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 .
Response to Arguments
Applicant’s arguments, set forth at pp. 7-8 of Applicant’s Remarks—particularly with respect to claims 1, 14, and 15—have been considered but are moot because the new ground of rejection relies on one or more reference not applied in the prior rejection of record for some teaching or matter specifically challenged in the argument.
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).
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, 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.
Claims 1-13 and 15 are rejected under 35 U.S.C. § 103 as being obvious over WO 2024/048803A1 (hereinafter, “HE”) (Note: The following citations are to the NPL machine translation of HE) in view of US 2022/0225466 (hereinafter, “WANG”).
Regarding claim 1, HE discloses:
An apparatus, comprising: (wireless device 200 / wireless device 300 / portable device 400)
at least one processor; and (processor 202 / control unit 320 / control unit 420)
at least one memory storing instructions that, when executed with the at least one processor, cause the apparatus to perform: (memory unit 204 / memory unit 330 / memory unit 400)
with an artificial intelligence agent (AI device 600), determining information that a command is to be transmitted to a user equipment, (Pg. 20, 4th ¶: [T]he base station transmits traffic prediction-related information to the terminal to proceed with adaptive DRX, and the terminal monitors only the PDCCH in a section without data transmission in the PDCCH-only section. Monitor signals and perform traffic prediction. If the time distribution value of the PDCCH-only section is above a certain value, a change request for DRX parameters and predicted traffic information (e.g. throughput and delay budget) are transmitted to the base station through UAI)
the command controlling entering of an inactive mode with the user equipment, (Pg. 4, 4th ¶: [O]ne command is such that the device receives configuration information related to a DRX (discontinuous reception) operation from a base station, performs the DRX operation based on the configuration information, and performs the DRX operation. Control to transmit a first message containing information related to at least one related parameter and receive a second message containing information related to resetting the at least one parameter, wherein the first message includes the at least one It may include information related to traffic characteristics determined by the UE, which is used to reset the parameters of; pg. 20, 4th ¶: [T]he base station transmits an RRC connection reconfiguration command to the terminal containing the information necessary to predict DRX parameters)
wherein said artificial intelligence agent comprises a reinforcement learning agent that receives a reward input providing information as to satisfaction of quality of service requirements of the user equipment responsive to a transmitted command, (Pg. 14, 1st ¶: In machine learning, data learning methods [include] reinforcement learning; pg. 20, 4th ¶: [T]he beta distribution information managed by the base station is determined based on reward information determined according to feedback from the terminal that selected the DRX parameter value. In other words, the base station holds beta distribution information that reflects compensation information determined according to whether or not the QoS of the terminal that selected the DRX parameter value is maintained and the DRX power saving gain in all throughput and delay budget states)
wherein the determining is using current state information associated with the user equipment; and (Pg. 20, 6th ¶: [T]he information needed to predict DRX parameters includes the allowable range of the DRX parameter that the terminal wants to change, selectable candidate DRX parameters, and the Thompson sampling model corresponding to the current state of the terminal (e.g., throughput and delay budget)
based on the state information, causing the command to be transmitted to the user equipment, (Pg. 20, 7th ¶: [T]he terminal predicts DRX parameters based on information (e.g., TS model beta distribution) that reflects the DRX parameter change history of all terminals. Then, the UE maintains QoS and selects a DRX parameter value with the highest DRX power saving gain (e.g., the largest value among values randomly sampled from the beta distribution of candidate DRX parameter values), and sends the selected DRX parameter value to the base station. send to Then, the terminal changes the value of the parameter to the selected value after the time set by the base station; pg. 17, 5th ¶: Thompson sampling is an algorithm that maintains QoS and changes DRX parameters with the greatest power savings effect when setting DRX parameters in the current state and usage environment of the terminal)
wherein, the command is a discontinuous reception . . . that comprises a set of rules using at least one of medium access control or timers associated with a medium access control at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode. (Pg. 8, last ¶: [P]rocessors 202a and 202b may operate on one or more layers (e.g., . . . media access control (MAC). . . ; pg. 13, 6th ¶: [A]ttempts have been made to integrate AI with wireless communication systems, but these are focused on the application layer and network layer, and in particular, deep learning is focused on wireless resource management and allocation. . . . However, this research is gradually advancing to the MAC layer; pg. 18, 5th ¶: [C]onfiguration information related to DRX may include at least one of the length of each section within the DRX cycle, a timer value related to DRX operation, DRX cycle length [The Examiner finds that the descriptive element: a set of rules using at least one of medium access control or timers associated with a medium access control at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode is due little to no patentable weight because one of ordinary skill in the art would not interpret the passively performed steps related to operation of the user equipment to further limit the structure of the apparatus to which claim 1 is directed.)
