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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/9/2026 has been entered.
Claim objection regarding to claim 19 is withdrawn.
Claim rejection related to 35 USC § 101 regarding to claims 1-20 is withdrawn.
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 1-9, 11-12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tahir GB2588958 in view of Chang et al. (Chang) US 11636161
In regard to claim 1, Tahir disclose A method comprising: (page 2, line 7-35 method)
collecting, by a processor of an electronic device and while the electronic device is worn by a user, measurement data from a set of sensors of the electronic device; (Fig. 2, page 2, line 7-35, page 13, line 24-30, a processor 102 of the wearable device 100, collecting biometric data from the sensors of the wearable device of the user)
providing, by the processor and to a machine-learning model:
the collected measurement data from the set of sensors; (page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-page 18, line 3 the model the biometric data sensed by the wearable device, the biometric data are combined information from various sensors)
and a set of classified feature sets, wherein at least one of the classified feature sets is classified as belonging to a known user; (page 2, line 7-35, page 7, line 8- page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25- page 18, line 3, page 27, line 25-page 28, line 34, inputting the biometric data collected from the sensors and the predetermined authentication information representing an authorized user to the feature extraction module and then a recognition module using a recognition algorithm, “In examples of the present disclosure, the recognition module 201 may indicate to the recognition algorithm that the extracted feature set belongs to the authorised user by adding the extracted feature set to a list of predetermined feature sets associated with the authorised user. The recognition module 201 will use the modified list of predetermined feature sets in future iterations of the recognition algorithm.” The feature set can be classified and belong to the known user) and extracting, using the machine-learning model, a feature set from the collected measurement data for each of a series of periodic measurements; (page 4, line 1-18, page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-page 18, line 3, page 20, line 27-page 21, line 19, the model extracting feature set from the biometric data sensed by the wearable device, the biometric data are combined information measured from various sensors from various different time points)
obtaining, by the processor:
an indication of whether the extracted feature set is similar to one or more of the classified feature sets, wherein at least one of the classified feature sets is classified as belonging to the known user and generated based on the previously collected sets of measurement data for the known user; (page 2, line 7-35, page 7, line 8-page 8, line 35, page 20, line 34-page 21, line 19, provide the indication with a confidence score, based on the score, the extracted feature set belong to the authorized user and the indication is generated based on predetermined authentication information collected from the authorized user) and
a distance between the extracted feature set and the classified feature sets classified as belonging to the known user; (page 4, line 1-18, page 7, line 8-page 8, line 35, page 16, line 25-col, 18, line 3, page 27, line 25-page 28, line 34, page 20, line 34- page 21, line 10 calculate a distance between the extracted feature set and the predetermined feature set and the predetermined feature set as belong to the known user)
and based on the obtained indication of whether the extracted feature set is similar to one or more of the classified feature sets, determining, by the processor, whether the user is the known user. (page 2, line 7-35, page 7, line 8-page 8, line 35, page 20, line 27-page 21, line 19, based on the extracted feature set is similar to the predetermined feature set belong to the authorized user with the similarity measure, determine if the user is the authorized user)
in accordance with the user being the known user and the distance between the extracted feature set and the set of classified feature sets is identified (page 4, line 1-18, page 5, line 1-line 26, page 7, line 8-page 8, line 35, page 16, line 25-page 18, line 3, page 19, line 12-24, page 20, line 34- page 21, line 10, page 27, line 25-page 28, line 34, based on a distance between the extracted feature set and the predetermined feature set and the predetermined feature set as belong to the known user, here it disclose the condition)
adding the extracted feature set to the set of classified feature sets; (page 5, line 1-line 26, page 7, line 33-page 8, line 15, page 19, line 12-24, adding the extracted feature set to the predetermined feature set associate with the authorized user)
But Tahir fail to explicitly disclose “and an indication of whether a distance between the feature sets has increased over time; and the distance between the feature sets having increased over time:”
Chang disclose and an indication of whether a distance between the feature sets has increased over time; and the distance between the feature sets having increased over time: (col. 2, line 30-col. 3, line 11, col. 7, line 35-col. 8, line 42, col. 13, line 50- col. 14, line 2, col. 19, line 3-col. 20, line 38, calculate the distance between the feature sets and to determine if the distance grow over the time since the user can change the weight of features in the feature set based on user defined rule and the distance is proportional to the weight of the feature. Note: please further define the similarity and the distance, etc. and the relationship between the two to help move forward the prosecution, call to discuss if necessary)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s method of feature clustering into Tahir’s invention as they are related to the same field endeavor of method of similar feature identification. The motivation to combine these arts, as proposed above, at least because Chang’s method of feature clustering based on the distance and weight of the feature would help to provide feature identification method into Tahir’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing feature identification based on the feature distance and weight would help to facilitate similar feature identification and therefore improve user experience using the device.
