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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 19, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Objections
Claim 6 is objected to because of the following informality:
“The wearable apparatus according to according to” should be amended to recite “The wearable apparatus according to” (lines 1-2).
Appropriate correction is required.
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.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claims 1-15 are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 1 recites “one or more devices for tracking the movement of one or more anatomical districts of a person” in lines 4-5, which encompasses a portion of the human body as part of the claimed invention. In order to overcome this rejection, the claim may be amended to recite “one or more devices configured for tracking the movement of one or more anatomical districts of a person”.
Claim 7 recites “one or more devices applied to upper limbs of said person” in line 3, “one or more devices applied to lower limbs of said person” in line 5, and “one or more devices applied on the head, or neck, or on the trunk or pelvis” in lines 7-8, which encompass a portion of the human body as part of the claimed invention. In order to overcome this rejection, the claim may be amended to use “one or more devices configured to be applied to…” language.
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.
Claims 1-15 are 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 recites “a person” in line 5. It is unclear if this is meant to be the same as “a person” in line 2. Further clarification is required.
Claim 1 recites the limitation “said devices” in lines, 19, 20, 35. It is unclear which devices are being referenced by this limitation. Are these devices intended to be the “one or more devices”? Further clarification is required.
Claim 1 recites the limitation "the wearable device" in lines 24-25. There is insufficient antecedent basis for this limitation in the claim. Additionally, it is unclear if the “wearable apparatus” (lines 1 and 32) and the “wearable device” are different devices or the same device. Further clarification is required.
Claim 1 recites the limitation "the remaining wearable devices" in line 34. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 1, the phrase "optionally in real time" in line 45-46 renders the claim indefinite because this creates confusion as to when direct infringement occurs. See MPEP § 2173.05(d).
Regarding claim 6, the phrase "i.e." in line 14 renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 6 recites “the result of the classification” in line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the analysis of anomalies" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites “a person” in line 3. It is unclear if this is meant to be the same as “a person” in line 2 of claim 1. Further clarification is required.
Claim 11 recites limitations with improper antecedent basis due to its dependence on claim 5, which is dependent on claim 1. Claim 11 recites “a wearable apparatus”, “one or more motion detectors”, “a centralized or distributed control unit”, “at least one anatomical district”, and “one or more artificial intelligence algorithms”, which are previously recited in claim 1. If these limitations are intended to reference the same components as claim 1, the words “the” or “said” should precede any subsequent recitation of the limitation. Claim 11 also recites “a first dataset”, “a processing operator”, “a second data set”, and “a third dataset”, which are previously recited in claim 5. If these limitations are intended to reference the same components as claim 5, the words “the” or “said” should precede any subsequent recitation of the limitation.
Claim 11 recites “a person” in line 1. It is unclear if this is meant to be the same as “a person” in line 2 of claim 1. Further clarification is required.
Claim 11 recites the limitation "the transmission channels" in line 28. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites “the result of the classification” in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites “a person” in line 2. It is unclear if this is meant to be the same as “a person” in line 2 of claim 1. Further clarification is required.
Claim 15 recites “the sports performance” in line 3. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1-2, 4-7 and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170188895 A1 (Nathan, Anoo) in view of US 20170265785 A1 (Vaughn et al.).
