DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1-18, as filed 05/11/2026, are currently pending and have been considered below.
Foreign priority is generally acknowledged to INDIA 202221027968 which was filed 05/16/2022.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon and the rationale supporting the rejection would be the same under either status.
Claim Objections
Claim 11 is objected to for the following informality. The claim’s final “transmit” step should read “transmitting” for grammatical consistency.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
A system (110) for generating personalized and community-based recommendations, the system (110) comprising:
a processor (202); and a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions, which when executed by the processor (202), causes the processor (202) to: (additional element – general purpose computer applying the abstract idea)
receive one or more data parameters via one or more primary sensors communicatively coupled to the processor (202), wherein the received one or more data parameters are based on one or more inputs provided by a user (102) via a computing device (104); (additional element – insignificant extra-solution activity; mere data-gathering)
receive one or more health parameters from the user (102) via a wearable device (108), wherein the wearable device (108) is adaptively secured to the user (102) and connected to the processor (202) via a network (106); (additional element – insignificant extra-solution activity; mere data-gathering)
generate, via an artificial intelligence (Al) engine (112), (additional element – general purpose computer applying the abstract idea) a personalized model comprising a personalized digital twin model generated by training machine-learning parameters based on the received one or more data parameters and the received one or more health parameters; (mental process because generating a personalized model can be done by writing a linear regression on a piece of paper; mathematical concept. To the extent that the limitation is not abstract, it amounts to merely applying the idea with a computer.)
secure the personalized model in a vault service, wherein the personalized model is reusable when switching the computing device; (abstract idea – mental process or certain method of organizing human activity, namely, using access rules to restrict access to information; to the extent that the vault service requires hardware, it is an additional element amounting to a general purpose computer applying the abstract idea)
update the personalized model by invoking one or more microservices, wherein the one or more microservices include execution of a smart contract that updates a distributed ledger of a blockchain network; (mental process because using a blockchain ledger can be done by writing values on a piece of paper. To the extent that the limitation is not abstract, it amounts to merely applying the idea with a computer.) and
generate the personalized and community-based recommendations based on the generated personalized mode,. (mental process because generating recommendations based on a personalized model can be done by thinking about the model and then thinking of a recommendation) and
transmit the generated recommendations to the user (102) via the computing device (104) (additional element; insignificant extra-solution activity; data output).
Claim 1 is presented as an exemplary claim but the same analysis applies to the other claims 11 and 18.
Step 2A Prong One
The broadest reasonable interpretation of the italicized steps encompasses mental processes because a person can consider various information, generate a model, and generate recommendations based on the model. But for the “via an artificial intelligence (AI) engine” language, generating a personalized model could be performed by a care manager thinking about the needs and habits of a particular patient and is thus in the context of this claim analogous to steps a human could practicably perform mentally or with pen and paper.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claim 4-6, 9-10, 12-14, and 17 add the abstract idea of certain methods of organizing human activity by including micro services, blockchain networks, and distributed ledgers. Claim 8 adds another mental process because mapping a user to a model can be performed mentally or with pen and paper.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, a processor and a memory operatively couped to the processor and via an artificial intelligence (AI) engine amount to invoking computers as a tool to perform the abstract idea, see applicant’s specification as published [0061]-[0067], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, receiving data from sensors and computing devices amounts to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
generally link the abstract idea to a particular technological environment or field of use such as via an artificial intelligence (AI) engine, see MPEP 2106.05(h))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claims 2 and 3 invoke additional generic computing equipment as tools to perform the abstract idea such as functionally-recited sensors. Claim 7-8 and 15-16 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea such as functionally recited computer components. The “vault service” of claims 7 and 15 and the “identification service” of claims 8 and 16 amount to using general-purpose computers to perform portions of information processing that is part of the abstract ideas. Claim recites additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii), electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii), and/or storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ekambaram (US20190378431A1) in view of Zelocchi (US20220051276A1) and Kutzko (US20200273578A1).
