CTNF 19/265,069 CTNF 84915 DETAILED ACTION Claims 1-20 are presented for examination. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Double Patenting 08-33 AIA 3. 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. 4. Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,406,025. Although the conflicting claims are not identical, they are not patentably distinct from each other. Claim 1 of US Patent 12,406,025 Claim 1 of US Patent Application 19/265,069 A computer-implemented method for detecting and resolving non-synchronization in a complex system, comprising: A computer-implemented method for detecting and resolving non- synchronization in a complex system, comprising: acquiring monitoring data from multiple computers and devices within the complex system; acquiring monitoring data from multiple computers and devices within the complex system; preparing the acquired data by aligning data sequences from different sources based on timestamps, and segmenting the prepared data into time windows; preparing the acquired data by aligning data sequences from different sources based on timestamps, and segmenting the prepared data into time windows; extracting a plurality of features from the data within each of the time windows; extracting a plurality of features from the data within each of the time windows; selecting significant features from the extracted features based on their relevance to non-synchronization detection, the relevance being determined by feature ranking and recursive feature elimination; selecting significant features from the extracted features based on their relevance to non-synchronization detection, the relevance being determined by feature ranking and recursive feature elimination; applying detection algorithms to the selected features to identify non-synchronization events within the system, the detection algorithms including unsupervised neural networks; and applying detection algorithms to the selected features to identify non- synchronization events within the system, the detection algorithms including unsupervised neural networks, the applying including: applying point detection methods to analyze features in individual time slots independently, applying sequential detection methods to analyze temporal patterns across multiple time slots, calculating anomaly scores for each time slot using both point detection and sequential detection methods, and combining the anomaly scores using a weighted ensemble approach to generate final anomaly scores for identifying non-synchronization events; and generating alerts, responsive to the detection of non-synchronization events, which trigger targeted, automatic corrective measures including adjusting particular system parameters to resolve the non-synchronization events and prevent occurrence of future non-synchronization events for enhanced stability and performance of the complex system. generating alerts, responsive to the detection of non-synchronization events, which trigger targeted, automatic corrective measures including adjusting particular system parameters to resolve the non-synchronization events and prevent occurrence of future non-synchronization events for enhanced stability and performance of the complex system. It is noted that the claimed limitations of claims 1-20 of Patent Application 19/265,069 are almost identical to that of claims 1-20 of U.S. Patent No. 12,406,025 except the limitations bolded above. It appears to be proper to apply the judicially created doctrine of obvious-type double patenting (see Kearns (US 2024/0119386), Lesi (US 2022/0224501) and Awad (US 2017/0192872)) to the claims at issue. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 5. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 6. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA 7. The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-21-aia AIA 8. Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kearns et al. (US 2024/0119386) hereinafter Kearns, in view of Lesi et al. (US 2022/0224501) hereinafter Lesi, and further in view of Awad et al. (US 2017/0192872) hereinafter Awad . In claim 1, Kearns discloses “A computer-implemented method for detecting and resolving non-synchronization in a complex system, comprising: acquiring monitoring data from multiple computers and devices within the complex system ([0005] monitoring computer service incidents for the plurality of organizations to identify computer services having current computer service incidents [0091] The ingestion tool 402 may be configured to receive or obtain one or more different types of events provided by various sources, here represented by events 401A, 401B); preparing the acquired data by aligning data sequences from different sources based on timestamps ([0091] Grouping events into partitions can be used to enable parallel processing and/or scaling of the system 400 so that the system 400 can handle (e.g., process, etc.) more and more events and/or more and more organizations [0092] The ingestion tool 402 may assign (e.g., associate, etc.) an ingested timestamp with an accepted event), and segmenting the prepared data into time windows ([0005] aggregating a count of organizations of the plurality of organizations that utilize a particular computer service and that have a current computer service incident related to the particular computer service within a plurality of time windows to generate an aggregate count for the particular computer service for each time window of the plurality of time windows); extracting a plurality of features from the data within each of the time windows ([0005] generating an outage risk