Regarding claim 1, Nathan teaches a wearable apparatus for analyzing the movement of a person (Abstract, [0009]; [0164] “The wearable device, disclosed in the present invention, detects tremors by analyzing sensor data acquired using the one or more sensors of the wearable device”), the apparatus comprising:
one or more devices for tracking the movement of one or more anatomical districts of a person, wherein said one or more devices each comprise one or more motion detectors which are configured to generate a movement data flow of a speed or acceleration or position of said one or more anatomical
districts ([0009] “human body motion analysis (which can include movements of hands, legs, head, torso) using body worn sensors”; [0010] “the invention will acquire a first set of parameters from the various body physical/geometric sensors on the body worn device, such as acceleration sensors (accelerometers), angle sensors (gyros), location sensor (GPS), directional sensor (compass or magnetometers).”);
a distributed or centralized control unit in functional connection with said one or more devices or with said one or more devices and with one or more remote electronic devices, said control unit comprising a processor and a memory unit having stored a computer program stored therein, said computer program being executable by said processor and being configured to process the data flow so as to obtain one or more processed datasets ([0011] “The invention will typically employ one or more processors (e.g. compute processors such as microprocessors, and the like) and various types of motion analytics algorithms to analyze the characteristics of various daily normal activities”; [0037] “a CPU or microprocessor or microcontroller or a central-processing-unit to collect these sensor data, process, store it and send communications out.”);
a communication module which in association with said control unit, allows data exchange between two or more of said devices, or between at least one of said devices and said remote electronic device, wherein said data are the data flow generated by the motion detectors or said data is said one or more datasets processed by the control unit ([0037] “the invention's system hardware may be implemented by an electronic device that is small and includes one or more of these sensors. The system will generally also include a CPU or microprocessor or microcontroller or a central-processing-unit to collect these sensor data, process, store it and send communications out.”; [0038] “Communication unit: This can be one or more of these Wired or wireless communication unit such as Ethernet, Wi-Fi, Bluetooth, Zigbee or 3G/cellular modem units.”);
a centralized or distributed power device to power the wearable device ([0038] “All electronics needs a power source such as battery, solar cell, or external power input.”; [0232]);
one or more wearable casings to contain said one or more devices, and at least a part of said communication module ([0317] “the body worn device is worn on any one of, or a combination of, but not limited to, body parts of the patient, such as wrist, waist, neck, arm, leg, abdomen, chest, thigh, head, ear and fingers. Further, the body worn device may be any one of, or a combination of, a wristband, a watch, an armband, a necklace, a headband, an earring, a waist belt and a ring.”; [0319] “the data captured by the sensors and various devices including body worn devices can communicate with the processor over the network via a short range wireless communication medium. Examples of the short range wireless communication medium include Bluetooth, ZigBee, Infrared, Near Field Communication (NFC) and Radio-frequency identification (RFID).”);
a user interface which in association with said software allows configuration and functions activation of said wearable device as well as activation of an alarm condition by means of an alarm device ([0143]; [0163] “These messages or alerts or alarms are output from the device to the mobile phone or the internet cloud via the above mentioned wired or wireless connectivity methods.”; [0230-0231]),
wherein said computer program includes one or more artificial intelligence algorithms, which, when executed by the processor of said processing unit ([0088]; [0165]; [0168] “The sensor data acquired from the sensors is used to establish the baseline for detecting the gait related abnormal event/motion, based on the machine learning methods”), direct said processor to:
identify in said one or more processed datasets, optionally in real time, potential anomalies in the movement of the person with respect to a normal movement pattern according to a paradigm of low-latency detection ([0239] “The positive and negative examples of motion patterns that are fed to a neural network or other learning algorithm may include each component of acceleration and/or the magnitude of the acceleration.”; [0248]; [0325] “the trending module may detect a threat by flagging a threshold discrepancy of an event between the baseline pattern and the trending summary. The flagging a threshold discrepancy of an event may be determined by machine learning algorithms”) and
associate to said anomalies the probability that an anomalous event is in progress or may arise in said person ([0055] “The final analytics detection/classification can be either some rule/heuristic based (such as thresholds), or machine learning based, or any combination thereof. Also, the system may choose to give a probabilistic output (a confidence value between 0 and 1) instead of a binary decision.”).
Nathan does not explicitly teach said wearable apparatus wherein: one of said one or more devices is a master device and the remaining wearable devices are slave devices; or said devices are all slave devices and the master device is said remote electronic device, said master device receives from said slave devices said data flow or said one or more processed datasets, and performs a first classification or a further classification in addition to the classifications performed locally by the individual slave devices.