Regarding claim 1, Ekambaram discloses: A system for generating personalized and community-based recommendations, the system comprising:
--a processor; and a memory operatively coupled with the processor, wherein said memory stores instructions, which when executed by the processor, causes the processor (“another embodiment of the invention or elements thereof can be implemented in the form of a system including a memory and at least one processor that is coupled to the memory and configured to perform noted method steps,” [0005]; FIG. 4) to:
--receive one or more data parameters via one or more primary sensors communicatively coupled to the processor, wherein the received one or more data parameters are based on one or more inputs provided by a user via a computing device (User Registers with Personal Profile Data 102 in FIG. 1 via a mobile software application including data received from wearables such as sleep and exercise data in [0017]);
--receive one or more health parameters from the user via a wearable device, wherein the wearable device is adaptively secured to the user and connected to the processor via a network (Monitoring User Deviations 108 in FIG. 1; “the software application can be based on one or more Internet of Things (IoT) systems (such as, for example, home and/or wearable sensors), which collect data pertaining to a user for monitoring,” [0018]; “Step 304 includes monitoring, based at least in part on data derived from one or more wearable sensors worn by the user, user activity. Monitoring the user activity can include monitoring one or more user exercise habits, user caloric intake, user stress level, and/or one or more user sleep patterns,” [0025]; monitoring 304 in FIG. 3);
--generate, via an artificial intelligence (Al) engine, a personalized model comprising a personalized digital twin model generated by training machine-learning parameters based on the received one or more data parameters and the received one or more health parameters (“the pattern matching algorithm 212 learns one or more correlations between components 202, 204, 206, 208, and 210, and generates a prediction 214 for new user data (given components 202, 204, 206, 208 and 210),” [0022]; personalized model in [0021]). The Examiner notes that the claimed digital twin is a personalized model in [0059] of the Specification as published: “This personalized model may act as a digital twin.” To the extent Applicant maintains that this limitation requires an express teaching of machine learning beyond Ekambaram’s learned correlations, that teaching is provided by Zelocchi as described below.
--generate the personalized (the Examiner notes that the specification provides no definition or even example of a community-based recommendation. Based on the plain meaning of these terms, the Examiner interprets it to be any recommendation in any way based on data from other users) recommendations based on the generated personalized model (“consider an example scenario wherein a recommendation is generated for a user to perform exercise for one hour every day. Using inputs such as the created context information, the user profile data, and the calculated percentage deviation(s), method 114 can determine that the user has recently undergone a surgery and/or accident that would make it difficult to successfully carry out the recommendation. In such a scenario, it may not be useful and/or effective to penalize the user for failing to carry out the recommendation, and as such, the method 114 can generate (and output to the user) a new and/or revised recommendation (such as to avoid exercise for a proscribed period of time (relevant to recovery form the recent surgery and/or accident),” [0021]; recommendations 302 in FIG. 3).
Ekambaram does not expressly disclose but Zelocchi teaches: generating community based recommendations (various community data sources 806 in FIG. 10 drive recommendations as in [0183]; “because the system includes an AI assisted machine learning algorithm, the system is monitored and updated continuously, providing information beyond user input, wherein such additional information is neither uploaded or recommended by a physical user,” [0233]; incorporating feedback from other users into the recommendations in [0173]) and updating the model with microservices (“The Machine Learning Algorithms 1202 also integrate bi-directionally with microservice 1208 and are influenced by the monitoring system 1210,” [0180]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the wearable-based recommendations of Ekambaram to include the community-based recommendations with microservices of Zelocchi because including data sources beyond the user himself would improve the accuracy and usefulness of the recommendations (see Zelocchi [0163] and [0173]).
Ekambaram discloses various incentives (e.g., “an insurance company providing monetary incentives (such as premium discounts) to a user based at least in part on the user's current premium” in [0020]) but does not expressly disclose that the incentive is part of the personalized model. Ekambaram does not expressly disclose but Kutzko teaches:
--secure the personalized model in a vault service, wherein the personalized model is reusable when switching the computing device (104) (“patients control their own healthcare data 120 and all of the healthcare data 120 is in one unified location on the blockchain database 113,” [0117]; “The healthcare information may be maintained as a continuously growing ledger or listing of the data which may be referred to as blocks, secured from tampering and revision,” [0096]; “When a patient joins the healthcare system of the present invention, they authorize the sharing of their healthcare data to the blockchain database 113, and the artificial intelligence module 152 has, by default, access to all healthcare data stored on the blockchain database 113,” [0123]). The Examiner notes that the reusability feature is a property of a secured model, not an active step that is met, e.g., by Kutzko’s unified blockchain location or Ekambaram’s cloud architecture which contemplates “one or more user devices” in [0018]. Applicant identifies no mechanism by which the claimed reusability differs from the ordinary consequence of storing the model in one unified network location or in the cloud.
