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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim recites “the first physiological metric is associated with a first physiological phenomenon and the second physiological metric is associated with a second physiological phenomenon that is distinct from the first physiological phenomenon.” However, the claim’s recitation of a physical phenomenon is so broad that it could be any one of a number of physiological phenomena (e.g., back pain, Alzheimer’s, vision impairment, cancer, diabetes, etc).
The written description requirement for a claimed genus (in the claim’s case, a physiological phenomenon) may be satisfied through sufficient description of a representative number of species by actual reduction to practice (see i)(A) above), reduction to drawings (see i)(B) above), or by disclosure of relevant, identifying characteristics, i.e., structure or other physical and/or chemical properties, by functional characteristics coupled with a known or disclosed correlation between function and structure, or by a combination of such identifying characteristics, sufficient to show the inventor was in possession of the claimed genus… [T]he written description must lead a person of ordinary skill in the art to understand that the inventor possessed the entire scope of the claimed invention. [Ariad, 598 F.3d at 1353–54]. (MPEP 2163, II, 3(a), ii).
The specification discloses the metrics used to determine a physiological phenomenon (para. [0122]; “a breathing disturbance metric, a sleep staging metric, a blood oxygen metric, a blood pressure metric, a heartbeat metric (e.g., arrhythmia metric), or a respiratory rate metric.” Although support is provided for a system that can detect physiological phenomena associated with mentioned metrics, the claim, as cited, encompasses a range of phenomena much broader than the metrics described in paragraph [0122]. “Physiological phenomenon” encompasses every possible physiological phenomenon in existence. Further, since the claimed generic physiological phenomena are not well defined, and since the representative species (i.e. the physiological metrics from para. [0122]) is insufficient to show that the inventor was in possession of a device that determines the genus physiological phenomenon, the written description is therefore inadequate to prove ownership of a system that can associate metrics for every possible physiological phenomenon. In short, the limited number of examples of physiological metrics do not represent the entirety of physiological phenomenon claimed.
Claim 9 is rejected for the same issue.
Regarding claim 4, the claim recites “The system of claim 1, wherein a training accuracy associated with training the multi-output neural network to compute the one or more values of the first physiological metric is increased based at least in part on the multi-output neural network being trained to compute the one or more values of the second physiological metric.”
However, the claim recites a functional result (in this case, the increase in training accuracy), but does not disclose how the result is achieved. Citing MPEP § 2161.01: “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
The specification uses generic claim language to describe the increased training accuracy due to contributions from the neural network’s computation of the second physiological metric (para. [0181]). However, the specification does not further describe how to achieve the intended increase in training accuracy. The lack of description raises questions regarding what metrics are input into the network, and how the implementation of these metrics can be used to increase the training accuracy.
Regarding claim 7, the claim recites “ train the multi-output neural network based on a plurality of features within a training physiological dataset associated with a plurality of users.” However, the recitation of “a plurality of features” is an inadequate description for the specific features in which the neural network is based.
The written description requirement for a claimed genus (in this case, a plurality of features) may be satisfied through sufficient description of a representative number of species by actual reduction to practice (see i)(A) above), reduction to drawings (see i)(B) above), or by disclosure of relevant, identifying characteristics, i.e., structure or other physical and/or chemical properties, by functional characteristics coupled with a known or disclosed correlation between function and structure, or by a combination of such identifying characteristics, sufficient to show the inventor was in possession of the claimed genus… [T]he written description must lead a person of ordinary skill in the art to understand that the inventor possessed the entire scope of the claimed invention. [Ariad, 598 F.3d at 1353–54]. (MPEP 2163, II, 3(a), ii).
The specification recites generic claim language to describe the features identical to the language claimed (para. [0139], [0161], [0184], and [0201]). However, no further description of these features is provided, and the description is therefore inadequate. Additionally, no examples of species of features are provided to sufficiently represent the genus. The lack of description raises questions regarding the specific features within the dataset that are used to train the neural network.
Regarding claim 8, the claim recites “wherein the one or more processors are further configured to: process the physiological data simultaneously prior to inputting the physiological data into the multi-output neural network.”