However,
Vaughn teaches said wearable apparatus wherein:
one of said one or more devices is a master device and the remaining wearable devices are slave devices ([0021]; [0023]; [0026] “if the devices 116 and 118 are considered to be “slaves” in a master-slave architecture, server 104 may perform the processing functions and provide the processed results (e.g., query results) to the devices 116,”); or
said devices are all slave devices and the master device is said remote electronic device,
said master device receives from said slave devices said data flow or said one or more processed datasets, and performs a first classification or a further classification in addition to the classifications performed locally by the individual slave devices ([0021]; [0023]; [0026] “if a large volume of data is analyzed, server 104 may be better suited to perform such functions than devices 116 or 118. As another example, if the devices 116 and 118 are considered to be “slaves” in a master-slave architecture, server 104 may perform the processing functions and provide the processed results (e.g., query results) to the devices 116, 118. As still another example, because the user motions 114 include users' sensor based data classified into specific motions, querying the user motions 114 may be less processing intensive and thus, the query module 108 may be included in the devices 116, 118.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the apparatus taught by Nathan to include a master device and slave device structure for local classification by the slave devices. One would have been motivated to make this modification because Nathan teaches processing by other computerized devices such as mobile phones, computers, and internet servers (Abstract), and a master device can analyze a large volume of data external from a slave wearable device and perform classification of specific motions, as suggested by Vaughn [0021, 0023, 0026].
Regarding claim 2, Nathan teaches the wearable apparatus according to claim 1, wherein said one or more motion detectors are of the type selected from the group consisting of: an inertial sensor, a gyroscope, an accelerometer, a magnetometer, a camera, and combinations thereof ([0010] “various body physical/geometric sensors on the body worn device, such as acceleration sensors (accelerometers), angle sensors (gyros), location sensor (GPS), directional sensor (compass or magnetometers)”; [0228] “cameras and/or motion detector”).
Regarding claim 4, Nathan teaches the wearable apparatus according to claim 1, wherein said one or more motion detectors, at least one processing unit and at least a part of the communication module are contained in a wearable enclosure, said enclosure comprising means for retaining said device on an anatomical district (Abstract - “The device, often configured to be worn on the user's wrist, arm, neck, belt or other location, comprises a processor, an output device (often a wireless transceiver), and at least one accelerometer, angle, location, direction, or physiological state sensor.”; [0227] “The sensor that is used as motion detector may a small device that can fit into an enclosure the size of a wristwatch.”; [0298] “The device in this case can be a pendant or watch, which will continuously listen to the communication port for these commands or notification events.”).
Regarding claim 5, Nathan teaches the wearable apparatus according claim 1, wherein said one or more processed datasets include:
a first dataset obtained by sampling said data flow (Fig. 7; [0042], [0143] “Data flow typically happens as follows. From Sensor [0144] Temporarily hold in memory, To CPU/Processor [0145] More processed results in memory, Communication outside, Reports and alerts to outside”; [0091] “Signal data obtained from a sensor such as an accelerometer could be used to cluster (either directly using raw data or transformed into another feature space) into different types of activities.”);
a second dataset obtained by applying a processing operator to said first dataset ([0086] “There can be pre-processing step on the input sensor data to normalize, eliminate noise, clean up, adjust, and prepare for final algorithm processing”);
a third dataset obtained by applying said one or more artificial intelligence algorithms to said second dataset ([0088] “In supervised learning based methods, the system is presented with labeled “positive” and “negative” samples and a training algorithm is used to learn a classifier which can classify the data accordingly”).
Regarding claim 6, Nathan teaches the wearable apparatus according to according to one or more of the claim 5, wherein the devices exchange data by means of said communication module with one of the following sharing levels:
a total sharing level, when said first dataset is generated by each individual device and is sent to the single master device, which processes and classifies said first dataset, so that said second dataset and said third dataset are generated;
a partial sharing level, when each individual slave device generates said second dataset and shares it with the master device;
a minimum sharing level, when each individual slave device generates said second dataset and said third dataset, and shares with the master device only said third dataset i.e. the result of the classification performed independently by each individual slave device ([0320] “The network may be any other type of network that is capable of transmitting or receiving data to/from host computers, personal devices, telephones, video/image capturing devices, video/image servers, or any other electronic devices. Further, the network is capable of transmitting/sending data between the mentioned devices”; [0231] “One example of an embodiment encompassed may include input sensors (video and/or motion sensors, such as accelerometers), one or more computers for receiving and processing data from the sensors, connectivity interfaces, and a system for remotely monitoring and for alerting (e.g., sending an alarm) a concerned party.”; This is an example of a total sharing level in which the data from the sensors is communicated to the computers to generate second and third datasets for output.).
