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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 20 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, the claim requires that one reduces a clock frequency of a sensor; all recitations of clock frequencies in the disclosure prior are in reference to a processing device instead (e.g. paragraphs 0046 and 0100), with no mention of a clock frequency for a sensor. Such a requirement is thus considered new matter and beyond what was originally detailed in the disclosure.
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 taught 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4, 7-12, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Torabi (US20210402942A1) in view of Stent (US20200342303A1) and Rice (US20190080602A1).
Regarding claim 1, Torabi teaches;
A system (taught as a system for hazard prevention and control for autonomous machine applications, paragraph 0020) comprising:
memory to store data regarding a context of operation (taught as a data stores containing instructions to be executed by a processor, paragraph 0139-0140); and
at least one artificial neural network (ANN) processor (taught as a deep neural network, paragraph 0027) configured to adjust a processing capability of the at least one ANN processor based on the context of operation (taught as implementing power management, including allowed power states and wakeup times, paragraph 0115, and changing power states and modes based on detected information, including temperature, paragraph 0140).
However, Torabi does not explicitly disclose; based on a command generated from an output of an ANN evaluating the context of operation, wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Stent teaches; based on a command generated from an output of an ANN evaluating the context of operation (taught as, upon analyzing a road scene, determining a predicted hazardous event, paragraph 0031, and focusing a computational resources on determined salient potions of the road scene, paragraph 0021).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to direct operating characteristics of sensors as taught by Stent in the system taught by Torabi in order to improve data collection where needed. As suggested in Stent, such mechanisms of generating a context specific risk weighted saliency map to direct computational resources and/or sensors to focus on the salient portions can further improve warning systems (paragraph 0021). As Torabi already teaches the use of neural networks related to data collection and analysis [processors execute neural networks, such as in cabin monitoring (paragraph 0146), one of ordinary skill in the art would recognize that a processor already implementing a neural network to analyze sensor data could additionally implement a neural network to analyze other sensor data.
However, Stent does not explicitly teach; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Rice teaches; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state (taught as detecting objects and class, paragraph 0051, and further adjusting behavior based on sensor data, such as disabling sensors or reducing resolution, paragraph 0069, which would reduce power consumption).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sensor dependent sensor disabling as taught by Rice in the system taught by Torabi in order to improve vehicle performance. As described in Rice, such modifications would reduce power consumption and prevent excessive heat generation (paragraph 0069), and further aid the low power mode states taught by Torabi (paragraph 0142).
Regarding claim 2, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). Torabi further teaches; wherein adjusting the processing capability comprises reducing a number of active cores (taught as implementing power management, including allowed power states and wakeup times, paragraph 0115, including a low power mode in the event of temperature faults, paragraph 0140).
Regarding claim 4, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). Torabi further teaches; wherein the context of operation includes a type of object to be detected (taught as factoring in information and status of objects perceived, paragraph 0098, and sensor use based on object classification/detection, based on whether a network has been trained, paragraph 0105, to control sensors or processing power).
Regarding claim 7, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). Torabi further teaches; wherein the context of operation includes data received in a communication regarding a traffic infrastructure (taught as receiving information from infrastructure [I2V], paragraph 0178).
Regarding claim 8, Torabi teaches;
An apparatus (taught as a system for hazard prevention and control for autonomous machine applications, paragraph 0020) comprising:
At least one sensor to generate image data (taught as a camera(s), paragraph 0174); and
at least one processing device (taught as processor running a neural network, paragraph 0124, part of controllers) configured to: determine, based on the image data, at least one characteristic of at least one object (taught as neural networks evaluating sensor data for functions such as object identification, paragraph 0125).
However, Torabi does not explicitly teach;
adjust, based on the characteristic, at least one operating characteristic of the at least one sensor wherein the at least one operating characteristic is adjusted based on a command generated from an output of an artificial neural network (ANN) evaluating the context of operation, wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Stent teaches; adjust, based on the characteristic, at least one operating characteristic of the at least one sensor wherein the at least one operating characteristic is adjusted based on a command generated from an output of an artificial neural network (ANN) evaluating the context of operation (taught as, upon analyzing a road scene, determining a predicted hazardous event, paragraph 0031, and focusing a sensor [modifying sensor characteristics of imaging devices or lidar] on determined salient potions of the road scene, paragraph 0021).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to direct operating characteristics of sensors as taught by Stent in the system taught by Torabi in order to improve data collection where needed. As suggested in Stent, such mechanisms of generating a context specific risk weighted saliency map to direct computational resources and/or sensors to focus on the salient portions can further improve warning systems (paragraph 0021). As Torabi already teaches the use of neural networks related to data collection and analysis [processors execute neural networks, such as in cabin monitoring (paragraph 0146), one of ordinary skill in the art would recognize that a processor already implementing a neural network to analyze sensor data could additionally implement a neural network to analyze other sensor data.
