fail to explicitly teach, but Spanos et al. teaches the claimed:
wherein the health data exchange platform is built on an Ethereum blockchain or a Bitcoin blockchain ((Spanos et al. [0068]) (A digital medical currency can be created, much like bitcoin, where doctors, nurses, or hospitals get paid small amounts of the currency for each block of information that is provided to the data base, or for providing the computing power necessary to solve for new blocks. This currency is then spent on accessing the database to read patient information. This system incentivizes more disclosure of patient information to the database because the currency that is provided is required to access the database later)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the digital medical currency for accessing a database to read patient data such as bitcoin within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al., the inferring of a missing value from a patient as taught by Das et al. and the present and historically acquired environmental and physiological sensor data as taught by Sobol et al. with the motivation of providing a system that allows for changes and updates to the rules or protocol governing the blockchain for updates (Spanos et al. at [0008]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Pub. No. 2018/0165588 A1 (hereinafter “Saxena et al.”) in view of U.S. Patent Application Pub. No. 2019/0209022 A1 (hereinafter “Sobol et al.).
RE: Claim 20 (Currently Amended) Saxena et al. teaches the claimed:
20. A system for exchanging health data including a phenotype network comprising: at least one user device and at least one remote server including a processor and a memory in network communication with a health data exchange platform ((Saxena et al., [0005], [0024], [0313]) (a processor; The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server; EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the at least one remote server includes a phenotype database, a report database, a recommendation database, and a permissions database ((Saxena et al., [0152], [0160], [0170], [0313], [0320]) (the blockchain exchange 948 may be implemented with permission and identity management controls to determine the degree to which data associated with the public 912 and private 932 blockchains can be respectively accessed by the private 924 and hosted 904 cognitive platforms; universal knowledge repositor implemented as a cognitive graph; data may be stored in a relational database management system; a composite electronic health record for the patient stored in a collection of electronically-stored patient healthcare information stored in a digital format; As an example, a CILS may process a combination of healthcare-related data and blockchain-associated data to generate a healthcare-related, blockchain-associated cognitive insight 1302 for business operations 1312 associated with the provision of a particular healthcare service));
wherein the report database includes health data ((Saxena et al., [0313]) (an EHR may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information));
wherein the at least one user device is operable to authorize sharing of selected health data with a third-party device via a graphical user interface (GUI), thereby creating selected permissible data ((Saxena et al., [0146], [0313]) (permission controls typically associated with private blockchains can provide dynamic control over who can connect, send, receive and enact individual transactions, based upon any number of parameters that may not be available or implementable in public blockchains; In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the health data exchange platform is operable to exchange the selected permissible data with the third-party device ((Saxena et al., [0060], [0313]) (various embodiments, the application accelerators 306 include widgets, user interface (UI) components, reports, charts, and back-end integration components familiar to those of skill in the art; In certain embodiments, an EHR can be shared between various caregivers who are authorized to access the information they contain. In various embodiments, and EHR can be accessed through network connected, enterprise-wide information systems or other information networks and exchanges that are authorized to do so));
wherein the health data exchange platform is operable to process a micropayment between the at least one user device and the third-party device ((Saxena et al., [0135]) (the transaction record may also contain a list of validated digital assets and instruction statements, such as transactions made, their associated financial amounts, and the addresses of the parties to those transactions));
wherein the phenotype network includes a plurality of user nodes, a plurality of phenotype or concept nodes, a plurality of edges connecting the plurality of user nodes and/or the phenotype or concept nodes ((Saxena et al., [0049], [0151], [0278]) (a cognitive graph 228 refers to a representation of expert knowledge, associated with individuals and groups over a period of time, to depict relationships between people, places, and things using words, ideas, audio and images; nodes within the hosted or private universal graph contain one or more knowledge elements; each individual cognitive session graph that is associated with the user and stored in a repository of cognitive session graphs '1' through 'n' 1252 introduces edges that are not already present in the application cognitive graph)),