HE does not explicitly disclose:
the command is a discontinuous reception medium access control elements layer 2 command
In the same field of endeavor, however, WANG teaches:
the command is a discontinuous reception medium access control elements layer 2 command (¶ 0064: [T]he sidelink DRX configuration may be sent using layer 2 . . . signaling, such as via . . . a medium access control control element (MAC-CE))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify HE’s DRX parameter setting procedure to provide MAC-CEs as taught by WANG to improve network performance by extending reach and reducing data latency, and improve user equipment performance by enabling additional data sending and receiving opportunities while improving battery power, such that the additional data enables improved functions and capabilities at the UE. See WANG, at ¶ 0034.
Regarding claim 2, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the state information comprises information indicating a current mode of the user equipment. (Pg. 20, 6th ¶: [T]he Thompson sampling model corresponding to the current state of the terminal (e.g., throughput and delay budget)
Regarding claim 3, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the state information comprises
information indicating a current mode of the user equipment, (Pg. 20, 6th ¶: [T]he Thompson sampling model corresponding to the current state of the terminal (e.g., throughput and delay budget)
a channel between the user equipment and a base station from which the command is to be transmitted, and (Pgs. 7-8, bridging ¶: Through wireless communication/connection (150a, 150b, 150c), a wireless device and a base station/wireless device, and a base station and a base station can transmit/receive wireless signals to each other. For example, wireless communication/connection 150a, 150b, and 150c may transmit/receive signals through various physical channels)
information about scheduling of data to be transmitted to the user equipment. (Pg. 19, 2nd ¶: [T]he base station may check the DRX cycle and on-section based on configuration information related to the UE's DRX operation and transmit a control signal for data scheduling within the confirmed on-section; pg. 19, 4th ¶: [T]he base station may check the DRX cycle and on-section based on at least one reset parameter and transmit a control signal for data scheduling within the confirmed on-section)
Regarding claim 4, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the state information comprises information about a state of one or more timers running on the user equipment which control the current mode of the user equipment. (Pg. 18, 5th ¶: [C]onfiguration information related to DRX may include at least one of the length of each section within the DRX cycle, a timer value related to DRX operation, DRX cycle length)
Regarding claim 5, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the determining that the command is to be transmitted to a user equipment takes instructions, when executed with the at least one processor, take into account one or more of:
a traffic pattern of the user equipment; or (Pg. 18, 7th ¶: [T]he UE performs signaling to reset at least one parameter related to DRX. The UE may transmit or receive a message to reset at least one DRX parameter. According to one embodiment, the UE transmits a first message indicating traffic characteristics and receives a second message containing information necessary to determine the value of at least one DRX parameter based on the traffic characteristics; pg. 20, 4th ¶: FIG. 23 illustrates a first situation 2310 with traffic characteristics of low traffic, such as a messaging service, and a second situation 2312, with traffic characteristics of high traffic, such as video streaming. In FIG. 23, TON is an on-section, meaning an active state section, TIN is a section in which an inactive timer is maintained, and TS is a sleep state section)
one or more quality of service requirements associated with the user equipment. (Pg. 20, 4th ¶: [T]he beta distribution information managed by the base station is determined based on reward information determined according to feedback from the terminal that selected the DRX parameter value. In other words, the base station holds beta distribution information that reflects compensation information determined according to whether or not the QoS of the terminal that selected the DRX parameter value is maintained and the DRX power saving gain in all throughput and delay budget states)
Regarding claim 6, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 5. HE further discloses:
wherein one or more quality of service requirements associated with the user equipment comprise one or more of latency or bit rate. (Pg. 20, 9th ¶: [I]f QoS is adversely affected as latency increases, the compensation value is 0. If QoS is maintained, compensation can be determined based on the energy efficiency of the DRX operation based on the new DRX parameter value, that is, the power saving gain)
Regarding claim 7, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the determining that the command is to be transmitted to a user equipment takes instructions, when executed with the at least one processor, take into account previous state information of the user equipment. (Pg. 11, 6th ¶: control unit 620 collects history information including the operation content of the AI device 600 or user feedback on the operation, and stores it in the memory unit 630 or the learning processor unit 640c, or the AI server (It can be transmitted to an external device such as Figure 1, 140). The collected historical information can be used to update the learning model)