detection alert for the particular computer service responsive to the aggregate count for a time window of the plurality of time windows surpassing a threshold level [0087] The system 400 uses data associated with the event (including data associated with objects related to the event, such as an alert) to identify a source that triggered the event. The data associated with the incident can include an attribute or a combination of attributes, descriptive data, payload data, or other data. For example, a source identifier might be used to identify a source that triggered the event [0121] each time bucket is associated with a plurality of historical time windows that correspond to the time bucket at a past time. The outage risk determination tool 600 may determine a baseline aggregate count of the number of distinct entities for a time bucket and a measure of historical variability. In some implementations, the baseline aggregate count is a statistical norm such as a mean or median of the aggregate count of distinct entities in the plurality of time windows and the historical variability is a statistical deviation such as a median absolute deviation or standard deviation of the number of distinct entities in the plurality of time windows. The plurality of time windows can include time windows for a current time interval, such as the most recent week. In other words, the statistical norm measures a typical number of distinct entities in a time bucket and the statistical deviation measures how the typical number of distinct entities varies. Other statistical information may be calculated based on the number of distinct entities experiencing an incident in each time window. The statistical norm and the statistical deviation of the number of distinct entities in historical time windows can be used to calculate a risk threshold for each time bucket. For example, each threshold may correspond to the statistical norm number of distinct entities plus a multiple of the number of statistical deviations)”. Kearns does not appear to explicitly disclose however, Lesi discloses “selecting significant features from the extracted features based on their relevance to non-synchronization detection, the relevance being determined by feature ranking and recursive feature elimination ([0040] to facilitate transmission of packets (e.g., packet 112, etc.) during protected windows (e.g., Qbv window 110 a , etc.), nodes in network 100 a are time synchronized and scheduled to transmit TC packets (e.g., packet 112, etc.) using non overlapping protected windows (e.g., Qbv window 110 a , etc.). It is to be appreciated that providing latency bounded communication (e.g., as depicted in timing diagram 100 b ) requires tight synchronization of time between nodes in network 100 a . With such dependency on time synchronization, reliable TSN operation can be disrupted by attacking the timing of the network, sometimes referred to as a desynchronization attack or event [0042] FIG. 2B depicts timing diagram 200 b illustrating Qbv window 110 b misaligned with Qbv window 110 a and Qbv window 110 c and overlapping with Qbv window 110 a . As a result, packets (e.g., packet 114 in the figure) arrive too late with respect to the attacked switch protected window (e.g., Qbv window 110 b ) causing them to be buffered and sent in the next protected window. As a result of the delay in transmitting packet 114, relay node 202 breaks the latency bound of the stream that it is serving and can result in errors or comprise the safety of the system in which the nodes are operating); applying detection algorithms to the selected features to identify non-synchronization events within the system, the detection algorithms including unsupervised neural networks ([0050] The IDS 324 can utilize any number of different detection methods to detect an attack. For instance, the IDS 324 may implement a signature-based method, a statistical anomaly-based method, a stateful protocol analysis method, machine-learning based, or some combination of all four methods. A signature-based IDS monitors packets in the network and compares with pre-configured and pre-determined attack patterns known as signatures. A statistical anomaly-based or machine-learning based IDS monitors network traffic and compares it against an established baseline. The baseline will identify what is “normal” for that network, such as what sort of bandwidth is generally used and what protocols are used. For instance, ensemble models that use Matthews correlation co-efficient to identify unauthorized network traffic have obtained 99.73% accuracy. A stateful protocol analysis IDS identifies deviations of protocol states by comparing observed events with defined profiles of generally accepted definitions of benign activity); and generating alerts, responsive to the detection of non-synchronization events, which trigger targeted, automatic corrective measures including adjusting particular system parameters to resolve the non-synchronization events and prevent occurrence of future non-synchronization events for enhanced stability and performance of the complex system ([0048] The device 312 may further include an intrusion detection system (IDS) 324. In general, the IDS 324 is a device or software application that monitors a device, network or systems for malicious activity or policy violations. The IDS 324 may be specifically tuned to detect a timing attack, such as a desynchronization attack, or other TSN specific attack vector. Any intrusion activity or violation is typically reported either to other devices in the same network, an