and a user attribute inference module ((Saxena et al., [0032], [0165]) (Cognitive systems achieve these abilities by combining various aspects of artificial intelligence; an inferred 1008 cognitive learning style broadly refers to the use of inferred data by a CILS to perform a corresponding cognitive learning operation. In various embodiments the inferred data may include data inferred from the processing of source data. In certain embodiments, the source data may include data associated with one or more blockchains. In various embodiments, the inferred data may include concepts that are inferred from the processing of other concepts. In these embodiments, the inferred data resulting from the processing of the source data, the concepts, or a combination thereof, may result in the provision of new information that was not in the source data or other concepts)),
wherein the health data exchange platform receives a request from the at least one user device to generate a requested phenotype network based on one or more designated criteria ((Saxena et al., [0078]) (to configure the insight/learning engine 330 to provide access to predetermined outputs from one or more cognitive graph algorithms that are executing in the cognitive platform 310. In certain embodiments, the cognitive insight API 410 is implemented to subscribe to, or request, such predetermined outputs));
wherein the health data exchange platform includes an artificial intelligence module operable to automatically generate the requested phenotype network ((Saxena et al., [0093], [0094]) (the insight/learning engine 330 is implemented to encapsulate a predetermined algorithm, which is then applied to a target cognitive graph to generate a result, such as a recommendation, a cognitive insight, a blockchain-associated cognitive insight, or some combination thereof. In certain embodiments, one or more such algorithms may contribute to answering a specific question and provide additional cognitive insights or recommendations. In these and other embodiments, the insight/learning engine 330 is implemented to perform insight/learning operations; the discover/visibility 430 component is implemented to provide detailed information related to a predetermined topic, such as a subject or an event, along with associated historical information; the historical information may be related to a particular industry sector, process, or operation, such as healthcare. In certain embodiments, the predict 431 component is implemented to perform predictive operations to provide insight into what may next occur for a predetermined topic. In various embodiments, the rank/recommend 432 component is implemented to perform ranking and recommendation operations to provide a user prioritized recommendations associated with a provided cognitive insight)).
Saxena et al. fails to explicitly teach, but Sobol et al. teaches the claimed:
wherein the health data includes historical objective data collected from at least one body sensor and at least one environmental sensor ((Sobol et al., [0165], [0167]) (both the presently acquired data and any historical or baseline data (including those with significant temporal components as discussed herein) may be placed in memory 173B in order to provide appropriate signatures that correspond to the LEAP data for subsequent comparison or analysis purposes. the sensors 121 may be placed into three major groups for the acquisition of the other components of the LEAP data. the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like));
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the present and historically acquired environmental and physiological sensor data as taught by Sobol et al. within the method and system for providing healthcare-related, blockchain associated cognitive insights as taught by Saxena et al. with the motivation of providing data-informed care insights for family members, nurses, doctors or other caregivers for patients suffering with adverse health conditions (Sobol et al. at [0002], [0008], [0261]).
Response to Arguments
Applicant's arguments filed 11/05/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 11/05/2025.
In the remarks, Applicant argues in substance that:
Regarding the Double Patenting rejection of claims 1-20, Applicant argues that a terminal disclaimer has been filed;
Regarding the 112 rejection of claims 1-20, Applicant argues the currently amended claim limitations overcome the prior rejection;
Regarding the 101 rejection of claim 101, Applicant argues the claims are not directed to an abstract idea, and even if so, recite an improvement to technology and provides an inventive concept amounting to significantly more; and
Regarding the 102 rejection of claim 20, Applicant argues the amendments overcome the previously cited prior art;
Regarding the 103 rejection of claims 1-19, Applicant argues the amendments
In response to Applicant’s argument that (a) regarding the Double Patenting rejection of claims 1-20, Examiner is persuaded in view of applicant’s arguments and the currently amended limitations of the claims and, accordingly, the Double Patenting rejection has been withdrawn.
In response to Applicant’s argument that (b) regarding the 112 rejection of claims 1-20, Examiner is persuaded and has withdrawn the prior 112 rejection.
In response to Applicant’s argument that (c) regarding the 101 rejection of claims 1-20, Examiner respectfully disagrees.