--update the personalized model by invoking one or more microservices, wherein the one or more microservices include execution of a smart contract that updates a distributed ledger of a blockchain network (“As used herein, the term “blockchain” shall generally mean a distributed database that maintains a continuously growing ledger or list of records, called blocks, secured from tampering and revision using hashes. Every time data may be published to a blockchain database the data may be published as a new block,” [0081]; [0096]);
One of ordinary skill in the art before the effective filing date would have been motivated to expand the health recommendations of Ekambaram and Zelocchi to include the AI-integrated smart contracts, blockchain, and vault service of Kutzko because this would improve the accuracy and usefulness of the health recommendations while ensuring that the user data is secure and secure from tampering (Kutzko [0081] and [0123]).
Additionally, it can be seen that each element is taught by either Ekambaram, Zelocchi, or Kutzko. The community-based machine learning features of Zelocchi and the blockchain features of Kutzko do not affect the normal functioning of the elements of the claim which are taught by Ekambaram. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Kutzko and Zelocchi with the teachings of Ekambaram since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Regarding claim 2, Ekambaram does not expressly disclose but Zelocchi teaches: wherein the one or more primary sensors comprise at least one of: a temperature sensor, a blood pressure sensor, an oxygen saturation sensor, and a heart-rate sensor (“wearable devices 204 include a plurality of sensors 210 which sense for sudden falls, blood pressure, glucose levels, and body temperature,” [0139]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the profile registration with a mobile app of Ekambaram [0017] in combination with Zelocchi and Kutzko to include the wearables of Zelocchi because his make the inputted medical and habit information more complete and thus allow for more accurate and useful of the recommendations (see Zelocchi [0163] and [0173]).
Regarding claim 3, Ekambaram does not expressly disclose but Zelocchi teaches: wherein the wearable device (108) is configured with one or more secondary sensors that comprise at least one of: a temperature sensor, a blood pressure sensor, an oxygen saturation sensor, a heart rate sensor, a motion sensor, a camera, a global positioning system (GPS), and a microphone (“wearable devices 204 include a plurality of sensors 210 which sense for sudden falls, blood pressure, glucose levels, and body temperature,” [0139]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the wearable monitoring (108 in FIG. 1) of Ekambaram (i.e., The software application can also include capabilities to monitor user actions such as, for example, user exercise time, user caloric intake, user calorie burn, user stress level, user sleep patterns, user eating habits, etc) in combination with Zelocchi and Kutzko to include the wearables of Zelocchi because his make the inputted medical and habit information more complete and thus allow for more accurate and useful of the recommendations (see Zelocchi [0163] and [0173]).
Regarding claim 4, Ekambaram further discloses: wherein the processor (202) is configured with one or more micro services to provide inputs to the user (102) based on the generated personalized and community-based recommendations (“With respect to providing incentives, an example embodiment of the invention can include an insurance company providing monetary incentives (such as premium discounts) to a user based at least in part on the user's current premium,” [0020]).
Regarding claim 5, Ekambaram further discloses: wherein the one or more micro services comprise at least one of: a blockchain ledger and an internet of things (IoT) service (“the software application can be based on one or more Internet of Things (IoT) systems (such as, for example, home and/or wearable sensors), which collect data pertaining to a user for monitoring,” [0018]).
Regarding claim 6, Ekambaram further discloses: wherein the one or more micro services are configured to update the generated personalized model and aid in the generation of the personalized and community-based recommendations (“generating a user score based at least in part on comparing, for the user to that of one or more previous users, (i) the user-provided information, (ii) the deviation information, (iii) the one or more additional health-related recommendations, (iv) the one or more incentives related to carrying out the one or more additional health-related recommendations, and (v) the one or more penalties related to failing to carry out the one or more additional health-related recommendations,” [0004]).
