DETAILED ACTION
Claims 1-20 are presented for examination
This office action is in response to submission of application on 14-DECEMBER-2022.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 13-FEBURARY-2026 in response to the non-final office action mailed 17-NOVEMBER-2025 has been entered. Claims 1-20 remain pending in the application.
With regards to the claim objection of claim 20, the amendment to claim 20 has rendered the objection overcame.
With regards to the 101 rejection, the rejection to claim 1 has not been overcome by the applicant’s amendments. Despite applicant’s amendments, claim 1 still remains rejected under 35 U.S.C. 101 on the basis of being an abstract idea.
With regards to the 103 rejections, the applicant’s amendments to the claims have not overcome the rejections to claims 1-20 as newly added prior art sufficiently teaches the newly added limitations of the amended claims.
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
an abstract idea (Abstract Idea) without significantly more.
Regarding claim 1, in Step 1 of the 101 analysis set forth in MPEP 2106, the claim recites a system for determining a variable. A system is one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101 analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, covers
a mental process but for recitation of generic computer components:
generating a set of contextual profiles for the first subset of the plurality of users based on the analyte measurements and the psychographic data, the set of contextual profiles including psychographic features and analyte-derived features; (one can mentally generate a set of data corresponding to a set of users based on a dataset as a process of simply evaluating the data and making a determination based of the data)
determining that contextual data for a second subset of the plurality of users is incomplete or not available; (one can mentally determine that a dataset is lacking data or is incomplete as a process of simply evaluating the data and making a determination based of the data)
Calculating one or more confidence scores based on the generated values of the imputed psychographic features for the second subset of the plurality of users; (one can mentally calculate a score corresponding to a set of data based on a dataset as a process of simply evaluating the data and making a determination based of the data)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform a method including: (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h))
collecting contextual data for a first subset of a plurality of users, where in the contextual data comprises analyte measurements obtained from an analyte monitoring device and psychographic data; (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
For each psychographic feature included in the set of contextual profiles, training one or more imputation models based on the contextual data for the first subset of the plurality of users to develop a corresponding imputed psychographic feature for the second subset of the plurality of users, thereby developing imputed psychographic features for the second subset of the plurality of users; (In step 2A prong 2 training a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
generating the values of the imputed psychographic features for the subset of the plurality of users using the one or more imputation models; (In step 2A prong 2 generating data is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.)
and initiating a user interface to request user input to validate at least one of the imputed psychographic features when at least one of the one or more confidence scores is below a threshold (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g))
Since the claim does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the additional elements (iv) recite generally linking the use of the judicial exception to a particular technological environment or field of use, (v) recites mere data gathering, (vi) and (vii) recites a mere application of a computer tool, which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 2 recites The non-transitory computer readable medium of claim 1, wherein: the contextual data for the first subset of the plurality of users corresponds to psychographic data for the first subset of the plurality of users; the first set of contextual profiles for the first subset of the plurality of users corresponds to a first set of psychographic profiles for the first subset of the plurality of users; the contextual data for the second subset of the plurality of users corresponds to psychographic data for the second subset of the plurality of users; and the second set of contextual profiles for the second subset of the plurality of users corresponds to a second set of psychographic profiles for the second subset of the plurality of users. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites The non-transitory computer readable medium of claim 1, wherein the method further comprises performing an exploration-exploitation phase by: dividing the plurality of users into an exploration subset of users and an exploitation subset of users; (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) randomly assigning at least one user interaction policy to each of the exploration subset of users; (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) and determining at least one user interaction policy for each of the exploitation subset of users using one or more contextual models trained using contextual data corresponding to the exploitation subset of users, wherein the contextual data comprises at least some of the first set of contextual profiles and the second set of contextual profiles. (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 4 recites The non-transitory computer readable medium of claim 3, wherein the exploration-exploitation phase is further performed by: receiving user feedback telemetry from the exploitation subset of users, wherein the feedback telemetry provides information regarding effectiveness of the at least one user interaction policy assigned to each user of the exploitation subset of users. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Further, claim 5 recites The non-transitory computer readable medium of claim 4, wherein at least one of the one or more imputation models or at least one of the contextual models is retrained using the user feedback telemetry. (In step 2A prong 2 training a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 6 recites The non-transitory computer readable medium of claim 3, wherein the exploration-exploitation phase is further performed by: measuring outcomes associated with the exploitation subset of users, wherein the measured outcomes provide information regarding effectiveness of the at least one user interaction policy assigned to each user of the exploitation subset of users. (In step 2A, prong 1, this recites an abstract idea but for recitation of generic computer components which is not indicative of integration into a practical application.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 6, and thereby incorporates the limitations of, and corresponding analysis applied to claim 6. Further, claim 7 recites The non-transitory computer readable medium of claim 6, wherein at least one of the contextual models is retrained using the measured outcomes. (In step 2A prong 2 training a model is a mere application of a computer tool (M.L. Model), which is not indicative of integration into a practical application. In step 2B, merely applying a computer tool is not indicative of significantly more.) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further, claim 8 recites The non-transitory computer readable medium of claim 1, wherein at least one of the contextual models is a contextual multi-armed bandit model. (Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 9-16, they comprise of limitations similar to those of claims 1-8 and are therefore rejected for similar rationale. Regarding claims 17-20, they comprise of limitations similar to those of claims 1-4 and are therefore rejected for similar rationale.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-4, 6, 8, 9-12, 14, 16, 17-20 is rejected under 35 U.S.C. 103 as being unpatentable over WANG (U.S. Pub. No. US 20180268073 A1) in view of CHU (U.S. Pub. No. US 20130226842 A1) in view of LYKE (U.S. Pub. No. US 11745058 B2) in view of GUGGILLA (U.S. Pub. No. US 20200073882 A1)
Regarding claim 1, WANG substantially teaches the claimed invention, including:
A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to perform a method including: collecting contextual data for a first subset of a plurality of users; ([0015] A first strategy involves the use of selection rules that consider a specific targeted content, and filters through the available observable user information to predict a user (or more than one users) that may enjoy the specific targeted content. A predictor is created as a selection rule that selects users predicted to enjoy the specific targeted content based on the analyzed observable user information. For example, a first predictor may describe a first selection rule that states all females between ages sixteen and twenty one will enjoy advertisement content offering a sale at a teen clothing store. (i.e. contextual data) The advertisement content may be a standalone offering from the teen clothing store, or part of an advertisement campaign promoted by the teen clothing store. Then users that satisfy the first selection rule may be grouped into a first user set created by the first predictor.)
While WANG does teach generating contextual data for a plurality of users, it does not explicitly teach:
Wherein the contextual data comprises analyte measurements obtained from an analyte monitoring device and psychographic data
However, in analogous art that similarly handles user context data, LYKE teaches:
Wherein the contextual data comprises analyte measurements obtained from an analyte monitoring device and psychographic data (((Col 13, line 28-38) In alternative embodiments, user identifying information may be used e.g., to improve accuracy. For example, a first set of profiles may be generated from anonymized user workout data for users that do not want to release sensitive information and a second profile may be generated from private user workout data for users that waive their privacy. The second profile may offer additional benefits and/or better coaching based on the improved specificity. For example, location and/or demographics (age, ethnicity, sex) information may provide better psychographic profiling useful for niche motivational messaging.) (Col 21, lines 6-24) Other examples of health parameter data may include data that the particular device 550 is configured to collect (such as athletic activity, biometric information, and environmental data). For example, an activity tracking device may be configured to collect activity data such as steps taken, distance traveled, rate or pace of a run, and/or flights of stairs climbed, etc.; a heart rate monitor may be configured to collect heartbeat data; a sleep tracking device collects data relating to how much time a user/wearer spends sleeping; a nutrition tracking device collects data relating to food and drinks consumed by a user; a smart scale collects data relating to a body weight, body fat percentage, and/or body mass index (BMI), etc. Furthermore, a smartwatch and/or smartphone, may be utilized as an activity tracking device, a heart rate monitor, a sleep tracking device, and/or a nutrition tracking device. The user device 550 may comprise any of the foregoing types of devices and/or may receive collected data from a first device at one or more applications running on the user device 550.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with LYKE‘s psychographic data and, with WANG‘s, user context data sets, with a reasonable expectation of success, sets of data that include psychographic profiling, as in LYKE, used on user data sets, as found in WANG. A person of ordinary skill would have been motivated to improve feedback (LYKE, Col 2, lines 39-48).
