DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Nonfinal Office Action is in response to the RCE filed 12/22/2025. Claims 1, 2, 4-15, 21 and 22 are amended. Claims 16-20 are cancelled. Claims 1, 2, 4-15, 21 and 22 are pending and considered herein.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/22/2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 4-15, 21 and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the acronym “PPG” is not described in the claims and is indefinite. For purposes of examination, it will be defined as “photoplethysmogram” (See Specification at Para. [0037]) and related sensors.
Appropriate correction is required.
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, 2, 4-15, 21 and 22 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract elements are not emboldened:
A system, comprising: a wearable ring device configured to measure physiological data from a user, the wearable ring device comprising: a ring-shaped housing having an inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user; one or more temperature sensors arranged along or within the inner curved surface of the ring-shaped housing; one or more PPG sensors arranged on the inner curved surface of the ring-shaped housing; a curved battery disposed at least partially within the ring- shaped housing, the curved battery electrically coupled with the one or more temperature sensors, and the one or more PPG sensors; and a communication module electrically coupled with one or more processors, the communication module configured to transmit the physiological data; a user device communicatively coupled to the wearable ring device; and one or more processors communicatively coupled with the wearable ring device, the user device, and the communication module, the one or more processors configured to: measure, via the wearable ring device during a first time interval, a first set of physiological data of the user; generate a first physiological metric based at least in part on the first set of physiological data; predict, via the one or more processors using a first machine learning model, a future physiological metric for the user for a second time interval based at least in part on a set of feature vectors, wherein the set of feature vectors are based at least in part on the first set of physiological data; generate, via the one or more processors using a second machine learning model, a predictive weighting for each feature vector of the set of feature vectors, wherein the predictive weighting for each feature vector indicates a relative contribution of the respective feature vector on a difference between the first physiological metric and the future physiological metric; generate a plurality of groups of feature vectors, wherein each group of the plurality of groups corresponds to a user-recognizable category of a plurality of user-recognizable categories, wherein each user-recognizable category is associated with a cumulative weighting that is based at least in part on respective predictive weightings for each feature vector within a corresponding group of feature vectors; measure, via the wearable ring device during the second time interval, a second set of physiological data of the user; generate, via the one or more processors, an actual physiological metric for the user based at least in part on the second set of physiological data; generate a signal to cause a graphical user interface (GUI) of the user device associated with the wearable ring device to output an indication of the actual physiological metric and an indication of one or more user-recognizable categories, from the plurality of user-recognizable categories, based at least in part on the actual physiological metric relative to the future physiological metric, wherein the one or more user-recognizable categories are associated with respective cumulative weightings that exceed a predetermined threshold; determine a difference between the actual physiological metric and the future physiological metric; and update the first machine learning model based at least in part on the difference between the actual physiological metric and the future physiological metric exceeding a first threshold.
The claimed invention is broadly directed to the abstract idea of collecting patient physiological information, analyzing the information, and predicting results related to the physiological information based on the analyses.
The limitations to “generate a first physiological metric based at least in part on the first set of physiological data; predict, a future physiological metric for the user for a second time interval based at least in part on a set of feature vectors, wherein the set of feature vectors are based at least in part on the first set of physiological data; generate, a predictive weighting for each feature vector of the set of feature vectors, wherein the predictive weighting for each feature vector indicates a relative contribution of the respective feature vector on a difference between the first physiological metric and the future physiological metric; generate a plurality of groups of feature vectors, wherein each group of the plurality of groups corresponds to a user-recognizable category of a plurality of user-recognizable categories, wherein each user-recognizable category is associated with a cumulative weighting that is based at least in part on respective predictive weightings for each feature vector within a corresponding group of feature vectors; generate, an actual physiological metric for the user based at least in part on the second set of physiological data; an indication of the actual physiological metric and an indication of one or more user-recognizable categories, from the plurality of user-recognizable categories, based at least in part on the actual physiological metric relative to the future physiological metric, wherein the one or more user-recognizable categories are associated with respective cumulative weightings that exceed a predetermined threshold; determine a difference between the actual physiological metric and the future physiological metric; and update a model based at least in part on the difference between the actual physiological metric and the future physiological metric exceeding a first threshold,” as drafted, is a process that, under the broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic recitation of a processor, memory, and a non-transitory computer-readable medium storing code (claim 20), signals generating an output on a GUI and first and second machine learning models, analyzing patient physiological data and determining future progression rates and determinations based on the analyses, in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing access to patient data that is analyzed and a future result or prediction is generated based on the analysis. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing personal behavior or relationships or interactions between people including a physician and her patient. Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic computer, processor, memory, wearable ring device, its sensors and modules, a GUI and machine learning models does not remove the claims from the method of organizing human interactions grouping. Thus, the claims recite an abstract idea.
