DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Argument
Applicant's arguments, filed 06/29/26, with respect to claims have been considered but are moot in view of the new ground(s) of rejection.
Claims 10 and 13 have been canceled.
Claim 22 is added.
Claims 1-9, 11-12 and 14-22 are pending.
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 of this title, 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 7-9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bonev et al. (U.S. 20250098984) in view of Kenward et al. (U.S. 20230172301).
For claim 7, Bonev et al. disclose a device configured to be worn on a user’s wrist, the device comprising:
one or more network interfaces (at least [0029]. The wearable computing device can include one or more sensors (e.g., accelerometers, gyroscopes, etc.) configured to obtain data indicative of motion of the user. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event. For instance, the notification can include a text notification displayed on a display screen of the wearable computing device. Additionally, in some instances, the wearable computing device can notify emergency personnel in the event the user fails to take some action (e.g., dismiss notification));
one or more accelerometers (at least [0049]. The wearable computing device 300 can include a plurality of sensors 310. For instance, in some implementations, the plurality of sensors 310 can include an accelerometer 312 (e.g., a multi-axis accelerometer) and a gyroscope 314.);
one or more processors (at least [0048]. The wearable computing device 300 can include one or more processors 302.); and
one or more computer-readable media storing instructions that, when executed, cause the device to perform operations comprising:
receiving, from an electronic device, first data (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316),
determining, based at least in part on the first data, a threshold associated with issuing notifications (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.),
storing the threshold for use in determining whether to issue the notifications (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.),
receiving second data from the one or more accelerometers (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316. The fall event may be detected when the fall indicating parameter exceeds the fall detection threshold. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.);
determining, based at least in part on the second data, a characteristic of an event experienced by the device (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316. The fall event may be detected when the fall indicating parameter exceeds the fall detection threshold. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.);
determining that the characteristic satisfies the threshold acceleration (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316. The fall event may be detected when the fall indicating parameter exceeds the fall detection threshold. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.);
generating, based at least in part on the characteristic satisfying the threshold, a notification associated with a user of the device experiencing a fall event (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. The fall event may be detected when the fall indicating parameter exceeds the fall detection threshold. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.); and
sending, via the one or more network interfaces, third data associated with the notification at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. The fall event may be detected when the fall indicating parameter exceeds the fall detection threshold. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.) However, Bonev et al. do not disclose a device configured to be worn on a hardhat.
In the same field of endeavor, Kenward et al. disclose a device configured to be worn on a hardhat (at least [0002]. Conventional safety helmets, or hard hats, are worn for protection by industrial workers such as construction workers, electricians or engineers on building sites.), the device comprising: one or more network interfaces (please Figure. 9); one or more accelerometers (at Fig. 1, [0065], [0145], [0219], [0258]-[0259] and [0281]. The input device comprises a shock sensor, and wherein the controller is configured to cause the output device to generate an alert when the data from the shock sensor indicates the safety helmet has been subject to an impact. For example, the shock sensor may be an accelerometer. For example, the alert may be to identify the level of damage to the user and report this to the site manager at the central controller for assessment.); one or more processors; and one or more computer-readable media storing instructions that, when executed, cause the device to perform operations comprising receiving second data from the one or more accelerometers; determining, based at least in part on the second data, an amount of acceleration experienced by the device; determining that the amount of acceleration satisfies the threshold acceleration (at Fig. 1, [0065], [0145], [0219], [0258]-[0259] and [0281]. An accelerometer can be used to determine whether a user is getting tired. One or more of the sensors may be configured to trigger an alert if the sensor records a measurement of a parameter which exceeds a threshold value for that parameter. In another embodiment, the input device comprises a shock sensor, and wherein the controller is configured to cause the output device to generate an alert when the data from the shock sensor indicates the safety helmet has been subject to an impact. For example, the shock sensor may be an accelerometer. For example, the alert may be to identify the level of damage to the user and report this to the site manager at the central controller for assessment. The shock sensor may be arranged to determine whether the helmet 100 has sustained too much damage, for example by detecting shocks or accelerations in excess of a particular “single event” threshold, which are likely to have resulted in sufficient damage to the helmet 100 that the structural safety shell needs replacing. Additionally or alternatively the shock sensor may monitoring shocks or accelerations below the “single event” threshold, but above a lower “possible damage” threshold. By integrating the signals from these events over time, a second manner of identifying the helmet 100 having accumulated sufficient damage and needing replacement is provided (i.e. by monitoring general, but serious, “wear and tear” type damage); generating a notification associated with a user of the device experiencing a fall (at least [0131], [0136], [0145], [0253]-[0258] and [0275]. The input device comprises a shock sensor, and wherein the controller is configured to cause the output device to generate an alert when the data from the shock sensor indicates the safety helmet has been subject to an impact. The input device can receive data from the environment, such as sensor data or record camera data. The output device is capable of emitting an effect, such as sound or light.); and
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Bonev et al.as taught by Kenward et al. for purpose of identifying and monitoring workers to improve the safety of workers on site.