Regarding claim 8, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
comprising an artificial intelligence agent to determine that the command is to be transmitted, said artificial intelligence agent receiving the current state information. (Pg. 17, 5th ¶: Thompson sampling is an algorithm that maintains QoS and changes DRX parameters with the greatest power savings effect when setting DRX parameters in the current state and usage environment of the terminal)
Regarding claim 9, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 8. HE further discloses:
wherein the artificial intelligence agent is a reinforcement learning artificial intelligence agent. (Pg. 14, 1st ¶: In machine learning, data learning methods [include] reinforcement learning)
Regarding claim 10, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 8. HE further discloses:
wherein the instructions, when executed with the at least one processor, cause the apparatus to perform providing a reward input to the artificial intelligence agent, (Pg. 6, 3rd ¶: Figure 26 shows a reward structure based on Thompson sampling in a wireless communication system)
the reward input providing information as to satisfaction of quality of service requirements of the user equipment responsive to a transmitted command. (Pg. 20, 4th ¶: [T]he beta distribution information managed by the base station is determined based on reward information determined according to feedback from the terminal that selected the DRX parameter value. In other words, the base station holds beta distribution information that reflects compensation information determined according to whether or not the QoS of the terminal that selected the DRX parameter value is maintained and the DRX power saving gain in all throughput and delay budget states)
Regarding claim 11, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the command causes instructions, when executed with the at least one processor, cause the user equipment to be in the inactive mode for a predetermined amount of time. (Pg. 22, 4th ¶: In step S2511, the UE transmits information about DRX parameters to be changed. That is, the UE may transmit a message containing at least one of a DRX parameter to be changed (e.g., inactivity timer) and a value to be changed (e.g., timer value))
Regarding claim 12, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE further discloses:
wherein the instructions, when executed with the at least one processor, cause the apparatus to perform selecting the command from a plurality of different commands, each of the plurality of the commands causing the user equipment to be in the inactive mode for a different predetermined amount of time. (Pg. 11, 6th ¶: [C]ontrol unit 620 may request, search, receive, or utilize data from the learning processor unit 640c or the memory unit 630, and may select at least one operation that is predicted or determined to be desirable among the executable operations; pg. 18, 7th ¶: [T]he second message may include at least one of an allowable range for DRX parameter resetting, selectable candidate DRX parameter values, and probability distribution information about the candidate DRX parameter values. Here, the acceptable range may include a quantitative range (e.g., at least one parameter item) and a temporal range; pg. 22, 3rd ¶: UE selects one value among candidate parameter values based on information related to the model for DRX parameter prediction included in the RRC connection reconfiguration message received in step S2507. Then, the UE changes the DRX parameter using the selected value)
Regarding claim 13, the combination of HE and WANG, as applied above, renders obvious the apparatus of claim 1. HE does not explicitly disclose:
wherein the command is a layer 2 command.
In the same field of endeavor, however, WANG teaches:
wherein the command is a layer 2 command. (¶ 0064: [T]he sidelink DRX configuration may be sent using layer 2 . . . signaling, such as via . . . a medium access control control element (MAC-CE))
Regarding claim 15, HE discloses:
A computer program comprising computer executable instructions which when executed cause non-transitory program storage device readable with an apparatus (wireless device 200 / wireless device 300 / portable device 400), tangibly embodying a program of instructions executable with the apparatus for performing using an artificial intelligence agent, (AI device 600)
determining that a command is to be transmitted to a user equipment, (Pg. 20, 4th ¶: [T]he base station transmits traffic prediction-related information to the terminal to proceed with adaptive DRX, and the terminal monitors only the PDCCH in a section without data transmission in the PDCCH-only section. Monitor signals and perform traffic prediction. If the time distribution value of the PDCCH-only section is above a certain value, a change request for DRX parameters and predicted traffic information (e.g. throughput and delay budget) are transmitted to the base station through UAI)
