DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the claims
The arguments received on March 12, 2025 have been acknowledged and entered. Claims 1, 11, and 20 are amended. Thus, 1, 3-11, and 13-22 are currently pending.
Response to Arguments
Applicant’s arguments filed March 12, 2025 with respect to claim(s) 1, 3-11, and 13-22 under 35 U.S.C. 101 have been considered but are moot because the new ground of rejection.
Applicant’s arguments filed March 12, 2025 with respect to claim(s) 1, 3-11, and 13-22 under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection. However, since the rejection below relies on previously cited prior art, Applicant’s arguments with respect to Balakrisnan are addressed as follows:
On the page 10 of the Remarks, Applicant alleges that “[B]alakrishnan and Luo do not teach or suggest at least receiving orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb…generating, using the trained prediction model, an exercise recommendation based on the sensor data, the exercise prediction, and historical data associated with the user; or transmit a signal representing the exercise prediction and the exercise recommendation for display on a user interface.”
Examiner respectfully disagrees. Balakrishnan discloses, in Fig. 1 and para. [0070], that acceleration values may be processed by a processor, such as processor 202, associated with a device, such as device 112, 126, 128, 130, and/or 400 to calculate one or more attributes. Further, Balakrishnan discloses, in para. [0102], that the physical orientation of an accelerometer circuit and/or chip in a device, such as device 112, 126, 128, 130, and/or 400, may control the output values from the accelerometer. Examiner interprets that “acceleration values may be processed by a processor, such as processor 202, associated with a device, such as device 112, 126, 128, 130, and/or 400” in para. [0070] and “the physical orientation of an accelerometer circuit” in para. [0102] as “receive orientation data from a second sensor circuit coupled to a mobile computing device.”
Further, Balakrishnan discloses, in para. [0069], that training data may be recorded for one or more individuals performing one or more specific activities. Further, Balakrishnan discloses, in para. [0078], that one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting. Further, Balakrishnan discloses, in para. [0027], being calculated athletic attributes, feedback signals to provide guidance, and/or other information. Examiner interprets that “training data may be recorded for one or more individuals performing one or more specific activities” in para. [0069], “one or more walking and/or running models for predicting” in para. [0078], and “calculated athletic attributes, feedback signals to provide guidance, and/or other information” as “generate, using the trained prediction model, an exercise recommendation based on the sensor data, the exercise prediction, and historical data associated with the user, wherein the exercise recommendation includes a recommendation about the exercise activity.”
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, 3-11, and 13-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A fitness tracking device worn on a user limb comprising:
a first sensor circuit configured to generate sensor data, wherein the first sensor circuit is configured to measure motion of the user limb about at least one axis;
a processor coupled to the sensor circuit;
a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to:
buffer sensor data associated with motion of the user limb to filter noise data associated with the motion;
receive orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb:
training a prediction model using the sensor data associated with motion of the user limb and the orientation data;
generate an exercise prediction based on the trained prediction model, the sensor data, and the orientation data, the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about at least one sensor axis, wherein the genus prediction represents a type of exercise activity and the exercise prediction represents a species prediction associated with at least one of equipment type or user position during motion of the user limb, and wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb and the orientation data associated with the orientation of the mobile computing device;
generate, using the trained prediction model, an exercise recommendation based on the sensor data, the exercise prediction, and historical data associated with the user, wherein the exercise recommendation includes a recommendation about the exercise activity; and
transmit a signal representing the exercise prediction and the exercise recommendation for display on a user interface.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
For example, the step of “training a prediction model using the sensor data associated with motion of the user limb and the orientation data (see paras. [0025], [0054]-[0061], [0101] of instant application),” “generate an exercise prediction based on the trained prediction model and the sensor data, and the orientation data the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about at least one sensor axis, wherein the genus prediction represents a type of exercise activity and the exercise prediction represents a species prediction associated with at least one of equipment type or user position during motion of the user limb, and wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb and the orientation data associated with the orientation of the mobile computing device (see paras. [0101], [00213]-[00214] of instant application)” are mathematical calculations. Further, “generate, using the trained prediction model, an exercise recommendation based on the sensor data, the exercise prediction, and historical data associated with the user, wherein the exercise recommendation includes a recommendation about the exercise activity (see para. [00189] of instant application)” is mental process (evaluation/judgment/opinion) based on mathematical calculations (i.e. trained prediction model). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical calculation, then it falls within the “Mathematical Concepts” of abstract ideas. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation (see MPEP 2016.04(a)(2)C).
Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 11 and 20.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: a fitness tracking device worn on a user limb (preamble); a processor; a memory; a first sensor circuit configured to generate sensor data, wherein the first sensor circuit is configured to measure motion of the user limb about at least one axis; buffer sensor data associated with motion of the user limb to filter noise data associated with the motion; transmitting a signal representing the exercise prediction for display on a user interface; receive orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb;
In Claim 11: a method of fitness exercise tracking (preamble); buffering sensor data associated with motion of the user limb, the sensor data generated by a first sensor circuit, wherein the sensor data is buffered to filter noise data associated with the motion; receiving orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb; and
In Claim 20: a non-transitory computer-readable medium or media having stored thereon machine interpretable instructions which, when executed by a processor (preamble); buffering sensor data associated with motion of the user limb, the sensor data generated by a first sensor circuit, wherein the sensor data is buffered to filter noise data associated with the motion; receiving orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb.
The generically recited preamble represents field-of-use limitation that is not meaningful. The additional element such as a first sensor circuit configured to generate sensor data, a second sensor circuit coupled to a mobile computing device (i.e. smart phone or mobile phone), a processor, a memory, and a non-transitory computer-readable medium or media are recited at a high-level of generality (MPEP 2106.05(d)).
The additional elements of “a first sensor circuit configured to generate sensor data, wherein the first sensor circuit is configured to measure motion of the user limb about at least one axis” in claim 1 is not meaningful and represent insignificant (gathering data) extra-solution activity to perform abstract idea. see MPEP 2106.05(g). The limitation of “measure motion of the user limb about at least one axis” in claim 1 is merely a part of the model, which is abstract idea and is also merely a description of sensor data to perform abstract idea. Further the additional elements of “buffer sensor data associated with motion of the user limb to filter noise data associated with the motion” in claim 1 and “buffering sensor data associated with motion of the user limb, the sensor data generated by a first sensor circuit, wherein the sensor data is buffered to filter noise data associated with the motion” in claims 11 and 20 are not meaningful and represent insignificant (gathering data) extra-solution activity to perform abstract idea (see MPEP 2106.05(g)). The limitation of “where the sensor data is buffered to filter noise data associated with the motion” is merely a description of sensor data to perform abstract idea. Also, the additional elements of “receive orientation data from a second sensor circuit coupled to a mobile computing device, wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb” in claims 1, 11, 20 is not meaningful and represent insignificant (gathering data) extra-solution activity to perform abstract idea (see MPEP 2106.05(g)). The limitation of “measuring an orientation of the mobile computing device during the motion of the user limb” is merely a part of the model, which is abstract idea and is also merely a description of sensor data to perform abstract. Furthermore, the additional element of “transmitting a signal representing the exercise prediction and the exercise recommendation for display on a user interface” is not meaningful and represent insignificant (post-solution activity) extra-solution activity merely after performing abstract idea (i.e., exercise prediction and the exercise recommendation) (see MPEP 2106.05(g)). Merly “transmitting a result (i.e., a signal representing the exercise prediction and the exercise recommendation) for display” is nothing more than outputting a signal or displaying result. There is established case law (electric power group for example) to prove that such a feature is insufficient extra solution activity. See MPEP 2106.05(g).
As discussed above, with respect to integration of the abstract idea into a practical application, using a computer system to perform the processes of " training a prediction model using the sensor data associated with motion of the user limb and the orientation data; generate an exercise prediction based on the trained prediction model, and-the sensor data, and the orientation data, the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about the at least one sensor-axis, wherein the genus prediction represents a type of exercise activity and the exercise prediction represents a species prediction associated with at least one of equipment type or user position during motion of the user limb, and wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with the motion of the user limb and the orientation data associated with the orientation of the mobile computing device; generate, using the trained prediction model, an exercise recommendation based on the sensor data, the exercise prediction, and historical data associated with the user, wherein the exercise recommendation includes a recommendation about the exercise activity; " amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept cannot provide statutory eligibility. Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 1 is not patent eligible.
Regarding claims 3-10, 13-19, and 21-22,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations. The explanation for the rejection of Claims 1, 11, and 20, therefore is incorporated herein and applied to Claims 3-10, 13-19, and 21-22. These claims therefore stand rejected for similar reasons as explained in above Claims 1, 11, and 20.
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 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.
