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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1-4, 5-11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-4, 6-12 of copending Application No. 18511103 in view of Thigpen et al. (US 20220287622 A1).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Thigpen for the purpose of having smart watch (i.e., wearable smart jewelry) having a casing to be worn as a bracelet by a user so that the various physical activities of the user can be easily monitored in the watch.
Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations of the instant application are also presented in the application No. 18511103.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 and 11 recites “a casing formed to be appear as an ornamental piece of jewelry that is designed…”. It is unclear as what type of casing to be called for appearing as an ornamental piece of jewelry. For the purpose of examination, any watch or ring with casing will be considered as appearing as an ornamental piece. Applicant is suggested to remove the unclear language for the purpose of clarity.
Claim 3 recites Semiconductor Crystal configured to provide a clock signal. It is unclear what type or kind of semiconductor crystal the claim mean and how a semiconductor crystal provides a clock signal. The purpose of examination, examiner considers any semiconductor with crystal that generate signal as a semiconductor crystal for providing a clock signal.
Claim 11 recites the limitation "a 3-axis …. for the smart watch." in. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites “the back-end processing …fully customized to the user…”. The term fully creates an unclear language in the claim, as what condition to be considered a fully customized plan for the user. Applicant is suggested to remove fully for the purposed of clarity.
Dependent claims 2-10, 12 and 13 are also rejected under 35 U.S.C 112 due to dependency on their independent claims 1 and 11.
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, 2, 4, 11, 12 is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Thigpen et al. (US 20220287622 A1) herein after Thigpen.
Regarding claim 11, Thigpen teaches a wearable smart jewelry system comprising (para [0027] FIG. 1 illustrates an example of a system 100 that supports menstrual cycle tracking in accordance with aspects of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., wearable devices 104, user devices 106) which may be worn and/or operated by one or more users 102.):
Examiner views wearable devices like ring or watch (104) are viewed as wearable smart jewelry.
a 3-axis accelerometer configured to measure and store position and acceleration information for the wearable smart jewelry (for the smart watch) (para [0028] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring wearable devices, watch wearable devices, etc.)
para [0083] As another example, the ring 104 may include one or more gyro sensors that generate gyro signals that indicate angular motion (e.g., angular velocity) and/or changes in orientation. The motion sensors 245 may be included in one or more sensor packages. An example accelerometer/gyro sensor is a Bosch BM1160 inertial micro electro-mechanical system (MEMS) sensor that may measure angular rates and accelerations in three perpendicular axes.);
The wearable device, ring or watch include a 3dimensional accelerometer to measure and store orientation (i.e., position) and acceleration information of the wearable device 104 (i.e., wearable smart jewelry or smart watch or ring).
a casing formed to appear as an ornamental piece of jewelry that is designed and dimensioned to be clipped on to a user's clothing or worn as a necklace or a bracelet (para [0029] Example wearable devices 104 may include wearable computing devices, such as a ring computing device (hereinafter “ring”) configured to be worn on a user's 102 finger, a wrist computing device (e.g., a smart watch, fitness band, or bracelet) configured to be worn on a user's 102 wrist, and/or a head mounted computing device (e.g., glasses/goggles).);
In Fig. 1 examiner views the smart ring and watch (i.e., the wearable smart jewelry) with a housing or casing appear as an ornamental piece of jewelry that is designed and dimensioned to be worn on as a bracelet.
a memory configured to store instructions (para [0059] Furthermore, memory 215 may include instructions that, when executed by one or more processing circuits); and
a processor communicatively connected to the 3-axis accelerometer (para [0084] The processing module 230-a may sample the motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample acceleration signals to determine acceleration of the ring 104) and the memory (para [0080] The processing module 230-a may store the pulse waveform in memory 215 in some implementations.), the processor configured to execute the instructions at least to:
receive the stored position and acceleration information from the 3-axis accelerometer (para [0084] The processing module 230-a may sample the motion signals at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signals. For example, the processing module 230-a may sample acceleration signals to determine acceleration of the ring 104);
In Fig. 2, examiner views the processor 230-a is connected to sensors (i.e., 3-axis accelerometer) and memory to receive the stored sensors data.
determine one or more physical activities corresponding to the stored position and acceleration information (para [0015] Some wearable devices may be configured to collect physiological data from users, including temperature data, heart rate data, and the like. Acquired physiological data may be used to analyze the user's movement and other activities, such as sleeping patterns. Many users have a desire for more insight regarding their physical health, including their sleeping patterns, activity, and overall physical well-being.