Regarding claim 7, Nathan teaches the wearable apparatus according to claim 1, comprising
one or more devices applied to upper limbs of said person; or
one or more devices applied to lower limbs of said person; or
one or more devices applied on the head, or neck, or on the trunk or pelvis ([0164] “Examples of the one body part of the user include, but are not limited to, hands, legs, torso, and head.”; [0240] “The accelerometer is secured to the patient's hand, arm, legs, and/or any other part of body that shakes and has the seizure movements.”; [0317] “the body worn device is worn on any one of, or a combination of, but not limited to, body parts of the patient, such as wrist, waist, neck, arm, leg, abdomen, chest, thigh, head, ear and fingers”).
Regarding claim 9, Nathan teaches the wearable apparatus according to claim 1, for use in the analysis of anomalies affecting the movement of a person, wherein said anomalies are predictive of, or related with, a human pathological condition associated with features which are detectable by the movement of one or more anatomical districts of said person ([0232] “In some cases, there may be a threshold for even suspected conditions may be recorded. In one embodiment, one threshold or level may indicate that there is a problem that is observed, which may record the event or condition without actually activating an alert and at a second level or threshold an alert may be activated. Activating the alert may include sending a communication indicating the alert. In an embodiment, alerts are sent for only conditions or abnormal motions that pass certain conditions or levels.”; [0164] “Tremors are one of the abnormal events/motions related with Parkinson's disease. The wearable device, disclosed in the present invention, detects tremors by analyzing sensor data acquired using the one or more sensors of the wearable device”).
Regarding claim 10, Nathan teaches the wearable apparatus according to claim 9 wherein said pathological condition is selected from:
Transitory Ischemic Attack, stroke, epilepsy, Asperger's syndrome, Parkinson's syndrome, autism spectrum disorders, or a combination thereof ([0164] “Tremors are one of the abnormal events/motions related with Parkinson's disease. The wearable device, disclosed in the present invention, detects tremors by analyzing sensor data acquired using the one or more sensors of the wearable device.”; [0166]; [0090] “For e.g. consider the case where it desired to find out the different types of epileptic seizure patterns that a patient has over a period of time.”).
Regarding claim 11, Nathan teaches a method for a movement analysis of a person by means of the wearable apparatus according to claim 5, said method comprising the following steps:
a) choosing a wearable apparatus having one or more devices each including one or more motion detectors in functional connection with a centralized or distributed control unit, said one or more devices positioned on at least one anatomical district of said person for tracking movement thereof when said
person moves ([0164] “The wearable device, disclosed in the present invention, detects tremors by analyzing sensor data acquired using the one or more sensors of the wearable device. Examples of a sensor of the one or more sensors include, but are not limited to an accelerometer, a magnetometer and a gyroscope. Each of the one or more sensors monitors motion/acceleration of at least one body part of the user.”);
b) initializing and training said wearable apparatus by providing an ensemble of one or more classification algorithms with a set of normal movement patterns of the person, or with a set of abnormal movement patterns associated with abnormal movement of the person, said classification algorithms being included in the software of said control unit ([0036] “Algorithms—primarily the software, math processing and getting detection, recognition and alerting to work and the method to do the software”; [0088] “the system is presented with labeled “positive” and “negative” samples and a training algorithm is used to learn a classifier which can classify the data accordingly. The input to the classification algorithm can be the raw signal data itself or some transformation/mapping of the raw data.”; [0125]);
c) acquiring the movement data flow of the person generated by said motion detectors and sample said flow so as to generate a first dataset ([0010] “the invention will acquire a first set of parameters from the various body physical/geometric sensors on the body worn device, such as acceleration sensors (accelerometers), angle sensors (gyros), location sensor (GPS), directional sensor (compass or magnetometers)”);
d) applying a processing operator to said first dataset so as to obtain a second dataset, said operator being at least one of:
a time synchronization operator if said first dataset consists of signals generated from different devices ([0056]; [0172] “We also calculate the relative motion, speed and acceleration at each time instance.”),
an operator able to obtain a representation of said first dataset, in the form of a features vector ([0095] “We divide the classification time segment into S overlapping segments. For each segment we calculate the histogram of gradients with B bins. We then create a feature vector of length S*B. This feature vector is then used as an input for learning SVM classifier.”);
an operator able to extract useful information by reducing the flow of data on the transmission channels ([0181] “. Two frequency slices of the enhanced time-frequency distribution are then extracted and subjected to the smoothed nonlinear energy operator (SNEO). Finally, the output of the SNEO is thresholder to localize the position of the spikes in the signal. The SNEO is employed to accentuate the spike signature in the extracted frequency slices. A spike is considered to exist in the time domain signal if the spike signature is detected at the same position in both frequency slices.”).