Regarding claim 7, Ekambaram further discloses: wherein the processor (202) is configured with a vault service to secure the generated personalized model to enable the user (102) to access the vault service via the computing device (104) (various types of cloud architecture in [0061]-[0066] are interpreted as vault services; user accesses the “vault” via one or more user devices in [0018]; see also the teachings of Kutzko applied to claim 1).
Regarding claim 8, Ekambaram further discloses: wherein the processor (202) is configured with an identification service (IS) that enables a mapping between the user (102) and the generated personalized model (FIG. 2; “the pattern matching algorithm 212 can use such data, in conjunction with the above-noted inputs, to generate a pattern match with respect to a current user,” [0022]).
Regarding claim 9, Ekambaram does not expressly disclose but Kutzko teaches: wherein the processor (202) is communicatively coupled to a blockchain network that generates a reward based on the one or more data parameters and the generated personalized model (“The blockchain network also utilizes token governance rulesets based on crypto-economic incentive mechanisms that determine under which circumstances blockchain network transactions are validated and new blocks are created… providing of incentives to individuals or organizations,” [0142]; [0143]; [0121]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the wearable-based recommendations and incentives of Ekambaram in combination with Zelocchi and Kutzko to include the blockchain features of Kutzko because this would make the analysis more accurate and make the incentive transactions more secure (see Kutzko [0081] and [0123]).
Regarding claim 10, Ekambaram does not expressly disclose but Kutzko teaches: wherein the blockchain network comprises at least one of: a distributed consensus, the smart contract, a wallet service, and the distributed ledger (“Through the use of a peer-to-peer blockchain network 111 and a distributed timestamping server 300, a blockchain database 113 may be managed autonomously. Consensus ensures that the shared ledgers are exact copies, and lowers the risk of fraudulent transactions, because tampering would have to occur across many places at exactly the same time,” [0096]; smart contract in [0134]; virtual wallet in [0150]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand the wearable-based recommendations and incentives of Ekambaram in combination with Zelocchi and Kutzko to include the blockchain features of Kutzko because this would make the analysis more accurate and make the incentive transactions more secure (see Kutzko [0081] and [0123]).
Claims 11, 12, 13, 14, 15, 16, 17, and 18 are substantially similar to claims 1, 4, 5, 6, 7, 8, 9, and 1 (respectively) and are rejected with the same reasoning.
Response to arguments
Applicant's arguments filed 05/11/2026 have been fully considered and are not persuasive as discussed below.
Regarding the subject matter ineligibility rejections, the Examiner’s response to arguments dated 01/12/2026 are directly on-point regarding the current arguments, and are incorporated herein.
Applicant argues that the claimed invention is not directed to a mental process (Step 2A Prong One) because it requires generating a personalized digital twin model by training ML parameters, which Applicant characterizes as impracticable to perform in the human mind and as technical according to the USPTO guidance on AI inventions. Remarks pages 2-3. The Examiner disagrees. The claimed invention recites generating and training the model with no particular training algorithm, no technical improvement to machine learning, and no detail of how the parameters are trained. Per Applicant’s specification, the personalized digital twin model is a personalized model (i.e., (302) in FIG. 3; “This personalized model may act as a digital twin,” [0059]), and the generation of the personalized model and recommendations can be performed mentally or with pen and paper. Applicant’s arguments that this cannot be done mentally are bare assertions and import a complexity not required by the claim language. A claim that recites a ML limitation at a high level of generality may still recite an abstract idea where, as here, the claim covers performance in the mind but for the recitation of generic computer components when no improvement to the ML technology itself is claimed.
Applicant further argues that the receiving steps are not insignificant extra-solution activity because they rely on “wearable-derived health parameters” that are integral to the training and continuous improvement process. Remarks page 4. These steps merely gather parameters from sensors which are then fed into the abstract idea, and thus they are insignificant extra-solution activity as described in 2106.05(g). Unlike Thales, where a specific unconventional arrangement of inertial sensors and a method of using the raw data from the sensors solved a technical problem, the present claims merely recite “one or more primary sensors” and “a wearable device” which perform their ordinary data-collection functions. The asserted technical improvement lies in the analysis of the gathered data, not in any particular sensor or arrangement of sensors.
