DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 8-9, 11-15, 18, and 20 of U.S. Patent No. 12,204,693. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are broader than the patented claims.
Current Claim
Patented Claim
1. A method comprising: polling a proximity sensor of a wearable device, the wearable device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on a low-power processor of the wearable device, the high-power gesture recognition application configured to be executed on a high-power processor of the wearable device; and in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor, wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor.
1. A method comprising: polling a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, performing a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activating a high-power gesture recognition application executed on the high-power processor.
2. The method of claim 1, further comprising: recognizing, using the low-power gesture recognition application, the gesture of the user of the wearable device based on the proximity sensor data, wherein the low-power gesture recognition application is configured to execute a gesture detection and a recognition routine using the low-power processor.
1. A method comprising: polling a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, performing a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activating a high-power gesture recognition application executed on the high-power processor.
3. The method of claim 1, further comprising: determining that the low-power gesture recognition application fails to recognize the gesture of the user of the wearable device; and in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating the high-power gesture recognition application with the high-power processor.
1. A method comprising: polling a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, performing a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activating a high-power gesture recognition application executed on the high-power processor.
4. The method of claim 1, further comprising: detecting a proximity event based on the proximity sensor data, wherein operating the low-power gesture recognition application is in response to detecting the proximity event.
1. A method comprising: polling a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, performing a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activating a high-power gesture recognition application executed on the high-power processor.
5. The method of claim 1, further comprising: generating a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and identifying the gesture in response to the level of confidence exceeding a preset threshold.
2. The method of claim 1, further comprising: generating a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and identifying the gesture in response to the level of confidence exceeding a preset threshold.
6. The method of claim 1, further comprising: identifying an operation corresponding to the gesture based on the high-power gesture recognition application; and requesting the high-power processor to perform the operation.
3. The method of claim 2, further comprising: identifying an operation corresponding to the gesture; and requesting one of the low-power processor or the high-power processor to perform the operation.
7. The method of claim 1, further comprising: identifying an operation of a mixed reality application corresponding to the gesture; and requesting the high-power processor to perform the operation of the mixed reality application.
4. The method of claim 2, further comprising: identifying an operation of a mixed reality application corresponding to the gesture; and requesting the high-power processor to perform the operation of the mixed reality application.
8. The method of claim 1, further comprising: returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture.
5. The method of claim 1, further comprising: returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture.
9. The method of claim 1, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.
8. The method of claim 1, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.
10. The method of claim 1, wherein the low-power gesture recognition application uses a neural network to detect and recognize a hand gesture of the user of the wearable device based on proximity data from the proximity sensor of the wearable device, the neural network being configured to recognize a first set of hand gestures, and wherein the high-power processor comprises a high-power hand-tracking application configured to recognize a second set of hand gestures using camera data from a higher resolution camera of the wearable device, the second set of hand gestures being larger than the first set of hand gestures.
9. The method of claim 1, wherein the low-power hand tracking gesture recognition application uses a neural network to detect and recognize a hand gesture of a user of the wearable device based on the proximity data, the neural network being configured to recognize a first set of hand gestures, and wherein the high-power processor comprises a high-power hand-tracking application configured to recognize a second set of hand gestures using camera data from a higher resolution camera of the wearable device, the second set of hand gestures being larger than the first set of hand gestures.
11. A wearable device comprising: a proximity sensor; a low-power processor; a high-power processor; and a memory storing instructions that, when executed by one of the low-power processor or the high-power processor, configure the wearable device to perform operations comprising: polling the proximity sensor, the wearable device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on the low-power processor, the high-power gesture recognition application configured to be executed on the high-power processor; and in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor, wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor.
11. A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: poll a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, perform a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determine that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activate a high-power gesture recognition application executed on the high-power processor.
12. The wearable device of claim 11, wherein the operations further comprise: recognizing, using the low-power gesture recognition application, the gesture of the user of the wearable device based on the proximity sensor data, wherein the low-power gesture recognition application is configured to execute a gesture detection and a recognition routine using the low-power processor.