In regard to claim 2, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
Tahir disclose wherein the machine-learning model is based on a trained neural network. (page 21, line 20-page 22, line 15, page 4, line 1-26, the model is based on an ANN trained)
In regard to claim 3, Tahir and Chang disclose The method of claim 2, the rejection is incorporated herein.
Tahir disclose wherein the neural network is a Siamese neural network. (page 21, line 20-page 22, line 15, page 4, line 1-26, the model is based on an ANN trained, the choice of the NN is an implementation choice and but not an invention)
In regard to claim 4, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
Tahir disclose wherein the measurement data from the set of sensors is acquired during authentication of the known user. (page 2, line 7-35, page 12, line 10-40, page 7, line 8-page 8, line 35, the biometric data are collected during authentication of the user)
In regard to claim 5, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
Tahir disclose wherein the set of sensors includes: at least one of a temperature sensor, a heartrate sensor, a (PPG) sensor, or an (ECG) sensor; and at least one inertial measurement unit (IMU) sensor including an accelerometer, a magnetometer, or a gyroscope. (page 2, line 7-35, page 13, line 24- page 16, line 30, page 22 line 34- page 23 line 5, the sensors of the wearable device of the user, such as a temperature sensor, a heartrate sensor, a PPG sensor, ECG, inertial measurement unit, an accelerometer, a magnetometer, or a gyroscope, etc.)
In regard to claim 6, Tahir and Chang disclose The method of claim 5, the rejection is incorporated herein.
Tahir disclose wherein the set of sensors further includes a number of optical sensors. (page 2, line 7-35, page 13, line 24- page 16, line 30, page 22 line 34- page 23 line 5, the sensors with optical sensors)
In regard to claim 7, Tahir and Chang disclose The method of claim 6, the rejection is incorporated herein.
Tahir disclose wherein the number of optical sensors includes an infrared sensor and a visible light sensor. (page 2, line 7-35, page 13, line 24- page 16, line 30, page 22 line 34- page 23 line 5, the sensors are infrared light and visible light sensor)
In regard to claim 8, Tahir and Chang disclose The method of claim 7, the rejection is incorporated herein.
Tahir disclose wherein the measurement data from the set of sensors includes data from two or more channels of the infrared sensor, data from two or more channels of the visible light sensor, and data from two or more channels of the at least one IMU sensor. (page 2, line 7-35, page 13, line 24- page 16, line 30, page 22 line 34- page 23 line 5, the sensors are measure data from infrared light and with photodetectors and the sensors measure data from visible light with photodetectors and IMU unit measure data accelerometer, gesture sensor, tactile sensor, etc.)
In regard to claim 9, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
Tahir disclose further comprising generating the indication of whether the extracted feature set is similar to the one of the classified feature sets based on a distance between a first vector generated corresponding the extracted feature set and at least one second vector generated corresponding to at least one of the classified feature sets. (page 7, line 8-page 8, line 35, page 16, line 24- page 18, line 5, page 20, line 34-page 21, line-19, the determine a similarity of the feature sets with the predetermined feature sets and the predetermined features sets are generated based on the distance between the first vector corresponding the extracted feature set and the second vector corresponding to the predetermined feature sets)
In regard to claim 11, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
Tahir disclose further comprising tuning a machine-learning model sensitivity in response to a number of the previously collected sets of measurement data for the known user. (page 16, line 24- page 18, line 5, page 20, line 34-page 21, line-19, page 25, line 10-page 26, line 2, the ML can be updated with collecting the biometric data with different time point or frequency)
In regard to claim 12, Tahir and Chang disclose The method of claim 11, the rejection is incorporated herein.