generating a set of contextual profiles for the first subset of the plurality of users based on the analyte measurements and the psychographic data, the set of contextual profiles including psychographic features and analyte-derived features; ([0015] A first strategy involves the use of selection rules that consider a specific targeted content, and filters through the available observable user information to predict a user (or more than one users) that may enjoy the specific targeted content. A predictor is created as a selection rule that selects users predicted to enjoy the specific targeted content based on the analyzed observable user information. For example, a first predictor may describe a first selection rule that states all females between ages sixteen and twenty one will enjoy advertisement content offering a sale at a teen clothing store. The advertisement content may be a standalone offering from the teen clothing store, or part of an advertisement campaign promoted by the teen clothing store. Then users that satisfy the first selection rule may be grouped into a first user set created by the first predictor. (i.e. profile, in this case, the collection of users that holds user relevant data does meet the definition of a ‘profile’.))
While WANG, as modified by LYKE, does teach collecting context data and making a user profile for a plurality of users, it does not explicitly teach:
determining that contextual data for a second subset of the plurality of users is incomplete or not available; for each psychographic feature included in the set of contextual profiles training one or more imputation models based on the contextual data for the first subset of the plurality of users to develop a corresponding imputed psychographic feature for the second subset of the plurality of users, thereby developing imputed psychographic features for the second subset of users; generating values of the imputed psychographic features for the second subset of the plurality of users using the one or more imputation models;
However, in analogous art that similarly handles data sets, CHU teaches:
determining that contextual data for a second subset of the plurality of users is incomplete or not available; ( [0032] FIG. 2 illustrates, in a flow diagram, missing value imputation for a small data source in accordance with certain embodiments. In certain embodiments, a small data source is a single data source. Control begins at block 200 with the missing value imputation system 110 receiving data 202 from a data source. The data 202 is input to block 204 and block 208. ) for each psychographic feature included in the set of contextual profiles training one or more imputation models based on the contextual data for the first subset of the plurality of users to develop a corresponding imputed psychographic feature for the second subset of the plurality of users, thereby developing imputed psychographic features for the second subset of users; ([0032] In block 204, the missing value imputation system 110 builds an imputation model 206 based on target variable information.) generating values of the imputed psychographic features for the second subset of the plurality of users using the one or more imputation models; ([0032] The imputation model 206, in addition to the data 202, is input to block 208. In block 208, the missing value imputation system 110 imputes missing values and outputs completed data 210.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with CHU‘s imputation and, with WANG‘s, as modified by LYKE, user context data sets, with a reasonable expectation of success, a method that imputes missing data from a data set, as in CHU, used to complete a second user data set, as found in WANG, as modified by LYKE. A person of ordinary skill would have been motivated to increase efficiency (CHU [0003]).
While WANG, as modified by LYKE and CHU, does teach imputing data, it does not explicitly teach:
Calculating one or more confidence scores based on the generated values of the imputed psychographic features for the second subset of the plurality of users; and intiating a user interface to request user input to validate at least one of the imputed psychographic features when at least one of the one or more confidence scores is below a threshold.
However, in analogous art that similarly handles imputed data, GUGGILLA teaches:
Calculating one or more confidence scores based on the generated values of the imputed psychographic features for the second subset of the plurality of users; ([0046] With continued reference to FIG. 3 and the sub-process-1 that is implemented by the entity and relation annotator 102, for each predicted entity, the entity and relation annotation model 106 may determine a confidence score in terms of predicted probabilities.) and initiating a user interface to request user input to validate at least one of the imputed psychographic features when at least one of the one or more confidence scores is below a threshold. ([0055] Further, the entity and relation annotator 102 may generate another inquiry (e.g., to a subject matter expert) for verification of the entity and the relation that respectively include the entity confidence score and the relation confidence score that are respectively less than the entity confidence score threshold and the relation confidence score threshold. [0082] Referring to FIG. 4, at 400, an input set of documents may be passed through the entity and relation annotation model 106 at 402 developed using machine learning and deep learning techniques as disclosed herein to extract entities along with the confidence scores for each entity. The output at 404 may be fed at 406 to a user interface system, where a user may correct any low confidence and conflicting entities.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with GUGGILLA‘s confidence calculation and, with WANG‘s, as modified by LYKE and CHU, user context data sets, with a reasonable expectation of success, a method that calculates a confidence score and prompts a user for correction if its low, as in GUGGILLA, using user context data, as found in WANG, as modified by LYKE and CHU. A person of ordinary skill would have been motivated to improve data useability (GUGGILLA [0019]).