However, the claim recites a functional result (in this case, simultaneous data processing), but does not disclose how the result is achieved. Citing MPEP § 2161.01: “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.”
The specification does not provide support for processing the physiological data simultaneously. The generic claim language is recited in the specification (para. [0118], [0162], and [0202]). Additionally, the specification recites that the multi-output neural network, not the processor, is trained to “simultaneously compute one or more values” (para. [0037], [0094], [0155] and [0195]). The claim recites the functional result of simultaneous data processing, but does not clearly detail any algorithm or steps for achieving this result. Lastly, besides the recited generic claim language, no further description is given to the processor’s configuration. The inadequacy of the description raises questions as to how the processor is configured to process simultaneously, the structure that enables simultaneous processing, and how simultaneous processing is achieved.
Claims 2 and 3, 5 and 6, and 10-14 are rejected for their dependency on claim 1.
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 1-14 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 claim recites “the physiological data comprising heartbeat data collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable device, motion data collected via one or more accelerometers of the wearable device, or both” (ln. 2-6) and then, recites “wherein the physiological data comprises a first input stream corresponding to the heartbeat data and a second input stream corresponding to the motion data” (ln. 11-13). Due to the optional terms “or the motion data” in line 6 and the terms “and a second input stream corresponding to the motion data” it is unclear if both the PPG and accelerometer are necessary for the claimed system.
Further regarding claim 1, the claim recites “the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric, wherein the first physiological metric is associated with a first physiological phenomenon and the second physiological metric is associated with a second physiological phenomenon that is distinct from the first physiological phenomenon.” However, the use of “first physiological phenomenon” and “second physiological phenomenon” is unclear since no definition is provided as to what is considered a physiological phenomenon. Further, the recitation of “distinct” is undefined, and it is unclear what can be considered distinct phenomenon. Lastly, no distinction can be made between a first and second phenomenon because each phenomenon is not defined. As recited, the phenomena are associated with metrics, which are later defined as one of “a breathing disturbance event, a sleep staging metric, a blood oxygen metric, a blood pressure metric, a heartbeat metric, or a respiratory rate metric” (para. [0094]). However, it is unclear with which physiological phenomenon these metrics are in association. As explained above (see 112(a) rejection, there is a great number of biological processes that can be considered physiological phenomenon.
Regarding claim 10, the claim recites “wherein the one or more values associated with the first physiological metric comprises a first time scale and the one or more values associated with the second physiological metric comprise a second time scale same as the first time scale.” .” Due to the claim’s distinction between a “first time scale” and “a second time scale,” and that the second time scale is the “same as the first time,” it is unclear what the difference is between the time scales.
Regarding claim 11, the claim recites “wherein the one or more values associated with the first physiological metric comprises a first resolution and the one or more values associated with the second physiological metric comprise a second resolution same as the first resolution.” Due to the claim’s distinction between a “first resolution” and “a second resolution,” and that they are the “same resolution,” it is unclear what the difference is between the resolutions.
Claims 2-14 are rejected due to their dependency on claim 1.
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.
Claim 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Step 1
Claim 1-14 recites a machine (e.g. a system).
Step 2A, Prong 1
Claim 1 recites “the multi-output neural network is trained to simultaneously compute one or more values associated with a first physiological metric and one or more values associated with a second physiological metric.” Calculating values for a metric is a mathematical concept abstract idea in that the values are created using mathematical functions and computing one or more values associated with a physiological metric is so broadly claimed as to encompass a mathematical process abstract idea. The specification recites calculating average temperature, respiratory rates, acceleration and angular values, sleep metrics, activity metrics, and breathing quality metrics (para. [0213]; “the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium”; All of the mentioned data and metrics are derived from mathematical functions). Likewise, the calculations are so broadly claimed that a human could derive the values and metrics in their head.
Additionally, claim 1 recites “generat[ing]… the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric, wherein the first physiological metric is associated with a first physiological phenomenon and the second physiological metric is associated with a second physiological phenomenon that is distinct from the first physiological phenomenon.” The one or more values are mathematical concept abstract ideas that are essentially the results from previous calculations. The recitation is also an abstract mental process since one could generate values in their head based off the readings of a PPG sensor or accelerometer. Similar to the previously recited calculations, a human could perform the mathematical calculations and obtain results, thus generating the one or more values.