Tahir disclose wherein tuning the machine-learning model sensitivity comprises collecting the measurement data from the set of sensors over a window of time. (page 16, line 24-page 18, line 5, page 20, line 34-page 21, line-19, page 25, line 10-page 26, line 2, the ML can be updated with collecting the biometric data from the sensors with different time point or frequency)
In regard to claim 14, Tahir disclose A wearable electronic device, (page 2, line 7-35 wearable device) comprising:
a memory configured to store instructions; a set of sensors including at least two sensors; and a processor configured to execute the instructions stored in the memory, which causes the processor to perform operations comprising: (Fig. 2, page 2, line 7-35, page 13, line 24-30, a processor 102 of the wearable device 100, with sensors)
collecting measurement data from the set of sensors while the wearable electronic device is worn by a user; (Fig. 2, page 2, line 7-35, page 13, line 24-30, a processor 102 of the wearable device 100, collecting biometric data from the sensors of the wearable device of the user)
providing, to a trained machine-learning model:
the collected measurement data from the set of sensors; (page 2, line 7-35, page 7, line 8- page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-col, 18, line 3, page 27, line 25-page 28, line 34, inputting the biometric data collected from the sensors and the predetermined authentication information representing an authorized user to the feature extraction module and then a recognition module using a recognition algorithm, “In examples of the present disclosure, the recognition module 201 may indicate to the recognition algorithm that the extracted feature set belongs to the authorised user by adding the extracted feature set to a list of predetermined feature sets associated with the authorised user. The recognition module 201 will use the modified list of predetermined feature sets in future iterations of the recognition algorithm.” Thus the predetermined feature set were inputted previously based on the previous biometric information collected)
and a set of classified feature sets, wherein at least one of the classified feature sets is classified as belonging to a known user; (page 2, line 7-35, page 7, line 8- page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25- page 18, line 3, page 27, line 25-page 28, line 34, inputting the biometric data collected from the sensors and the predetermined authentication information representing an authorized user to the feature extraction module and then a recognition module using a recognition algorithm, “In examples of the present disclosure, the recognition module 201 may indicate to the recognition algorithm that the extracted feature set belongs to the authorised user by adding the extracted feature set to a list of predetermined feature sets associated with the authorised user. The recognition module 201 will use the modified list of predetermined feature sets in future iterations of the recognition algorithm.” The feature set can be classified and belong to the known user) and
extracting, using the machine-learning model, a feature set from the collected measurement data for each of a series of periodic measurements; (page 4, line 1-18, page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-page 18, line 3, page 20, line 27-page 21, line 19, the model extracting feature set from the biometric data sensed by the wearable device, the biometric data are combined information measured from various sensors from various different time points) and
determining whether the user is the known user based on a comparison of the extracted feature set with each classified feature set of the set of classified feature sets, (page 2, line 7-35, page 7, line 8-page 8, line 35, page 20, line 34-page 21, line 19, comparing the feature sets with predetermined feature sets and obtain a score, and based on the score reached a threshold value to determine the likelihood the extracted feature set belongs to the authorized user) at least one of classified feature set of the set of classified feature sets classified as belonging to the known user and generated based on the previously collected sets of measurement data for the known user; (page 2, line 7-35, page 7, line 8-page 8, line 35, page 12, line 26- page 13, line 19, page 20, line 34-page 21, line 19, the predetermined feature set belong to the authorized user and is generated based on predetermined authentication information collected from the authorized user) and
in accordance with the user being the known user and the distance between the extracted feature set and the set of classified feature sets is identified (page 4, line 1-18, page 5, line 1-line 26, page 7, line 8-page 8, line 35, page 16, line 25-page 18, line 3, page 19, line 12-24, page 20, line 34- page 21, line 10, page 27, line 25-page 28, line 34, based on a distance between the extracted feature set and the predetermined feature set and the predetermined feature set as belong to the known user, here it disclose the condition)
adding the extracted feature set to the set of classified feature sets; (page 5, line 1-line 26, page 7, line 33-page 8, line 15, page 19, line 12-24, adding the extracted feature set to the predetermined feature set associate with the authorized user)
But Tahir fail to explicitly disclose “and a determined distance between the feature sets having increased over time:”
Chang disclose and a determined distance between the feature sets having increased over time: (col. 2, line 30-col. 3, line 11, col. 7, line 35-col. 8, line 42, col. 13, line 50- col. 14, line 2, col. 19, line 3-col. 20, line 38, calculate the distance between the feature sets and to determine if the distance grow over the time since the user can change the weight of features in the feature set based on user defined rule and the distance is proportional to the weight of the feature. Note: please further define the similarity and the distance, etc. and the relationship between the two to help move forward the prosecution, call to discuss if necessary)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s method of feature clustering into Tahir’s invention as they are related to the same field endeavor of method of similar feature identification. The motivation to combine these arts, as proposed above, at least because Chang’s method of feature clustering based on the distance and weight of the feature would help to provide feature identification method into Tahir’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing feature identification based on the feature distance and weight would help to facilitate similar feature identification and therefore improve user experience using the device.