However, Stent does not explicitly teach; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Rice teaches; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state (taught as detecting objects and class, paragraph 0051, and further adjusting behavior based on sensor data, such as disabling sensors or reducing resolution, paragraph 0069, which would reduce power consumption).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sensor dependent sensor disabling as taught by Rice in the system taught by Torabi in order to improve vehicle performance. As described in Rice, such modifications would reduce power consumption and prevent excessive heat generation (paragraph 0069), and further aid the low power mode states taught by Torabi (paragraph 0142).
Regarding claim 9, Torabi as modified by Stent and Rice teaches;
The apparatus of claim 8 (see claim 8 rejection). Torabi further teaches; wherein determining the characteristic is based on an output from an artificial neural network having the image data as input (taught as neural networks evaluating sensor data for functions such as object identification, paragraph 0125).
Regarding claims 10-11, it has been determined that no further limitations exist apart from those previously addressed in claims 1 and 4 respectively. Therefore, claims 10-11 are rejected under the same rationale as claims 1 and 4 respectively.
Regarding claim 12, Torabi as modified by Stent and Rice teaches;
The apparatus of claim 8 (see claim 8 rejection). Torabi further teaches; wherein the processing device is further configured to steer a vehicle based on detecting the object (taught as controlling steering inputs, paragraph 0096, which use sensor information for controls such as collision avoidance, paragraph 0104).
Regarding claim 15, Torabi teaches;
A method comprising:
receiving, by a wireless interface of a vehicle (taught as a network interface, paragraph 0159), at least one communication (taught as communicating with other vehicles, paragraph 0159, or infrastructure, paragraph 0178); and
controlling, based on the communication, at least one characteristic of at least one sensor of the vehicle (taught as controlling a vehicle based on the communication, such as cooperative adaptive cruise control, paragraph 0159, and managing power states, paragraph 0115, including low power states of sensor, paragraph 0140 and 0142).
However, Torabi does not explicitly teach; wherein the at least one characteristic is adjusted based on a command generated from an output of an artificial neural network (ANN) evaluating the context of operation, wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Stent teaches; wherein the at least one characteristic is adjusted based on a command generated from an output of an artificial neural network (ANN) evaluating the context of operation (taught as, upon analyzing a road scene, determining a predicted hazardous event, paragraph 0031, and focusing a sensor [modifying sensor characteristics of imaging devices or lidar] on determined salient potions of the road scene, paragraph 0021).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to direct operating characteristics of sensors as taught by Stent in the system taught by Torabi in order to improve data collection where needed. As suggested in Stent, such mechanisms of generating a context specific risk weighted saliency map to direct computational resources and/or sensors to focus on the salient portions can further improve warning systems (paragraph 0021). As Torabi already teaches the use of neural networks related to data collection and analysis [processors execute neural networks, such as in cabin monitoring (paragraph 0146), one of ordinary skill in the art would recognize that a processor already implementing a neural network to analyze sensor data could additionally implement a neural network to analyze other sensor data.
However, Stent does not explicitly teach; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state.
Rice teaches; wherein adjusting the operating characteristic comprises selecting a portion of sensors of the at least one sensor used to control at least one function for an inactive state (taught as detecting objects and class, paragraph 0051, and further adjusting behavior based on sensor data, such as disabling sensors or reducing resolution, paragraph 0069, which would reduce power consumption).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sensor dependent sensor disabling as taught by Rice in the system taught by Torabi in order to improve vehicle performance. As described in Rice, such modifications would reduce power consumption and prevent excessive heat generation (paragraph 0069), and further aid the low power mode states taught by Torabi (paragraph 0142).