11. A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: poll a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, perform a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determine that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activate a high-power gesture recognition application executed on the high-power processor.
13. The wearable device of claim 11, wherein the operations further comprise: determining that the low-power gesture recognition application fails to recognize the gesture of the user of the wearable device; and in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating the high-power gesture recognition application with the high-power processor.
11. A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: poll a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, perform a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determine that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activate a high-power gesture recognition application executed on the high-power processor.
14. The wearable device of claim 11, wherein the operations further comprise: detecting a proximity event based on the proximity sensor data, wherein operating the low-power gesture recognition application is in response to detecting the proximity event.
11. A computing apparatus comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the apparatus to: poll a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, perform a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor; determine that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activate a high-power gesture recognition application executed on the high-power processor.
15. The wearable device of claim 11, wherein the operations further comprise: generating a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and identifying the gesture in response to the level of confidence exceeding a preset threshold.
12. The computing apparatus of claim 11, wherein the instructions further configure the apparatus to: generate a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and identify the gesture in response to the level of confidence exceeding a preset threshold.
16. The wearable device of claim 11, wherein the operations further comprise: identifying an operation corresponding to the gesture based on the high-power gesture recognition application; and requesting the high-power processor to perform the operation.
13. The computing apparatus of claim 12, wherein the instructions further configure the apparatus to: identify an operation corresponding to the gesture; and request one of the low-power processor or the high-power processor to perform the operation.
17. The wearable device of claim 11, wherein the operations further comprise: identifying an operation of a mixed reality application corresponding to the gesture; and requesting the high-power processor to perform the operation of the mixed reality application.
14. The computing apparatus of claim 12, wherein the instructions further configure the apparatus to: identify an operation of a mixed reality application corresponding to the gesture; and request the high-power processor to perform the operation of the mixed reality application.
18. The wearable device of claim 11, wherein the operations further comprise: returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture.
15. The computing apparatus of claim 11, wherein the instructions further configure the apparatus to: returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture.
19. The wearable device of claim 11, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.
18. The computing apparatus of claim 11, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.
20. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: polling a proximity sensor of a wearable device, the wearable device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on a low-power processor of the wearable device, the high-power gesture recognition application configured to be executed on a high-power processor of the wearable device; and in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor, wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor.
20. A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: poll a proximity sensor of a wearable device to detect a proximity event, the wearable device comprising a low-power processor and a high-power processor; in response to detecting the proximity event, perform a gesture detection and recognition routine using a low-power gesture recognition application executed on the low-power processor based on proximity data from the proximity sensor by operating a low power hand tracking application on the low power processor; determine that the gesture detection and recognition routine executed on the low-power processor fails to recognize a gesture of a user of the wearable device; and in response to determining that the gesture detection and recognition routine executed on the low-power processor fails to recognize the gesture activate a high-power gesture recognition application executed on the high-power processor.
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 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Weber et al., US Patent Publication 2015/0049017 in view of Potts et al., US Patent Publication 2017/0220119.
Regarding independent claim 1, Weber et al. teaches a method comprising:
polling a proximity sensor of a device (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”), the device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on a low-power processor of the wearable device, the high-power gesture recognition application configured to be executed on a high-power processor of the device (paragraph 0035 describes a lower power mode and a normal or higher power mode where the processor in a low power mode is a low-power processor and a processor in a high or normal power mode is a high-power processor); and
in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once),
wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Weber et al. does not specify that the device is a wearable device. Potts et al. teaches the use of a wearable device that performs gesture recognition that realizes power conservation (paragraphs 0008-0010). It would have been obvious to one of ordinary skill in the art before the effective filing date to use a wearable device as taught by Potts et al. as the device in the system of Weber et al. The rationale to combine would be to expand the use of the invention by increasing the types of devices that can be used while improving user experience by allowing more natural user inputs (paragraph 0001 of Potts et al.).