Applicant argues that the claimed invention integrates any abstract idea into a practical application (Step 2A Prong Two) because of the vault service and the smart contracts. Remarks pages 4-5. The claim recites that the model is “reusable when switching the computing device” and that the smart contract “updates the distributed ledger,” but recites only generic technical mechanisms for achieving these results. The specification provides only the intended benefits of these features rather than technical detail establishing an unconventional arrangement.
Applicant argues that the claimed invention amounts to significantly more than any abstract idea (Step 2B) because they amount to an unconventional arrangement. Remarks pages 6-7. Unlike Thales and Enfish, where technical problems associated with self-referential databases and configuration of inertial sensors, the present claims recite generic computing equipment performing its ordinary functions to implement the abstract idea.
Regarding the prior art rejections, Applicant argues that Ekambaram fails to teach the claimed personalized digital twin model generating by training machine-learning parameters. Remarks page 9. The Examiner disagrees because, as described above, Ekambaram’s pattern-matching algorithm learns from user data and generates a per-user prediction. This is a personalized model which fails under the BRI of “digital twin.” See [0059] of the Specification as published: “This personalized model may act as a digital twin.” Applicant appears to rely on a definition of “digital twin” that is narrower than the BRI of the term, particularly when informed by [0059] of the specification. Applicant never articulates what a “digital twin” requires that a trained personalized model lacks. Similarly, Applicant never describes what the claimed training of parameters requires that the learned correlations of Ekambaram lacks. The claim recite no particular training algorithms, loss functions, or architecture that would distinguish these analogous features.
Applicant argues that Zelocchi and Kutzko fail to teach updating the personalized model (i.e., digital twin) using microservices to execute a smart contract that updates a distributed ledger. Remarks pages 10-11. Claim 1, as written, has two relevant features that are met by the cited combination: (i) updating a model by invoking microservices and (ii) the microservices “include[ing] execution of a smart contract” that “updates a distributed ledger”. Regarding (i), Zelocchi meets this limitation by bi-directionally integrating the ML algorithms with a microservice in [0180]. For (ii), Kutzko describes updating a blockchain ledger in [0081] to account for contract creation and execution in [0096]-[0098]. If Applicant intends the smart contract to be the mechanism of updating the model, the claim must be amended accordingly.
Applicant argues that Kutzko secures healthcare data rather than a trained model reusable across devices. Remarks pages 11-12. While Kutzko does not use the term “vault service”, the specification gives this term no special meaning and instead uses the term to describe an intended function of the storage. The BRI of a vault service is any service that securely stores something. Kutzko’s data having “one unified location on the blockchain database” in [0117] that is “secured from tampering and revision” in [0096] with patient-authorized access control in [0123] securely stores the data and thus amounts to a vault service. The reusability feature is a property of a secured model, not an active step that is met, e.g., by Kutzko’s unified blockchain location or Ekambaram’s cloud architecture which contemplates “one or more user devices” in [0018]. Where the prior art is capable of performing the claimed function, the functional limitation is met. MPEP 2114. Applicant identifies no mechanism by which the claimed reusability differs from the ordinary consequence of storing the model in one unified network location or in the cloud.
Applicant argues that the motivation to incorporate Kutzko’s blockchain relies on impermissible hindsight. Remarks page 12. This is not persuasive because the motivation to combine is drawn expressly from Kutzko’s own teaching that securing the ledger prevents “tampering and revision” in [0096]. A POSITA building the Ekambaram/Zelocchi system in which the personalized model is valuable and continuously updated would recognize that Kutzko’s secure ledger would prevent costly tampering and revision. This motivation comes directly from the references rather than from Applicant’s specification.
Conclusion
Prior art that is made of record but is not relied upon for any rejection includes Mohammed (US-20210219910-A1) and Schiatti (US-20210067339-A1). Mohammed discloses a “whole body” (Title) digital twin in which “biosignals” are collected from a patient, input into an ML model, and used to generate personalized recommendations (Abstract). Schiatti discloses a federated smart contract stored on a blockchain where the model updates are recorded on a distributed ledger (Abstract).
THIS ACTION IS MADE FINAL. 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 extension fee 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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/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624
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