Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/23/2025 has been entered.
DETAILED ACTION
Notices to Applicant
This communication is a non-final rejection. Claims 1-18, as filed 10/23/2025, 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 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 based on the received one or more data parameters and the received one or more health parameters, wherein the personalized model includes a predefined criteria to reward the user (102) when at least one of the received one or more data parameters or the received one or more health parameters matches the predefined criteria; (mental process because generating a personalized model can be done by writing a linear regression on a piece of paper; mathematical concept)
secure the personalized model in a vault service to prevent unauthorized access; (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 using one or more microservices including a blockchain ledger that records consensus-verified updates to ensure data integrity; (mental process because using a blockchain ledger can be done by writing values on a piece of paper) and
generate the personalized and community-based recommendations based on the generated personalized model. (mental process because generating recommendations based on a personalized model can be done by thinking about the model and then thinking of a recommendation)
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 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])
--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:
--wherein the personalized model includes a predefined criteria to reward the user (102) when at least one of the received one or more data parameters or the received one or more health parameters matches the predefined criteria (“The artificial intelligence module 152 is located on top of the blockchain database,” [0119]; “It is an object of the present invention to provide a computer system that utilizes AWL, blockchain and cryptocurrency technologies that incentivize individuals to improve their health through rewarding them with set health goals that are linked to smart contracts. In one embodiment, health goals comprise lab values, exam results, adherence levels, compliance, and persistence,” [0029]; “The smart contract further ensures compliance between two individuals or organizations in relation to what they have agreed, such as between a physician and a patient, wherein the patient is rewarded if their health is improved,” [0135]);
--secure the personalized model in a vault service to prevent unauthorized access (“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])
--update the personalized model using one or more microservices including a blockchain ledger that records consensus-verified updates to ensure data integrity (“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]).
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, a smart contract, a wallet service, and a 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 10/23/2025 have been fully considered and are discussed below.
Regarding the subject matter ineligibility rejections, the examiner notes that the responses to arguments in the previous action are all still pertinent and are incorporated herein. Applicant argues that the claimed invention is not directed to a mental process (Step 2A Prong One) because the AI engine “generates personalized models using multidimensional user data collected from sensors and a wearable device” which is “computationally infeasible for a human and requires processor-executed pattern recognition and real-time computation.” Remarks pages 3-4. The examiner disagrees. A human with pen and paper can generate a simple model using multidimensional (i.e., data from multiple sources) by observing various sensors and thinking about a couple relationships or trends. Applicant’s bare assertion that this cannot be done mentally or with pen and paper is not persuasive.
Despite applicant’s argument that “[t]he vault service ensures encrypted, authenticated access to the models,” such features are not found in the claims or the specification. To the extent that the vault service goes beyond certain methods of organizing human activity, namely using access rules to limit who can read a particular piece of data, it amounts to mere instructions to implement the access rules with a computer. If the vault service went beyond an abstract idea generally implemented with a computer, the examiner would expect to see technical detail in the specification fleshing out technical details to that effect. The same reasoning applies to the blockchain ledger and microservices which are described at high levels and merely apply abstract ideas with computers.
Applicant argues that the claimed invention includes an unconventional arrangement because as in BASCOM because “the vault service ensures encrypted, authenticated access to the models, while the blockchain ledger records consensus-verified updates, ensuring immutable, time-stamped data with tamper-resistance across a distributed network.” Remarks page 4. The examiner disagrees because the vault service and blockchain ledgers simply amount to generic data processing by one or more general-purpose computers. The only detail set forth in the claims for the vault service is an intended benefit of the feature: “a vault service to prevent unauthorized access”. The only detail of the ledger is “a blockchain ledger that records consensus-verified updates to ensure data integrity.” If this combination were unconventional as in BASCOM, then the applicant should present evidence such as proof that preventing unauthorized access to a data model or using blockchain consensus to control a piece of data required solving technical problems. Instead, these limitations amount to instructions to use generic computing functions to implement the abstract ideas.
Applicant argues that “[t]he Al engine is deployed at an edge device, enabling the system to deliver personalized recommendations in real-time that ensures efficient decision making and provides accurate predictions/alerts based on personalized and community-based models. This technical improvement addresses the specific problem of efficient decision making and providing accurate predictions in distributed health monitoring systems, a problem that cannot be solved manually or with generic automation.” This is not persuasive because it has no nexus to the pending claims which do not recite an edge device and because the edge device described in the specification has no limiting definition that would require it be implemented with any particular device.
Applicant argues that the claimed invention amounts to significantly more than any abstract idea (Step 2B) because it includes a nonconventional arrangement. Remarks pages 5-6. This is not persuasive because Applicant appears to conflate 102/103 with eligibility. “Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.” MPEP 2106.05. The invention in BASCOM was not found eligible because it was novel and nonobvious. Rather, the claimed invention “Add[ed] a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content.” MPEP 2106.05(A). While filtering content in general did not confer eligibility in BASCOM, performing the filtering at a location remote from end-users was not the conventional technique for performing the filtering and thus amounted to an unconventional arrangement. There is nothing in the claimed invention here that is an additional element and amounts to an unconventional arrangement. The additional elements here are generic computer components that merely apply the abstract ideas.
Regarding the prior art rejections, Applicant’s arguments are moot in light of the new grounds of rejection containing Kutzko applied above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624