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 .
Claim Rejections - 35 USC § 101
Claim(s) 1-12 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “receive the physiological data acquired via the wearable ring device via one or more electronic signals;”, “input the physiological data into a neural network, the neural network trained to detect breathing disturbance events based on a plurality of features within a training physiological dataset associated with a plurality of users, the plurality of features comprising a first decrease in an oxygen saturation level relative to a baseline oxygen saturation level of the plurality of users, a second decrease in a pulse wave amplitude relative to a baseline heartbeat amplitude of the plurality of users, an increase in intensity of movement relative to a baseline intensity of the plurality of users, a third decrease in temperature data relative to baseline temperature data of the plurality of users, or some combination thereof”, “generate, using the neural network, a breathing disturbance metric based at least in part on the plurality of features within the physiological data during the time interval, the breathing disturbance metric associated with a probability that the user experienced a breathing disturbance event during the time interval”, “determine, a triggering condition for triggering a set of oxygen saturation measurements from the user, wherein the triggering condition corresponds to at least one of a physical state of the wearable ring device or a physiological state of the user, and wherein the first machine learning model comprises a mapping between previous sets of oxygen saturation measurements, a respective oxygen saturation measurement accuracy for each of the previous sets of oxygen saturation measurements, and one or more respective conditions associated with each of the previous sets of oxygen saturation measurements;”, “determine the triggering condition has been met;” and “transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable ring device, the instruction configured to cause the GUI to display information associated with the breathing disturbance metric.”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “a wearable ring device configured to acquire physiological data from a user”, “first/second wireless communication link”, “the PPG system comprises at least one optical transmitter and at least one optical receiver, and wherein activation of the PPG system comprises the at least one optical transmitter transmitting light, the at least one optical receiver generating a PPG signal indicative of an amount of the light received by the at least one optical receiver”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0068]: “finger rings on both hands… for electrical impulse measurement across the heart as used in EKG/ECG type systems (currently the gold standard in biometric medical sensing applications.)” of Day et al. (US 20190021616 A1) and para [0025]: “wireless communication links operating according to any known communications protocol or standard,” of Nazarro et al. (US 20220203022 A1). The PPG system comprising at least one optical transmitter and at least one optical receiver, and wherein activation of the PPG system comprises the at least one optical transmitter transmitting light, the at least one optical receiver generating a PPG signal indicative of an amount of the light received by the at least one optical receiver is well-known, routine, and/or conventional as evidenced by para [0010]: “a light receiver 110 of a conventional PPG signal measurement apparatus receives not only light irradiated onto a body part 120 of a user by a light emitter (not shown) and reflected from the body part of the user, but also ambient light irradiated from an external light source” of Shin et al (US 20170020421 A1). Regarding the “one or processors”, “machine learning model” and “user device”, a generic computer structure is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements does not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements does not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-12 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. The additional element “microphone” recited is claim 12 is not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0028]: “conventional microphones” of Bidichandani et al. (US 20160206277 A1). Thus, claim(s) 1-12 is/are rejected under 35 U.S.C. 101.
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.
Claim 14 is 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.
Claim 14 recites the limitation “wherein the first machine learning model and the second machine learning model are the same”. It is unclear whether the limitation indicates the first and second model share the same structure, are the same model, or are the same in some other way. If the first model and the second model are the same model, then the scope of the claim is broader than claim 13 as opposed to narrower.
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.
Claim(s) 1-8 and 10-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gowda et al. (US 20230270377 A1) in view of Laput et al. (US 20210063434 A1) Seo et al. (US 20230000353 A1), in view of Smit et al. (US 20220104737 A1).