In addition, the claims recite under its broadest reasonable interpretation, an abstract idea that covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, memory, and a non-transitory computer-readable medium storing code (claim 20), signals generating an output on a GUI and first and second machine learning models, nothing in the claim element precludes the steps from being performed in the mind. For example, but for the generic computing device language, a system for determining a patient’s future condition in the context of this claim encompasses one skilled in the pertinent art to manually determine the details of a patient’s future disease progression based on physiological and other relevant data. The wearable ring device is merely used as a tool to obtain the physiological metrics. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of being implemented by a generic processor, memory, and a non-transitory computer-readable medium storing code (claim 20), GUI and machine learning models for the sending and receiving and calculation of information related to assessment of a patient. The devices in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying or sending selected information, or as mathematical concepts) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient analysis and diagnosis and general techniques between a physician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. In addition, the wearable ring device and its modules and sensors are merely applying the abstract idea using the device as a tool to execute the known functions of the device (i.e., to measure physiological metrics). See MPEP 2106.05(f)(2). The claim is thus directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of being implemented by a processor, memory, and a non-transitory computer-readable medium storing code (claim 20), GUI and machine learning models amounts to no more than mere instructions to apply the exception using a computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea and do not overcome the rejection under 35 U.S.C. §101. Claim 2 details physiological data and metrics and causing the GUI to output an indication, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the additional data and GUI do not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 4, 8 and 13 further describes the machine learning model, which is recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the machine learning model does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Claims 5-7, 9-12 and 14-15 further detail feature vectors, physiological metrics, clustered groups and inputs and further limits the abstract idea. Therefore, the claims are not patent eligible. Claims 21 and 22 further detail feature vectors and a cumulative weighting and are recited at a high level of generality such that it amounts no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the feature vectors and cumulative weighting does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself.
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 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.
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 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407684 A1 to Pho et al., hereinafter “Pho,” in view of U.S. 2024/0127078 A1 to Kadkhodaie Elyaderani et al., hereinafter “Kadkhodaie” and further in view of U.S. 2021/0315525 A1 to Mairs et al., hereinafter “Mairs.”
Regarding claim 1, Pho discloses A system, comprising: a wearable ring device configured to measure physiological data from a user (See Pho at least at Abstract; Paras. [0032]-[0034]; Figs. 1, 2, 12-19), the wearable ring device comprising: a ring-shaped housing having an inner curved surface and an outer curved surface, wherein at least a portion of the inner curved surface is configured to contact a tissue of the finger of the user (See id. at least at Paras. [0034], [0057]-[0067] (“[T]he ring 104 may be configured to be worn around a user's finger, and may determine one or more user physiological parameters when worn around the user's finger […] The ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of the ring 104 may store or otherwise include various components of the ring.”); Figs. 1, 2, 12-19); one or more temperature sensors arranged along or within the inner curved surface of the ring-shaped housing (See id. at least at Paras. [0057]-[0067] (“Example measurements and determinations may include, but are not limited to, user skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, blood oxygen levels, and the like.”); Figs. 1-2, 12-10); one or more PPG sensors arranged on the inner curved surface of the ring-shaped housing (See id. at least at Paras. [0057]-[0067] (“the ring 104 may send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, and the like) to the user device 106 […] The inner housing 205-a may be transparent to light emitted by the PPG light emitting diodes (LEDs).”), [0082]-[0090]; Figs. 