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 . 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.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 and 21-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-10 are directed to a machine, claims 21-25 are directed to a process, and claims 26-30 are directed to a manufacture. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“feed source data to a machine learning algorithm, wherein the source data includes data points for recorded quantities of the customer-requested executions at corresponding timestamps; obtain confidence data from the machine learning algorithm, wherein the confidence data includes lower confidence interval values for the corresponding timestamps, and wherein the lower confidence interval values are indicative of lower bounds of expected quantities of the customer-requested executions on the mobile app at the corresponding timestamps; identify a trigger index and a corresponding trigger timestamp from the source data; identify a trigger interval value from the lower confidence interval values that corresponds with the trigger timestamp; detect an anomaly indicative of one or more unperformed customer-requested executions in response to determining that the trigger index is less than the trigger interval value at the trigger timestamp; and generate and transmit an alert for remediation in response to detecting the one or more unperformed customer-requested executions” Claim 1.
With recited additional elements reserved for consideration alone and all together combined with their recited role(s) in the claim under step 2A prong two, a careful analysis of the remaining limitations above results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract ideas fall within the groupings of abstract ideas described as mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment), and certain methods of organizing human activity, for example commercial or legal interactions (including agreements in the form of contracts, legal obligations, or business relations) and managing personal behavior or relationships or interactions between people (including following rules or instructions). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in present claims 1-10 are a memory configured to store instructions and one or more processors configured to execute the instructions. Claims 21-25 include only one or more processors, while claims 26-30 include only a non-transitory computer readable medium and “a machine.” These additional elements have been considered individually, in combination, and altogether as a whole with the functions they perform, e.g., the recited “memory” (potentially transitory), processor(s), non-transitory computer readable medium, and “machine” (potentially any contrivance, including those that are abstract, see “Machine,” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/machine) are tangentially recited as broadly and generally performing all steps without direct reference to any specific operation performed with respect to any particular step. The steps themselves are recited only in terms of the intended results of functionally nonspecific activities.
These additional elements do not integrate the judicial exception into a practical application because they amount to no more than a mere suggestion to apply the exception using generic computer components. The claims are almost entirely a recitation of abstract ideas. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Claimed elements that are not abstract are identified only tangentially as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any specific operations performed by any particularly identified machines, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The elements fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that the remaining dependent claims 2-10, 22-25, and 27-30, do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the data and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-10 and 21-30 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
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.
Claims 1-10 and 21-30 are rejected under 35 U.S.C. 103 as being unpatentable over Watt et al. (Pub. No.: US 2022/0114437 A1) in view of Wasserstrom et al. (Patent No.: US 11,645,075 B1).
Watt teaches, a) an automated system for detecting anomalous execution patterns, b) customer requested executions, c) execution failures lowering execution rates, and d) notification of detected non-performance, and discloses regarding
Claim 1. An automated system for detecting and notifying of non-performance of customer-requested executions on a mobile app for rental property shopping, the automated system comprising: ● memory configured to store instructions (see at least Watt fig.11, ¶0025 “processing device generally comprises at least one processor and an associated memory”); and ● one or more processors (see at least Watt fig.11, ¶0025 “processing device generally comprises at least one processor and an associated memory”) configured to execute the instructions to: ● feed source data to a machine learning algorithm, wherein the source data includes data points for recorded quantities of the customer-requested executions at corresponding timestamps (see at least Watt abstract “obtaining multiple data streams pertaining to one or more data center resources in at least one multitenant executing environment; correlating one or more portions of