Regarding claim 1, Gowda discloses a system for detecting breathing disturbance (title), comprising: a wearable device configured to acquire physiological data from a user ([0006]: “a wearable activity tracker, or other wearable electronic device”), the physiological data comprising oxygen saturation data ([0040]: "may generate PPG data usable to calculate heart rate, heart rate variability, respiration rate, and/or oxygen saturation”) collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable ring device ([0044]: “PPG sensors described herein may include a single light source and a single light detector”), heartbeat data collected via the PPG measurements from the one or more light-emitting components and the one or more light-receiving components of the wearable ring device ([0032]: “determining the heart rate of the user based on an optical PPG signal (e.g., received from the green PPG sensor"), heartbeat data collected via an electrocardiogram sensor of the wearable ring device, pulse wave amplitude data collected via the PPG measurements ([0046]: “pulse oximetry (e.g., as part of a PPG system utilizing PPG data)”), movement data comprising an intensity of movement of the user collected via one or more accelerometers of the wearable ring device ([0007]: "accelerometer data"), temperature data collected via one or more temperature sensors of the wearable ring device ([0028]: " temperature sensor"), or some combination thereof, wherein the physiological data is collected throughout a time interval that includes one or more sleep periods of the user ([0007]: "a sleep period during which the user of the wearable activity tracker was asleep"); and one or more processors communicatively coupled with the wearable ring device ([0007]: " and at least one processor”), wherein the one or more processors are configured to: receive the physiological data acquired via the wearable ring device via one or more electronic signals, wherein the oxygen saturation data included in the physiological data comprises the set of oxygen saturation measurements ([0011]: "configure the one or more processors to: receive sensor data generated by one or more sensors of a wearable device worn by a use", [0040]: “oxygen saturation”); input the physiological data into a neural network ([0059]: “the system uses a random forest classifier. Other classification methods include neural networks, hidden-Markov models, support vector machines, k-mean clustering, and decision tree"), the neural network trained to detect breathing disturbance events based on a plurality of features within a training physiological dataset associated with a plurality of users ([0059]: "These statistical features may be used to train a classifier to classify temporal windows as either breathing disturbed or normal.”, [0070]: “user categories can be generated and/or updated based on the demographics data and the classifications/breathing scores gathered across a plurality of users.”), the plurality of features comprising a first decrease in an oxygen saturation level relative to a baseline oxygen saturation level of the plurality of users ([0010]: “determining a baseline physiological metric associated with the user based on the additional sensor data, and classifying said each respective temporal window further based on the baseline physiological metric associated with the use”, [0026]: “Such physiological metric(s) may include… oxygen saturation (SpO2)”), a second decrease in a pulse wave amplitude relative to a baseline heartbeat amplitude of the plurality of users ([0057]: “whether heart rate exceeded/went-below some threshold over the course of some interval.”), an increase in intensity of movement relative to a baseline intensity of the plurality of users ([0008]: “when the accelerometer data indicates at least a threshold level of movement for a threshold amount of time”), a third decrease in temperature data relative to baseline temperature data of the plurality of users ([0010]: “determining a baseline physiological metric associated with the user based on the additional sensor data, and classifying said each respective temporal window further based on the baseline physiological metric associated with the use”, [0026]: “Such physiological metric(s) may include… body temperature)”), or some combination thereof; generate, using the neural network, a breathing disturbance metric based at least in part on the plurality of features within the physiological data during the time interval ([0007]: “a breathing disturbed minute”), the breathing disturbance metric associated with a probability that the user experienced a breathing disturbance event during the time interval ([0009]: "the respective temporal window can be classified, such as for one of (i) breathing disturbed or (ii) breathing not disturbed (or regular), based on the sensor data generated during the respective temporal window and data corresponding to a time period outside the sleep period. A breathing disturbance score, sleep fitness score, or other such value can be determined based on the classification of the plurality of temporal windows ", [0066]: “The epoch (or temporal window) classifier outputs a probability of the minute being breathing disturbed”); and transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable ring device, the instruction configured to cause the GUI to display information associated with the breathing disturbance metric ([0007]: "to be displayed on a display screen of the mobile device, or otherwise made available to a respective user, along with the updated benchmark breathing, sleep fitness, or other such score associated with the user category.").
Gowda fails to disclose a wearable ring device.
Laput discloses a wearable ring device ([0015]: “entirely in other wearable devices (e.g., ring”).
As Gowda discloses the disclosed system may be implemented in any wearable device ([0024]: “the concepts described herein may be implemented on any type of portable or wearable devices including one or more sensors”), it would have been obvious to a person of ordinary skill in the art to substitute the wrist worn device disclosed by Gowda to the ring-shaped device disclosed by Laput for the predictable result of non-invasive physiological monitoring.
Gowda as modified by Laput fails to disclose wherein the one or more processors are further configured to: determine a condition to trigger acquisition of the physiological data from the user associated with the wearable ring device, wherein the condition corresponds to a physical state of the wearable ring device, a physiological state of the user, or both, and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable ring device, application data, physiological data from the wearable ring device, or any combination thereof; receive the oxygen saturation data associated with the user from the wearable ring device based at least in part on the condition; and cause the GUI to display an indication of the oxygen saturation data for the user.