1-2); a curved battery disposed at least partially within the ring-shaped housing (See id. at least at Para. [0074] (“The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curve of the ring 104.”); Figs. 1-2), the curved battery electrically coupled with the one or more temperature sensors, and the one or more PPG sensors (See id. at least at Paras. [0073]-[0076] (“The one or more temperature sensors 240 may be electrically coupled to the processing module 230-a.”), [0082]-[0090]; Figs. 1-2); and a communication module electrically coupled with one or more processors, the communication module configured to transmit the physiological data (See id. at least at Paras. [0038]-[0040], [0057]-[0065] (“[T]he ring 104 may send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, and the like) to the user device 106 […] The device electronics may include device modules (e.g., hardware/software), such as: a processing module 230-a, a memory 215, a communication module 220-a, a power module 225, and the like. The device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, a PPG sensor assembly (e.g., PPG system 235), and one or more motion sensors 245.”); Figs. 1-2; 12-19); a user device communicatively coupled to the wearable ring device (See id.); and one or more processors communicatively coupled with the wearable ring device, the user device, and the communication module (See id. at least at Paras. [0067]-[0073], [0082]-[0090]; Figs. 1-2, 12-19), the one or more processors configured to: measure, via the wearable ring device during a first time interval, a first set of physiological data of the user (See id. at least at Paras. [0026]-[0029], [0046]-[0049] (“[T]echniques described herein may compare physiological data (and rhythm parameters thereof) collected over different time intervals (e.g., first/reference time interval, second/prediction time interval) to identify a satisfaction of deviation criteria.”); Figs. 1-2, 12-19); generate a first physiological metric based at least in part on the first set of physiological data (See id. at least at Paras. [0049]-[0052], [0104], [0151]-[0152] (generate illness assessment scores); Figs. 1-2, 12-19); predict, via the one or more processors using a first machine learning model, a future physiological metric for the user for a second time interval based at least in part on a set of feature vectors, wherein the set of feature vectors are based at least in part on the first set of physiological data (See id. at least at Abstract; Paras. [0026]-[0029] (“[T]o detect a transition from a healthy state to an unhealthy state, the systems and methods of the present disclosure may utilize one or more classifiers (e.g., machine learning classifiers, algorithms, etc.).”), [0042]-[0044], [0048]-[0055] (predictive weighting, machine learning), [0110]-[0111]); generate, via the one or more processors using a second machine learning model, a predictive weighting for each feature vector of the set of feature vectors, wherein the predictive weighting for each feature vector indicates a relative contribution of the respective feature vector on a difference between the first physiological metric and the future physiological metric (See id. at least at Paras. [0116]-[0118], [0119]-[0121] (“[A] feature engineering pipeline of the system 200 (and/or machine learning classifier) may determine frequency and amplitude (e.g., raw spectral power) of high-frequency signals and low-frequency signals in rolling time windows (e.g., rolling two-minute time intervals) across a night of collected physiological data. The system 200 may then compute statistics on determined features (e.g., 50% quantile of high-frequency amplitude across the whole night of rolling two-minute intervals) to determine a single set of parameters/values for the respective night of physiological data.”), [0158], [0179]-[0180] (“[T]he system 200 may select/identify different time intervals (e.g., different windows) such that physiological data acquired during each of the respective time intervals may be compared to each other.”), [0220]-[0222] (“[U]sers in colder climates may have a higher relative “predictive weight” as compared to temperature changes/temperature readings for users in warmer climates. In some cases, the location data may be used to determine a geographical position and/or climate data for the user (e.g., ambient temperature data), which may be used to generate the predictive weights.”), [0261]-[0262] (“[P]redictive weights may refer to some weighting metric or other metric which are associated with a relative predictive accuracy for detecting illness. For instance, a higher predictive weight may be associated with higher relative accuracy for predicting illness (e.g., more accurate at identifying illness), whereas a lower predictive weight may be associated with a lower relative accuracy for predicting illness (e.g., less accurate at identifying illness).”), [0280]).