For claim 8, the combination of Kenward et al. and Bonev et al. disclose the device of claim 7. Bonev et al. disclose at least one of one or more speakers or one or more lighting elements, the operations further comprising based at least in part on the characteristic satisfying the threshold, at least one of: causing, via the one or more speakers, output of audio; or causing, via the one or more lighting elements, output of light (at least [[0029] and [0032]. The wearable computing device can generate a notification (e.g., visual, auditory, etc.) in response to the one or more sensors obtaining data indicative of motion corresponding to a fall event.)
For claim 9, the combination of Kenward et al. and Bonev et al. disclose the device of claim 7. Kenward et al. disclose at least one of a voltage detector or a current detector (Fig. 1 and [0065] and [0218]. The sensor comprises a sensor for detecting electrical current, electrical fields, magnetic fields or ionising radiation. Optionally the sensor comprises a skin contact sensor, e.g. to determine whether the helmet is being worn correctly, or indeed at all. Optionally the sensor comprises an SOS switch to allow a user to trigger an alert that they require assistance, medical attention, etc. Optionally, the sensor comprises a temperature sensor.)
For claim 12, the combination of Kenward et al. and Bonev et al. disclose the device of claim 7. Bonev et al. disclose receiving fourth data associated with the threshold acceleration, wherein the fourth data is received from at least one of: an input component of the device; the electronic device; or a second electronic device (at least [0030]-[0038], [0041]-[0042], [0053]-[0059] and [0070]. Data from at least one of the accelerometer 312 (FIG. 3), the gyroscope 314 (FIG. 3), or the barometer 316 can be provided as an input to the one or more machine-learned models 320. The one or more machine-learned models 320 can process the data from the one or more sensors 310 to determine an adjusted fall detection threshold for a fall event. In some implementations, the output of the one or more machine-learned models 320 can be a single numerical value that can be used to determine whether a fall event has occurred. In alternative implementations, the output of the one or more machine-learned models 320 can include a plurality of outputs. Each of the plurality of outputs can correspond to a fall detection threshold for one or more of the sensors 310. For instance, a first output of the one or more machine-learned models 320 can correspond to a first fall detection threshold for the accelerometer 312. Conversely, a second output of the one or more machine-learned models 320 can correspond to a second fall detection threshold for the gyroscope 314. Still further, in some implementations, a third output of the one or more machine-learned models 320 can correspond to a third fall detection threshold for the barometer 316.)
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Bonev et al. (U.S. 20250098984) in view of Kenward et al. (U.S. 20230172301) further in view of Brown et al. (U.S. 20170350754).
For claim 11, the combination of Kenward et al. and Bonev et al. disclose do not the device of claim 1, further comprising an ultra violet (UV) sensor or one or more lighting element, the operations further comprising: receiving fourth data from the UV sensor; determining, based at least in part on the fourth data, an illuminance of a source; determining that the illuminance satisfies a threshold illuminance; and causing, based at least in part on the illuminance satisfying the threshold illuminance, output of a second notification via the one or more lighting elements.