Seo discloses a system for detecting breathing disturbance, comprising a wearable device (abstract, fig 5b), wherein the one or more processors ([0011]: “at least one processor”) determine, using a first machine learning model ([0038]: “The auxiliary processor 123 may control at least some of functions or states related to at least one component (e.g., the display module 160, the sensor module 176, or the communication module 190) among the components of the electronic device 101, instead of the main processor…. An artificial intelligence model may be generated by machine learning. Such learning may be performed, e.g., by the electronic device 101”), a triggering condition for triggering a set of oxygen saturation measurements from the user, ([0083]: “may identify whether the user state is the state requiring an oxygen saturation check by identifying whether the medical records include information about a disease or treatment related to oxygen saturation.”) wherein the condition corresponds to a physical state of the wearable ring device, a physiological state of the user, or both, ([0124]: “an embodiment may identify (e.g., select, determine, or change) a specified measurement period and a specified reference range as an oxygen saturation measurement period and a reference oxygen saturation, based on the medical records related to oxygen saturation.”) and wherein the condition is determined based at least in part on one or more relationships between sensor data from the wearable ring device, application data, physiological data from the wearable ring device, or any combination thereof ([0080]: “various user states related to a medical treatment state, a health state, or an activity of the user”); receive the oxygen saturation data associated with the user from the wearable ring device based at least in part on the condition ([0011]: “measure oxygen saturation based on the specified oxygen saturation measurement period by using the biosensor, identify whether the measured oxygen saturation satisfies the specified reference oxygen saturation range,”); transmitting, via a signaling link ([0054]: “least one antenna appropriate for a communication scheme used in the communication network, such as the first network 198 or the second network 199, may be selected”) a control signal to the wearable ring device to trigger the set of oxygen saturation measurements at the one or more light-emitting components and the one or more light-receiving components of the wearable ring device ([0167]: “processor (e.g., the processor 120) of the machine (e.g., the electronic device 101) may invoke at least one of the one or more instructions stored in the storage medium, and execute it, with or without using one or more other components under the control of the processor”, [0110]: “the light receiver 424 senses light which has been irradiated to the body from the light emitter 422 and reflected from the body, the PPG sensor 372-1 (or the processor 320) may measure an oxygen saturation level”), based at least in part on the condition being met at the one or more light-emitting components and the one or more light-receiving components of the wearable ring device ([0167], [0110]); and causing a GUI to display an indication of the measure of the oxygen saturation for the user ([0011]: “and display the measured oxygen saturation and information indicating whether the measured oxygen saturation satisfies the specified reference oxygen saturation range on the display.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Gowda as modified by Laput to include the determination of a condition to trigger acquisition of the physiological data from the user associated with the wearable ring device as disclosed by Seo in order to provide user context to the obtained measurements (Seo [0006]).
Gowda as modified by Laput and Seo fails to disclose inputting, into a machine learning model a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements.
Smit discloses inputting, into a machine learning model, a set of previous oxygen saturation measurements associated with a user, a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements ([0111]: “calibrate oxygen saturation prediction model 124 based on a history of accuracy of oxygen saturation prediction model 124.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method disclosed by Seo to include inputting a respective measurement accuracy associated with each previous oxygen saturation measurement of the set of previous oxygen saturation measurements as disclosed by Smit in order to improve the accuracy of the machine learning model.
Regarding claim 2, Gowda further discloses cause the GUI to display an indication of the oxygen saturation data ([0007]: "to be displayed on a display screen of the mobile device, or otherwise made available to a respective user, along with the updated benchmark breathing, sleep fitness, or other such score associated with the user category.").
Regarding claim 3, Seo further discloses acquire the physiological data based at least in part on a sleep state of the user, wherein, to determine the triggering condition, the one or more processors are further configured to ([0117]: “change the oxygen saturation measurement period to a shorter measurement period in the sleep state”), detect the sleep state of the user ([0104]: “ and/or a sleep state”).
Regarding claim 4, Gowda further discloses wherein the breathing disturbance metric comprises a probability of an apnea event, a probability of a hypopnea event, a probability of an oxygen desaturation event, a probability of a respiratory effort-related arousal event, or some combination thereof ([0071]: “the apnea-hypopnea index (AHI”).