Regarding claim 2, GUGGILLA further teaches:
The non-transitory computer readable medium of The non-transitory computer readable medium of wherein the user interface is initiated to request user input to validate an imputed psychographic feature only when a confidence score associated with the imputed psychographic feature is below the threshold. ([0055] Further, the entity and relation annotator 102 may generate another inquiry (e.g., to a subject matter expert) for verification of the entity and the relation that respectively include the entity confidence score and the relation confidence score that are respectively less than the entity confidence score threshold and the relation confidence score threshold. [0082] Referring to FIG. 4, at 400, an input set of documents may be passed through the entity and relation annotation model 106 at 402 developed using machine learning and deep learning techniques as disclosed herein to extract entities along with the confidence scores for each entity. The output at 404 may be fed at 406 to a user interface system, where a user may correct any low confidence and conflicting entities.)
Regarding claim 3, WANG further teaches:
The non-transitory computer readable medium of claim 1, wherein the method further comprises performing an exploration-exploitation phase by: dividing the plurality of users into an exploration subset of users and an exploitation subset of users; ([0060] The MAB problem is a known model for studying the exploration vs. exploitation trade-off in sequential decision making. The MAB problem has been presented as a MAB slot machine including a plurality of arms, where each arm has a different probability of resulting in a win, and the basic solution to the MAB problem is to select the arm having the best probability of resulting in a win. [0062] Under the MAB method, each user set from the user set proposal 500 represents an arm within the MAB slot machine, where each pull of an arm on the MAB slot machine is allocated its own unique predicted likelihood the user will enjoy the targeted content. Each impression of the targeted content to the users within the user set proposal 500 is considered a pull of the respective arm, and the corresponding conversion performance (i.e., reward) of the impression may be recorded and used to update the predicted likelihood the user will enjoy the targeted content for each of the user sets. (A MAB works by splitting a dataset between exploitation and exploration subsets. The rewards in this case are being given to the exploitation))randomly assigning at least one user interaction policy to each of the exploration subset of users; ([0060] The MAB problem is a known model for studying the exploration vs. exploitation trade-off in sequential decision making. The MAB problem has been presented as a MAB slot machine including a plurality of arms, where each arm has a different probability of resulting in a win, and the basic solution to the MAB problem is to select the arm having the best probability of resulting in a win. Each time an arm is pulled (i.e., played), a random reward is generated according to a fixed, sometimes unknown, distribution between [0, 1] (win is a 1, loss is a 0). The random reward from playing the arm is independent and identically distributed, and independent of the plays of the other arms. The random reward is observed and fed into the fixed distribution for subsequent plays of the arms. (in a mab, the reward is determined by random selection of a ‘policy’ or ‘rule’. This is implicit.)) and determining at least one user interaction policy for each of the exploitation subset of users using one or more contextual models trained using contextual data corresponding to the exploitation subset of users, wherein the contextual data corresponding to the exploitation subset of users comprises at least some of the set of contextual profiles and at least some of the generated values of the imputed psychographic features for the second subset of the plurality of users. ([0066] When the user is determined to qualify for the selected user set, the targeted content is presented to the user on the website (608). Following the presentation of the targeted content to the user on the website, the selection tool updates an impression count being monitored for the targeted content and the user (609). The impression count monitors a number of times the targeted content has been presented to the user on the website, or in some embodiments, other websites that are O&O by the common website publisher. Following the presentation of the targeted content to the user on the website, the selection tool monitors the user's interaction with the targeted content to determine whether the user interacts with the targeted content (610). The interactions may be in the form of the user clicking on the targeted content, or otherwise converting on the targeted content. The interaction monitoring may generally be described in a click count updated by the selection tool.)