the multiple data streams … determining one or more anomalies within the multiple data streams by processing the one or more correlated portions of the multiple data streams using a machine learning-based anomaly detection engine,” figs.2, 7, ¶0039 “workload refers to an executable job with a finite lifetime … trace logs refer to information output from a running workload to known system file locations (e.g., system logging protocol (syslog), standard output (stdout), etc.), and metrics refer to profiling information collected on infrastructure on a regular cadence (e.g., CPU usage per 60 second intervals, etc.),” ¶0047 “data are collected from data center log source steams 301-1, 301-2, and 301-N. The data center logs and related data can be collected and pushed using a data processing tool (e.g., Logstash) 312 within a log pipeline 303 (within multi-tenant execution environment resource correlation system 305) … . date-time stamps can be extracted from such logs” . Please note: a system log includes timestamps.); ● identify a trigger index and a corresponding trigger timestamp from the source data (see at least Watt figs. 5-6, ¶0047 “date-time stamps can be extracted from such logs,” ¶¶0048-0049 “system capable of performing cross-table and/or cross-index queries and fulfilling requirements of machine learning system correlation needs. [0049] Using the structured data log output(s), at least one embodiment includes storing, indexing, and searching one or more data sources via the multi-tenant-capable search engine,” ¶0053 “job completion trigger 507 represents an event hook that is part of an operational workflow for workload distribution system 502. …workload distribution system 502 fires an automated event that triggers tagging and/or correlation of multi-source data ( e.g., data 504, 506, and/or 508, as further detailed below) via job completion trigger”); and ● generate and transmit an alert for remediation in response to detecting the one or more unperformed customer-requested executions (see at least Watt figs. 2, 7, ¶0043 “alerts can be generated and output when the percentage ratio of total workloads running versus total failed workloads crosses a given threshold,” ¶0045 “identifying, using the trained machine learning model, one or more workloads with a higher error or failure rate from system norms and/or one or more workloads with higher deviation from system norms, …. alert(s) in connection with the dynamically identified workload(s) outside of system norm(s)”).
Watt teaches all of the above as noted. It teaches, a) an automated system for detecting anomalous execution patterns, b) customer requested executions, c) execution failures lowering execution rates, and d) notification of detected non-performance, but does not explicitly disclose obtain confidence data from the machine learning algorithm, wherein the confidence data includes lower confidence interval values for the corresponding timestamps, and wherein the lower confidence interval values are indicative of lower bounds of expected quantities of the customer-requested executions on the mobile app at the corresponding timestamps, and the trigger interval value.
Wasserstrom also teaches a) an automated system for detecting anomalous execution patterns, b) customer requested executions, c) execution failures lowering execution rates, and d) notification of detected non-performance, and further discloses, wherein the method further comprises:
● obtain confidence data from the machine learning algorithm, wherein the confidence data includes lower confidence interval values for the corresponding timestamps, and wherein the lower confidence interval values are indicative of lower bounds of expected quantities of the customer-requested executions on the mobile app at the corresponding timestamps (see at least Wasserstrom fig.7, c3:30-40 “events statistics can be collected at a periodic interval,” c6:45-50 “prediction hit-rate bounds 512 can track the minimum and maximum prediction hit rates that correspond to a normal program flow,” c7:1-10 “inspection into the program flow (e.g., the monitoring time period) can be accelerated to increase the confidence level that the system may have been compromised”);
● identify a trigger interval value from the lower confidence interval values that corresponds with the trigger timestamp (see at least Wasserstrom figs. 6-7, c5:25-30 “time intervals of a day (e.g., 8 am to 10 am, 10 am to noon, etc.) when the program is executed can be correlated with the execution events,” c7:5-25 “triggering of specific interrupts, and/or utilization levels of execution units ( or any combination thereof) can be used as the execution events. The statistics collected for these execution events can include, for example, the number of times an instruction or sequence of instructions has been executed in a time period; the number of times a line of code is executed in a time period; the number of certain type of interrupt being triggered in a time period; or an average, minimum, and maximum utilization level of an execution unit during a time period”); ● detect an anomaly indicative of one or more unperformed customer-requested executions in response to determining that the trigger index is less than the trigger interval value at the trigger timestamp (see at least Wasserstrom abstract “execution events statistics can be utilized for both training a machine learning model, and later on for making classification inferences to determine whether a program run contains any abnormality,” figs. 5-7).