In the same field of endeavor, Brown et al. disclose comprising an ultra violet (UV) sensor or one or more lighting element, the operations further comprising: receiving fourth data from the UV sensor; determining, based at least in part on the fourth data, an illuminance of a source; determining that the illuminance satisfies a threshold illuminance; and causing, based at least in part on the illuminance satisfying the threshold illuminance, output of a second notification via the one or more lighting elements (at least [0017] and [0028]. Sunscreen monitor program 112 receives UV data from UV control sensor 106 and UV monitoring sensor 108 and determines the amount of UV radiation the sensors receive. Sunscreen monitor program 112 determines whether the UV radiation received by UV monitoring sensor 108 exceeds a threshold, and if so, then the program transmits a warning or an alert to the user. Sunscreen monitor program 112 may transmit a warning message by activating a flashing light on client computing device 110.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Bonev et al. as taught by Brown et al. for purpose of determining the amount of ultraviolet radiation received by the first ultraviolet radiation sensor is not below an alert threshold, the one or more computer processors transmit an alert message to the user.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Bonev et al. (U.S. 20250098984) in view of Kenward et al. (U.S. 20230172301) further in view of Zheng et al. (U.S. 20190357618).
For claim 7, the combination of Kenward et al. and Bonev et al. do not disclose receiving third data from the one or more accelerometers; and determining, based at least in part on the second data and the third data, the characteristic of the fall event experienced by the device.
In the same field of endeavor, Zheng et al. disclose receiving third data from the one or more accelerometers; and determining, based at least in part on the second data and the third data, the characteristic of the fall event experienced by the device (at least Fig. 1, [0011] and [0021]-[0031]. When a change variable of the acceleration of each axis is smaller than the third threshold and a duration of the acceleration is greater than the third duration, the motionlessness event occurs. The change variable refers to a difference value between a maximal value and a minimal value detected at least within a range of the third duration, and during the third duration, the rider barely moved, indicating that the rider is most likely to be in an unconscious or unable to move.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Bonev et al. as taught by Zheng et al. for purpose of notifying to rescue, hazards caused by falling.
Claims 14-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (U.S. 20190357618) in view of Ales et al. (U.S. 20080265170).
For claim 14, Zheng et al. disclose a device comprising:
one or more network interfaces (Fig. 3 and [0046]. The smart helmet 100 can be connected with a cellphone via the wireless communication module 41, for embodiment, a Bluetooth module, a control signal sent by the controller 20 controls the cellphone to send the emergency information to a predetermined number (for embodiment, a number of an emergency contact registered by the rider or a number of an emergency center), wherein the emergency information includes emergency message or emergency phone call.);
one or more accelerometers (Fig. 3 and [0039]. Smart helmet 100 provided in a second embodiment of the present invention includes a three-axis acceleration sensor 10 and a controller 20, wherein the three-axis acceleration sensor 10 is configured to detect acceleration of three axe.);
one or more processors; and one or more computer-readable media storing instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving second data from the one or more accelerometers (at least [0015]. The acceleration is measured by the three-axis acceleration sensor installed on the smart helmet, and whether the falling event occurs is determined by analyzing change of the acceleration),
receiving third data from the one or more accelerometers (at least Fig. 1, [0011] and [0021]-[0031]. When a change variable of the acceleration of each axis is smaller than the third threshold and a duration of the acceleration is greater than the third duration, the motionlessness event occurs. The change variable refers to a difference value between a maximal value and a minimal value detected at least within a range of the third duration, and during the third duration, the rider barely moved, indicating that the rider is most likely to be in an unconscious or unable to move.),
determining, based at least in part on the second data and the third data, a change in acceleration experienced by the device (at least Fig. 1, [0011] and [0021]-[0031]. When a change variable of the acceleration of each axis is smaller than the third threshold and a duration of the acceleration is greater than the third duration, the motionlessness event occurs. The change variable refers to a difference value between a maximal value and a minimal value detected at least within a range of the third duration, and during the third duration, the rider barely moved, indicating that the rider is most likely to be in an unconscious or unable to move.),
determining that the chang in acceleration satisfies a threshold acceleration (at least Fig. 1, [0011] and [0021]-[0031]. When a change variable of the acceleration of each axis is smaller than the third threshold and a duration of the acceleration is greater than the third duration, the motionlessness event occurs. The change variable refers to a difference value between a maximal value and a minimal value detected at least within a range of the third duration, and during the third duration, the rider barely moved, indicating that the rider is most likely to be in an unconscious or unable to move.),
determining, based at least in part on the change in acceleration satisfying the threshold, to issue an alert (at least [0031]. The emergency signal is sent by the controller in a way of sending a control signal to a corresponding emergency module, wherein a form of the emergency signal is unlimited, for embodiment, an emergency sound and a light indication are sent, a report is made to a cellphone which sends an emergency message or make an emergency call, and so on.),
determining an identifier associated with a second user (at least claim 10. The wireless communication module being configured to send emergency information to a predetermined number via a mobile terminal associated with the smart helmet, wherein the emergency information carries the current geographical position information.),
causing, via the one or more output devices, output of an indication (at least [0031]. The emergency signal is sent by the controller in a way of sending a control signal to a corresponding emergency module, wherein a form of the emergency signal is unlimited, for embodiment, an emergency sound and a light indication are sent, a report is made to a cellphone which sends an emergency message or make an emergency call, and so on.); and
sending, via the one or more network interfaces, third data to an electronic device associated with the second user, the third data associated with the alert (at least claim 10. The wireless communication module being configured to send emergency information to a predetermined number via a mobile terminal associated with the smart helmet, wherein the emergency information carries the current geographical position information.)