Step 2A, Prong 2
Claim 1 does not include any additional elements that amount to integration of the abstract idea into a practical application. Claim 1 recites the additional elements of “a wearable device configured to acquire physiological data from a user; the physiological data comprising heartbeat data collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable device, motion data collected via one or more accelerometers of the wearable device, or both; and one or more processors communicatively coupled with the wearable device, wherein the one or more processors are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals; input the physiological data into the multi-output neural network, wherein the physiological data comprises a first input stream corresponding to the heartbeat data and a second input stream corresponding to the motion data; wherein the first physiological metric and the second physiological metric each comprises an input stream from at least one of the first input stream, the second input stream, or both; transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric.” Acquiring physiological data from a user, collecting the heartbeat and motion data, receiving the data, inputting it into a neural network, and transmitting the data to a GUI amounts to the insignificant, extra-solution activity of data gathering. The common computer elements and data gathering sensors are recited at such a high level of generality to simply amount to generic computer implementation of an abstract idea. Lastly, displaying one or more messages on the GUI amounts to insignificant, post-solution activity (data display and/or reporting).
Step 2B
Claim 1 does not include any additional elements that amount to significantly more than the abstract idea into a practical application. Claim 1 recites the additional elements of “a wearable device configured to acquire physiological data from a user; the physiological data comprising heartbeat data collected via photoplethysmogram (PPG) measurements from one or more light-emitting components and one or more light-receiving components of the wearable device, motion data collected via one or more accelerometers of the wearable device, or both; and one or more processors communicatively coupled with the wearable device, wherein the one or more processors are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals; input the physiological data into the multi-output neural network, wherein the physiological data comprises a first input stream corresponding to the heartbeat data and a second input stream corresponding to the motion data; wherein the first physiological metric and the second physiological metric each comprises an input stream from at least one of the first input stream, the second input stream, or both; transmit an instruction to a graphical user interface (GUI) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric.” Acquiring physiological data from a user, collecting the heartbeat and motion data, receiving the data, inputting it into a neural network, and transmitting the data to a GUI amounts to the insignificant, extra-solution activity of data gathering. The common computer elements and data gathering sensors are recited at such a high level of generality to simply amount to generic computer implementation of an abstract idea. Lastly, displaying one or more messages on the GUI amounts to insignificant, post-solution activity (data display and/or reporting).
Claims 2, 3, and 12 further define extra-solution activity for data gathering.
Claims 4, 5, 6, 7, 9, 10, 11, and 13 further define mathematical concept abstract ideas.
Claim 8 further defines generic computer structure.
Claim 14 further defines the mathematical concept and mental process abstract ideas by generally linking the abstract idea to a field of use.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 7, 8, 10, and 11-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US 20230114833 A1, “Zhang”).
Regarding claim 1, Zhang discloses a system for utilizing a multi-output neural network (Abstract; Fig. 1 and 2), comprising: a wearable device (wearable device 104; Fig. 1) configured to acquire physiological data from a user, the physiological data comprising heartbeat data collected via photoplethysmogram (PPG) (para. [0043]; "a PPG sensor assembly") measurements from one or more light-emitting components (para. [0071]; “optical transmitter”) and one or more light-receiving components (para. [0071]; “optical receiver”) of the wearable device (wearable device 104; Fig. 1), motion data collected via one or more accelerometers (para. [0028]; "accelerometer data (e.g., movement/motion data; para. [0075]; "The ring 104 may include one or more motion sensors 245, such as one or more accelerometers); of the wearable device (wearable device 104; Fig. 1), or both; and one or more processors communicatively coupled with the wearable device (para. [0053]; " The processing module 230-a of the ring 104 may include one or more processors (e.g., processing units)"), wherein the one or more processors (Fig. 2; processing module 230-a) are configured to: receive the physiological data acquired via the wearable device via one or more electronic signals (Fig. 2; para. [0053]; "For example, the processing module 230-a may transmit/receive data to/from the modules and other components of the ring 104, such as the sensors"; input the physiological data (para. [0116]; "In particular, the motion data collected/processed at 305 through 320, and the PPG data collected and processed at 325 through 335, may be inputted into a machine learning model" into the multi-output neural network [para. [0116]; “machine learning model”, wherein