Regarding claim 18, Torabi as modified by Stent and Rice teaches;
The method of claim 15 (see claim 15 rejection). Torabi further teaches; further comprising:
sending, by the wireless interface, a location of the vehicle to a server (taught as sending information to a server, such as road conditions or road work or other image data, paragraph 0194, wherein the map information getting updated from the provided information necessitates location information being sent);
wherein the communication is received from the server (taught as the server sending information, such as updated map information, to vehicles, paragraph 0194), and
the communication is configured by the server based on the location (taught as transmitting information for HD maps, including potholes, detour, flooding and other obstructions in the area, paragraph 0194).
Regarding claim 19, Torabi as modified by Stent and Rice teaches;
The method of claim 15 (see claim 15 rejection). Torabi further teaches; wherein the communication is received from traffic infrastructure (taught as communicating with infrastructure, paragraph 0178).
Regarding claim 20, Torabi as modified by Stent teaches;
The method of claim 15 (see claim 15 rejection). However, Torabi doesn’t explicitly teach; wherein controlling the characteristic of the at least one sensor comprises reducing a clock frequency.
Rice teaches; wherein controlling the characteristic of the at least one sensor comprises reducing a clock frequency (taught as reducing emission frequency [e.g. firing rate] of a sensor in certain operating contexts for reduced power consumption, paragraph 0069).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the sensor dependent sensor disabling as taught by Rice in the system taught by Torabi in order to improve vehicle performance. As described in Rice, such modifications would reduce power consumption and prevent excessive heat generation (paragraph 0069), and further aid the low power mode states taught by Torabi (paragraph 0142).
Claim(s) 3, 6, 13-14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Torabi (US20210402942A1) as modified by Stent (US20200342303A1) and Rice (US20190080602A1) and further in view of Do (US20150375756A1).
Regarding claim 3, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). However, Torabi does not explicitly teach;, wherein the context of operation includes a level of activity around a vehicle.
Do teaches; wherein the context of operation includes a level of activity [interpreted to indicate presence and movement of objects around the host vehicle, as suggested in paragraph 0094] around a vehicle (taught as identifying a number of vehicles by sensor data, paragraph 0036).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a number of objects as taught by Do in the system taught by Torabi in order to improve collision risk detection. Identifying and classifying proximate vehicles, as suggested in Do, contribute as extra vehicle risk factors (paragraph 0036), which is further analyzed to anticipate collisions (paragraph 0037) and thus avoid risk to passengers.
Regarding claim 6, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). However, Torabi does not explicitly teach; wherein the context of operation includes a number of objects to be identified.
Do teaches; wherein the context of operation includes a number of objects to be identified (taught as identifying a number of vehicles by sensor data, paragraph 0036).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a number of objects as taught by Do in the system taught by Torabi in order to improve collision risk detection. Identifying and classifying proximate vehicles, as suggested in Do, contribute as extra vehicle risk factors (paragraph 0036), which is further analyzed to anticipate collisions (paragraph 0037) and thus avoid risk to passengers.
Regarding claims 13-14, it has been determined that no further limitations exist apart from those previously addressed in claims 6 and 3 respectively. Therefore, claims 13-14 are rejected under the same rationale as claims 6 and 3 respectively.
Regarding claim 17, Torabi as modified by Stent and Rice teaches;
The method of claim 15 (see claim 15 rejection). However, Torabi does not explicitly teach; wherein the communication indicates to the vehicle that the vehicle has moved to a location in which traffic density is reduced.
Do teaches; wherein the communication indicates to the vehicle that the vehicle has moved to a location [interpreted to indicate that the traffic information is related to the vehicle’s location] in which traffic density is reduced (taught as network based data determining traffic information and number of vehicles, with current GPS information, paragraph 0036; while not explicitly indicating that the density is reduced, such broad traffic information would indicate that traffic has been reduced).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify a number of objects as taught by Do in the system taught by Torabi in order to improve collision risk detection. Identifying and classifying proximate vehicles, as suggested in Do, contribute as extra vehicle risk factors (paragraph 0036), which is further analyzed to anticipate collisions (paragraph 0037) and thus avoid risk to passengers.
Claim(s) 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Torabi (US20210402942A1) as modified by Stent (US20200342303A1) and Rice (US20190080602A1) and further in view of Hara (US20200283022A1).
Regrading claim 5, Torabi as modified by Stent and Rice teaches;
The system of claim 1 (see claim 1 rejection). However, Torabi does not explicitly teach; wherein the context of operation includes an amount of energy available from a power supply.