e) classifying said second dataset by applying one or more artificial intelligence algorithms in order to generate a third dataset which expresses the degree of similarity of the movement of the person, associated with the data flow detected in step c), with respect to said set of normal movement patterns or said set of anomalous movement patterns ([0055] “The final analytics detection/classification can be either some rule/heuristic based (such as thresholds), or machine learning based, or any combination thereof.”; [0084]), wherein the classification is:
performed independently by each individual slave device only on the signals collected, sampled and processed by said slave device, and therefore only on elements of the first dataset or of the second dataset, or
performed by the master device on all the signals collected by the individual slave devices, and therefore on the entire first dataset or on the entire second dataset ([0142] “The system both inside the body worn device; and/or in the mobile phone; and/or on the internet server, can continue to analyze the body motions from recent past to before or long time back. These movements are compared and analyzed for the variations”; [0230]; [0319] “the data captured by the sensors and various devices including body worn devices can communicate with the processor over the network via a short range wireless communication medium”);
f) evaluating, upon processing said third dataset, the likelihood of a real anomaly in the movement of said person associated with the data flow detected in step c) ([0055] “The final analytics detection/classification can be either some rule/heuristic based (such as thresholds), or machine learning based, or any combination thereof. Also, the system may choose to give a probabilistic output (a confidence value between 0 and 1) instead of a binary decision.”), and activate an alarm signal by means of said control unit according to an outcome of the likelihood evaluation ([0125] “Any deviation from the normal/baseline sensor or pattern can be treated as abnormal and can be output as an abnormal or deviation event. Then that event can be sent out as alert.”; [0228]).
Regarding claim 12, Nathan teaches the method according to claim 11 wherein a sharing level between the devices is:
a total sharing level, when said first dataset is generated by each individual device and is sent to the single master device which processes and classifies said first dataset, so that said second dataset and said third dataset are generated;
a partial sharing level, when each individual slave device generates said second dataset and shares it with the master device;
a minimum sharing level, when each individual slave device generates said second dataset and said third dataset, and said slave device shares with the master device only said third dataset being the result of the classification performed independently by each individual slave device ([0320] “The network may be any other type of network that is capable of transmitting or receiving data to/from host computers, personal devices, telephones, video/image capturing devices, video/image servers, or any other electronic devices. Further, the network is capable of transmitting/sending data between the mentioned devices”; [0231] “One example of an embodiment encompassed may include input sensors (video and/or motion sensors, such as accelerometers), one or more computers for receiving and processing data from the sensors, connectivity interfaces, and a system for remotely monitoring and for alerting (e.g., sending an alarm) a concerned party.”; This is an example of a total sharing level in which the data from the sensors is communicated to the computers to generate second and third datasets for output.).
Regarding claim 13, Nathan teaches the method according to claim 11. wherein said ensemble of classifiers are selected from the group consisting of: recurrent neural networks, convolutional networks, one-dimensional convolutional neural networks, random forests, support vector machines, k-nearest neighbor, and a combination thereof ([0088] “The learning algorithm could be Bayesian methods, Logistic regression, Neural Networks, Support Vector Machines, Hidden Markov Models, Decision Trees, Boosting methods etc.”; [0100] “The classification technique is not limited to SVM. We can use neural networks, hidden Markov model, K-nearest neighbor, decision trees and random forests.”).