In regard to claim 15, Tahir and Chang disclose The wearable electronic device of claim 14, the rejection is incorporated herein.
Tahir disclose wherein the operations further comprise: in response to determining that the user is not the known user, requesting the user to authenticate using any one of: a password, a PIN, a design pattern, a fingerprint, or a facial recognition feature. (page 3, line 15-page 5, line 26, if the first source of authentication is rejected with confidence level score is less than the threshold, asking the user to input password, fingerprint, etc.)
In regard to claim 16, Tahir and Chang disclose The wearable electronic device of claim 14, the rejection is incorporated herein.
Tahir disclose wherein the operations further comprise, in response to determining that the user is the known user, allowing the user access to a function of the wearable electronic device. (page 3, line 15-page 5, line 26, if the first source of authentication is accepted with confidence level score is greater than the threshold, the user can access functions of the wearable device)
In regard to claim 17, Tahir and Chang disclose The wearable electronic device of claim 14, the rejection is incorporated herein.
Tahir disclose wherein the measurement data is collected by sampling the measurement data from a number of channels of the at least two sensors of the set of sensors. (page 2, line 7-35, page 13, line 24- page 16, line 30, page 22 line 34- page 23 line 5, measurement data are collected from infrared light and with photodetectors and from visible light with photodetectors and IMU, accelerometer, gesture sensor, tactile sensor, etc.)
In regard to claim 18, Tahir and Chang disclose The wearable electronic device of claim 17, the rejection is incorporated herein.
Tahir disclose further comprising sampling a first subset of channels of the number of channels of a sensor of the at least two sensors at a first sampling rate and a second subset of channels of the number of channels of the sensor of the at least two sensors at a second sampling rate. (page 2, line 7-35, page 13, line 24-page 18, line 5, page 20, line 34-page 21, line-19, page 22 line 34- page 23 line 5, page 25, line 10-page 26, line, measurement data are collected from infrared light and with photodetectors and from visible light with photodetectors and IMU, accelerometer, gesture sensor, tactile sensor, etc. collecting the biometric data from the sensors with different time point or frequency)
In regard to claim 19, Tahir disclose A system, (page 2, line 7-35 system) comprising:
a first wearable electronic device comprising at least one photoplethysmography (PPG) sensor; (Fig. 2, page 2, line 7-35, page 13, line 24-30, a processor 102 of the wearable device 100, collecting biometric data from the sensors of the wearable device of the user, such as PPG sensor)
a second wearable electronic device comprising at least one Inertial Measurement Unit (IMU) sensor; (page 22 line 34- page 23 line 25, inertial measurement unit of the user electronic device, the user electronic device can be the second wearable electronic device (for example, mobile phone)
a processor configured to: (Fig. 2, page 2, line 7-35, page 13, line 24-30, a processor 102 of the wearable device 100)
collect measurement data from the at least one PPG sensor and the at least one IMU sensor while the first and second wearable electronic devices are worn by a user; (Fig. 2, page 2, line 7-35, page 13, line 24-30, page 22 line 34- page 23 line 5, a processor 102 of the wearable device 100, collecting first biometric data from the PPG sensor and collecting second biometric data from IMU of the wearable device of the user)
provide, to a machine-learning model: the collected measurement data from the at least one PPG sensor and the at least one IMU sensor; (page 2, line 7-35, page 7, line 8- page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-col, 18, line 3, page 27, line 25-page 28, line 34, inputting the biometric data collected from the sensors and the predetermined authentication information representing an authorized user to the feature extraction module and then a recognition module using a recognition algorithm, “In examples of the present disclosure, the recognition module 201 may indicate to the recognition algorithm that the extracted feature set belongs to the authorised user by adding the extracted feature set to a list of predetermined feature sets associated with the authorised user. The recognition module 201 will use the modified list of predetermined feature sets in future iterations of the recognition algorithm.” Thus the predetermined feature set were inputted previously based on the previous biometric information collected ) the machine-learning model trained for extracting a feature set from a fusion of measurement data obtained by a set of sensors(page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-col, 18, line 3, the model extracting feature set from the biometric data sensed by the wearable device, the biometric data are combined information from various sensors)