Pho may not specifically describe but Kadkhodaie teaches to generate a plurality of groups of feature vectors, wherein each group of the plurality of groups corresponds to a user-recognizable category of a plurality of user-recognizable categories, wherein each user-recognizable category is associated with a cumulative weighting that is based at least in part on respective predictive weightings for each feature vector within a corresponding group of feature vectors (See Kadkhodaie at least at Paras. [0106]-[0107] (weights, sub-groups of user-recognizable categories), [0117]-[0130] (feature vectors and aggregating and weights for categories a user could recognize); measure, via the wearable device during the second time interval, a second set of physiological data of the user (See id. at least at Paras. [0181]-[0189] (time intervals and data collection/history sequences), [0228]-[0240] (machine learning and labeled sensor data, first labels and second labels)); generate, via the one or more processors, an actual physiological metric for the user based at least in part on the second set of physiological data (See id. at least at Paras. [0067]-[0069], [0072]-[0076] (actual physiological metrics), [0097], [0216]-[0220] (physiological signals and metrics), [0226]-[0239]; Figs. 1-8, 14-20); generate a signal to cause a graphical user interface (GUI) of a user device associated with the wearable device to output an indication of the actual physiological metric and an indication of one or more user-recognizable categories, from the plurality of user-recognizable categories, based at least in part on the actual physiological metric relative to the future physiological metric (See id. at least at Paras. [0048]-[0052], [0131]-[0132]), wherein the one or more user-recognizable categories are associated with respective cumulative weightings that exceed a predetermined threshold (See id. at least at Paras. [0098]-[0101], [0107]-[0112], [0124]-[0132]; Figs. 1-8,. 14-19); determine a difference between the actual physiological metric and the future physiological metric (See id. at least at Paras. [0067]-[0069], [0072]-[0076] (actual physiological metrics), [0097], [0216]-[0220] (physiological signals and metrics), [0226]-[0239]; Figs. 1-8, 14-20).
The references may not specifically describe but Mairs teaches to update the first machine learning model based at least in part on the difference between the actual physiological metric and the future physiological metric exceeding a first threshold (See id. at least at Paras. [0004]-[0006] (“[U]pdating the translation model with a reduced weighting associated with the input variable when a difference between the simulated output and the reference output is greater than a threshold. A notification is generated based at least in part on an output of the updated translation model when one or more subsequent values for the input variable derived from one or more electrical signals output by the instance of the sensing element are input to the updated translation model with the reduced weighting.”), [0017] (machine learning models and updating based on physiological metrics), [0041]-[0042], [0059]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho to incorporate the teachings of Kadkhodaie and Mairs and provide certain metrics and thresholds and updated models. Kadkhodaie is directed to learning techniques for sensor devices and patients. Mairs relates to mitigating sensor error for physiological metrics. Incorporating the learning techniques for sensor devices and patients in Kadkhodaie with the sensor error mitigation techniques of Mairs and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the claimed health-related metric explainer.
Regarding claim 21, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein each user-recognizable category corresponds to one or more common characteristics of a respective group of feature vectors, the one or more common characteristics understandable by the user (See Kadkhodaie at least at Paras. [0106]-[0107] (weights, sub-groups of user-recognizable categories), [0117]-[0131] (feature vectors and aggregating and weights for categories a user could recognize); Figs. 1-8, 14-21).
Regarding claim 22, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1 and Kadkhodaie further teaches wherein each cumulative weighting indicates a cumulative impact of each respective user-recognizable category on the future physiological metric (See id.).
Claims 2, 4 and 6-15 are rejected under 35 U.S.C. 103 as being unpatentable over Pho, in view of Kadkhodaie, in view of Mairs and further in view of U.S. 2016/0302671 A1 to Shariff et al., hereinafter “Shariff.”
