CTFR 17/909,771 CTFR 90409 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Amendment As per the instant Application having Application number 17/909,771 the examiner acknowledges the applicant's submission of the amendment dated 01/30/2026. At this point, claims 1-3, 5, 7-9, 13, 17, and 19 have been amended. Claim 18 has been cancelled. Claims 12, 20, and 23-25 have been previously cancelled. Claims 26 and 27 have been newly added. Claims 1-11, 13-17, 19, 21-22, and 26-27 are pending. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1-6 a nd 21 is/ are rejected under 35 U.S.C. 102(a)(2) as being ant icipated by S he n et al., (US 2022/0346132 A1, hereinafter Shen ). Reg arding claim 1 and 21, taking claim 1 as exemplary: Shen shows: “An electronic device for a central processing apparatus, wherein the central processing apparatus performs federated learning (FL) with a plurality of distributed computing apparatuses,” ( Paragraph [0037]: Embodiments of the present application provide a resource scheduling method, an apparatus and a readable storage medium. In a process in which a terminal device participates in federated learning , a network device first generates first information, the first information including configuration information, and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information in the first information . In the embodiments of the present application, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device, that the terminal device participates in a current round of federated learning, and generates and sends the configuration information of the training parameter that matches the terminal device, thereby realizing scheduling of the training parameter in a wireless network .”) “and the electronic device comprises a processing circuit configured to cause the central processing apparatus to: for a first distributed computing apparatus of the plurality of distributed computing apparatuses: receive a report message from the first distributed computing apparatus the report message including at least one of training data set information or device information of the first distributed computing apparatus;” ( Paragraph [0037]: Embodiments of the present application provide a resource scheduling method, an apparatus and a readable storage medium. In a process in which a terminal device participates in federated learning , a network device first generates first information, the first information including configuration information, and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information in the first information . In the embodiments of the present application, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device, that the terminal device participates in a current round of federated learning, and generates and sends the configuration information of the training parameter that matches the terminal device, thereby realizing scheduling of the training parameter in a wireless network .” In paragraph [0096]: “In the present embodiment, the network device first generates first information, the first information including configuration information of the training parameter for the terminal device to perform model training , and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training ; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information included in the first information. In the present embodiment, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device , that the terminal device participates in a current round of federated learning, and generates and sends the configuration information of the training parameter that matches the terminal device, thereby realizing scheduling of the training parameter in a wireless network .”) “evaluate an performance of the first distributed computing apparatus based on the report message of the first distributed computing apparatus; and determine resource requirements of the first distributed computing apparatus based on the FL performance of the first distributed computing apparatus.” ( Paragraph [0037]: Embodiments of the present application provide a resource scheduling method, an apparatus and a readable storage medium. In a process in which a terminal device participates in federated learning , a network device first generates first information, the first information including configuration information, and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information in the first information . In the embodiments of the present application, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device, that the terminal device participates in a current round of federated learning, and generates and sends the configuration information of the training parameter that matches the terminal device, thereby realizing scheduling of the training parameter in a wireless network .” In paragraph [0067]: “In a process of federated learning, transmission of the AI model/ML model (i.e. the above mentioned model to be trained) on a communication network will gradually become one of important services in the communication network. In an existing process of federated learning, the network device schedules training resources of the connected terminal devices based on a wired network .” In paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” And in paragraph [0272]: “Continuing referring to FIG. 19, as the data transmission rate of the terminal device 1 is half of the data transmission rate of the terminal device 2, terminal device 3 and terminal device 4, the time required by the terminal device 1 to send the training result of the (n−1)-th training period to the network device in the n-th training period is 2 times of the time required by the terminal device 2, terminal device 3 and terminal device 4. That is, if the terminal device 1 participates in each round of federated learning, then the training period of the entire federated learning will be doubled, which will cause the training rate to be reduced by half. In addition, the utilization rate of the data processing capabilities of the terminal device 2, terminal device 3 and terminal device 4 is only 50%, where n is an integer greater than or equal to 0.” And in paragraph [0096]: “In the present embodiment, the network device first generates first information , the first information including configuration information of the training parameter for the terminal device to perform model training, and specifically, the configuration information is used to indicate characteristics of sample data, and/or, characteristics of a model to be trained for the terminal device to perform model training ; and then, the