Regarding claim 2, Weber et al. teaches the method of claim 1, further comprising:
recognizing, using the low-power gesture recognition application, the gesture of the user of the wearable device based on the proximity sensor data (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”),
wherein the low-power gesture recognition application is configured to execute a gesture detection and a recognition routine using the low-power processor (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Regarding claim 3, Weber et al. teaches the method of claim 1, further comprising:
determining that the low-power gesture recognition application fails to recognize the gesture of the user of the wearable device (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once); and
in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating the high-power gesture recognition application with the high-power processor (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once).
Regarding claim 4, Weber et al. teaches the method of claim 1, further comprising:
detecting a proximity event based on the proximity sensor data (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”),
wherein operating the low-power gesture recognition application is in response to detecting the proximity event (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Regarding claim 5, Weber et al. teaches the method of claim 1, further comprising:
generating a level of confidence of the gesture based on a gesture dictionary (paragraph 0023 explains that “the collection of points for a given motion or gesture then can be compared against sets of points stored in a library or other such data repository” to recognize a gesture with a minimum level of certainty or confidence), using a gesture recognition algorithm operating on the low-power processor (the gesture recognition algorithm starts operation by or uses the object detection of the low power scan mode described in paragraph 0035); and
identifying the gesture in response to the level of confidence exceeding a preset threshold (paragraph 0023 describes the necessary minimum level of certainty or confidence that must be met to identify a gesture).
Regarding claim 6, Weber et al. teaches the method of claim 1, further comprising:
identifying an operation corresponding to the gesture based on the high-power gesture recognition application (paragraph 0015 explains how gesture recognition is performed with the intent of recognizing the corresponding action to be performed); and
requesting the high-power processor to perform the operation (paragraph 0015 recites that “the recognition of the gesture can cause a corresponding action or function to be performed” such that the processor must perform the operation).
Regarding claim 7, Weber et al. teaches the method of claim 1, further comprising:
identifying an operation of a mixed reality application corresponding to the gesture (paragraph 0041 discusses the use of the device in a virtual reality setting that uses inputs as given in paragraph 0041-0042 that can include gesture control as given in paragraph 0038); and
requesting the high-power processor to perform the operation of the mixed reality application (paragraph 0035 explains that the normal or high power mode is used to recognize the gesture that is part of performing the operation of the gesture as given in 0015).
Weber et al. does not specify that the extended reality is specifically mixed reality. Potts et al. teaches specifically the use of mixed reality (paragraph 0011). It would have been obvious to one of ordinary skill in the art before the effective filing date to use a wearable device in mixed reality as taught by Potts et al. as the device in the system of Weber et al. The rationale to combine would be to expand the use of the invention by increasing the types of devices that can be used while improving user experience by allowing more natural user inputs (paragraph 0001 of Potts et al.).
Regarding claim 8, Weber et al. teaches the method of claim 1, further comprising:
returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture (paragraph 0035 explains how if there is no object to detect or track, the device reverts back to the low power scan mode 810 of figure 8).
Regarding claim 9, Weber et al. teaches the method of claim 1, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device (paragraph 0035 explains that proximity detection occurs in a low power scan mode and upon detection of “an object in close proximity 804…at least one of the above mentioned sensors could enter (or initiate) a normal (or higher) power analysis mode to further analyze the object 806. If an object is detected 808, the sensor data is analyzed to identify a hand gesture of the user of the computing device 812”).
Regarding independent claim 11, Weber et al. teaches a device comprising:
a proximity sensor (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”);
a low-power processor (processing unit 902 of figure 9(a) as given in paragraph 0045 where paragraph 0035 describes a lower power mode and a normal or higher power mode where the processor in a low power mode is a low-power processor and a processor in a high or normal power mode is a high-power processor);
a high-power processor (processing unit 902 of figure 9(a) as given in paragraph 0045 where paragraph 0035 describes a lower power mode and a normal or higher power mode where the processor in a low power mode is a low-power processor and a processor in a high or normal power mode is a high-power processor); and
a memory storing instructions that, when executed by one of the low-power processor or the high-power processor, configure the device to perform operations (described in paragraph 0045) comprising:
polling the proximity sensor (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”), the device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on the low-power processor, the high-power gesture recognition application configured to be executed on the high-power processor (paragraph 0035 describes a lower power mode and a normal or higher power mode where the processor in a low power mode is a low-power processor and a processor in a high or normal power mode is a high-power processor); and
in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once),
wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor (paragraph 0035 explains how the device switches to normal or high power to identify or recognize the detected gesture, which ends the low power mode and low power hand tracking, in response to detecting without recognizing the gesture once).