the physiological data comprises a first input stream corresponding to the heartbeat data (para. [0116]; Fig. 3; the input stream going from 325 to 335) and a second input stream corresponding to the motion data (para. [0116]; Fig. 3; the input stream going from 305 to 320), wherein the multi-output neural network is trained to simultaneously compute one or more values associated with a first physiological metric (para. [0078]; “For example, the processing module 230 may calculate and store various metrics, such as sleep metrics”) and one or more values associated with a second physiological metric (para. [0078; “The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion) wherein the first physiological metric and the second physiological metric each comprises an input stream from at least one of the first input stream, the second input stream, or both (Fig. 3; The input stream from the PPG data (325) and the motion data (305) are input into 340 to output heart rate (345)). generate, via a single pass of the multi-output neural network, the one or more values associated with the first physiological metric (para. [0118]; " As such, the machine learning model may be trained to receive PPG data and motion data as inputs, and generate heart rate”) and the one or more values associated with the second physiological metric (para. [0078; “The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion), wherein the first physiological metric is associated with a first physiological phenomenon (para. [0078]; The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion. Example derived values for motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalence of task values (METs), and orientation values) and the second physiological metric is associated with a second physiological phenomenon that is distinct from the first physiological phenomenon (para. [0025]; " Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters"; The PPG data and heart phenomenon are distinct from the accelerometer and motion phenomenon; and transmit an instruction to a graphical user interface (GUI) (para. [0141]; GUI 500) of a user device associated with the wearable device, the instruction configured to cause the GUI to display one or more messages (The user device may display recommendations and/or information associated with the heart rate data via a message) based at least in part on generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric (para. [0025]; " Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters").
It should be noted that the specification never defines the physiological phenomenon in the specification. Further, the distinction between the first and second physiological phenomenon is also lacking definition. Therefore, any physiological phenomena that are different could be considered distinct. Zhang discloses determining distinctly different physiological parameters (para. [0025]; “Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters". Zhang also discloses a variety of different metrics/values that are measured (para. [0078]; “the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., a Sleep Score), activity metrics, and readiness metrics.”).
Regarding claim 7, Zhang discloses the system of claim 1 (see above), wherein the one or more processors are further configured to: train the multi-output neural network based on a plurality of features within a training physiological dataset associated with a plurality of users. (para. [0119]; the system 200 may train multiple versions of a machine learning model, such as for different demographics of users (e.g., different age groups, varying levels of activity/performance, different skin tones, etc.), for users with varying medical conditions, and the like. In this regard, different models which are tailored to different demographics of users may be used to further fine-tune the ability of the respective models to perform heart rate detection. For example, the system 200 may acquire physiological data from a user who is an avid runner, and may utilize a machine learning model trained on data from other runners to perform heart rate detection for the user.”).
Regarding claim 8, Zhang discloses the system of claim 1 (see above), wherein the one or more processors are further configured to: process the physiological data simultaneously prior to inputting the physiological data into the multi-output neural network. (para. [0099]; “At 310, the wearable device, the user device, the servers, or any combination thereof, may preprocess the motion data.”; para. [0105]; “At 330, the wearable device, the user device, the servers, or any combination thereof, may preprocess the PPG data.”; Both 310 and 330 process physiological data simultaneously prior to merging the data and calculating the heart rate output).
Regarding claim 10, Zhang discloses the system of claim 1 (see above), wherein the one or more values associated with the first physiological metric (para. [0093]; “PPG data”) comprises a first time scale and the one or more values associated with the second physiological metric (para. [0093]; “motion data.”) comprise a second time scale same as the first time scale. (para. [0093]; “Additionally, or alternatively, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data).
Regarding claim 11, Zhang discloses the system of claim 1 (see above), wherein the one or more values associated with the first physiological metric comprises a first resolution and the one or more values associated with the second physiological metric comprise a second resolution same as the first resolution. (para. [0093]; “Additionally, or alternatively, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data).