network device sends the first information to the terminal device, and the terminal device acquires the configuration information included in the first information. In the present embodiment, the network device determines, according to a data processing capability and/or a data transmission capability of the terminal device, that the terminal device participates in a current round of federated learning , and generates and sends the configuration information of the training parameter that matches the terminal device, thereby realizing scheduling of the training parameter in a wireless network .”) Regarding claim 2: Shen shows the device of claim 1 as claimed and specified above. And Shen shows “wherein the training data set information comprises at least one of attribute information, data amount, data freshness, or data diversity of the training data, and wherein the evaluating of the FL performance of the first distributed computing apparatus comprises at least one of the following: the more the effective dimensions in the attribute information of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus; the greater the amount of data of the first distributed computing apparatus. the better the FL performance of the first distributed computing apparatus; the fresher the data of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus; or the more diverse the data of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus.” ( Paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” Note that the claim is written in the alternative and not all claim elements needs to be taught (i.e. only the better performance has to be taught) for the teaching by the reference to be satisfied.) Regarding claim 3: Shen shows the device of claim 1 as claimed and specified above. And Shen shows “wherein the device information comprises at least one of battery power or processing capability information, and wherein the evaluating of the FL performance of the first distributed computing apparatus comprises at least one of the following: the higher the battery power of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus; or the higher the processing capability of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus.” ( Paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” Note that the claim is written in the alternative and not all claim elements needs to be taught (i.e. only the higher processing capability has to be taught) for the teaching by the reference to be satisfied.) Regarding claim 4: Shen shows the device of claim 1 as claimed and specified above. And Shen shows “wherein the evaluating of the FL performance of the first distributed computing apparatus comprises: the better the uplink and/or downlink quality of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus; or the more the uplink and/or downlink time-frequency resources of the first distributed computing apparatus, the better the FL performance of the first distributed computing apparatus.” ( Paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” Note that the claim is written in the alternative and not all claim elements needs to be taught (i.e. only uplink and/or downlink quality has to be taught—which is shown through data transmission capability) for the teaching by the reference to be satisfied.) Regarding claim 5: Shen shows the device of claim 1 as claimed and specified above. And Shen shows “wherein the determining of the resource requirements of the first distributed computing apparatus comprises: the better the FL performance of the first distributed computing apparatus is, the higher the resource requirements are determined for the first distributed computing apparatus, the resource requirements specifying at least one of uplink or downlink transmission power, or uplink or downlink time-frequency resources of the first distributed computing apparatus.” ( Paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” And in paragraph [0272]: “Continuing referring to FIG. 19, as the data transmission rate of the terminal device 1 is half of the data transmission rate of the terminal device 2, terminal device 3 and terminal device 4, the time required by the terminal device 1 to send the training result of the (n−1)-th training period to the network device in the n-th training period is 2 times of the time required by the terminal device 2, terminal device 3 and terminal device 4. That is, if the terminal device 1 participates in each round of federated learning, then the training period of the entire federated learning will be doubled, which will cause the training rate to be reduced by half. In addition, the utilization rate of the data processing capabilities of the terminal device 2, terminal device 3 and terminal device 4 is only 50%, where n is an integer greater than or equal to 0.” Note that the claim is written in the alternative and not all claim elements needs to be taught (i.e. only uplink and/or downlink power has to be taught—which is shown through data transmission capability including that of rate) for the teaching by the reference to be satisfied.) Regarding claim 6: Shen shows the device of claim 1 as claimed and specified above. And Shen shows “wherein the processing circuit is further configured to cause the central processing apparatus to: based on at least one of the training data set information or the device information, determine or update the distributed computing apparatuses participating in the FL.” ( Paragraph [0076]: “The purpose of the resource scheduling method of the present solution is to ensure that the terminal device participating in federated learning can make full use of its own data processing capability, data resources and data transmission resources, so as to cause the multiple terminal devices participating in federated learning to complete the task of model training as soon as possible, thereby improving efficiency of federated learning. In the implementation of the present solution, factors that influence the efficiency of federated learning at least include: factors of data processing capability of the terminal device and the data transmission capability of the terminal device. As can be known that , the network device can improve efficiency of federated learning according to the data processing capabilities of the respective terminal devices participating in federated learning and the data transmission capabilities of the respective terminal devices.” And in paragraph [0272]: “Continuing referring to FIG. 19, as the data transmission rate of the terminal device 1 is half of the data transmission rate of the terminal device 2, terminal