Claims 1, 3-11, and 13-22 are rejected under 35 U.S.C. 102 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Balakrisnan et al. (US 2016/0223580 A1, hereinafter referred to as “Balakrisnan”).
Regarding claim 1, Balakrisnan discloses fitness tracking device worn on a user limb (para. [0027]; para. [0046]; para. [140]; Fig.1) comprising:
a first sensor circuit configured to generate sensor data (para. [0009]: the motion data point outputs from the sensor; para. [0047]: one or more sporting devices may comprise one or more sensors, such as one or more of the sensors discussed above in relation to FIGS. 1-3) wherein the first sensor circuit is configured to measure motion of the user limb about at least one axis (para. [0009]: sensor data that includes a value for each of an x-axis, a y-axis, and a z-axis; para. [0111]: an attribute may generally be a calculated value representing one or more motions of a user, or parts thereof, and which may be used to subsequently predict an output from a model; para. [0135]: many users often swing their arms in a predictable manner during running and/or walking to provide “arm swing peaks”, note that the above feature of “sensor data that includes a value for each of an x-axis, a y-axis, and a z-axis” in para. [0009], “an attribute may generally be a calculated value representing one or more motions of a user” in para. [0111] and “swing their arms” in para. [0135] reads on “in response to motion of the user limb about at least one axis”);
a processor coupled to the sensor circuit (para. [0070]: a plurality of acceleration values associated with the respective axes to which an accelerometer sensor is sensitive (x-, y-, and z-axis) may be grouped as a single acceleration data point. In another implementation, acceleration values may be processed by a processor);
a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to (para. [0035]: Memory 212 may be in communication with the processors 202 via a chipset 216):
buffer the sensor data associated with motion of the user limb to filter noise data (para. [0094]: the data point may be identified as noise, and disregarded) associated with the motion(para. [0095]: a buffer is being populated with acceleration data); the above feature of acceleration data” reads on “motion of the user limb”)
receive orientation data from a second sensor circuit coupled to a mobile computing device (Fig. 1 and para. [0070]: acceleration values may be processed by a processor, such as processor 202, associated with a device, such as device 112, 126, 128, 130, and/or 400 to calculate one or more attributes; para. [0102]: the physical orientation of an accelerometer circuit and/or chip in a device, such as device 112, 126, 128, 130, and/or 400, may control the output values from the accelerometer), wherein the second sensor circuit includes at least one sensor configured to measure an orientation of the mobile computing device during the motion of the user limb (Fig. 1 and para. [0070]: see above; para. [0102]: the physical orientation of an accelerometer circuit and/or chip in a device, such as device 112, 126, 128, 130, and/or 400, may control the output values from the accelerometer);
generate an exercise prediction based on the trained prediction model (para. [0069]: training data may be recorded for one or more individuals performing one or more specific activities; para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting), the sensor data (para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting), and the orientation data (para. [0102]: the physical orientation of an accelerometer circuit and/or chip in a device, such as device 112, 126, 128, 130, and/or 400, may control the output values from the accelerometer),
the prediction model defined by one or more oscillating signal profiles (para. [0008]: one or more motion attributes may be compared to one or more activity models comprising motion data from a plurality of individuals… in certain embodiments, both the motion data within the models and the motion attributes of the user are independent of any activity type, note that the above feature of “motion data from a plurality of individuals” and “models” is considered to oscillating signal profiles to identify genus prediction) to identify genus predictions (para. [0008]: one or more activity models comprising motion data from a plurality of individuals…in certain embodiments, both the motion data within the models and the motion attributes of the user are independent of any activity type; para. [0111]: an attribute may generally be a calculated value representing one or more motions of a user, or parts thereof, and which may be used to subsequently predict an output from a model) for respective limb movement types (para. [0008]; para. [0135]: many users often swing their arms in a predictable manner during running and/or walking to provide “arm swing peaks”) about at least one sensor axis (para. [0009]); Examiner interprets the above feature of “one or more activity models” and “models” in para. [0008] as “genus predictions.“ Also, Examiner interprets the above feature of “motion data within the models” in para. [0008], “a calculated value representing one or more motions of a user” in para. [0111], “predictable manner during running and/or walking to provide “arm swing peaks” in para. [0135] as “exercise prediction representing a species prediction associated with user position during motion of the user limb;
wherein the genus prediction represents a type of exercise activity and the exercise prediction represents a species prediction associated with at least one of equipment type or user position during motion of the user limb (para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models (i.e., genus prediction); para. [0111]: an attribute may generally be a calculated value representing one or more motions of a user, or parts thereof, and which may be used to subsequently predict an output from a model; para. [0135]: many users often swing their arms in a predictable manner during running and/or walking to provide “arm swing peaks, note that Examiner interprets “one or more attributes may be calculated (i.e., genus prediction) from received sensor data and used as inputs to one or more walking and/or running models (i.e., a type of exercise activity)” in para. [0078], “an attribute may generally be a calculated value (i.e., genus prediction) representing one or more motions of a user” in para. [0111], and “many users often swing their arms in a predictable manner during running and/or walking (i.e., i.e., a type of exercise activity)” in para. [0135] as “the genus prediction represents a type of exercise activity and the exercise prediction represents a species prediction associated with at least one of equipment type or user position during motion of the user limb).”
wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb and (para. [0077]; para. [0069]: In one implementation the sensor data stored in addition to the oxygen consumption data may include data from one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), light sensor, temperature sensor (including ambient temperature and/or body temperature), heart rate monitor, image-capturing sensor, moisture sensor and/or combinations thereof) and the orientation data (para. [0102]: the physical orientation of an accelerometer circuit and/or chip in a device, such as device 112, 126, 128, 130, and/or 400, may control the output values from the accelerometer) associated with the orientation of the mobile computing device (Fig. 1 and para. [0070]: acceleration values may be processed by a processor, such as processor 202, associated with a device, such as device 112, 126, 128, 130, and/or 400 to calculate one or more attributes; para. [0102]: orientation of an accelerometer, note that the above feature of “orientation of an accelerometer circuit and/or chip in a device” in para. [0102] and “Fig. 1 and processor in para. [0070] reads on “he orientation of the mobile computing device”);
generate, using the trained prediction model (para. [0069]: training data may be recorded for one or more individuals performing one or more specific activities; para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting), an exercise recommendation (para. [0027]: calculated athletic attributes, feedback signals to provide guidance, and/or other information ) based on the sensor data (para. [0078]: see above), the exercise prediction (para. [0078]: see above), and historical data associated with the user (para. [0069]: training data may be recorded for one or more individuals performing one or more specific activities, note that “training data may be recorded for one or more individuals performing one or more specific activities” in para. [0069] reads on “historical data”), wherein the exercise recommendation includes a recommendation about the exercise activity (para. [0027]: calculated athletic attributes, feedback signals to provide guidance, and/or other information; para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting); and
transmit a signal representing the exercise prediction and the exercise recommendation (para. [0027]: calculated athletic attributes, feedback signals to provide guidance, and/or other information) for display on a user interface (para. [0077]: Accordingly, information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes… the device comprises a display device configured to display an output relating to energy expenditure. In further embodiments, the device may comprise a communication element configured to transmit information relating to energy expenditure to a remote device).
Regarding claim 3, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses that the respective oscillating signal profiles define one or more stages of user limb movement for an associated exercise type (para. [0009]: the one or more motion attributes are calculated from one or more data points representing variation between the motion data point outputs from the sensor), and
wherein the processor-executable instructions, when executed, configure the processor to: determine in substantial real-time an exercise repetition count based on the defined stages of user limb movement for the exercise prediction (para. [0004]: repetitive behavior as being classified as a specific activity, such as for example, running and/or walking; para. [0009]: the one or more motion attributes are calculated from one or more data points representing variation between the motion data point outputs from the sensor).
Regarding claim 4, Balakrisnan discloses all the limitation of claim 3, in addition, Balakrisnan discloses that the environment data includes sensor data representing post-exercise motion of the user limb, and wherein determining the exercise repetition count is based on identifying post-exercise motion of the user limb (para. [0069]: in one implementation the sensor data stored in addition to the oxygen consumption data may include data from one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), light sensor, temperature sensor (including ambient temperature and/or body temperature), heart rate monitor, image-capturing sensor, moisture sensor and/or combinations thereof; para. [0076]: the device may determine that a user has a heart rate indicative of vigorous exercise, but accelerometer data may indicate that said user has been at rest for a period of time. Accordingly the device may determine that the user has a sustained elevated heart rate after a period of activity but is now resting after said activity, and the like).