Due to the lack of specificity in the claim as to what is considered a resolution, the claim is interpreted that a resolution can be any type of resolution. Additionally, the specification recites a “thirty second resolution (e.g., time scale) (para. [0131]).” Therefore, using the broadest reasonable interpretation of a resolution to mean any type of resolution, Zhang’s disclosure of time scales read as resolutions.
Regarding claim 12, Zhang discloses the system of claim 1 (see above), wherein the physiological data is acquired throughout a time interval that includes one or more sleep intervals of the user. (para. [0032]; “In this example, the ring 104-a may collect physiological data associated with the user 102-a, including temperature, heart rate, HRV, respiratory rate, and the like. In some aspects, data collected by the ring 104-a may be input to a machine learning classifier, where the machine learning classifier is configured to determine periods of time that the user 102-a is (or was) asleep. Moreover, the machine learning classifier may be configured to classify periods of time into different sleep stages…”.
Regarding claim 13, Zhang discloses the system of claim 1 (see above), wherein at least one of the first physiological metric and the second physiological metric comprises at least one of a breathing disturbance event, a sleep staging metric, a blood oxygen metric, a blood pressure metric, a heartbeat metric, or a respiratory rate metric. (para. [0040]; “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).
Regarding claim 14, Zhang discloses the system of claim 1 (see above), wherein the wearable device comprises a wearable ring device. (Fig. 1; para. [0040]; “ring 104”).
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) 2-6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230114833 A1, “Zhang”) in view of Baker et al. (US 20090287070 A1, “Baker”).
Regarding claim 2, Zhang et. al discloses the system of claim 1 (see rejection above). Zhang also discloses wherein the physiological data comprises a third input stream corresponding to temperature data (para. [0077]; “The ring 104 may store a variety of data described herein. For example, the ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data.”
However, Zhang does not disclose wherein the first physiological metric and the second physiological metric each comprises an input stream from at least one of the first input stream, the second input stream, the third input stream, or a combination thereof.
Baker, in the same field of endeavor of using neural networks to predict physiological parameters, discloses a neural network used to process physiological data. Baker discloses wherein the first physiological metric (Fig. 3; 312-1) and the second physiological metric (Fig. 3; 312-2; para. [0037]; “ In the embodiment shown, the output layer 308 delivers the estimated physiological parameter, in this case SpO.sub.2, in an output 312-1.”) each comprises an input stream from at least one of the first input stream (Fig. 3; 310-1), the second input stream (Fig. 3; 310-2), the third input stream (Fig. 3; 310-n), or a combination thereof. (See Fig. 3 below where the outputs (312-1-2) are shown as a combination of inputs (310-1-n); Additionally, a multi-output neural network is clearly shown in Fig. 3 below).
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It would have been obvious for one of ordinary skill in the art to combine the system, including its components, and the temperature sensor and data of Zhang with the neural network system of Baker since doing so would enable faster data processing. Further, the combination of Zhang and Baker would enable the system to calculate physiological metrics that require temperature data in combination with other data, enhancing the versatility of the system to predict a wider range of physiological metrics.
Regarding claim 3, the combination of Zhang and Baker disclose the system of claim 2 (see rejection above). However, the combination does not disclose wherein the physiological data comprises a fourth input stream corresponding to blood oxygen data, wherein the first physiological metric and the second physiological metric each comprises an input stream from at least one of the first input stream, the second input stream, the third input stream, the fourth input stream, or a combination thereof.
Zhang discloses wherein the physiological data comprises a fourth input stream (310-n) corresponding to blood oxygen data (para. [0007]; “Another aspect of the disclosure is a pulse oximeter that incorporates a neural network for the calculation of blood oxygen saturation), wherein the first physiological metric (312-1) and the second physiological metric (312-2) each comprises an input stream from at least one of the first input stream, the second input stream, the third input stream, the fourth input stream, or a combination thereof. (As shown in Fig. 3 above, the multi-input neural network of Baker is equipped to handle a number of inputs (denoted by 310-n), Although only 3 inputs are shown, the “n” represents a variable denoting any number of inputs; The inputs are shown contributing in combination to outputs 312-1 and 2; Additionally, Baker discloses that the neural network is not limited to only blood oxygen metrics; para. [0020]; “Although described in detail in this context of a pulse oximeter displaying oxygen saturation measurements, the reader will understand that the systems and methods described herein may be equally adapted to the estimation and display of any physiological parameter of any patient (human or non-human) generated by any monitoring device, including but not limited to pulse rate, blood pressure, temperature, cardiac output, respiration parameters, and measures of blood constituents other than oxygenation.”).