Weber et al. does not specify that the device is a wearable device. Potts et al. teaches the use of a wearable device that performs gesture recognition that realizes power conservation (paragraphs 0008-0010). It would have been obvious to one of ordinary skill in the art before the effective filing date to use a wearable device as taught by Potts et al. as the device in the system of Weber et al. The rationale to combine would be to expand the use of the invention by increasing the types of devices that can be used while improving user experience by allowing more natural user inputs (paragraph 0001 of Potts et al.).
Regarding claim 12, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
recognizing, using the low-power gesture recognition application, the gesture of the user of the wearable device based on the proximity sensor data (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”),
wherein the low-power gesture recognition application is configured to execute a gesture detection and a recognition routine using the low-power processor (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Regarding claim 13, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
determining that the low-power gesture recognition application fails to recognize the gesture of the user of the wearable device (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once); and
in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating the high-power gesture recognition application with the high-power processor (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once).
Regarding claim 14, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
detecting a proximity event based on the proximity sensor data (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”),
wherein operating the low-power gesture recognition application is in response to detecting the proximity event (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Regarding claim 15, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
generating a level of confidence of the gesture based on a gesture dictionary (paragraph 0023 explains that “the collection of points for a given motion or gesture then can be compared against sets of points stored in a library or other such data repository” to recognize a gesture with a minimum level of certainty or confidence), using a gesture recognition algorithm operating on the low-power processor (the gesture recognition algorithm starts operation by or uses the object detection of the low power scan mode described in paragraph 0035); and
identifying the gesture in response to the level of confidence exceeding a preset threshold (paragraph 0023 describes the necessary minimum level of certainty or confidence that must be met to identify a gesture).
Regarding claim 16, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
identifying an operation corresponding to the gesture based on the high-power gesture recognition application (paragraph 0015 explains how gesture recognition is performed with the intent of recognizing the corresponding action to be performed); and
requesting the high-power processor to perform the operation (paragraph 0015 recites that “the recognition of the gesture can cause a corresponding action or function to be performed” such that the processor must perform the operation).
Regarding claim 17, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
identifying an operation of a mixed reality application corresponding to the gesture (paragraph 0041 discusses the use of the device in a virtual reality setting that uses inputs as given in paragraph 0041-0042 that can include gesture control as given in paragraph 0038); and
requesting the high-power processor to perform the operation of the mixed reality application (paragraph 0035 explains that the normal or high power mode is used to recognize the gesture that is part of performing the operation of the gesture as given in 0015).
Weber et al. does not specify that the extended reality is specifically mixed reality. Potts et al. teaches specifically the use of mixed reality (paragraph 0011). It would have been obvious to one of ordinary skill in the art before the effective filing date to use a wearable device in mixed reality as taught by Potts et al. as the device in the system of Weber et al. The rationale to combine would be to expand the use of the invention by increasing the types of devices that can be used while improving user experience by allowing more natural user inputs (paragraph 0001 of Potts et al.).
Regarding claim 18, Weber et al. teaches the wearable device of claim 11, wherein the operations further comprise:
returning the wearable device to an idle state that polls the proximity sensor at a regular interval after the high-power gesture recognition application recognizes the gesture (paragraph 0035 explains how if there is no object to detect or track, the device reverts back to the low power scan mode 810 of figure 8).
Regarding claim 19, Weber et al. teaches the wearable device of claim 11, wherein the low-power processor is configured to only operate the proximity sensor and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device (paragraph 0035 explains that proximity detection occurs in a low power scan mode and upon detection of “an object in close proximity 804…at least one of the above mentioned sensors could enter (or initiate) a normal (or higher) power analysis mode to further analyze the object 806. If an object is detected 808, the sensor data is analyzed to identify a hand gesture of the user of the computing device 812”).