Para [0028] The electronic devices associated with the respective users 102 may include one or more of the following functionalities: 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing outputs (e.g., via GUIs) to a user 102 based on the processed data, and 5) communicating data with one another and/or other computing devices.
Para [0035] The physiological data may include any physiological data known in the art including, but not limited to, temperature data, accelerometer data (e.g., movement/motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.)
Examiner views the stored data (i.e., position and acceleration) from the 3-axis accelerometer are processed by the processor to determine about activities like movement, sleep of the user.; and
store the determined one or more physical activities (para [0084] the processing module 230-a may store motion data in memory 215. Motion data may include sampled motion data as well as motion data that is calculated based on the sampled motion signals (e.g., acceleration and angular values.
[0086] The ring 104, or other computing device, may calculate and store additional values based on the sampled/calculated physiological data. For example, the processing module 230 may calculate and store various metrics, such as sleep metrics (e.g., a Sleep Score), activity metrics, and readiness metrics.)
Examiner views the determined physical activities of the user are by the computing device.
the external computer device, wherein the external computer device is configured to be communicatively connected to the wearable smart jewelry (para [0028] 5) communicating data with one another and/or other computing devices. Different electronic devices may perform one or more of the functionalities. Para [0049] System 200 further includes a user device 106 (e.g., a smartphone) in communication with the ring 104.).
In Fig. 2 examiner views system 200 (i.e., user’s smartphone 106, external computer) is communicatively connected to wearable smart jewelry, ring 104.
wherein the external computer device obtains data from the wearable smart jewelry (para [0049] In some implementations, the ring 104 may send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion/accelerometer data, ring input data, and the like) to the user device 106), and
wherein external computer device displays information related to the data to a user (para [0172] As shown in FIG. 7, the application page 705-a may display an indication of the identified menstrual cycle phases via alert 710…. Additionally, in some implementations, the application page 705-a may display one or more scores (e.g., Sleep Score, Readiness Score, etc.) for the user for the respective day.
[0176] For users whose body signals (e.g., body temperature, heart rate, HRV, and the like) may react to the phase of the menstrual cycle, the system may display low activity goals around the identified menstrual cycle phase).
In fig. 7 examiner views the external computer 705 displays information related to the data (i.e., sensor data, activities label and goals, sleep score, menstrual cycle) to a user.
Claim 1 is rejected as claim 11 having same claim limitation.
Regarding claim 2, Thigpen teaches the wearable smart jewelry according to claim 1, Thigpen teaches further comprising a power source configured to provide power to the wearable smart jewelry (para [0064] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210).).
Battery is viewed to provide power to the wearable smart jewelry.
Regarding claim 4, Thigpen teaches the wearable smart jewelry according to claim 1, Thigpen teaches further comprising a transceiver configured to connect the wearable smart jewelry to an external computer device for data communication (para [0034] In some aspects, wearable devices 104 (e.g., rings 104, watches 104) and other electronic devices may be communicatively coupled to the user devices 106 of the respective users 102 via Bluetooth, Wi-Fi, and other wireless protocols.).
Examiner views bluetooth as a transceiver to connect the wearable smart jewelry to an external computer for data communication.
Regarding claim 12 Thigpen teaches the wearable smart jewelry system of claim 11 comprising: Thigpen teaches one or more servers configured to communicatively connect to the wearable smart jewelry and the external computer device (para [0037] The electronic devices of the system 100 (e.g., user devices 106, wearable devices 104) may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols.), and perform back-end processing of information collected from the wearable smart jewelry and the external computer device (para [0038] The system 100 may offer an on-demand database service between the user devices 106 and the one or more servers 110. In some cases, the servers 110 may receive data from the user devices 106 via the network 108, and may store and analyze the data.).
Examiner views the server (that include an external computer) is connected to the wearable smart jewelry. Receive data from the wearable smart jewelry to store and analyze the data (i.e., perform back-end processing).
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) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thigpen in view of McDonald et al (WO 2010093461 A1).