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Watt to include obtain confidence data from the machine learning algorithm, wherein the confidence data includes lower confidence interval values for the corresponding timestamps, and wherein the lower confidence interval values are indicative of lower bounds of expected quantities of the customer-requested executions on the mobile app at the corresponding timestamps, as taught by Wasserstrom since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Wasserstrom in the method of Watt. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Watt et al. in view of Wasserstrom teaches, regardingClaim 2. The automated system of claim 1, further comprising one or more databases configured to store the source data and the confidence data for subsequent retrieval (see at least Watt figs.1-3).Claim 3. The automated system of claim 1, wherein, prior to checking for the one or more unperformed customer-requested executions, the one or more processors are further configured to: ● collect raw source data at a predefined interval (see at least Watt figs.2-5, ¶¶0007-0010, 0039 “information collected on infrastructure on a regular cadence (e.g., CPU usage per 60 second intervals, etc.”); and ● reformat the raw source data into the source data that is usable by the machine learning algorithm (see at least Watt ¶0047 “logs can be converted from a string format to a searchable database JavaScript object notation (j son) with one or more appropriate filters and/or transforms to render the logs more compatible”).Claim 4. The automated system of claim 3, wherein, to reformat the raw source data into the source data, the one or more processors are configured to: ● parse the raw source data to extract a portion with a list of entries having the recorded quantities of the customer-requested executions and the corresponding timestamps (see at least Watt ¶0047 “many logs can typically be verbose, and as such, in one or more embodiments, filters and/or transforms can be applied to logs at intake to render the logs more manageable for further processing. For instance, date-time stamps can be extracted from such logs and/or processing based on keywords in such logs can be performed”); ● convert the list of entries to another data structure that is compatible with the machine learning algorithm (see at least Watt ¶0047 “logs can be converted from a string format to a searchable database JavaScript object notation (j son) with one or more appropriate filters and/or transforms to render the logs more compatible”); ● rename columns with the recorded quantities of the customer-requested executions and the corresponding timestamps to be in accordance with a naming convention of the machine learning algorithm (see at least Watt ¶0039 “trace logs refer to information output from a running workload to known system file locations (e.g., system logging protocol (syslog), standard output (stdout), etc.)”); and ● reformat the timestamps to be in a predefined datetime format (see at least Watt ¶0047 “many logs can typically be verbose, and as such, in one or more embodiments, filters and/or transforms can be applied to logs at intake to render the logs more manageable for further processing. For instance, date-time stamps can be extracted from such logs and/or processing based on keywords in such logs can be performed”).Claim 5. The automated system of claim 1, wherein the one or more processors are configured to generate and transmit a graph that overlays each of the data points of the source data for the recorded quantities of the customer-requested executions onto the confidence data (see at least Watt “dashboards are used to project data using information such as, for example, statistical graphs and charts. Such dashboards may include details about the number of workloads, the total workloads running versus the total failed workloads, a percentage ratio for the same, etc.,” in view of Wasserstrom c3:35-45 “execution events statistics can be collected over back-to-back time periods (e.g., discrete time periods), or over a continuous sliding window of the time period duration such that each time period overlaps with the next”).Claim 6. The automated system of claim 1, wherein the trigger index is a second-to-last entry of the source data (see at least Watt figs. 5-7, ¶0053 “triggers tagging and/or correlation of multi-source data (e.g., data 504, 506, and/or 508, as further detailed below) via job completion trigger 507. Such action(s) can be carried out, for example, by contacting multi-tenant-capable search engine 514 and creating a correlation record between point-in-time data represented in streams 504, 506 and 508 that ties such data together in a searchable way that relates back to the tenant(s) and job-specific information,” ¶0054 “multi-tenant-capable search engine 514 can include a database and/or database management system capable of carrying out cross-table and/or cross-index queries,” ¶0055).Claim 7. The automated system of claim 1, wherein the expected quantities of the customer-requested executions are to fluctuate seasonally, and wherein the confidence data is fit for daily seasonality and weekly seasonality associated with performance of the customer-requested executions on the mobile app to facilitate detection of the anomaly during different seasons (see at least Wasserstrom fig.7, c3:5-10 “program