Regarding claim 2, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein causing the user device to output the indication of the one or more user-recognizable categories is based at least in part on the difference between the first physiological metric and the future physiological metric exceeding a second threshold (See Shariff at least at Paras. [0044]-[0047] (“[D]iscriminant function analysis is classification—the act of distributing things into groups, classes or categories of the same type. Discriminant analysis works by creating one or more linear combinations of predictors, creating a new latent variable for each function. These functions are called discriminant functions. The number of functions possible is either Ng−1 where Ng=number of groups, or p (the number of predictors), whichever is smaller. The first function created maximizes the differences between groups on that function. The second function maximizes differences on that function, but also must not be correlated with the previous function […] Upon classifying the physiological data with one or more probabilistic classification model(s) 214 the classification module 212 may return probabilities that the physiological data belongs to one or more classes representing health states (e.g. healthy, ambiguous, or sick).” Clustered groups), [0061]-[0065] (first and second metrics and first and second thresholds), [0071]-[0072] (“If the physiological data does not vary from the baseline by more than a threshold amount, process 500 returns to 502 where additional physiological data is received. Thus, process 500 may continue in a loop until physiological data is received which varies from the baseline by more than a threshold amount. If the physiological data varies from the baseline by more than a threshold amount, process 500 proceeds to 508 […] At 508, the physiological data is provided to a probabilistic classification model that returns probabilities that the physiological data belongs to one or more classes representing different health states. [clustered groups] The probabilistic classification model may be a mixture model, a discriminant analysis model, or a discriminative model. The probabilistic classification model may be the probabilistic classification model 214 shown in FIG. 2. In an implementation, the classes representing health states may be healthy, ambiguous, and sick.”), Physiological data that is received varies from the first physiological metric and future physiological metric by a threshold amount and then determining a probability that the groups are clustered because the metrics show a stronger indication a person will have fever/become sick; Figs. 2-5; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho and Kadkhodaie to incorporate the teachings of Shariff and provide metrics and thresholds. Shariff is directed to prediction of health status. Incorporating the prediction of health status as in Shariff with the learning techniques for sensor devices and patients in Kadkhodaie, the sensor error mitigation techniques of Mairs and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the claimed health-related metric explainer.
Regarding claim 4, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches updating the first machine learning model based at least in part on the difference between the actual physiological metric and the future physiological metric (See Shariff at least at Abstract; Paras. [0016]-[0018], [0028], [0038]-[0046], [0054]-[0064], [0079]-[0087]; Figs. 1-5).
Regarding claim 6, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the one or more user-recognizable categories comprises a greatest cumulative weighting of the plurality of user-recognizable categories (The current Specification at Para. [0013] states “[T]he system may identify a cumulative correlation value associated with each category and may identify one or more categories with the greatest cumulative correlation values, or a cumulative correlation value satisfying (e.g., exceeding) a threshold.” Shariff teaches this limitation. See Shariff at least at Paras. [0038]-[0042] (“[T]he system may cluster the features according to user- understandable categories, such as sleep, activity, tags, etc. […] The variance detection module 210 may determine if a given physiological datum varies from the corresponding baseline value by more than a threshold amount. The threshold amount may be a fixed amount (e.g., beats per minute for heart rate, 15 breaths per minute for respiration rate, etc.) or a variable amount that depends on the value of the baseline (e.g., 5%, 10%, 15%, 25%, etc. of the baseline). Thus, the variance detection module 210 may flag physiological data that is “abnormal” in that it differs from the baseline value for a given physiological data descriptor.” Deviation from the cumulative correlation value exceeding a threshold), [0065] (“In one implementation, someone who will not get a fever may be equated to the classification of “healthy,” someone who will get a fever in the future may be equated to the classification of “sick,” and someone who might get a fever in the future may be equated to the classification of “ambiguous.” Of course, classification into other groups is also possible such as, for example, a group that will develop a high fever (e.g., above 103° F. (39.4° C.)) and a group that will develop a low fever (e.g., between 100° F. and 103° F. (37-39.4° C.)). The second threshold length of time may represent some period of time in the near future or the idea of “soon.” In an implementation, the second threshold length of time is between about 12 hours and about 72 hours.”).