Regarding claim 3, Thigpen teaches the wearable smart jewelry according to claim 1, Thigpen teaches measuring and storing the position and acceleration information using the 3-axis accelerometer. However Thigpen does not clearly teach further comprising a semiconductor crystal configured to provide a clock signal, wherein the clock signal is used to determine a frequency.
McDonald teaches a semiconductor crystal configured to provide a clock signal, wherein the clock signal is used to determine a frequency. (para [0012] A micro-power integrated circuit frequency generator calibrated with a single high frequency quartz crystal is disclosed. The frequency generator described herein may be employed to generate multiple clock frequencies using semiconductor technology and one crystal rather than a plurality of crystals in systems that require a plurality of clock signals of various frequencies.
[0046] In various embodiments, frequency generator 100 may be used for any appropriate application such as analog and digital watches and clocks; computers; portable media players; cameras; cell phones; media and/or storage applications; notebook computers; netbooks; consumer, industrial, medical, automotive, communications, and/or military applications;),
Examiner views the McDonald teaches quartx crystal for semiconductor to provide a clock signal or frequencies for watches, clocks or medical devices that require measuring oscillation or position or acceleration.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated McDonald into Thigpen for the purpose of using a semiconductor with a crystal in a wearable smart jewelry so that a clock signal can be generated to measure and store position and acceleration from the wearable smart jewelry.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Thigpen in view of Li et al (US 20160089080 A1).
Regarding claim 5, Thigpen teaches the wearable smart jewelry according to claim 1, wherein the processor is further configured to execute the instructions at least to: Thigpen does not clearly teach calculated output data based on the stored position and acceleration information using one or more classifier algorithms,
wherein the one or more classifier algorithms include one or more of a physical activity classifier, a step count algorithm, and a step activity classifier.
Li teaches calculated output data based on the stored position and acceleration information using one or more classifier algorithms (para [0041] At decision block 304, the step counting device 102 can determine whether a user is inactive (e.g., the user is not currently walking or running, driving a car, etc.). One or more sensors 206 may provide data, and a classifier or some other model can be used to analyze the data and determine whether it is indicative of inactivity. In some embodiments, the data may include 3-axis acceleration data from a 3-axis accelerometer.),
Examiner views the 3-axis accelerometer data is used by classifier algorithm to calculate the output data (i.e., if the person is sleeping or waking).
wherein the one or more classifier algorithms include one or more of a physical activity classifier, a step count algorithm, and a step activity classifier (para [0046] At decision block 312, the step counting device 102 can determine whether the user is sleeping or merely idle. The determination may be made using one or more classifiers or other models trained to detect the signature of sleep or mere idleness in input data.).
Here examiner views the classifier include physical activity classifier, a step counting classifier and step activity of the user.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Li into Thigpen for the purpose of using a machine learning classifier algorithm so that the user’s activity or sleeping can be accurately monitored.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Thigpen in view of Utter (US 20150186609 A1).
Regarding claim 6, Thigpen teaches the wearable smart jewelry according to claim 1, however Thigpen does not clearly teach wherein the processor is further configured to execute the instructions at least to: divide the physical activities into two categories, wherein the two categories are rhythmic activities and non-rhythmic activities.
Utter teaches para wherein the processor is further configured to execute the instructions at least to: divide the physical activities into two categories, wherein the two categories are rhythmic activities and non-rhythmic activitie ( para [0048] Sensor system 340 may include one or more motion sensors (e.g., single-axis or multi-axis accelerometers, gyroscopes, vibration detectors, piezoelectric devices, etc.) that generate one or more of the signals S.sub.n, and those signals S.sub.n may be generated by motion and/or lack of motion (e.g., running, exercise, sleep, rest, eating, etc.) of the user 800, such as translation (Tx, Ty, Tz) and/or rotation (Rx, Ry, Rz) about an X-Y-Z axes 897 of the users body during day-to-day activities.)
Here Utter monitors rhythmic and non-rhythmic Physical activities, it can be understood that Utter categories these activities into rhythmic (for example running) and non rhythmic activities (for example rest).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Utter into Thigpen for the purpose of monitoring physical activities of a user so that the activities can be categorized as rhythmic or nonrhythmic based on the physical activities.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Thigpen in view of Newberry (US 20190286233 A1).