Regarding independent claim 20, Weber et al. teaches a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer (described in paragraph 0045), cause the computer to:
polling a proximity sensor of a wearable device (paragraph 0035 recites “a proximity sensor, such as an electro-magnetic field (EMF) sensor, could run in a low power scan mode until an object of interest is detected”), the device comprising a low-power gesture recognition application and a high-power gesture recognition application, the low-power gesture recognition application configured to be executed on a low-power processor of the device, the high-power gesture recognition application configured to be executed on a high-power processor of the device (paragraph 0035 describes a lower power mode and a normal or higher power mode where the processor in a low power mode is a low-power processor and a processor in a high or normal power mode is a high-power processor); and
in response to polling the proximity sensor, operating the low-power gesture recognition application with the low-power processor prior to operating the high-power gesture recognition application with the high-power processor (paragraph 0035 explains how the low power state is used for detection and a normal or higher power state is activated for recognition such that the device detects without recognizing the gesture before activating the high power state a number of tries of once),
wherein the low-power gesture recognition application is configured to recognize a gesture of a user of the wearable device based on proximity sensor data from the proximity sensor (paragraph 0034 describes a hand tracking based on detected motion that would indicate a certain proximity and explains that this “gesture detection is activated manually by the user or upon activation of an application, for example, but can also be continually active in at least a low power state”).
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Weber et al., US Patent Publication 2015/0049017 in view of Potts et al., US Patent Publication 2017/0220119, further in view of Risco et al., US Patent Publication 2021/0117660.
Regarding claim 10, Weber et al. teaches the method of claim 1. Weber et al. does not teach the method wherein the low-power hand-tracking application uses a neural network to detect and recognize a hand gesture of the user of the wearable device based on proximity data from the proximity sensor of the wearable device, the neural network being configured to recognize a first set of hand gestures, and
wherein the high-power processor comprises a high-power hand-tracking application configured to recognize a second set of hand gestures using camera data from a higher resolution camera of the wearable device, the second set of hand gestures being larger than the first set of hand gestures.
Potts et al. teaches the method wherein the low-power hand-tracking application uses a technique to detect and recognize the hand gesture of a user of the wearable device based on proximity data from the proximity sensor of the wearable device (using inertial motion sensors as given in paragraph 0010), and
wherein the high-power processor comprises a high-power hand-tracking application configured to recognize a second set of hand gestures using camera data from a higher resolution camera of the wearable device (using image sensor data as given in paragraph 0010 using the higher resolution cameras described in paragraphs 0019 and 0023), the second set of hand gestures being larger than the first set of hand gestures (to necessitate the extra power consumption since any number of hand gestures may be used).
It would have been obvious to one of ordinary skill in the art before the effective filing date to use multiple detection modes as taught by Potts et al. as the device in the system of Weber et al. The rationale to combine would be to achieve a suitable balance of power consumption and motion tracking accuracy in each object tracking context, saving power where less accuracy is needed while achieving higher accuracy where appropriate (paragraph 0010 of Potts et al.).
Weber et al. and Potts et al. do not specify the use of a neural network to detect and recognize a hand gesture of a user, the neural network being configured to recognize a first set of hand gestures. Risco et al. teaches a neural network to detect and recognize a hand gesture of a user (as described in paragraph 0025), the neural network being configured to recognize a first set of hand gestures (based on the set of images described in paragraph 0066-0071, especially 0071 to perform a low power gesture recognition as given in paragraph 0073). It would have been obvious to one of ordinary skill in the art before the effective filing date to use the gesture recognition techniques as taught by Risco et al. as the device in the system of Weber et al. and Potts et al. The rationale to combine would be that neural networks are highly effective at detecting patterns in input data. This is true even in the presence of variations in the input data (paragraph 0025 of Risco et al.).
Conclusion
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/PARUL H GUPTA/Primary Examiner, Art Unit 2627
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