and a set of classified feature sets, wherein at least one of the classified feature sets is classified as belonging to a known user; (page 2, line 7-35, page 7, line 8- page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25- page 18, line 3, page 27, line 25-page 28, line 34, inputting the biometric data collected from the sensors and the predetermined authentication information representing an authorized user to the feature extraction module and then a recognition module using a recognition algorithm, “In examples of the present disclosure, the recognition module 201 may indicate to the recognition algorithm that the extracted feature set belongs to the authorised user by adding the extracted feature set to a list of predetermined feature sets associated with the authorised user. The recognition module 201 will use the modified list of predetermined feature sets in future iterations of the recognition algorithm.” The feature set can be classified and belong to the known user) and
extracting, using the machine-learning model, a feature set from the collected measurement data for each of a series of periodic measurements; (page 4, line 1-18, page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 16, line 25-page 18, line 3, page 20, line 27-page 21, line 19, the model extracting feature set from the biometric data sensed by the wearable device, the biometric data are combined information measured from various sensors from various different time points) and
determine whether the user is the known user based on a comparison of the extracted feature set with each of the set of the classified feature sets, (page 2, line 7-35, page 7, line 8-page 8, line 35, page 20, line 34-page 21, line 19, comparing the feature sets with predetermined feature sets and obtain a score, and based on the score reached a threshold value to determine the likelihood the extracted feature set belongs to the authorized user) at least one of the set of the classified feature sets classified as belonging to the known user and generated based on the previously collected sets of measurement data for the known user. (page 7, line 8-page 8, line 35, page 13, line 26-page 16, line 10, page 20, line 34-page 21, line-19, based on the score reached a threshold value to determine the extracted feature set belongs to the authorized user and predetermined feature sets and the predetermined features sets are generated based on the classified collected biometric data from the authorized user) and
in accordance with the user being the known user and the distance between the extracted feature set and the set of classified feature sets is identified (page 4, line 1-18, page 5, line 1-line 26, page 7, line 8-page 8, line 35, page 16, line 25-page 18, line 3, page 19, line 12-24, page 20, line 34- page 21, line 10, page 27, line 25-page 28, line 34, based on a distance between the extracted feature set and the predetermined feature set and the predetermined feature set as belong to the known user, here it disclose the condition)
adding the extracted feature set to the set of classified feature sets; (page 5, line 1-line 26, page 7, line 33-page 8, line 15, page 19, line 12-24, adding the extracted feature set to the predetermined feature set associate with the authorized user)
But Tahir fail to explicitly disclose “and a determined distance between the feature sets having increased over time:”
Chang disclose and a determined distance between the feature sets having increased over time: (col. 2, line 30-col. 3, line 11, col. 7, line 35-col. 8, line 42, col. 13, line 50- col. 14, line 2, col. 19, line 3-col. 20, line 38, calculate the distance between the feature sets and to determine if the distance grow over the time since the user can change the weight of features in the feature set based on user defined rule and the distance is proportional to the weight of the feature. Note: please further define the similarity and the distance, etc. and the relationship between the two to help move forward the prosecution, call to discuss if necessary)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chang’s method of feature clustering into Tahir’s invention as they are related to the same field endeavor of method of similar feature identification. The motivation to combine these arts, as proposed above, at least because Chang’s method of feature clustering based on the distance and weight of the feature would help to provide feature identification method into Tahir’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing feature identification based on the feature distance and weight would help to facilitate similar feature identification and therefore improve user experience using the device.