administrator, and/or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources and uses alarm filtering techniques to distinguish malicious activity from false alarms. In addition to the device 312, the IDS 324 may be implemented for other devices in the TSN, such as relay nodes 104 a -104 c , to provide a more comprehensive security solution to an attacker [0049] once an attack is identified, or abnormal behavior is sensed, an alert can be sent to a SIEM, a network administrator, or a software application to automatically implement security protocols, such as dropping the message 310, isolating an infected device guarded by the IDS 324, and/or re-configuring one or more network paths for impacted devices in the TSN network [0060] the clock 424 is effectively isolated and immune to an attack by the attacker 320. The device 402 uses the clock 424 to recover the shared or common network time for the TSN when the clock manager 414 deems the clock 422 is no longer reliable or unsafe, such as due to synchronization with an infected clock leader or unavailability of a normal clock leader, for example. When such events occur, the clock manager 414 may enter a time recovery mode in an attempt to recover the shared or common network time of the TSN [0061] When in time recovery mode, the clock manager 414 may utilize different sets of timestamps to assist in time recovery operations. In general, a timestamp is a sequence of characters or encoded information identifying when a certain event occurred, usually giving date and time of day, sometimes accurate to a small fraction of a second. This data is usually presented in a consistent format, allowing for easy comparison of two different records and tracking progress over time. Timestamps are typically used for logging events or in a sequence of events (SOE), in which case each event in the log or SOE is marked with a timestamp [0089] During the timestamp correction mode, the clock manager 706 can recover past and potentially incorrect timestamps for the application 710 using the same or similar redundant timeline used to interpolate/extrapolate a corrected network timeline. In one embodiment, the clock manager 706 utilizes the regression model 416 and the application timestamps 718 to correct one or more application timestamps 714 for the application 710. The device 702 can receive the regression model 416 and timestamps 418, 420 from the device 402, to use this information to generate the redundant timeline 506. Alternatively, the device 702 can receive only the timestamps 418, 420, and use or build its own regression model similar to the regression model 416 to generate the redundant timeline 506. The clock manager 706 can reconstruct and/or correct the timestamps 714 to a corrected network time using a set of recovery information that includes, for example, the regression model 416, the application timestamps 714, 718, a valid (accurate) checkpoint prior to T1, and a valid (accurate) network time after T2)”. Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Kearns and Lesi, the suggestion/motivation for doing so would have been to provide an improved method for time management and recovery techniques for systems operating on strict time requirements, such as systems based on TSN (time-sensitive networking). TSN defines a set of standards with the aim to enable time synchronization and deterministic data delivery in converged networks where TC (time-critical) traffic coexists with other types of traffic ([0028]). Kearns and Lesi do not appear to explicitly disclose however, Awad discloses “applying detection algorithms to the selected features…, the applying including: applying point detection methods to analyze features in individual time slots independently ([0027] each event may be associated with an event identifier identifying a given event in the series of events, an event time identifier identifying a time when the given event occurred), applying sequential detection methods to analyze temporal patterns across multiple time slots ([0045] the anomaly type may be a Partial Pattern. The Partial Pattern anomaly type may be characterized by multiple events appearing repeatedly in the same time slot. For example, a set of 30 events may be identified in the selected time slot, where each event corresponds to a service shutdown message and/or alert), calculating anomaly scores for each time slot using both point detection and sequential detection methods ([0074] the anomaly processor 104 may detect system anomalies based on at least one of the feedback data and a previously processed event pattern. In some examples, the evaluator may determine, for a time interval, the anomaly fingerprint based on a set of relative contributions of event types to the anomaly intensity; where a fingerprint matching score for the anomaly fingerprint may be computed in a second time interval to determine presence or absence of similar system anomalies in the second time interval, and where the fingerprint matching score may be computed based on a correlation between the anomaly fingerprint and anomaly intensity amounts in the second time interval), and combining the anomaly scores using a weighted ensemble approach to generate final anomaly scores for identifying non-synchronization events ([0090] processor 702 executes instructions of an evaluator to determine, for the time interval, anomaly intensities and the anomaly score, and where each anomaly intensity may be transformed, with respect to a distribution of anomaly intensities of the same anomaly type in reference time-slots, based on a distinctive residual rarity extremity score, into comparable, additive, and