device 3 and terminal device 4, the time required by the terminal device 1 to send the training result of the (n−1)-th training period to the network device in the n-th training period is 2 times of the time required by the terminal device 2, terminal device 3 and terminal device 4. That is, if the terminal device 1 participates in each round of federated learning, then the training period of the entire federated learning will be doubled, which will cause the training rate to be reduced by half. In addition, the utilization rate of the data processing capabilities of the terminal device 2, terminal device 3 and terminal device 4 is only 50%, where n is an integer greater than or equal to 0.” Note that the claim is written in the alternative and not all claim elements needs to be taught (i.e. only uplink and/or downlink power has to be taught—which is shown through data transmission capability including that of rate) for the teaching by the reference to be satisfied.) Allowable Subject Matter 12-151-07 AIA 07-97 12-51-07 Claim s 13-17, 19 and 22 are allowed. Regarding claims 13 and 22, taking claim 13 as exemplary: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claims 1 and 21 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claims 1 and 21 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claims 1 and 21 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of allowable subject matter of independent claims 13 and 22 , taking claim 13 as exemplary, in the instant application, is the combination with the inclusion in these claims of the limitations of a device and method comprising: “ send a report message to the central processing apparatus, the report message including at least one of training data set information or device information of the first distributed computing apparatus; and receive wireless resource configuration information from a wireless network, wherein the wireless resource configuration information is determined by the wireless network based on an indication message from the central processing apparatus, wherein the processing circuit is further configured to send model update information of the first distributed computing apparatus to the central processing apparatus and wherein the model update information is used by the central processing apparatus to aggregate the model update information with a corresponding weight based on the FL performance of the first distributed computing apparatus to obtain a global model. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach federated learning with wireless resource configuration and wireless resources with learning and wireless configurations, it does not teach wherein the wireless resource configuration information is determined by the wireless network based on an indication message from the central processing apparatus, wherein the processing circuit is further configured to send model update information of the first distributed computing apparatus to the central processing apparatus and wherein the model update information is used by the central processing apparatus to aggregate the model update information with a corresponding weight based on the FL performance of the first distributed computing apparatus to obtain a global model. Dependent claim(s) 14-17 and 19 are allowable at least for the reasons recited above as including all of the limitations of the allowable independent base claim 13 upon which claims 14-17 and 19 depend. Would Be Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 7-11 and 26-27 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. 13-03-01 The following is a statement of reasons for the indication of would be allowable subject matter: Regarding claim 7: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claim 18 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claim 18 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claim 18 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of would be allowable subject matter of dependent claim 7 in the instant application, is the combination with the inclusion in these claims of the limitations of a device comprising: “ wherein the device information comprises device identification information, and the processing circuit is further configured to cause the central processing apparatus to: query the uplink or downlink quality of one or more distributed computing apparatuses by corresponding device identification information; and when initiating or during the FL, choose distributed computing apparatuses whose uplink or downlink quality meets a predetermined threshold from the one or more distributed computing apparatuses to participate in the FL. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach federated learning with wireless resource configuration and wireless resources with learning and wireless configurations, it does not teach wherein the device information comprises device identification information, and the processing circuit is further configured to cause the central processing apparatus to: query the uplink or downlink quality of one or more distributed computing apparatuses by corresponding device identification information; and when initiating or during the FL, choose distributed computing apparatuses whose uplink or downlink quality meets a predetermined threshold from the one or more distributed computing apparatuses to participate in the FL. Regarding claim 8: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claim 18 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claim 18 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claim 18 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of would be allowable subject matter of dependent claim 8 in the instant application, is the combination with the inclusion in these claims of the limitations of a device comprising: “ wherein the device information comprises device identification information, and the processing circuit is further configured to cause the central processing apparatus to: query location information of one or more distributed computing apparatuses by corresponding device identification information; and when initiating the FL or during the FL, choose distributed computing apparatuses with diverse locations from the one or more distributed computing apparatuses to participate in the FL. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach federated learning with wireless resource configuration and wireless resources with learning and wireless configurations, it does not teach wherein the device information comprises device identification information, and the processing circuit is further configured to cause the central processing apparatus to: query location information of one or more distributed computing apparatuses by corresponding device identification information; and when initiating the FL or during the FL, choose distributed computing apparatuses with diverse locations from the one or more distributed computing apparatuses to participate in the FL. Regarding claim 9: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claim 18 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claim 18 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claim 18 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of would be allowable subject matter of dependent claim 8 in the instant application, is the combination with the inclusion in these claims of the limitations of a device comprising: “ wherein the processing circuit is further configured to cause the central processing apparatus to: when initiating the FL or during the FL, choose distributed computing apparatuses with diverse data from the one or more distributed computing apparatuses to participate in the FL. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach federated learning with wireless resource configuration and wireless resources with learning and wireless configurations, it does not teach wherein the processing circuit is further configured to cause the central processing apparatus to: when initiating the FL or during the FL, choose distributed computing apparatuses with diverse data from the one or more distributed computing apparatuses to participate in the FL . Regarding claim 10: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claim 18 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claim 18 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claim 18 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of would be allowable subject matter of dependent claim 10 in the instant application, is the combination with the inclusion in these claims of the limitations of a device comprising: “ wherein the processing circuit is further configured to cause the central processing apparatus to: receive model update information from the first distributed computing apparatus; and aggregate the model update information with a corresponding weight based on the FL performance of the first distributed computing apparatus, to obtain a global model. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach wherein the processing circuit is further configured to cause the central processing apparatus to: receive model update information from the first distributed computing apparatus; and aggregate the model update information with a corresponding weight based on the FL performance of the first distributed computing apparatus, to obtain a global model. Dependent claim(s) 11 is marked as would be allowable at least for the reasons recited above as including all of the limitations of the would be allowable dependent base claim 10 upon which claim 11 depends. Regarding claims 26 and 27, taking claim 26 as exemplary: Though Shen et al., (US 2022/0346132A1), part of the prior art made of record, teaches the use of federated learning with wireless resource configuration of claim 18 in paragraph [0037] through the use of determining configuration of a terminal device within a wireless network for participation in federated learning. Though Wang et al., (US 2021/0064996 A1), part of the prior art made of record, teaches the use of learning with communications configurations of claim 18 through the use of a neural network formation based on network signals in paragraphs [0004] and [0005] . And though Chiang et al., (US 2019/0104422 A1), part of the prior art made of record, teaches the use of wireless configuration of claim 18 through the use of wireless mesh network configurations in paragraphs [0011] and [0012]. The primary reason for marking of would be allowable subject matter of dependent claims 26 and 27, taking claim 26 as exemplary , in the instant application, is the combination with the inclusion in these claims of the limitations of a device comprising: “ wherein the processing circuit is further configured to cause the central processing apparatus to notify a wireless network, via a message, of configuring wireless resources for the first distributed computing apparatus based on the resource requirements. ” The prior art of made of record above neither anticipates nor renders obvious the above-recited combinations. Specifically, though the prior art of made of record does teach federated learning with wireless resource configuration and wireless resources with learning and wireless configurations, it does not teach it does not teach determine wireless resource requirements for distributed computing based of federated learning performance that has been reported and notifying a wireless network, via a message, of configuring wireless resources for the first distributed computing apparatus based on the wireless resource requirement . Conclusion 07-40 AIA 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 SHANE D WOOLWINE whose telephone number is (571)272-4138. The examiner can normally be reached M-F 9:30-6:00 PM. 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, MIRANDA HUANG can be reached at (571) 270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. SHANE D. WOOLWINE Primary Examiner Art Unit 2124 /SHANE D WOOLWINE/Primary Examiner, Art Unit 2124 Application/Control Number: 17/909,771 Page 2 Art Unit: 2124 Application/Control Number: 17/909,771 Page 3 Art Unit: 2124 Application/Control Number: 17/909,771 Page 4 Art Unit: 2124 Application/Control Number: 17/909,771 Page 5 Art Unit: 2124 Application/Control Number: 17/909,771 Page 6 Art Unit: 2124 Application/Control Number: 17/909,771 Page 7 Art Unit: 2124 Application/Control Number: 17/909,771 Page 8 Art Unit: 2124 Application/Control Number: 17/909,771 Page 9 Art Unit: 2124 Application/Control Number: 17/909,771 Page 10 Art Unit: 2124 Application/Control Number: 17/909,771 Page 11 Art Unit: 2124 Application/Control Number: 17/909,771 Page 12 Art Unit: 2124 Application/Control Number: 17/909,771 Page 13 Art Unit: 2124 Application/Control Number: 17/909,771 Page 14 Art Unit: 2124 Application/Control Number: 17/909,771 Page 15 Art Unit: 2124 Application/Control Number: 17/909,771 Page 16 Art Unit: 2124 Application/Control Number: 17/909,771 Page 17 Art Unit: 2124 Application/Control Number: 17/909,771 Page 18 Art Unit: 2124 Application/Control Number: 17/909,771 Page 19 Art Unit: 2124 Application/Control Number: 17/909,771 Page 20 Art Unit: 2124
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