Regarding claim 5, Gowda further discloses wherein the information associated with the breathing disturbance metric comprises a breathing disturbance index, an apnea-hypopnea index ([0071]: “the apnea-hypopnea index (AHI”), an oxygen desaturation index, or some combination thereof.
Regarding claim 6, Gowda further discloses wherein the one or more processors are further configured to: transmit an instruction to the GUI to cause the GUI to display a timeline associated with the one or more sleep periods of the user, wherein the timeline comprises one or more breathing disturbance events and a timestamp associated with the one or more breathing disturbance events ([0071]: “The system may also output a graph illustrating the trend in the breathing disturbance scores or metrics of the user over time (e.g., daily, weekly, monthly, and/or yearly). ”).
Regarding claim 7, Regarding claim 7, Gowda in view of Laput and Seo fails to disclose wherein the one or more processors are further configured to: select the neural network from a set of neural networks, wherein each neural network of the set of neural networks is associated with one or more combinations of physiological data.
Smit further discloses wherein the one or more processors are further configured to: select the neural network from a set of neural networks, wherein each neural network of the set of neural networks is associated with one or more combinations of physiological data. ([0069]: “comprises a plurality of oxygen saturation prediction models in the form of a plurality of neural network algorithms… each of the plurality of oxygen saturation prediction models may be trained using a different set of inputs to predict the oxygen saturation level of patient 101 at the end of the predefined time period.”).
Regarding claim 8, Gowda further discloses perform a preprocessing of the training physiological dataset and of the physiological data, wherein the preprocessing comprises filtering the training physiological dataset and the physiological data, normalizing the training physiological dataset and the physiological data ([0058]: “. Heart rate, the derivative of heart rate, heart rate variability, and respiration may be normalized or standardized appropriately on a per-user basis account for natural variation across users”), resampling the training physiological dataset and the physiological data, padding the training physiological dataset and the physiological data, or some combination thereof; and train the neural network based at least in part on the preprocessed training physiological dataset ([0059]: “These statistical features may be used to train a classifier to classify temporal windows as either breathing disturbed or normal.”)
Regarding claim 10, Gowda discloses the one or more processors are further configured to: generate the breathing disturbance metric periodically during the time interval, wherein a periodicity of the breathing disturbance metric is uniform or irregular ([0064]: “which this classification can occur, other temporal windows having a different duration may be used (e.g., 30 second temporal windows, 5-minute temporal windows, etc.). In one embodiment, each temporal window has a fixed duration. In another embodiment, the duration of the temporal window is adjusted over time based on the user's sleeping or breathing patterns.”).
Regarding claim 11, Gowda discloses wherein, to input the physiological data into the neural network, the one or more processors are further configured to: input an auditory measurement into the neural network, the auditory measurement comprising a measurement of an ambient sound level during the time interval ([0098]: “the user state data includes… audio data”), wherein the plurality of features within the training physiological dataset further comprise an increase in ambient sound level relative to a baseline ambient sound level ([0046]: “(ii) disruption of normal audible breathing patterns as measured by an audio sensor;”, wherein the baseline is the normal breathing pattern).
Regarding claim 12, while Gowda discloses an audio sensor ([0046]: “audio sensor”), they fail to specifically disclose wherein the one or more processors are further configured to: perform the auditory measurement during the time interval via a microphone of the wearable ring device, via a charger of the wearable ring device, or via the user device.
Laput discloses perform the auditory measurements during a time interval via a microphone of the wearable ring device, via a charger of the wearable ring device, or via the user device ([0016]: “Wearable device 150 can include an audio sensing system, for example, including a microphone”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the generic audio sensor disclosed by Gowda to the microphone disclosed by Laput for the predictable result of obtaining audio data.
Regarding claim 13, Seo discloses selecting, an oxygen saturation measurement cycle from a set of oxygen saturation measurement cycles, wherein the machine learning model comprises a mapping between the set of oxygen saturation measurement cycles and one or more respective conditions associated with each of the set of oxygen saturation measurement cycles ([0008]: “ a measurement period and a reference range of oxygen saturation may be identified based on the disease history of the user”, [0116]: “In addition, in the state where a measured altitude value is higher than a specified altitude value (the high altitude state), oxygen saturation may significantly affect the respiration of the respiratory patient. Accordingly, the processor 320 may change the oxygen saturation measurement period to a shorter measurement period in the high altitude state than in a low altitude state (a state where a measured altitude value is not higher than the specified altitude value”); wherein the selected oxygen saturation measurement cycle includes at least one of a number of oxygen saturation measurements included in the set of oxygen saturation measurements and a periodicity of the oxygen saturation measurements ([0116]: “and that different oxygen saturation measurement periods and different reference oxygen saturation ranges may be applied based on various user states”), wherein the control signal indicates the selected oxygen saturation measurement cycle, and wherein the wearable ring device performs the set of oxygen saturation measurements in accordance with the selected oxygen saturation measurement cycle ([0009]: “measurement period and a reference range of oxygen saturation may be identified based on the medical treatment result of the user, and an oxygen saturation may be monitored based on the identified measurement period and reference range of oxygen saturation and provided based on the result of the monitoring.”).