In regard to claim 20, Tahir and Chang disclose The system of claim 19, the rejection is incorporated herein.
Tahir disclose wherein the measurement data is collected by sampling the measurement data from a number of channels of the at least one PPG sensor or the at least one IMU sensor at different sampling rates. (page 2, line 7-35, page 13, line 24-page 18, line 5, page 20, line 34-page 21, line-19, page 22 line 34- page 23 line 5, page 25, line 10-page 26, line, measurement data are collected from infrared light and with photodetectors (PPG sensor) and from visible light with photodetectors and IMU, accelerometer, gesture sensor, tactile sensor, etc. collecting the biometric data from the sensors with different time point or frequency)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tahir GB2588958 and Chang et al. (Chang) US 11636161 as applied to claim 1, further in view of Davis et al. (Davis) US 2020/0285836
In regard to claim 10, Tahir and Chang disclose The method of claim 1, the rejection is incorporated herein.
But Tahir and Chang fail to explicitly disclose “further comprising generating the indication of whether the extracted feature set is similar to the one of the classified feature sets based on a triplet loss function using a first vector generated corresponding to the extracted feature set, and a second and a third vector generated corresponding to the classified feature sets.”
Davis disclose further comprising generating the indication of whether the extracted feature set is similar to the one of the classified feature sets based on a triplet loss function using a first vector generated corresponding to the extracted feature set, and a second and a third vector generated corresponding to the classified feature sets. ([0020]-[0022] the similarity of the features are based on a triplet loss function using the first vector representing the identified feature set and second and third vectors corresponding to the classified feature sets )
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Davis’s method of information authentication using ML into Chang and Tahir’s invention as they are related to the same field endeavor of using ML to authenticating user. The motivation to combine these arts, as proposed above, at least because Davis’s authentication using ML with triplet loss function would help to identify the similarity between the user biometric data to Chang and Tahir’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that identifying the similarity between the user biometric data using the triplet loss function would help to facilitate user identification and authentication and therefore improve user experience using the device.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Tahir GB2588958 and Chang et al. (Chang) US 11636161 as applied to claim 1, further in view of Oleson et al. (Oleson) US 2015/0061891
In regard to claim 13, Tahir and Chang disclose The method of claim 12, the rejection is incorporated herein.
But Tahir and Chang fail to explicitly disclose “wherein the window of time is about five seconds, ten seconds, or fifteen seconds.”
Oleson disclose wherein the window of time is about five seconds, ten seconds, or fifteen seconds. ([0048]-[0055] detecting the biometric data every 5 seconds, etc. intervals of time based on the type of data collected)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Oleson’s method of collecting user biometric data into Chang and Tahir’s invention as they are related to the same field endeavor of user biometric data collection. The motivation to combine these arts, as proposed above, at least because Oleson’s collecting user biometric data every interval of time would help to sample user biometric data to Chang and Tahir’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that collecting user biometric data every interval of time would help to facilitate data collection and therefore improve user experience using the device.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 4/9/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20220369390 A1 2022-11-17 Kourtis et al.
Method And System To Pair An Article To A User
Kourtis et al. disclose A method and system to seamlessly pair a user to an article includes matching their respective inertial profile. The system comprises of a user sensor that can capture and communicate the motion profile of the user or a part of the user as well as an article sensor that can capture and communicate the motion profile of the article. Both motion profiles are communicated to a pattern matching module. When the article and the user are spatially interacting for at least a minimum period of time, the pattern matching module can determine the level of similarity between the respective profiles. A decision to pair the article to the user is produced based on said level of said profiles similarity…. See abstract
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
Prosecution Insights