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
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because of the following analysis:
1 – statutory category: Claims 1-11 and 20 recite a system, and therefore, falls under the statutory category of being a thing or products. See MPEP 2106.03. Claim 12-19 recite a series of steps and therefore, falls under the statutory category of being a process. See MPEP 2106.03.
2A – Prong 1: The independent claims 1, 12 and 20 recite a judicial exception by reciting the limitations of “generate, based on the heart rate values and the activity type or the movement values, a user-specific predicted heart rate for at least a part of the time period” for claims 1 and 12, and “determine an activity classification based on the movement values; evaluate the signal quality of the measured heart rate for the period of time, wherein if a confidence level of the measured heart rate is low, finding a predicted heart rate for the user during the period of time based on the movement values in a personalized lookup table associated with the activity” for claim 20. These limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in mind or by a person using a pen and paper. Therefore, an abstract idea is involved.
It is further noted that the act of inputting training data into a learning model and using a trained model falls under the judicial exception of mathematical calculations.
2A – Prong 2: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The independent claims 1, 12, and 20 recite the additional limitations of “one or more heart rate sensors” “one or more movement sensors”, “user interface”, “processors”, “memory”, “display”, etc. The mentioned limitations are recited at a high level of generality and are considered to be data gathering/processing which are mere extra-solution activity. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
2B: The emphasized elements cited above do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’I, 110 USPQ2d 1976 (2014)).
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claims 2-11 and 13-19 depend on claims 1 and 12 respectively. The mentioned dependent claims recite the same abstract idea as the independent claims. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the mental process). For example, the dependent claim recites the limitations “3-axis accelerometer and a gyroscope”, “photoplethysmography (PPG) sensor”, etc., are recited at a high level of generality and are mere extra-solution activity, and recited as performing generic computer functions. i.e., data processing. The elements amount to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.04(d) and 2106.05(f)).
The additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Thus, claims 1-20 are directed to an abstract idea and are therefore rejected.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-3, 5-7, 9-10, 12-14, 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Pat Pub 20190090756 to Lu et al. (hereinafter “Lu”).
Regarding claims 1 and 12. Lu discloses a device/method for determining a personalized heart rate (para 0012 “wearable computing device” figs 1 and 2), comprising: one or more heart rate sensors configured to obtain heart rate values for a user for a time period (para 0019 “heart rate sensor(s) 120 [] generating or producing sensor data indicative of a heart rate of the user”); one or more movement sensors configured to obtain movement values for the user for the time period (para 0018 “motion sensor(s) 114… motion data indicative of any type of characteristic of the motion of the user while the user performs a present activity”), wherein the movement values are associated with an activity type (para 0025 “determine a type of physical activity (e.g., running, walking, sitting)”); wherein one or more of the heart rate values are associated with a signal quality that is less than a signal quality threshold (para 0027 “determine that that the user's heart rate should be estimated if the quality of the heart rate sensor data is poor (e.g., does not satisfy a reference relationship with one or more threshold quality values)”, para 0048, block 404 “determine whether to estimate the heart rate by analyzing the quality of the heart rate sensor data/signals received”); one or more processors configured to execute a personalized heart rate prediction model, trained on user-specific movement data and user-specific heart rate data associated with signal qualities greater than the signal quality threshold (para 0047, 0058 “employ a machine learning or other training algorithm to update the heart rate estimation model based on heart rate sensor data produced by the heart rate sensors in block 444. That is, as discussed above, when the heart rate sensor data is of high quality (e.g., has a high confidence value associated with it), the measured heart rate sensor data may be used to update the heart rate estimation model 236”), to: receive the heart rate values and the activity type or the movement values(para 0051-0053, block 412, block 416, para 0056, “produce a final value for the user’s heart rate”); and generate, based on the heart rate values and the activity type or the movement values, a user-specific predicted heart rate for at least a part of the time period (para 0051-0053, block 412, block 416, para 0056, “produce a final value for the user’s heart rate”); and a user interface configured to output the user-specific predicted heart rate as the user heart rate for at least the part of the time period (para 0040 “display the estimated heart rate to the user (e.g., via display 128)”).
Regarding claims 2 and 13. Lu discloses the device of claim 1 and method of claim 12, wherein the activity type comprises a non-resting activity or other activity type (para 0018, 0025 “activities may include, for example, walking, running, sitting, resting, etc.”).