Regarding claim 5, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses the processor-executable instructions, when executed, configure the processor to (para. [0034]: Device 200 may include one or more processors; para. [0070]: acceleration values may be processed by a processor):
determine whether one or more windows of the buffered sensor data represent noise data (para. [0094]: if a received data point from an accelerometer has a numerical value (in one example, an absolute numerical value, and the like), of less than 10% of the maximum output value from the accelerometer sensor, the data point may be identified as noise, and disregarded;
para. [0095]: a buffer is being populated with acceleration data); and
upon determining that one or more windows of the buffered sensor data represents noise data beyond a threshold quantity of windows, generate the exercise prediction (para. [0078]: one or more attributes may be calculated from received sensor data and used as inputs to one or more walking and/or running models for predicting, among others, a speed/a pace of a user; para. [0094]: if a received data point from an accelerometer has a numerical value (in one example, an absolute numerical value, and the like), of less than 10% of the maximum output value from the accelerometer sensor, the data point may be identified as noise, and disregarded; para. [0095]: a buffer is being populated with acceleration data).
Regarding claim 6, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses that the environment data includes sensor data representing pre-exercise motion of the user limb (para. [0069]: In one implementation the sensor data stored in addition to the oxygen consumption data may include data from one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), light sensor, temperature sensor (including ambient temperature and/or body temperature), heart rate monitor, image-capturing sensor, moisture sensor and/or combinations thereof), the above feature of “oxygen consumption data may include data from one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), image-capturing sensor reads on “pre-exercise motion of the user limb,” because the above-described sensors can detect the user motion before activity, and
wherein the processor-executable instructions, when executed, configure the processor to (para. [0034]: Device 200 may include one or more processors; para. [0070]: acceleration values may be processed by a processor):
determine that one or more windows of the buffered sensor data represents pre-exercise motion of the user limb (para. [0069]: see above; para. [0095]: a buffer is being populated with acceleration data); and
generate the exercise prediction based on the combination of the genus prediction (para. [0077]: information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes. For example, a user may be performing an activity, such as playing soccer, while wearing a sensor device on an appendage; the above feature of “predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes” reads on “generate the exercise prediction based on the combination of the genus prediction; and
the identified pre-exercise motion of the user limb (para. [0069]: In one implementation the sensor data stored in addition to the oxygen consumption data may include data from one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), light sensor, temperature sensor (including ambient temperature and/or body temperature), heart rate monitor, image-capturing sensor, moisture sensor and/or combinations thereof ). The above feature of “one or more of an accelerometer, a gyroscope, a location-determining device (e.g., GPS), image-capturing sensor reads on “pre-exercise motion of the user limb,” because the above-described sensors can detect the user motion before activity.
Regarding claim 7, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses that the sensor circuit includes a magnetometer sensor, and wherein the environment data includes sensor data representing at least one of magnetic field strength or magnetic field direction (para. [0042]: In one embodiment sensor 128 may comprise an infrared (IR), electromagnetic (EM) or acoustic transceiver. For example, image-capturing device 118, and/or sensor 120 may transmit waveforms into the environment, including towards the direction of athlete 124 and receive a “reflection” or otherwise detect alterations of those released waveforms), and
wherein the buffered sensor data includes at least one of magnetic field strength or magnetic field direction data for predicting exercise equipment apparatus associated with motion of the user limb (para. [0009]: the one or more motion attributes are calculated from one or more data points representing variation between the motion data point outputs from the sensor; para. [0042]: In one embodiment electromagnetic (EM) or acoustic transceiver. For example, image-capturing device 118, and/or sensor 120 may transmit waveforms into the environment, including towards the direction of athlete 124 and receive a “reflection” or otherwise detect alterations of those released waveforms; para. [0043]: a user may use a sporting device (described below in relation to BAN 102) and upon returning home or the location of equipment 122, download athletic data into element 122 or any other device of system 100; para. [0077]: information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes; para. [0095]: a buffer is being populated with acceleration data). The above feature of “one or more data points representing variation between the motion data point outputs from the sensor” in para. [0009], “any other device of system 100” in para. [0043], “predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values)” reads on “predicting exercise equipment apparatus associated with motion of the user limb.”
Regarding claim 8, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses that generating the exercise prediction is based on a combination of the genus prediction and third-party motion data associated with geolocation of the user limb (para. [0069]: a location-determining device (e.g., GPS); para. [0077]: training data may be used to construct one or more models, otherwise referred to as experts, or expert models, for predicting, among others, a volume of oxygen consumption based upon (at least in part) one or more individual-specific properties such as a gender, a mass and/or a height of a user. Accordingly, information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes. For example, a user may be performing an activity, such as playing soccer, while wearing a sensor device on an appendage; the above feature of a “location-determining device (e.g., GPS) (i.e., gelocation of the user limb) in para. [0069], “one or more individual-specific properties such as a gender, a mass and/or a height of a user (i.e., third party)” and “predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes. For example, a user may be performing an activity, such as playing soccer” in para. [0077] reads on “generating the exercise prediction is based on a combination of the genus prediction and third-party motion data associated with geolocation of the user limb.”