Regarding claim 4, WANG further teaches:
The non-transitory computer readable medium of claim 3, wherein the exploration-exploitation phase is further performed by: receiving user feedback telemetry from the exploitation subset of users, wherein the user feedback telemetry provides information regarding effectiveness of the at least one user interaction policy assigned to each user of the exploitation subset of users. ([0066] When the user is determined to qualify for the selected user set, the targeted content is presented to the user on the website (608). Following the presentation of the targeted content to the user on the website, the selection tool updates an impression count being monitored for the targeted content and the user (609). The impression count monitors a number of times the targeted content has been presented to the user on the website, or in some embodiments, other websites that are O&O by the common website publisher. Following the presentation of the targeted content to the user on the website, the selection tool monitors the user's interaction with the targeted content to determine whether the user interacts with the targeted content (610). (i.e. feedback telemetry) The interactions may be in the form of the user clicking on the targeted content, or otherwise converting on the targeted content. The interaction monitoring may generally be described in a click count updated by the selection tool.
[0067] When the user is determined not to qualify for the selected user set, the targeted content is withheld from being presented to the user on the website (611).)
Regarding claim 6, WANG further teaches:
The non-transitory computer readable medium of claim 3, wherein the exploration-exploitation phase is further performed by: measuring outcomes associated with the exploitation subset of users, wherein the measured outcomes provide information regarding effectiveness of the at least one user interaction policy assigned to each user of the exploitation subset of users. ([0061] An algorithm representing the MAB problem looks to decide which arm to play for each arm pulling step or time, t, based on the previously recorded t−1 arm pulling outcomes. When μ.sub.i represents the expected reward for an arm i, the expected total reward in time T may be: E[Σ.sub.t=1.sup.Tμ.sub.i(t)], where i(t) is the arm being pulled at time t, and the expectation is over the random choices of i(t). Another representation of the MAB problem is measured as an expected total regret, where the regret is the amount lost because of not playing an optimal arm in each step. The total expected regret in time T is given by: E[R(T)]=E[Σ.sub.t=1.sup.T(μ*−μ.sub.i(t))], where μ*=max.sub.iμ.sub.i.
[0062] Under the MAB method, each user set from the user set proposal 500 represents an arm within the MAB slot machine, where each pull of an arm on the MAB slot machine is allocated its own unique predicted likelihood the user will enjoy the targeted content.)
Regarding claim 8, WANG further teaches:
The non-transitory computer readable medium of claim 3, wherein at least one of the one or more contextual models is a contextual multi-armed bandit model. (0059] When the warm start scenario is implemented, the selection tool selects one of the user sets created from the predictors shown in, for example, the user set proposal 500 (606). The selection tool selects the user set according to a known selection strategy for a multi-armed bandit (MAB) problem such as the Thompson Sampling method.
[0060] The MAB problem is a known model for studying the exploration vs. exploitation trade-off in sequential decision making.)
Regarding claims 9-12 and 17-20, they comprise of limitations similar to those of claims 1-4 and are therefore rejected for similar rationale. Regarding claim 14, it comprises of limitations similar to those of claim 6 and is therefore rejected for similar rationale. Regarding claim 16, it comprises of limitations similar to those of claim 8 and is therefore rejected for similar rationale.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over WANG (U.S. Pub. No. US 20180268073 A1), CHU (U.S. Pub. No. US 20130226842 A1) LYKE (U.S. Pub. No. US 11745058 B2) GUGGILLA (U.S. Pub. No. US 20200073882 A1) in further view of CHANDRASHEKAR (U.S. Pub. No. US 20220147934 A1)
Regarding claim 5, while WANG, as modified by CHU, does teach claim 4, which claim 5 is dependent upon, it does not explicitly teach:
The non-transitory computer readable medium of claim 4, wherein at least one of the one or more imputation models or at least one of the one or more contextual models is retrained using the user feedback telemetry.
However, in analogous art that similarly trains a model, CHANDRASHEKAR teaches:
The non-transitory computer readable medium of claim 4, wherein at least one of the one or more imputation models or at least one of the contextual models is retrained using the user feedback telemetry. ([0044] The management system may utilize the feedback to retrain the context model, the intelligent automation model, and/or the insights model, as described below.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with CHANDRASHEKAR‘s retraining and, with WANG‘s, as modified by CHU, LYKE, and GUGGILLA, model, with a reasonable expectation of success, a method for retraining a model using feedback, as in CHANDRASHEKAR, used on the imputation model using the feedback data, as found in WANG, as modified by CHU, LYKE, and GUGGILLA. A person of ordinary skill would have been motivated to improve resource management (CHANDRASHEKAR [0013]).