Regarding claims 3 and 14. Lu discloses the device of claim 2 and method of claim 13, wherein the non-resting activity type is running, walking, rowing, or biking (para 0018, 0025 “activities may include, for example, walking, running, sitting, resting, etc.”).
Regarding claims 5 and 16. Lu discloses the device of claim 1 and method of claim 12, wherein the personalized heart rate prediction model is trained on user-specific movement data and user-specific heart rate data associated with signal qualities greater than the signal quality threshold by storing the user-specific movement data and user-specific heart rate data in a lookup table associated with the activity type (para 0029, 0036 “user's average or median heart rate during various activities (e.g., baseline activities) [] perform an initialization procedure in which the user is requested to perform each of the various activities while the user's average heart rate is determined for the specific, requested activity” – the claim does not provide any details regarding the lookup table, therefore, under its BRI, any data stored and stores predetermined/ precalculated results, mapping input values to output values would read over the claimed limitation. Here, Lu discloses during initialization procedure, user's average heart rate is determined for the specific, requested activity. Thus, a precalculated average for each activity is provided serving the same function as a “lookup table”.).
Regarding claims 6 and 17. Lu discloses the device of claim 5 and method of claim 14, wherein the lookup table is generated based on a plurality of user-specific movement data and user-specific heart rate data for the activity type (para 0029 “the heart rate from the heart rate sensor(s) 120 is similar to the average received heart rate from the heart rate sensor(s) 120 for a given activity performed by the user”, para 0036 “user's average or median heart rate during various activities (e.g., baseline activities) [] perform an initialization procedure in which the user is requested to perform each of the various activities while the user's average heart rate is determined for the specific, requested activity” - Lu discloses comparing heart rate to average heart rate for a specific activity, therefore, it is understood that a “lookup table” or similar is inherently provided).
Regarding claims 7 and 18. The device of claim 6 and method of claim 17, wherein the activity type is determined based on motion frequency and density (para 0012, “collects motion data indicative of characteristics of the motion of the user (e.g., pace of user's motion, intensity of user's motion, step frequency, type of motion, identified user activity, etc.) and uses the collected motion data as an input to a heart rate estimation model.”, para 0018 “motion sensor(s) 114 may be indicative of a type, an intensity, and/or a pace of the present activity performed by the user”, para 0025, etc., “motion frequency” is interpreted as the intensity, and “density” is interpreted as the pace).
Regarding claims 9 and 19. Lu discloses the device of claim 1 and method of claim 12, wherein the one or more heart rate sensors comprise a photoplethysmography (PPG) sensor (para 0019 “the heart rate sensor(s) 120 may include one or more optical sensors 122 configured to generate heart rate sensor data based on optical signals transmitted through the skin of the user.”).
Regarding claim 10. Lu discloses the device of claim 5, wherein, to generate the user-specific predicted heart rate for at least a part of the time period, the one or more processors are further configured to: evaluate signal quality with a confidence interval for the measured heart rate for a period of time (para 0027 discussing “quality”, para 0026 “confidence analyzer”, 0029-0030, 0049).