the command controlling entering of an inactive mode with the user equipment, (Pg. 4, 4th ¶: [O]ne command is such that the device receives configuration information related to a DRX (discontinuous reception) operation from a base station, performs the DRX operation based on the configuration information, and performs the DRX operation. Control to transmit a first message containing information related to at least one related parameter and receive a second message containing information related to resetting the at least one parameter, wherein the first message includes the at least one It may include information related to traffic characteristics determined by the UE, which is used to reset the parameters of; pg. 20, 4th ¶: [T]he base station transmits an RRC connection reconfiguration command to the terminal containing the information necessary to predict DRX parameters)
the determining using current state information associated with the user equipment, (Pg. 20, 6th ¶: [T]he information needed to predict DRX parameters includes the allowable range of the DRX parameter that the terminal wants to change, selectable candidate DRX parameters, and the Thompson sampling model corresponding to the current state of the terminal (e.g., throughput and delay budget)
wherein said artificial intelligence agent comprises a reinforcement learning agent that receives a reward input providing information as to satisfaction of quality of service requirements of the user equipment responsive to a transmitted command; and (Pg. 14, 1st ¶: In machine learning, data learning methods [include] reinforcement learning; pg. 20, 4th ¶: [T]he beta distribution information managed by the base station is determined based on reward information determined according to feedback from the terminal that selected the DRX parameter value. In other words, the base station holds beta distribution information that reflects compensation information determined according to whether or not the QoS of the terminal that selected the DRX parameter value is maintained and the DRX power saving gain in all throughput and delay budget states)
based on the state information, causing the command to be transmitted to the user equipment, (Pg. 20, 7th ¶: [T]he terminal predicts DRX parameters based on information (e.g., TS model beta distribution) that reflects the DRX parameter change history of all terminals. Then, the UE maintains QoS and selects a DRX parameter value with the highest DRX power saving gain (e.g., the largest value among values randomly sampled from the beta distribution of candidate DRX parameter values), and sends the selected DRX parameter value to the base station. send to Then, the terminal changes the value of the parameter to the selected value after the time set by the base station; pg. 17, 5th ¶: Thompson sampling is an algorithm that maintains QoS and changes DRX parameters with the greatest power savings effect when setting DRX parameters in the current state and usage environment of the terminal)
wherein, the command is a discontinuous reception . . . that comprises a set of rules using at least one of medium access control or timers associated with a medium access control at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode. (Pg. 8, last ¶: [P]rocessors 202a and 202b may operate on one or more layers (e.g., . . . media access control (MAC). . . ; pg. 13, 6th ¶: [A]ttempts have been made to integrate AI with wireless communication systems, but these are focused on the application layer and network layer, and in particular, deep learning is focused on wireless resource management and allocation. . . . However, this research is gradually advancing to the MAC layer; pg. 18, 5th ¶: [C]onfiguration information related to DRX may include at least one of the length of each section within the DRX cycle, a timer value related to DRX operation, DRX cycle length [The Examiner finds that the descriptive element: a set of rules using at least one of medium access control or timers associated with a medium access control at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode is due little to no patentable weight because one of ordinary skill in the art would not interpret the passively performed steps related to operation of the user equipment to further limit the structure of the apparatus recited in claim 15.)
HE does not explicitly disclose:
the command is a discontinuous reception medium access control elements layer 2 command
In the same field of endeavor, however, WANG teaches:
the command is a discontinuous reception medium access control elements layer 2 command (¶ 0064: [T]he sidelink DRX configuration may be sent using layer 2 . . . signaling, such as via . . . a medium access control control element (MAC-CE))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify HE’s DRX parameter setting procedure to provide MAC-CEs as taught by WANG to improve network performance by extending reach and reducing data latency, and improve user equipment performance by enabling additional data sending and receiving opportunities while improving battery power, such that the additional data enables improved functions and capabilities at the UE. See WANG, at ¶ 0034.
Claim 14 is rejected under 35 U.S.C. § 103 as being obvious over HE (Note: The following citations are to the NPL machine translation of HE) in view of WANG, and further in view of US 2023/0043616 (hereinafter, “REN”).