distinctive anomaly intensity scores that may be combined to determine the anomaly score)”. Hence, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Kearns, Lesi and Awad, the suggestion/motivation for doing so would have been to provide a method of interactive detection of system anomalies to quickly solve operational issues ([0016]). In claim 2, Kearns teaches The method of claim 1, wherein the acquiring the monitoring data includes real-time capturing of system performance metrics, operational state logs, error messages, and anomalies detected by onboard diagnostics ([0067] Exchanged communications may include, queries, searches, messages, notification messages, events, alerts, performance metrics, log data, API calls [0096] Events may be variously formatted messages that reflect the occurrence of events or incidents that have occurred in the computing systems or infrastructures of one or more managed organizations. Such events may include facts regarding system errors, warning, failure reports, customer service requests, status messages [0097] the data store 410 may be arranged to store performance metrics, configuration information, event history, alert history, incident history). In claim 3, Kearns teaches The method of claim 1, wherein the segmenting the prepared data includes utilizing a sliding window technique, with a size of each window being predetermined based on a granularity of analysis required, a frequency of data recording, and system response characteristics ([0090] An event received from an organization may include an indication of one or more event processing services that are to operate on (e.g., process, etc.) the event. The indication of the event processing service may be referred to as a routing key. A routing key may be unique to a managed organization. As such, two events that are received from two different managed organizations for processing by a same event processing service would include two different routing keys. A routing key may be unique to the event processing service that is to receive and process an event. As such, two events associated with two different routing keys and received from the same managed organization for processing may be directed to (e.g., processed by) different event processing services [0092] The ingestion tool 402 may be arranged to receive the various events and perform various actions, including, filtering, reformatting, information extraction, data normalizing, or the like, or combination thereof, to enable the events to be stored (e.g., queued, etc.) and further processed. In at least one of the various embodiments, the ingestion tool 402 may be arranged to normalize incoming events into a unified common event format. Accordingly, in some embodiments, the ingestion tool 402 may be arranged to employ configuration information, including, rules, templates, maps, dictionaries, or the like, or combination thereof, to normalize the fields and values of incoming events to the common event format). In claim 4, Lesi teaches The method of claim 1, wherein the extracting the plurality of features includes calculating statistical measures including mean, variance, skewness, and kurtosis, and frequency-domain features including spectral density and dominant frequency components ([0064] In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression, which finds a line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). In one embodiment, the regression model 416 may be implemented as a least-squares regression model. Other linear and non-linear regression models may be implemented for regression model 416, however, such as Bayesian regression, percentage regression, least absolute regressions, non-parameter regressions, scenario optimization, distance metric learning, and so forth). In claim 5, Lesi teaches The method of claim 1, further comprising utilizing machine learning algorithms during the selecting of the significant features to determine a significance of each feature based on historical synchronization data, and enhance an accuracy of non- synchronization detection based on an analysis of extracted features ([0050] the IDS 324 can utilize any number of different detection methods to detect an attack. For instance, the IDS 324 may implement a signature-based method, a statistical anomaly-based method, a stateful protocol analysis method, machine-learning based, or some combination of all four methods. A signature-based IDS monitors packets in the network and compares with pre-configured and pre-determined attack patterns known as signatures. A statistical anomaly-based or machine-learning based IDS monitors network traffic and compares it against an established baseline. The baseline will identify what is “normal” for that network, such as what sort of bandwidth is generally used and what protocols are used. For instance, ensemble models that use Matthews correlation co-efficient to identify unauthorized network traffic have obtained 99.73% accuracy. A stateful protocol analysis IDS identifies deviations of protocol states by comparing observed events with defined profiles of generally accepted definitions of benign activity). In claim 6, Kearns teaches The method of claim 1, further comprising real-time automatic monitoring, troubleshooting, and iterative implementing of the automatic corrective measures for the complex system to improve operational efficiency and uptime of the complex system ([0026] an alert can include information about the event, such as a description of the affected process, time the event occurred, and severity. An alert may be sent to a team responsible for the operation that