Hara teaches; wherein the context of operation includes an amount of energy available from a power supply (taught as a battery-power manager determining a remaining amount of battery power or state of charge, paragraph 0120).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to track the state of charge as taught by Hara in the system taught by Torabi in order to improve power management. Torabi already includes low power mode consideration (paragraph 0142), and thus would obviously have need to determine when a low power mode is needed based on available energy, as Hara suggests, ensure cases to allow control and communication with a vehicle (paragraph 0004).
Regarding claim 16, Torabi as modified by Stent and Rice teaches;
The method of claim 15 (see claim 15 rejection). However, Torabi does not explicitly teach; further comprising evaluating available energy of a power supply of the sensor, wherein controlling the characteristic of the sensor is further based on the available energy.
Hara teaches; evaluating available energy of a power supply of the sensor, wherein controlling the characteristic of the sensor is further based on the available energy (taught as a battery-power manager determining a remaining amount of battery power or state of charge, paragraph 0120).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to track the state of charge as taught by Hara in the system taught by Torabi in order to improve power management. Torabi already includes low power mode consideration (paragraph 0142), and thus would obviously have need to determine when a low power mode is needed based on available energy, as Hara suggests, ensure cases to allow control and communication with a vehicle (paragraph 0004).
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 1-13, 15-16 and 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and 6-7 of U.S. Patent No. US11845470. Although the claims at issue are not identical, they are not patentably distinct from each other because;
Claims 1, 5, 8-10, 12, 15-16, and 20 correspond to claim 1 of US11845470 due to the similar recitations of ANN and energy supply considerations, and controlling aspects of the ANN and sensors in the vehicle.
Claim 2 corresponds to claim 11 of US11845470 due to reducing the number of active cores
Claim 3 corresponds to claim 7 of US11845470 due to identifying a level of activity
Claims 4 and 11 correspond to claim 16 of US11845470 due to identifying a type of object
Claims 6 and 13 correspond to claim 6 of US11845470 due to identifying a number of objects
Claims 7 and 19 correspond to claim 14 of US11845470 due to communications received from infrastructure.
Response to Arguments
Applicant argues on pages 2-4 of the remarks that the combination of cited prior art fails to disclose the amended material such that adjusting the operating characteristic by selecting a portion of sensors of the at least one sensor that are used to control at least one function for an inactive state.
The examiner respectfully disagrees. The disclosure and independent claims indicates that adjusting the at least one operating characteristic includes changing a state of a sensing device from an active state to an inactive state (e.g. paragraphs 0022 and 0098) which occurs based on the context of operation. Rice teaches that, within a context of operation, sensor characteristics are adjusted, such as by deactivating some [controlling them to be in an inactive state], reducing power consumption of others etc. (paragraph 0069). Deactivating some sensors would reasonably fulfill the requirement that a portion of the sensors are adjusted for an inactive state [i.e. to achieve an inactive state, reduce power consumption].
Applicant argues on pages 4-6 of the remarks that the combination of cited prior art fails to disclose the material to “adjust a processing capability of the at least one ANN processor and an operating characteristic for the at least one sensor based on the context of operation and based on a command generated from an output of an ANN evaluating the context of operation” (emphasis added).
The examiner respectfully disagrees. The combination of references is relied on to fully teach this limitation; Torabi teaches the general adjustment of processing capability based on context (paragraphs 0115 and 0140), and Stent teaches the adjustment based on analysis from a neural network (paragraph 0031). Additionally, Rice further teaches the adjustment of sensor power states/characteristics based on context of operation (paragraph 0069), using neural networks (paragraph 0025). The key is that the combination of using general context (Torabi) and analyzed context (Stent) to modify component characteristics (Torabi for general processing capability and Rice for sensor activation) meets the requirements of the claim.
Applicant states on pages 6-7 that the intent to file a terminal disclaimer later, once allowable subject material is indicated if appropriate at the time.
The examiner acknowledges the intent. The double patenting rejection will be sustained until the point that the claims are amended to significantly differentiate, or with the actual filing of the terminal disclaimer.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For further low power sensor system; US20180122235A1.
For further focusing of sensor/computing resources; US20220410882A1 and US20200356835A1
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 1:00-9:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached on (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662