Regarding claim 9 Thigpen teaches the wearable smart jewelry according to claim 1, Thigpen does not clearly teach wherein the processor is further configured to execute the instructions at least to:
generate a dataset of input features and corresponding output labels from a database of previously stored position and acceleration information;
train one or more machine learning classifiers using a first portion of the generated dataset;
determine whether the one or more machine learning classifiers are generalized by testing the one or more machine learning classifiers on a second portion of the generated dataset;
in a case where the one or more machine learning classifiers are not generalized, validate the one or more machine learning classifiers using a third portion of the generated dataset until the one or more machine learning classifiers are generalized; and
in a case where the one or more machine learning classifiers are generalized, determine the one or more physical activities corresponding to the stored position and acceleration information using the generalized one or more machine learning classifiers.
Newberry teaches generate a dataset of input features and corresponding output labels from a database of previously stored position and acceleration information (para [0115] Unique PPG signal patterns at one or more wavelengths may be stored in a database with corresponding motion data of a body part. [0146] FIG. 17 illustrates a schematic block diagram of an embodiment of a neural network processing device 1700. The neural network processing device 1700 obtains or generates an input vector 1702. [0148] The neural network processing device 1700 then obtains an output vector 1704);
Examiner views input vector as a generated dataset of input features and output vector as the corresponding output labels from a database of stored motion data (i.e., position and acceleration)
train one or more machine learning classifiers using a first portion of the generated dataset (para [0083] Pattern classification and recognition algorithms may be used in conjunction with predetermined PPG patterns to obtain the motion data. Alternatively, a neural network or Artificial Intelligence (AI) device may be used to analyze the PPG signal using training vectors to determine the motion data. para [0147] The neural network processing device 1700 may be pre-configured with weights, parameters or other learning vectors 1706 derived from a training set. The training set preferably included sets with the same type of information in the input vector and known movements of various body parts in various combinations);
In Fig. 17 Examiner views the neural network (i.e., machine learning) with classifier is trained using the input vector X1, (i.e., first portion of the generated data set).
determine whether the one or more machine learning classifiers are generalized by testing the one or more machine learning classifiers on a second portion of the generated dataset (para [0083] Pattern classification and recognition algorithms may be used in conjunction with predetermined PPG patterns to obtain the motion data. Alternatively, a neural network or Artificial Intelligence (AI) device may be used to analyze the PPG signal using training vectors to determine the motion data. para [0147] The neural network processing device 1700 may be pre-configured with weights, parameters or other learning vectors 1706 derived from a training set. The training set preferably included sets with the same type of information in the input vector and known movements of various body parts in various combinations);
Examiner views checking if training of the machine learning is different from the target output. In Fig. 17 second portion of data X2 is provided to neural network processing (i.e., machine learning classifier) for learning and training (i.e., generalization).
in a case where the one or more machine learning classifiers are not generalized, validate the one or more machine learning classifiers using a third portion of the generated dataset until the one or more machine learning classifiers are generalized para [0145] For example, a gradient descent training algorithm is used in case of supervised training model. In case, the actual output is different from target output, the difference or error is determined. The gradient descent algorithm changes the weights of the network in such a manner to minimize this error. Other learning algorithms include back propagation, least mean square (LMS) algorithm, etc. A set of examples or a training set is used for learning by the neural network
[0161] The NN processing device 1700 may be pre-configured with learning parameters, e.g. from a learning vector generated using a training set of a general population. The training set includes a similar data in an input vector and known results in an output vector. The learning vector may then be updated based on a user's PPG signals and indicated movements.; and
Here Examiner views, if the actual output is different from the target out (i.e., checking if machine learning classifier are not generalized), the training may be updated (i.e., using third portion of data) for training of machine learning classifier.
in a case where the one or more machine learning classifiers are generalized (para Para [0161] The NN processing device 1700 may be pre-configured with learning parameters, e.g. from a learning vector generated using a training set of a general population. The training set includes a similar data in an input vector and known results in an output vector. The learning vector may then be updated based on a user's PPG signals and indicated movements), determine the one or more physical activities corresponding to the stored position and acceleration information using the generalized one or more machine learning classifiers (para [0108] When we physically move our body parts, the PPG signal motion artifacts are due to neural activity (seen especially at initiation of the motion artifact), vasodilation, movement of tissue, and color hue changes due to movement/vasodilation, as explained in more detail herein. Para [0161] The NN processing device 1700 may be pre-configured with learning parameters, e.g. from a learning vector generated using a training set of a general population. The training set includes a similar data in an input vector and known results in an output vector. The learning vector may then be updated based on a user's PPG signals and indicated movements.).