Regarding claim 7, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the predetermined threshold is based at least in part on a deviation from a greatest cumulative weighting of the plurality of user-recognizable categories (See Shariff at least at Paras. [0038]-[0042] (“[T]he system may cluster the features according to user- understandable categories, such as sleep, activity, tags, etc. […] The variance detection module 210 may determine if a given physiological datum varies from the corresponding baseline value by more than a threshold amount. The threshold amount may be a fixed amount (e.g., beats per minute for heart rate, 15 breaths per minute for respiration rate, etc.) or a variable amount that depends on the value of the baseline (e.g., 5%, 10%, 15%, 25%, etc. of the baseline). Thus, the variance detection module 210 may flag physiological data that is “abnormal” in that it differs from the baseline value for a given physiological data descriptor.” Deviation from the cumulative correlation value exceeding a threshold or not exceeding a threshold.), [0065]; See also Khanna at least at Paras. [0032]-[0033] (“To further improve the performance of the machine learning model, in some embodiments, the system may perform inductive learning. Inductive learning may be performed by selecting an un-labeled feature vector from the first set of feature vectors, classifying the un-labeled feature vector using the machine learning model to get a model classified cluster with a confidence score [cumulative weighting], determining whether the confidence score is greater than a threshold, determining a distance of the un-labeled feature vector with respect to each labeled feature vector of the first set of feature vectors, when the confidence score is greater than the threshold, determining a statistically significant matching cluster of labeled feature vectors to which the un-labeled feature vector is closest based on the determined distance [deviation], determining whether the model classified cluster and the statistically matching cluster are the same.”), [0071]-[0075] (“The module 114 selects each feature vector from a set of feature vectors that have been labeled through the inductive learning, classifies the feature vector using the machine learning model to get a model classified cluster with a confidence score.”) Clustered groups with cumulative weighting and correlation values for respective feature vectors; Figs. 1, 2, 8)).
Regarding claim 8, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches inputting, into the first machine learning model, an age of the user, a sex of the user, a blood pressure of the user, a body mass index of the user, a skin tone of the user, a medical condition of the user, a physical state of the user, or any combination thereof, wherein predicting the future physiological metric is based at least in part on the age of the user, the sex of the user, the blood pressure of the user, the body mass index of the user, the skin tone of the user, the medical condition of the user, the physical state of the user, or any combination thereof (See Shariff at least at Paras. [0023]-[0025], [0036]-[0038], [0054], [0075]).
Regarding claim 9, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the set of feature vectors are associated with sleep of the user, an activity the user engaged in, a readiness of the user, a menstrual cycle of the user, a tag input by the user, a health metric associated with the user, a characteristic of an environment associated with the user, or any combination thereof (See Shariff at least at Paras. [0014]-[0017], [0036]-[0038], [0044]-[0046], [0106]-[0117]).
Regarding claim 10, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein a value for each feature vector of a subset of the set of feature vectors is based at least in part on an average value of each feature vector of the subset over a period of time (See Shariff at least at Paras. [0067]-[0071]; Fig. 5).
Regarding claim 11, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the set of feature vectors includes one or more lagged features associated with a third set of physiological data collected during a third time interval prior to the first time interval (See Shariff at least at Paras. [0036], [0061]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
Regarding claim 12, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the plurality of user-recognizable categories comprise an activity-related group, a sleep-related group, a menstrual cycle-related group, a tag-related group, or any combination thereof (See Shariff at least at Paras. [0014]-[0017], [0036]-[0038], [0044]-[0046], [0106]-[0117]).
Regarding claim 13, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches determining at least one trend over time based at least in part on the set of feature vectors and the future physiological metric; and updating the first machine learning model based at least in part on the at least one trend (See Shariff at least at Paras. [0033]-[0035], [0038]-[0042] (“The variance detection module 210 may determine if a given physiological datum [heart beat, respiratory rate, etc. are a trend over time] varies from the corresponding baseline value by more than a threshold amount […] that it differs from the baseline value for a given physiological data descriptor […] given a patient's heart rate and respiration rate a probabilistic classifier can determine a probability that the patient is healthy and a probability that the patient is sick […] Probabilistic classification includes supervised learning which is the machine learning task of inferring a function from labeled training data. The labeled training data may be the training data 116 from FIG. 1. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.” The models/classification can use new information input to the model and looks at trends of physiological data over time), [0063] (“The probabilistic classification model is created by supervised machine learning from a set of training data. The set of training data may be the training data 116 shown in FIG. 1. In an implementation the set of training data may include physiological data from a plurality of individuals within the patient who are classified as healthy and a plurality of individuals other than the patient who are classified as sick.”), [0065] (“Upon providing the plurality of time points of physiological data to the probabilistic classification model, the probabilistic classification model returns probabilities that the patient belongs to one of a number of different groups. In an implementation, the groups comprise two groups: a group that will develop a fever within a second threshold length of time and a group that will not develop a fever within the second threshold length of time.” Trends and thresholds), [0068]-[0073]; Claim 1; Figs. 1-5).