It would have been obvious for one of ordinary skill in the art to combine the multi-input multi-output neural network of Zhang and Baker with the blood oxygen data disclosed in Baker since doing so would enable faster processing. Further, the combination of Zhang and Baker would enable the system to calculate physiological metrics that require blood oxygen data in combination with other data, enhancing the versatility of the system to predict a wider range of physiological metrics.
Regarding claim 4, Zhang discloses the system of claim 1 (see 102 rejection above). However, Zhang does not disclose wherein a training accuracy associated with training the multi-output neural network to compute the one or more values of the first physiological metric is increased based at least in part on the multi-output neural network being trained to compute the one or more values of the second physiological metric.
Baker discloses wherein a training accuracy associated with training the multi-output neural network (para. [0019]; “neural networks”) to compute the one or more values of the first physiological metric (para. [0019]; “the accuracy of the estimate”) is increased (para. [0019]; “the improved estimates”) based at least in part on the multi-output neural network being trained to compute the one or more values of the second physiological metric. (para. [0019]; “This disclosure describes the training and use of backpropagation neural networks to calculate an improved estimate of physiological parameters such as a patient's pulsatile oxygen saturation, pulse rate, or the accuracy of the estimate”; Additionally, para. [0037]; “In the embodiment shown, the output layer 308 delivers the estimated physiological parameter, in this case SpO.sub.2, in an output 312-1. A second output 312-2 is also illustrated, such as would deliver an estimated accuracy.”
It would have been obvious for one of ordinary skill in the art to combine the system and components of Baker with the neural network of Baker to improve the accuracy of the output physiological metric. The training of the network would allow the system to continuously improve the accuracy of the output metrics of Zhang (such as heart beat, motion, and sleep metrics), which would provide the system’s user with more accurate representations of their physiological health.
Regarding claim 5, Zhang discloses wherein the multi-output neural network is trained to simultaneously compute one or more values associated with a third physiological metric (para. [0017]; “For instance, the machine learning model may be configured to differentiate between candidate heart rate measurements that are attributable to motion artifacts from candidate heart rate measurements that are indicative of the user's actual heart rate…By way of another example, the machine learning model may be configured to identify time-domain and/or frequency-domain features within the received PPG data and motion data (which may be used to identify candidate heart rate measurements), and may be configured to determine or estimate a heart rate for the user based on the identified features”), wherein the third physiological metric comprises an input stream from at least one of the first input stream, the second input stream, or both (Fig. 3; Motion data 305 and PPG data 325 both input into heart rate detection 340), wherein the one or more processors are further configured to: generate, via the single pass of the multi-output neural network, the one or more values associated with the third physiological metric, wherein the third physiological metric is associated with a third physiological phenomenon that is distinct from the first physiological phenomenon and the second physiological phenomenon (para. [0193]; The code may include instructions executable by a processor to receive physiological data associated with the user, the physiological data comprising PPG data and motion data collected throughout a first time interval via a wearable device associated with the user, determine a set of candidate heart rate measurements within the first time interval based at least in part on the PPG data, select a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the received motion data, and determine a first heart rate for the user within the first time interval based at least in part on the selected first heart rate measurement)
However, Zhang does not specifically disclose wherein the multi-output neural network is trained to simultaneously compute one or more values associated with a third physiological metric or wherein the one or more processors are further configured to: generate, via the single pass of the multi-output neural network, the one or more values associated with the third physiological metric.