Regarding claim 14, Nathan teaches the method according to claim 11. wherein the movement analysis of a person is addressed to detect one or more anomalies in the movement which are predictive of, or related with, a cognitive deficit or a pathology or posttraumatic condition, a Transitory Ischemic Attack event, stroke, epilepsy, Asperger syndrome, Parkinson's syndrome, autism spectrum disorder, or a combination thereof ([0164] “Tremors are one of the abnormal events/motions related with Parkinson's disease. The wearable device, disclosed in the present invention, detects tremors by analyzing sensor data acquired using the one or more sensors of the wearable device.”; [0166]; [0090] “For e.g. consider the case where it desired to find out the different types of epileptic seizure patterns that a patient has over a period of time.”).
Claims 3 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170188895 A1 (Nathan, Anoo) in view of US 20170265785 A1 (Vaughn et al.), further in view of US 20210258540 A1 (Rajan et al.).
Regarding claim 3, Nathan teaches the wearable apparatus according to claim 1 wherein:
the memory unit includes comprises a main memory and a mass memory ([0043] “The CPU will store the data in the local memory or RAM memory temporarily. For each sensor output, the CPU polls, collects data and stores in the memory. Each sensor is stored separately. This data will be used next for pre and post processing, analysis and output.”; [0148] “Local RAM temporary memory from Sensor, Local RAM or NAND temp or perm memory for results and summary data, Local Firmware perm memory for program and execution data”);
the remote electronic device includes comprises an external memory unit (81) and it is selected from: a smart-phone, a tablet, a personal computer, a gateway, a video game console, or a combination thereof ([0149] “Sensor and summary and results data and trending—stored in mobile phone”);
the communication module is a Bluetooth® Low Energy communication module ([0136] “Locally to the mobile phone via Bluetooth or Wi-Fi”).
Nathan in view of Vaughn does not explicitly teach Bluetooth® Low Energy communication module.
However,
Rajan teaches Bluetooth® Low Energy communication module ([0046] “The first wireless communication technology may be Bluetooth Classic or Bluetooth Low Energy (BLE) technology”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the apparatus of Nathan to include a BLE communication module. One would have been motivated to make this modification because Bluetooth Low Energy is a short-link wireless technology that can exchange data between portable device with low power consumption, as suggested by Rajan [0046].
Regarding claim 15, Nathan in view of Vaughn teaches the method according to claim 11.
Nathan in view of Vaughn does not explicitly teach wherein the analysis of the movement is addressed to optimize the sports performance of an athlete.
However,
Utter teaches wherein the analysis of the movement is addressed to optimize the sports performance of an athlete ([0033] “The term “gait correctness” pertains to motion posture, angle of landing, stride rate, contact time, bounce, and so forth of user 24 in comparison to known sports-specific techniques. Developing a “correct gait” may enhance athletic performance and minimize injury. Further, providing such information to athletes in real time as they traverse a real course can further enhance their development of good motion techniques.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the apparatus taught by Nathan to include optimizing the sports performance of an athlete. One would have been motivated to make this modification because assessing motion for an athlete allows the athlete to improve their technique and prevent injury, as suggested by Rajan [0033].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US 20170188895 A1 (Nathan, Anoo) in view of US 20170265785 A1 (Vaughn et al.), further in view of US 20130116514 A1 (Kroner et al.).
Regarding claim 8, Nathan teaches the wearable apparatus according to claim 1.
Nathan does not explicitly teach further comprising an administration means configured to automatically or manually administer a compound if an anomalous event is in progress or may arise in said person.
However,
Kroner teaches further comprising an administration means configured to automatically or manually administer a compound if an anomalous event is in progress or may arise in said person ([0123] “The treatment module 314, in one embodiment, administers a treatment that includes a medicinal fluid or other machine administrable treatment. For example, the treatment module 314 may administer a medicinal treatment using a drug pump or the like”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the apparatus taught by Nathan to include administering a compound if an anomalous event is identified. One would have been motivated to make this modification because administering treatment based on motion feature detection to ensure patient receives timely treatment in the form of a medicinal drug, as suggested by [0004, 0123].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVELYN GRACE PARK whose telephone number is (571)272-0651. The examiner can normally be reached Monday - Friday, 9AM - 5:00PM.
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/EVELYN GRACE PARK/Examiner, Art Unit 3791
/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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