Seo fails to disclose a second machine learning model.
Smit discloses a second machine learning model ([0069]: “oxygen saturation prediction model 124 comprises a plurality of oxygen saturation prediction models in the form of a plurality of neural network algorithms,”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the machine learning models disclosed by Seo to include a second machine learning model as disclosed by Smit in order to allow for customization using models trained with multiple inputs (Smit [0069]).
Regarding claim 14, Seo further discloses wherein the first machine learning model and the second machine learning model are the same ([0038]).
Regarding claim 15, Smit further disclose wherein the one or more processors are further configured to: select the neural network from one or more neural networks based at least in part on at least one of one or more inputs included in the physiological data or a quality of the one or more inputs ([0069]: “each of the plurality of oxygen saturation prediction models may be trained using a different set of inputs to predict the oxygen saturation level of patient 101 at the end of the predefined time period.”).
Regarding claim 16, Smit wherein, responsive to the quality of the one or more inputs being below a threshold, the neural network is selected to exclude the one or more inputs ([0037]: “may be configured to modify a raw signal to a usable signal by, for example, filtering (e.g., low pass, high pass, band pass, notch, or any other suitable filtering),”).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gowda in view of Laput in view of Seo in view of Smit in further view of Yoshida et al. (US 20240266061 A1)
Regarding claim 9, Gowda in view of Laput discloses the system of claim 1, but fails to disclose wherein training the neural network comprises: determining one or more parameter values associated with the neural network based at least in part on an error between one or more ground-truth labels associated with the preprocessed training physiological dataset and one or more predicted labels output from the neural network.
Yoshida discloses training a neural network (abstract) wherein training the neural network comprises: determining one or more parameter values associated with the neural network based at least in part on an error between one or more ground-truth labels associated with the preprocessed training physiological dataset and one or more predicted labels output from the neural network ([0115]: “evaluates the arithmetic operation result by using the sensitivity information included in the training data generated in step S207 and the sensitivity information acquired in step S209 (step S210)… Specifically, the control unit 21 is capable of evaluating the arithmetic operation result by using an error function (also referred to as an objective function, a loss function, and a cost function) based on the ground truth data and the arithmetic operation result obtained in step S208.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the system disclosed by Gowda as modified by Laput to include the determination of one or more parameter values based at least in part on an error between one or more ground-truth labels associated with the preprocessed training physiological dataset in order to improve the accuracy of the model (Yoshida [0115]).
Response to Arguments
Applicant’s arguments, see Remarks, filed 07/21/2026, with respect to the rejection of claims 1-12 35 U.S.C. § 101 have been fully considered and are not persuasive. Applicant's arguments filed with respect to the rejection of claims 1-20. Applicant argues that per the USPTO issued memorandum issued August 4th, claim limitations that encompass AI in a way that cannot be practically performed in the human mind are not considered as pertaining to a mental process grouping. However, applying a generic machine learning model to accomplish a process that may be performed in the human mind does constitute a mental process. Mapping previous oxygen saturation measurements to a set of current measurements and observing sensor data may be performed in the mind with pen/paper. As could coming up with a command signal to communicate to a device based on the calculation. However, as applicant argues on page 16 of Remarks, selecting a triggering condition to operate a measurement selectively is not an improvement in a technological field. Devices that cycle through turning on and off are known in the art. Therefore, the rejection is maintained. However, per para [0104] of applicant’s specification, transmission of only data within a configurable threshold to reduce power consumption by “reducing processing and activating antenna elements” may more persuasively define a technological improvement.
Applicant’s arguments, see Remarks, filed 07/21/2026, with respect to the rejection(s) of claim(s) 1-12 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 (see above).
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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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791