Examiner views the Neural network (machine learning classifier) when trained based on PPG signals from accelerometer, the physical activities are determined.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Newberry into Thigpen for the purpose of accurately determining physical activities of a user by using a machine learning technique.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Thigpen and Newberry view of Galarneau (US 20210308465 A1).
Regarding claim 10, the combination of Thigpen and Newberry teach the wearable smart jewelry according to claim 9, the combination does not clearly teach wherein the input features include signal strength, rhythmicity, and frequency stability, each of which is calculated from the database of previously stored position and acceleration information, and wherein the output labels correspond to the one or more physical activities.
Galarneau teaches wherein the input features include signal strength, rhythmicity, and frequency stability (para [0163] a motion signal such as an acceleration signal sensed by an accelerometer, that is correlated to the strength (amplitude), frequency (or rate) and/or regularity (relative to atrial events) of the ventricular event signals in the sensor signal over multiple atrial cycles. ),
Examiner views the motion signal (i.e, input feature) correlated to strength of signal, regularity (i.e., rhythmicity) and frequency stability. each of which is calculated from the database of previously stored position and acceleration information, and wherein the output labels correspond to the one or more physical activities ([0174] It is contemplated that external device 20 may be in wired or wireless connection to a communications network via a telemetry circuit that includes a transceiver and antenna or via a hardwired communication line for transferring data to a centralized database or computer to allow remote management of the patient. para [0193] In some examples, motion sensor 212 may be included within the housing 255 of the pacemaker 254, in addition to a lead-based motion sensor, and configured for sensing motion due to patient physical activity and/or acceleration signal changes due to patient posture changes.).
The above input features are stored in database and used to calculated an output label for the physical activities of a user.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Galarneau into Thigpen for the purpose of recording sensor data (i.e., acceleration and position) which includes signal strength, regularity and frequency stability so that the physical activities of a user can be accurately monitored.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Thigpen in view of Catani et al (US 20150364057 A1) herein after Catani.
Regarding claim 13, Thigpen teaches the wearable smart jewelry system of claim 12, wherein the back-end processing includes providing a tailored plan for exercise, meals, sleep and meditation schedules which are fully customized to the user based on the data collected from the wearable smart jewelry and from the external computing device (para[0208] The computer system 100 can also include a web server for generating and/or delivering the web pages to client computer systems. para [0325] Receiving information from sensors can help the system gather data that the user may not remember accurately (e.g., due to delay in inputting data to the system, due to the user not using a timer to time a length of exercise, etc.), may forget to input, and/or may be too difficult and/or time-consuming for the user to input (e.g., all food and drink consumed by the user every day, etc.). For non-limiting example, a sensor configured to monitor the user's physical activity, such as a sensor of a fitness watch, heart rate monitor, etc para [0384-0386] Nutrition,” “Sleep,” “Mood,” and “Movement… As discussed herein, the one or more recommended activities can be customized for the user, e.g., based upon analysis by an analysis module of the system,).
Examiner views the user’s activity data from sensors in the smart watch are communicated to the computers using server (i.e., back end processing of data) to provide a tailored plan for food, sleep, exercise, meditation customized for the user.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Catani into Thigpen for the purpose of recording sensor data from wearable smart jewelry of a user to be provided to back-end computer or server so that a tailored plan for food, sleep, exercise, meditation can be customized for the user.
Note: Regarding claim 7, the limitation of “in a case that a physical activity is classified as rhythmic, perform a spectral analysis on the data captured by the accelerometer to identify the frequency of movement” is taught or suggested by Mirtikka et al US 20170202486 A1 in paragraph [0083]; and
However, none of the prior arts teach or suggest “in a case that a physical activity is classified as non-rhythmic, perform a time-domain analysis, in which each oscillation in the data collected by the accelerometer is considered independently.”
Claim 8 dependents on claim 7. Therefore, claim 8 also does not have prior art rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
David US 20210169417 A1 teaches mobile wearable device for monitoring health condition.
Tholen US 10779802 B2 discusses health monitoring using wearable device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at 571-270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAD TIMILSINA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857