Baker, in the same field of endeavor of using neural networks to predict physiological parameters, discloses a neural network used to process physiological data (Fig. 3; neural network 300). Baker discloses wherein the neural network can be adapted to output a variety of physiological parameters (para. [0020]; “Although described in detail in this context of a pulse oximeter displaying oxygen saturation measurements, the reader will understand that the systems and methods described herein may be equally adapted to the estimation and display of any physiological parameter of any patient (human or non-human) generated by any monitoring device, including but not limited to pulse rate, blood pressure, temperature, cardiac output, respiration parameters, and measures of blood constituents other than oxygenation.”)
It would have been obvious for one of ordinary skill in the art to combine the system, including its components, and the temperature sensor and data of Zhang with the neural network system of Baker since doing so would enable faster data processing. Further, it would have been obvious to add more inputs and outputs to the neural network since doing so would allow the device to predict more physiological metrics. As disclosed in Baker (para. [0020]), one of ordinary skill in the art would understand that the neural network is not limited to one physiological output, and the network could be adapted for multiple output types. It would have been obvious to adapt the neural network with multiple physiological inputs and outputs for a device capable of generating a third physiological metric.
Regarding claim 6, Zhang discloses the multi-output neural network of claim 1 (see 102 rejection above). However, Zhang does not disclose wherein the one or more processors are further configured to: train the multi-output neural network based at least in part on inputting the physiological data into the multi-output neural network and generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric.
Baker discloses wherein the one or more processors (Fig. 6; para. [0065]; “microprocessor 608 performs the functions of the neural network module 610”) are further configured to: train the multi-output neural network based at least in part on inputting the physiological data into the multi-output neural network and generating the one or more values associated with the first physiological metric and the one or more values associated with the second physiological metric (para. [0036]; “Rather, the neural network, during the process of training, creates a non-linear and complex model that generates an output based on all the inputs.” Para. [0007]; “The pulse oximeter includes: a microprocessor capable of calculating an estimated value of oxygen saturation of a patient's blood at least in part upon information received from a sensor; a neural network module capable of receiving the estimated value as an input and calculating a revised value of oxygen saturation of a patient's blood using a neural network).
It would have been obvious to one of ordinary skill in the art to combine the neural network configured to be trained with the system of Zhang since doing so would improve the processing speed and accuracy of Zhang’s physiological data. Further, it would have been obvious to use multiple physiological outputs with Baker’s neural network since Baker discloses adapting the neural network for other types of outputs [para. 0020]). Doing this would enhance Zhang’s system by enabling it to predict multiple physiological values and metrics based on multiple physiological data types.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230114833 A1, “Zhang”) in view of Pho et al. (US 20220409187 A1, “Pho”).
Regarding claim 9, Zhang discloses the system of claim 1 (see 102 rejection above). Zhang also discloses a first and second physiological metrics associated with physiological phenomenon (para. [0078]; motion data; “The ring 104, or other computing/wearable device, may calculate a variety of values/metrics with respect to motion. Example derived values for motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalence of task values (METs), and orientation values”; heart beat data; para. [0025]; " Some electronic devices (e.g., wearable devices 104, user devices 106) may measure physiological parameters of respective users 102, such as photoplethysmography waveforms, continuous skin temperature, a pulse waveform, respiration rate, heart rate, heart rate variability (HRV), actigraphy, galvanic skin response, pulse oximetry, and/or other physiological parameters"). However, Zhang never specifies that the metrics are associated with a probability.
Pho, in the same field of endeavor of wearable sensor used to detect physiological phenomenon, discloses a wearable device used to detect illness during a menstrual cycle. Pho discloses wherein the first physiological metric is associated with a probability that a user experienced a first physiological phenomenon during a time period (para. [0054]; “In some implementations, techniques 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, where the satisfaction of one or more deviation criteria may be used to predict illness risk metrics (e.g., “risk scores”), illness prediction metrics, illness severity metrics, illness recovery metrics, and the like.)
It would have been obvious for one of ordinary skill to combine the system and physiological metrics of Zhang with Pho’s association between physiological metrics and probability metrics since doing so would enable Zhang’s device to not only calculate metrics, but also predict if the user is experiencing a phenomenon. Further, it would have been obvious to one of ordinary skill to try and predict multiple types of physiological phenomenon such as those disclosed in Zhang to increase the range of possible physiological phenomenon the device is capable of detecting.
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/OWEN LEWIS MARSH/
Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796