flow can be characterized by fluctuations of utilization levels of the various execution units of a computing system,” c3:35-45 “execution events statistics can be collected at a periodic interval, for example, every time period of 1 second, 5 seconds, or 10 seconds, etc. The execution events statistics can be collected over back-to-back time periods (e.g., discrete time periods), or over a continuous sliding window of the time period”).Claim 8. The automated system of claim 1, wherein the machine learning algorithm includes a nonparametric regression model (see at least Watt ¶0046 “machine learning models (such as detailed, for example, in connection with step 208 above), one or more embodiments include using shallow learning techniques and deep learning techniques. Using a shallow learning technique, an example embodiment can include utilizing principal component analysis (PCA) to reduce dimensionality and determine which parameter(s) is/are important for training. After the PCA is completed, such an embodiment can include utilizing a multivariate anomaly detection algorithm using at least one distance classifier (e.g., a Mahalanobis distance classifier). Additionally or alternatively, in a complex data center context, at least one embodiment includes using one or more deep learning techniques for anomaly detection. Using one or more deep learning techniques, such an embodiment can include leveraging an artificial neural network (ANN) (e.g., an autoencoder),” in view of Wasserstrom c2:1-15 “training a machine learning model (e.g., neural network model, linear regression model, etc.),” c8:30-40 “Process 700 may begin at block 702 by training a machine learning model (e.g., neural network model, linear regression model, etc.) using execution events statistics of a software program running on a computing system”).Claim 9. The automated system of claim 8, wherein the nonparametric regression model is an additive regression model (see at least Watt ¶0046 “machine learning models (such as detailed, for example, in connection with step 208 above), one or more embodiments include using shallow learning techniques and deep learning techniques. Using a shallow learning technique, an example embodiment can include utilizing principal component analysis (PCA) to reduce dimensionality and determine which parameter(s) is/are important for training. After the PCA is completed, such an embodiment can include utilizing a multivariate anomaly detection algorithm using at least one distance classifier (e.g., a Mahalanobis distance classifier). Additionally or alternatively, in a complex data center context, at least one embodiment includes using one or more deep learning techniques for anomaly detection. Using one or more deep learning techniques, such an embodiment can include leveraging an artificial neural network (ANN) (e.g., an autoencoder),” in view of Wasserstrom c2:1-15 “training a machine learning model (e.g., neural network model, linear regression model, etc.),” c8:30-40 “Process 700 may begin at block 702 by training a machine learning model (e.g., neural network model, linear regression model, etc.) using execution events statistics of a software program running on a computing system”).Claim 10. The automated system of claim 1, wherein the confidence data further includes a mean value and an upper confidence interval value for each of the timestamps (see at least Wasserstrom figs.5, c6:25-50 “BST 500 also includes a prediction hit-rate 508, a prediction hit-rate mean 510, and a prediction hit-rate bounds 512. … Prediction hit-rate mean 510 can be a running average of the prediction hit-rates 508 over time, and prediction hit-rate bounds 512 can track the minimum and maximum prediction hit rates that correspond to a normal program flow”).
Pertaining to method and computer readable medium claims 21-25 and 26-30 respectively
Rejection of claims 21-30 is based on the same rationale noted above. In addition Watt teaches, regarding
Claim 26. A non-transitory computer readable medium including instructions (see at least Watt ¶0029 “One or more embodiments include articles of manufacture, such as computer-readable storage media”), which, when executed, cause a machine to perform the method addressed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Lin, CN 109547426 A: teaches service response when verification fails, determined by authentication failure time reaching a pre-set threshold of time of not responding to the service request by verifying the accuracy of judging the low reliability of service response and when second authentication is successful responding to the service request.
● Viclizki et al., Patent No.: US 12,055,995 B2: teaches prediction of an error or anomaly in processing units using trained machine learning models that each output the probability of an error occurring within a predetermined time period.
● Kalamkar et al., Patent No.: US 12,547,933 B2: teaches using metrics including time series data to detect an anomaly for an asset and training models to determine that a metric is anomalous. Includes both an additive regressive model and seasonality in the time series data.
● Martin et al., Patent No.: US 12,332,971 B2: teaches using time series data to train an ML model to predict execution errors and proactively issue service requests in response to predicted errors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 September 5, 2026
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