Regarding claim 14, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the first set of physiological data is associated with the first time interval further comprising: receiving, from the wearable device, a measured set of baseline physiological data associated with the user prior to the first time interval, wherein the set of feature vectors are based at least in part on the measured set of baseline physiological data (See Shariff at least at Paras. [0015]-[0017], [0036], [0061]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
Regarding claim 15, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe but Shariff teaches wherein the future physiological metric comprises at least a heart rate variability (See Shariff at least at Paras. [0015]-[0017], [0023]-[0024], [0036], [0042], [0059]-[0065], [0067]-[0071], [0106]-[0117]; Figs. 1-5).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Pho, in view of Kadkhodaie, in view of Mairs and further in view of U.S. 2022/0083815 A1 to Khanna et al., hereinafter “Khanna.”
Regarding claim 5, Pho as modified by Kadkhodaie and Mairs discloses all the limitations of claim 1. The references may not specifically describe yet Khanna teaches receiving, via the user device, one or more user inputs indicating one or more tags associated with the user (See Khanna at least at Para. [0051] (“The ground truth vector identification module 108 identifies ground truth representative vectors for labeling through oracle identification, for example, in which a user, automated process, or other means (e.g., a database lookup) can tag ground truth representative vectors or use other means for tagging. As one may appreciate, the system 102 uses a set of ground truth representative feature vectors for training the ML model.”), [0063]-[0064]). While Shariff further discloses wherein predicting the future physiological metric is based at least in part on a second set of feature vectors of the one or more user inputs (See Shariff at least at Paras. [0061]-[0065] (“Upon providing the plurality of time points of physiological data to the probabilistic classification model, the probabilistic classification model returns probabilities that the patient belongs to one of a number of different groups. In an implementation, the groups comprise two groups: a group that will develop a fever within a second threshold length of time and a group that will not develop a fever within the second threshold length of time.” (first and second set of feature vectors and first and second thresholds), [0071]-[0072] (“In an implementation, the classes representing health states may be healthy, ambiguous, and sick.”), Physiological data that is received [one or more user inputs] varies from the first physiological metric by a threshold and a future physiological metric is predicted; Figs. 2-5; Claim 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Pho and Kadkhodaie to incorporate the teachings of Khanna and provide user inputs indicating tags. Khanna is directed to vector labeling for machine learning. Incorporating the feature vectors, clusters and tagging as in Khanna with the learning techniques for sensor devices and patients in Kadkhodaie, the sensor error mitigation techniques of Mairs and the illness detection and prediction techniques using a ring device as in Pho would thereby increase the applicability, utility, and efficacy of the claimed health-related metric explainer.
Response to Arguments
Applicant’s remarks filed November 19, 2025 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The amended claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments are moot in light of at least new references Pho and Mairs. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., techniques for clinical administration and assessment, teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative and the claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and Updated PEG. The arguments at pages 7-9 of Applicant’s Remarks are not persuasive. At pages 7-9 the Examiner disagrees that there is not an abstract idea, that there is any practical application thereof and there is a technological improvement recited in the claims. The claims are directed to the abstract idea of organizing human activity and mental processes, discussed above, and the wearable ring is leveraged merely as a tool to implement the abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: U.S. 2024/0105335 A1 to Rezai et al.
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/WILLIAM T. MONTICELLO/ Examiner, Art Unit 3682
/FONYA M LONG/ Supervisory Patent Examiner, Art Unit 3682