Regarding claim 13, it comprises of limitations similar to those of claim 5 and is therefore rejected for similar rationale.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over WANG (U.S. Pub. No. US 20180268073 A1), CHU (U.S. Pub. No. US 20130226842 A1) LYKE (U.S. Pub. No. US 11745058 B2) GUGGILLA (U.S. Pub. No. US 20200073882 A1) in further view of KAWAGUCHI (U.S. Pub. No. US 20200007540 A1)
Regarding claim 7, while WANG, as modified by CHU, does teach claim 6, which claim 7 is dependent upon, it does not explicitly teach:
The non-transitory computer readable medium of claim 6, wherein at least one of the one or more contextual models is retrained using the measured outcomes.
However, in analogous art that similarly trains a model, KAWAGUCHI teaches:
The non-transitory computer readable medium of claim 6, wherein at least one of the contextual models is retrained using the measured outcomes. ([0268] Furthermore in embodiments, the machine learning module 2136 may use outcomes associated with those predictions or classifications (e.g., user provided outcomes) to reinforce/retrain the models.)
It would have been obvious to a person skilled in the art before the effective filing date of the
invention to have combined with KAWAGUCHI‘s retraining and, with WANG‘s, as modified by CHU, LYKE, and GUGGILLA, model, with a reasonable expectation of success, a method for retraining a model using outcomes, as in KAWAGUCHI, used on the computation model using the outcome data, as found in WANG, as modified by CHU, LYKE, GUGGILLA. A person of ordinary skill would have been motivated to lower the cost (KAWAGUCHI [0003]).
Regarding claim 15, it comprises of limitations similar to those of claim 7 and is therefore rejected for similar rationale.
Response to Arguments
Applicant’s arguments filed 13-FEBURARY-2026 have been fully considered, but they are found to be non-persuasive
With regards to the applicant’s remarks regarding the 101 rejection towards an abstract idea, the applicant argues that the amendments to claim 1 overcome the rejection
Step 2A, Prong 1 requires determining whether the claims recite a judicial exception. The pending claims do not recite a mathematical concept, a fundamental economic practice, or a mental process performed in the human mind. Rather, the claims recite specific operations performed by a computing system using analyte measurements obtained from an analyte monitoring device, feature-specific imputation models, confidence scoring, and conditional user-interface initiation. Accordingly, the claims are not directed to an abstract idea.
With regards to this argument, while the claims may use a computing system and analyte measuring device, that does not change the processes claimed such as generating data, determining a result, and calculating a value. Simply performing these abstract ideas with a computing system is not enough to pull these abstract ideas into the practical application.
Step 2A, Prong 2 further supports eligibility. Even assuming, arguendo, that an abstract idea is implicated, the claims integrate any such concept into a practical application. The claims recite a specific technological solution in which imputed psychographic features derived from physiological analyte data are selectively validated via a user interface that is initiated when a confidence score falls below a threshold. This conditional, confidence-gated interaction improves system operation by reducing computational and network resource usage associated with unnecessary user interaction while maintaining accuracy of inferred features, and thus represents an improvement to computer-implemented data processing systems. Under current USPTO guidance, such integration into a practical application renders the claims patent eligible.
With regards to this argument, while the improvement may be intended to be the confidence gated interaction, it is not clearly claimed within the claim limitation. The claim must, itself, claim the improvement to the invention: MPEP 2106(a)(II), the claims themselves have to show the improvement as well: “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. “ The improvement cannot simply lay in the abstract idea itself. Further, there is no cited paragraph from the specification linking the user interface as part of the improvement of the invention.
With regards to the applicant’s remarks regarding the 103 rejection in the non-final action, the applicant argues that the prior art does not teach the newly amended claims 1, 9, and 17. The examiner acknowledges this argument and has adjusted the prior art of WANG, CHU, and LYKE to disclose the newly added limitations while adding newly found prior art GUGGILLA. Further, the examiner has adjusted all dependent claims accordingly.
Conclusion
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 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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/SKIELER ALEXANDER KOWALIK/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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