Regarding claim 9, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses the processor-executable instructions, when executed, configure the processor to (para. [0034]: Device 200 may include one or more processors; para. [0070]: acceleration values may be processed by a processor):
determine form quality of motion of the user limb associated with the exercise prediction based on comparing the buffered sensor data with benchmark sensor data representing benchmark motion form for the predicted exercise (para. [0077]: information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes. For example, a user may be performing an activity, such as playing soccer, while wearing a sensor device on an appendage; para. [0095]: a buffer is being populated with acceleration data); the above feature of “para. [0077]: information from one or more sensors associated with a device, such as device 112, 126, 128, 130, and/or 400, may be used to calculate one or more attributes. In turn, the calculated attributes may be compared to attributes associated with one or more constructed models, and thereby, used to predict a volume of oxygen being consumed by a user while outputting motion signals (sensor output values) corresponding to the calculated attributes. For example, a user may be performing an activity, such as playing soccer, while wearing a sensor device on an appendage” and “a buffer is being populated with acceleration data” in para. [0095] reads on “determine form quality of motion of the user limb associated with the exercise prediction based on comparing the buffered sensor data with benchmark sensor data representing benchmark motion form for the predicted exercise”; and
transmit a signal representing the determined form quality of motion of the user limb for feedback to the user (para. [0077]: the device comprises a display device configured to display an output relating to energy expenditure. In further embodiments, the device may comprise a communication element configured to transmit information relating to energy expenditure to a remote device).
Regarding claim 10, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses comprising at least one of a smart watch, a fitness tracking band, wireless audio devices, or smart garments (para. [0050]: I/O devices may be formed within or otherwise associated with user's 124 clothing or accessories, including a watch, armband, wristband, necklace, shirt, shoe, or the like).
Regarding claim 11, it is a method type claim and has similar limitations as of claim 1 above, Therefore, it is rejected under the same rational as of claim 1 above.
Regarding claim 13, it is dependent on claim 11 and has similar limitation as of claim 3 above. Therefore, it is rejected under the same rational as of claim 3 above.
Regarding claim 14, it is dependent on claim 13 and has similar limitation as of claim 4 above. Therefore, it is rejected under the same rational as of claim 4 above.
Regarding claim 15, it is dependent on claim 11 and has similar limitation as of claim 5 above. Therefore, it is rejected under the same rational as of claim 5 above.
Regarding claim 16, it is dependent on claim 11 and has similar limitation as of claim 6 above. Therefore, it is rejected under the same rational as of claim 6 above.
Regarding claim 17, it is dependent on claim 11 and has similar limitation as of claim 7 above. Therefore, it is rejected under the same rational as of claim 7 above.
Regarding claim 18, it is dependent on claim 11 and has similar limitation as of claim 8 above. Therefore, it is rejected under the same rational as of claim 8 above.
Regarding claim 19, it is dependent on claim 11 and has similar limitation as of claim 9 above. Therefore, it is rejected under the same rational as of claim 9 above.
Regarding claim 20, it is a non-transitory computer-readable medium or media type claim and has similar limitations as of claim 1 above, Therefore, it is rejected under the same rational as of claim 1 above. The additional element of a non-transitory computer-readable medium or media (para. [0035]: Memory 212), taught by Balakrisnan.
Regarding claim 21, Balakrisnan discloses all the limitation of claim 1, in addition, Balakrisnan discloses the type of exercise activity (paras. [00780, [0135]: running and/or walking) includes a classification of a physical exercise associated with the motion of the user limb (para. [0135]: many users often swing their arms in a predictable manner during running and/or walking to provide “arm swing peaks”, note that above feature of “many users often swing their arms (i.e., limb) in a predictable manner during running and/or walking to provide “arm swing peaks” reads on “a classification of a physical exercise associated with the motion of the user limb” because a classification of physical exercise is possible using arm swing peaks).
Regarding claim 22, it is dependent on claim 11 and has similar limitation as of claim 21 above. Therefore, it is rejected under the same rational as of claim 21 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858