Regarding claim 14, HE discloses:
A method, comprising:
using an artificial intelligence agent (AI device 600), determining that a command is to be transmitted to a user equipment, (Pg. 20, 4th ¶: [T]he base station transmits traffic prediction-related information to the terminal to proceed with adaptive DRX, and the terminal monitors only the PDCCH in a section without data transmission in the PDCCH-only section. Monitor signals and perform traffic prediction. If the time distribution value of the PDCCH-only section is above a certain value, a change request for DRX parameters and predicted traffic information (e.g. throughput and delay budget) are transmitted to the base station through UAI)
the command controlling entering of an inactive mode with the user equipment, (Pg. 4, 4th ¶: [O]ne command is such that the device receives configuration information related to a DRX (discontinuous reception) operation from a base station, performs the DRX operation based on the configuration information, and performs the DRX operation. Control to transmit a first message containing information related to at least one related parameter and receive a second message containing information related to resetting the at least one parameter, wherein the first message includes the at least one It may include information related to traffic characteristics determined by the UE, which is used to reset the parameters of; pg. 20, 4th ¶: [T]he base station transmits an RRC connection reconfiguration command to the terminal containing the information necessary to predict DRX parameters)
the determining using current state information associated with the user equipment, (Pg. 20, 6th ¶: [T]he information needed to predict DRX parameters includes the allowable range of the DRX parameter that the terminal wants to change, selectable candidate DRX parameters, and the Thompson sampling model corresponding to the current state of the terminal (e.g., throughput and delay budget)
wherein said artificial intelligence agent comprises a reinforcement learning agent that receives a reward input providing information as to satisfaction of quality of service requirements of the user equipment responsive to a transmitted command, (Pg. 14, 1st ¶: In machine learning, data learning methods [include] reinforcement learning; pg. 20, 4th ¶: [T]he beta distribution information managed by the base station is determined based on reward information determined according to feedback from the terminal that selected the DRX parameter value. In other words, the base station holds beta distribution information that reflects compensation information determined according to whether or not the QoS of the terminal that selected the DRX parameter value is maintained and the DRX power saving gain in all throughput and delay budget states)
wherein; and based on the state information, causing the command to be transmitted to the user equipment, (Pg. 20, 7th ¶: [T]he terminal predicts DRX parameters based on information (e.g., TS model beta distribution) that reflects the DRX parameter change history of all terminals. Then, the UE maintains QoS and selects a DRX parameter value with the highest DRX power saving gain (e.g., the largest value among values randomly sampled from the beta distribution of candidate DRX parameter values), and sends the selected DRX parameter value to the base station. send to Then, the terminal changes the value of the parameter to the selected value after the time set by the base station; pg. 17, 5th ¶: Thompson sampling is an algorithm that maintains QoS and changes DRX parameters with the greatest power savings effect when setting DRX parameters in the current state and usage environment of the terminal)
wherein, the command is a discontinuous reception . . . that comprises a set of rules using at least one of medium access control or timers associated with a medium access control . . . . (Pg. 8, last ¶: [P]rocessors 202a and 202b may operate on one or more layers (e.g., . . . media access control (MAC). . . ; pg. 13, 6th ¶: [A]ttempts have been made to integrate AI with wireless communication systems, but these are focused on the application layer and network layer, and in particular, deep learning is focused on wireless resource management and allocation. . . . However, this research is gradually advancing to the MAC layer; pg. 18, 5th ¶: [C]onfiguration information related to DRX may include at least one of the length of each section within the DRX cycle, a timer value related to DRX operation, DRX cycle length)
HE does not explicitly disclose:
the command is a discontinuous reception medium access control elements layer 2 command . . . at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode.
In the same field of endeavor, however, WANG teaches:
the command is a discontinuous reception medium access control elements layer 2 command (¶ 0064: [T]he sidelink DRX configuration may be sent using layer 2 . . . signaling, such as via . . . a medium access control control element (MAC-CE))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify HE’s DRX parameter setting procedure to provide MAC-CEs as taught by WANG to improve network performance by extending reach and reducing data latency, and improve user equipment performance by enabling additional data sending and receiving opportunities while improving battery power, such that the additional data enables improved functions and capabilities at the UE. See WANG, at ¶ 0034.
Also, in the same field of endeavor, however, REN teaches:
at the user equipment to cause a change of an active mode to the inactive mode with the user equipment immediately and to define a length of time of the inactive mode. (¶ 0073: [T]he DRX configuration comprises at least one of a configuration for a long DRX cycle, a configuration for a short DRX cycle, a DRX pattern, or a configuration for the UE to stay in a connected state with the at least one BS and disable the DRX cycle)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify HE’s DRX parameter setting procedure to provide DRX configurations as taught by REN to provide a connected state in which the UE is to monitor PDCCH occasions for a portion of the off duration of the DRX cycle or the entire off duration of the DRX cycle, such that during the off duration of a long or short DRX cycle, the UE may remain in connected state to monitor PDCCH occasions. See REN, at ¶ 0089.
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 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 Garth D Richmond whose telephone number is (703)756-4559. The Examiner can normally be reached M-F 8 a.m. - 5 p.m. ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kathy Wang-Hurst can be reached at 571-270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GARTH D RICHMOND/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644