Regarding claim 20. Lu discloses a system for providing a personalized heart rate for a user of a heart rate monitoring apparatus (para 0012 system associated with “wearable computing device” figs 1 and 2), the system comprising: one or more heart rate sensors (para 0019 “heart rate sensor(s) 120 [] generating or producing sensor data indicative of a heart rate of the user”); one or more movement sensors (para 0018 “motion sensor(s) 114… motion data indicative of any type of characteristic of the motion of the user while the user performs a present activity”); and the heart rate monitoring apparatus, comprising: one or more processors; one or more memory (para 0016); and a display (para 0040 “display the estimated heart rate to the user (e.g., via display 128)”), wherein the one or more processors of the heart rate monitoring apparatus are configured to: measure a heart rate of the user during an activity for a period of time via the one or more heart rate sensors (para 0019 “heart rate sensor(s) 120 [] generating or producing sensor data indicative of a heart rate of the user”); measure movement values for the user during the activity for the period of time via the one or more movement sensors (para 0018 “motion sensor(s) 114… motion data indicative of any type of characteristic of the motion of the user while the user performs a present activity”); determine an activity classification based on the movement values (para 0025 “determine a type of physical activity (e.g., running, walking, sitting)”); evaluate the signal quality of the measured heart rate for the period of time (para 0027 “determine that that the user's heart rate should be estimated if the quality of the heart rate sensor data is poor (e.g., does not satisfy a reference relationship with one or more threshold quality values)”, para 0048, block 404 “determine whether to estimate the heart rate by analyzing the quality of the heart rate sensor data/signals received”), wherein if a confidence level of the measured heart rate is low, finding a predicted heart rate for the user during the period of time (para 0027 discussing “quality”, para 0026 “confidence analyzer”, 0029-0030 “the heart rate determination manager 204 may determine to estimate the heart rate of the user when the confidence score falls below a confidence score threshold value”, 0049) based on the movement values in a personalized lookup table associated with the activity (para 0029, 0036 “user's average or median heart rate during various activities (e.g., baseline activities) [] perform an initialization procedure in which the user is requested to perform each of the various activities while the user's average heart rate is determined for the specific, requested activity” – the claim does not provide any details regarding the lookup table, therefore, under its BRI, any data stored and stores predetermined/ precalculated results, mapping input values to output values would read over the claimed limitation. Here, Lu discloses during initialization procedure, user's average heart rate is determined for the specific, requested activity. Thus, a precalculated average for each activity is provided serving the same function as a “lookup table”.); and display a heart rate diagram based on the heart rate of the user during the activity via a device display (para 0040 “display the estimated heart rate to the user (e.g., via display 128)”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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.
Claim(s) 4, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 20190090756) in view of US Pat Pub 20160058367 to Raghuram et al. (hereinafter “Raghuram”).
Regarding claims 4 and 15. Lu discloses the device of claim 1 and method of claim 12, but fails to disclose wherein the one or more heart rate sensors are configured to obtain a heart rate signal having a plurality of different frequency components, wherein the one or more processors are configured to execute the personalized heart rate prediction model to select one of the plurality of different frequency components as corresponding to the heart rate value.
Raghuram, from a similar field of endeavor teaches using different frequency components to analyze and identify dominant frequency corresponding to the user's heart rate (para 0051). It would have been obvious before the effective filing date of the claimed invention to one of ordinary skills in the art to modify the disclosure of Lu with the teachings of Raghuram to provide the predictable result of identify dominant frequency corresponding to the user's heart rate to calculate user’s heart rate.
Regarding claim 8. Lu discloses the device of claim 1, wherein the one or more movement sensors comprise a[n] accelerometer and a gyroscope (para 0018 “one or more accelerometers 116 and/or one or more gyroscopes”). Lu fails to explicitly disclose the accelerometer to be a 3-axis accelerometer.
Raghuram, from a similar field of endeavor teaches the accelerometer to be a 3-axis accelerometer (para 0034, 0080 “accelerometer 914 can incorporate a 3-axis”). It would have been obvious before the effective filing date of the claimed invention to one of ordinary skills in the art to modify the disclosure of Lu with the teachings of Raghuram to provide the predictable result measuring motion in all planes/directions.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 20190090756) in view of US Pat Pub 20230190120 to Pacheco et al. (hereinafter “Pacheco”).
Regarding claim 11. Lu discloses the device of claim 10, wherein, to generate the user-specific predicted heart rate for at least a part of the time period, when the evaluated signal quality has a low confidence interval (para 0029-0030 “the heart rate determination manager 204 may determine to estimate the heart rate of the user when the confidence score falls below a confidence score threshold value”, 0049), but fails to disclose the one or more processors are further configured to: calculate a maximum prediction heart rate for the activity; and perform post-processing smoothing prediction for the heart rate.
Pacheco, from a similar field of endeavor teaches when the quality flag changes from reliable to unreliable (para 0087) calculating a maximum prediction heart rate for various activities (figs 1-2, para 0087 “manager always estimates the HR”) and performing post-processing smoothing prediction for the heart rate (para 0106) to ensure the smoothness and integrity of the HR final curve (para 0106) to provide a more natural and closer to a HR curve (para 0085). It would have been obvious before the effective filing date of the claimed invention to one of ordinary skills in the art to modify the disclosure of Lu with the teachings of Pacheco to provide the predictable result of providing a more smooth, natural and closer to a HR curve.
Conclusion
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/SANA SAHAND/Examiner, Art Unit 3796