However, Zheng et al. do not disclose one or more output devices oriented to output indications from a first side of the device; at least one of a button, a toggle, or a switch disposed on the first side of the device; and an ultra violet (UV) sensor disposed on a second side of the device, the second side extending transverse to the first side; and receiving first data associated with monitoring an environment of a first user wearing the device.
In the same field of endeavor, Ales et al. disclose one or more output devices oriented to output indications from a first side of the device (at least Fig. 1 and [0035]. The UV detection apparatus 10 includes an ultraviolet radiation sensor 12, a visual and/or audible display 14, an input panel 16, and a skin type sensor 18 all contained in a single housing 20.);
at least one of a button, a toggle, or a switch disposed on the first side of the device (at least Fig. 1 and [0035]. The UV detection apparatus 10 includes an ultraviolet radiation sensor 12, a visual and/or audible display 14, an input panel 16, and a skin type sensor 18 all contained in a single housing 20.);
an ultra violet (UV) sensor disposed on a second side of the device, the second side extending transverse to the first side (at least Fig. 1 and [0035]. The UV detection apparatus 10 includes an ultraviolet radiation sensor 12, a visual and/or audible display 14, an input panel 16, and a skin type sensor 18 all contained in a single housing 20.); causing, via the one or more output devices, output of an indication (at least Fig. 1 and [0035]. The UV detection apparatus 10 includes an ultraviolet radiation sensor 12, a visual and/or audible display 14, an input panel 16, and a skin type sensor 18 all contained in a single housing 20.), and
and receiving first data associated with monitoring an environment of a first user wearing the device (at least [0030]. The UV detection device may be calibrated or otherwise used in conjunction with the skin type measurement to provide output information to the user regarding exposure to ultraviolet radiation within the environment.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Zheng et al. as taught by Ales et al. for purpose of providing information to the user regarding the amount of ultraviolet radiation present in the environment.
For claim 15, the combination of Zheng et al. and Ales et al. disclose the device of claim 14. Zheng et al. disclose the one or more output devices include at least one of one or more speakers or one or more lighting elements, the operations further comprising causing, based at least in part on the change in acceleration satisfying the threshold, at least one of: output of audio via the one or more speakers; or output of light via the one or more lighting elements (at least [0031]. The emergency signal is sent by the controller in a way of sending a control signal to a corresponding emergency module, wherein a form of the emergency signal is unlimited, for embodiment, an emergency sound and a light indication are sent, a report is made to a cellphone which sends an emergency message or make an emergency call, and so on.)
For claim 16, the combination of Zheng et al. and Ales et al. disclose the device of claim 14. Zheng et al. disclose determining, based at least in part on the change in acceleration satisfying the threshold acceleration, to issue an alert; and generating a notification associated with the alert, wherein the third data includes the notification (at least [0031]. The emergency signal is sent by the controller in a way of sending a control signal to a corresponding emergency module, wherein a form of the emergency signal is unlimited, for embodiment, an emergency sound and a light indication are sent, a report is made to a cellphone which sends an emergency message or make an emergency call, and so on.)
For claim 17, the combination of Kenward et al., Ales et al. and Shearman et al. disclose the device of claim 14. Zheng et al. disclose receiving the first data comprises: receiving the first data via the at least one of the button, the toggle, or the switch; or receiving the first data via a second electronic device (at least [0095]. The light sensor can be configured to activate the UV detection device should light at a particular intensity be present in the environment. The UV detection device as described above can be configured to measure UVA radiation, UVB radiation, and/or UVC radiation.).