First, Applicant argues that the claims are not directed to an abstract idea in view of Thales Visionix. See Remarks at pgs. 10-12. Examiner respectfully disagrees and submits that although the instant claims recite sensors, the claim recites an abstract idea e.g. identifying that an unknown, incomplete, and/or inaccurate user attribute for a particular user is present, determine an inferred user attribute corresponding to the unknown, incomplete, and/or inaccurate user attribute, and determine an inferred user attribute confidence value and generating a phenotype network for the at least one user based on a request with one or more designated criteria, which but for the recitation of generic computer components and machinery, the claim is directed to limitations that can practically be performed in the mind such that they fall under the Mental Process grouping of Abstract ideas. Examiner respectfully submits that the mere recitation of health data includes data historically collected from at least one body sensor and at least one environmental sensor does not preclude the claim for being directed to the abstract ideas recited i.e. generating a network via rules/conditions/permissions for exchanging health data, which is distinguishable from Thales Visionix. Next, Applicant argues in view of DDR Holdings and USPTO guidance examples arguing the claims are inextricably linked and rooted in computer technology. See Remarks at pgs. 12-15. Examiner respectfully disagrees and submits that the claims merely recite generic computer components i.e. a remote server including a processor, databases, sensors, and modules as tools, but the claim is directed to the recitation of the abstract idea as discussed above. Applicant argues that the claim is directed to overcoming the problem of “aggregating incomplete, inaccurate, and inconsistent data from multiple sources,” which is not a problem rooted in technology but the abstraction of data aggregation through data analysis i.e. a Mental Process. Examiner further notes that the abstract idea of Certain Methods of Organizing Human Activity could also be applied to the instant claims, through the managing personal behavior or interactions between people through following rules or instructions as the generating a network via rules/conditions/permissions for exchanging health data. Accordingly, the claim recites an abstract idea.
Second, Applicant argues under Step 2A, Prong Two that the claim recites an improvement to the functioning of a computer analogous to Example 47 and CardioNet LLC. See Remarks at pgs. 16-21. Examiner respectfully disagrees. Example 47 is rooted in solving the technical problem of proactively preventing network intrusions in real time. The instant claims in view of the present Application Specification do not analogously recite a technical improvement to a technical problem through “specialized hardware and structured databases,” but rather, the databases, sensors, and AI module are recited at a high level of generality such that they amount to merely using generic computer components and machinery using in their normal functions as tools to perform the limitations of the abstract idea. See (Application Specification [0064]-[0069], [0080], [0098], [00187], [00188], [00195])); See also MPEP 2106.05(f)(2). Unlike CardioNet that recites particular beat detectors and an improvement to the technical problem of cardiac monitoring technology, the instant claims invoke the body sensor and environmental sensor at a high level of generality, merely stating that he health data includes data historically collected from at least one body sensor and at least one environmental sensor. Further, the additional element of using the sensors amounts to are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Accordingly, the claim fails to recite an integration into a practical application under Step 2A, Prong 2.
Lastly, under Step 2B, Applicant argues that the claim recites significantly more than generic computer components to apply the judicial exception. See Remarks at pgs. 21-28. Examiner respectfully disagrees. The claim recites generic computer components (e.g. a processor, server, memory, databases, and AI module) and invokes machinery (body sensor and environmental sensor) at a high level used in its normal function to perform the limitations of the abstract idea. See MPEP 2106.05(f)(2). Further, the this additional element of using the claimed sensors amounts to are mere data gathering and output receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. That is, unlike the 2014 interim guidance reciting that the method could not be performed without a GPS sensor, the instant claims recite the body and environmental sensors as mere extra-solution activity using well-understood, routine, and conventional machinery in their normal function for mere data gathering and sending data over a network. Accordingly, the claim fails to recite significantly more under Step 2B.
Examiner respectfully maintains the 101 rejection as applied in the above Office Action.
In response to Applicant’s argument that (d) regarding the 102 rejection of claim 20, Examiner Applicant’s arguments, see Remarks at pgs. 28-30, with respect to the rejection of claim 20 under 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of secondary reference, Sobol et al., in obvious combination under 103.
In response to Applicant’s argument regarding the 103 rejection of claims 1-19, Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Examiner notes newly applied reference Sobol et al. as teaching the newly amended limitation in obvious combination as applied in the above Office Action.
Accordingly, Examiner respectfully maintains the 103 rejection of claims 1-19 and further claim 20 as applied in the above Office Action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220199208 A1 teaches a system and method of managing access of a user’s health information stored over a healthcare network (abstract);
US 20180337769 A1 teaches a digital currency micropayment, thus, may be used, at least in part, to monitor health of any appropriate and/or applicable infrastructure and/or associated system of IoT devices, even if certain audit trail information, such as data values, miner identities, etc. is encrypted (e.g., in a public blockchain, etc.) ([0026]); and
US 20180060496 A1 teaches a distributed ledger, such as a healthcare blockchain, employed to set, host and adjudicate permissions to access HIR (Abstract).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682