triggered the event. An incident can be a task associated with an event and that requires a resolution. For example, non-limiting examples of tasks include determining the cause of an event, rectifying the cause of the event, and mitigating issues related to the event. The incident may be assigned to a responder (e.g., a person or a group of persons) who may become responsible for resolving the incident. The responder may be a part of the team associated with the computer service that generated the event [0080] a recommendation engine 326 (which may be or include a machine-learning model)). In claim 7, Lesi teaches The method of claim 1, further comprising generating a report identifying the detected non-synchronization events and probable causes, the report including recommendations for system adjustments to mitigate the future non-synchronization events to further enhance reliability and operational continuity of the complex system ([0029] if an attacker located on a network device (e.g., switch or relay) modifies a critical attribute on a specific port, then all downstream nodes from that network device will suffer a desynchronization event. Therefore, it becomes important to detect and localize an attack as quickly as possible. Furthermore, upon detection, it becomes important for the TSN to quickly isolate the compromised network device and thereby prevent the desynchronization attack from spreading to downstream nodes [0030] If a compromised network device is a clock leader, the clock followers downstream from the compromised clock leader will need to recover a network time without using any time information received from the compromised clock leader. In a TSN with one primary clock leader, for example, it may be necessary for a clock follower to take the primary clock leader role if an attack response causes disconnection of the primary clock leader. Further, applications that use incorrect time due to attack (e.g., logging) may need to be retroactively corrected [0032] The log files may have inconsistent entries and/or timestamps during or subsequent to the attack. The latter case may occur even when an intrusion detection system (IDS) prevents propagation of abrupt attacks, since less aggressive attackers may persist in the system for some time duration. In such cases, it becomes important to retroactively correct the incorrect timestamps before the logs are used, such as during a security review or threat detection in the case of autonomous or cyber-physical systems, or correcting the incorrect timestamps that PHC2SYS (in Unix-based systems) has propagated to system time, among other use cases [0054] When the IDS 324 determines the message 310 is malicious, the device 312 can disconnect from the device 302 to isolate itself from the attacker 320. Once disconnected, the device 312 will no longer receive or trust synchronization messages or update messages from the device 302, including the message 310. The clock manager 314 can enter a time recovery mode. In time recovery mode, the clock manager 314 assumes the clock 316 is no longer synchronized with a correct network time for the TSN, and it must therefore attempt to recover a correct network time without using any information from the compromised device 302, including the time information contained within the message 310. Time recovery can be accomplished using the clock 318, which is unsynchronized with the clock 306 of the device 302 or any other clock within the TSN). Claims 8-14 are essentially same as claims 1-7 except that they recite claimed invention as a system and are rejected for the same reasons as applied hereinabove. Claims 15-20 are essentially same as claims 1-4 and 6-7 except that they recite claimed invention as a computer program product and are rejected for the same reasons as applied hereinabove . Conclusion 07-96 AIA 9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed on 892 form . Examiner’s Note: Examiner has cited particular figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUAWEN A PENG whose telephone number is (571)270-5215. The examiner can normally be reached Mon thru Fri 9 am to 5 pm. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HUAWEN A PENG/Primary Examiner, Art Unit 2169 Application/Control Number: 19/265,069 Page 2 Art Unit: 2169 Application/Control Number: 19/265,069 Page 3 Art Unit: 2169 Application/Control Number: 19/265,069 Page 4 Art Unit: 2169 Application/Control Number: 19/265,069 Page 5 Art Unit: 2169 Application/Control Number: 19/265,069 Page 6 Art Unit: 2169 Application/Control Number: 19/265,069 Page 7 Art Unit: 2169 Application/Control Number: 19/265,069 Page 8 Art Unit: 2169 Application/Control Number: 19/265,069 Page 9 Art Unit: 2169 Application/Control Number: 19/265,069 Page 10 Art Unit: 2169 Application/Control Number: 19/265,069 Page 11 Art Unit: 2169 Application/Control Number: 19/265,069 Page 12 Art Unit: 2169 Application/Control Number: 19/265,069 Page 13 Art Unit: 2169 Application/Control Number: 19/265,069 Page 14 Art Unit: 2169 Application/Control Number: 19/265,069 Page 15 Art Unit: 2169 Application/Control Number: 19/265,069 Page 16 Art Unit: 2169 Application/Control Number: 19/265,069 Page 17 Art Unit: 2169 Application/Control Number: 19/265,069 Page 18 Art Unit: 2169 Application/Control Number: 19/265,069 Page 19 Art Unit: 2169 Application/Control Number: 19/265,069 Page 20 Art Unit: 2169 Application/Control Number: 19/265,069 Page 21 Art Unit: 2169 Application/Control Number: 19/265,069 Page 22 Art Unit: 2169 Application/Control Number: 19/265,069 Page 23 Art Unit: 2169
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