For claim 20, the combination of Zheng et al. and Ales et al. disclose the device of claim 14. Zheng et al. disclose receiving fourth data from the one or more accelerometers; determining, based at least in part on the fourth data, a second amount of acceleration experienced by the device; determining that the second amount of acceleration fails to satisfy the threshold acceleration; and refraining from sending, via the one or more network interfaces, fourth data to the electronic device (at least [0037]. If the motionlessness event is not detected continuously three times, it means that a person falling down can move, then an alarm cancellation signal is generated, and the monitoring is continued after the three-axis sensor is initiated.)
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (U.S. 20190357618) in view of Ales et al. (U.S. 20080265170) and further in view of Kenward et al. (U.S. 20230172301).
For claim 18, the combination of Zheng et al. and Ales et al. do not disclose the device of claim 14, further comprising at least one of a voltage detector or a current detector.
In the same field of endeavor, Kenward et al. disclose (at least [0065]. The sensor comprises a sensor for detecting electrical current, electrical fields, magnetic fields or ionising radiation.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Zheng et al. as taught by Kenward et al. for purpose of identifying and monitoring workers to improve the safety of workers on site.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (U.S. 20190357618) in view of Ales et al. (U.S. 20080265170) and further in view of Gonzalez et al. (U.S. 10365156).
For claim 19, the combination of Zheng et al. and Ales et al. do not disclose the device of claim 14, wherein the one or more output devices include the one or more output devices include at least one of one or more speakers or one or more lighting elements, the operations further comprising: receiving fourth data from the UV sensor; determining, based at least in part on the fourth data, an illuminance of a source; determining that the illuminance satisfies a threshold illuminance; and causing, based at least in part on the illuminance satisfying the threshold illuminance, output of a notification via at least one of: one or more lighting elements, or one or more speakers.
In the same field of endeavor, Gonzalez et al. disclose the one or more output devices include at least one of one or more speakers or one or more lighting elements, the operations further comprising: receiving fourth data from the UV sensor; determining, based at least in part on the fourth data, an illuminance of a source; determining that the illuminance satisfies a threshold illuminance; and causing, based at least in part on the illuminance satisfying the threshold illuminance, output of a notification via at least one of: one or more lighting elements, or one or more speakers (at least col. 6, lines 43-67. The processor(s) 202 can be configured to cause the output device 104 to provide an alert to the wearer in response to determining that total exposure has reached the threshold amount of exposure to UV radiation. In some variations, the processor(s) 202 can be configured to instruct the LED drive circuit 206 to illuminate one or more LEDs 208 as the alert. For example, the LED(s) 208 can be configured to illuminate red in response to a determination, by the processor 202, that the wearer has been exposed to a threshold amount of UV radiation. In some variations, there may be more than one threshold amount of exposure that the processor(s) 202 determine. For example, a first threshold may be determined at which point the wearer would be notified that they are about to reach a second threshold and to take corrective action to protect themselves against UV radiation. In such a variation, the processor(s) 202 can be configured to instruct the LED drive circuit 206 to cause a different color LED 208 to illuminate, such as an orange LED, prior to illuminating a red LED at the next threshold.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Zheng et al. as taught by Ales et al. for purpose of providing information to the user regarding the amount of ultraviolet radiation present in the environment.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (U.S. 20190357618) in view of Ales et al. (U.S. 20080265170) and further in view of Fu (U.S. 20110118023).
For claim 21, the combination of Zheng et al. and Ales et al. do not disclose the device of claim 14, the operations further comprising: determining an identifier associated with the first user; and determining the threshold acceleration based at least in part on the identifier associated with the first user.
In the same field of endeavor, Fu discloses determining an identifier associated with the first user; and determining the threshold acceleration based at least in part on the identifier associated with the first user (at least claim 11. Determining the identity of a user of the game controller; and set the acceleration threshold based upon the identity of the user.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the invention of Zheng et al. as taught by Fu for purpose of providing accurate input and/or altered in their operation to enact improved input operations.
Allowable Subject Matter
Claims 1-6 are allowed.
The following is an examiner’s statement of reasons for allowed:
Please see the reason cited, on pages 9-10 of the remarks, by applicant filed on 12/08/25.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/DAI PHUONG/Primary Examiner, Art Unit 2644
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