Final Rejection
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 .
Claims 1-20 are rejected under 35 U.S.C. 101
Claims 1-6, 8, 12-15, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2)
Claims 7, 9-11, 16, and 18-19 are rejected under 35 U.S.C. 103
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the Republic of India on May 29th, 2024. It is noted, however, that applicant has not filed a certified copy of the 202411041602 application as required by 37 CFR 1.55.
Response to Amendment
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite mental processes and mathematics. This judicial exception is not integrated into a practical application because the claims generally link abstract ideas to a generic computer and perform mere data gathering in relation to the abstract ideas. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they include mere instructions to perform abstract ideas on a generic computer.
The claimed invention collects data and analyzes it to create patterns. The analysis is done at a high level by a generic computer algorithm. The invention recites a generic solution to a generic problem, as the algorithm itself is not tied to any specific issue, and the only limitations that tie the invention to a particular problem recite the type and source of data to be used and manipulated. This is also specified in Paragraphs 0087 and 00121 of Applicant’s specification, which describe analysis related to various data and other fields.
Claim 1
Step 2A Prong 1: Identification of Abstract Ideas
Claim 1 recites:
analyzing (MPEP 2106.04(a)(2)(III)(A), judgments, particularly “collecting information, analyzing it…” and identifying patterns are mental processes) …
… to identify/identifying (MPEP 2106.04(a)(2)(III)(A), “observations, evaluations, judgments, and opinions,” and identifying patterns are mental processes) …
correlating (MPEP 2106.04(a)(2)(III)(A), “observations, evaluations, judgments, and opinions,” and identifying patterns are mental processes) …
and predicting (MPEP 2106.04(a)(2)(III)(A), “evaluations, judgments, and opinions,” and identifying patterns are mental processes) …
Step 2A Prong 2: Identification of Additional Elements
Claim 1 recites:
… via [the] at least one processor (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) …
receiving (MPEP 2106.05(g), mere data gathering is considered insignificant extra-solution activity; MPEP 2106.05(f)(2), “using a computer in its ordinary capacity … e.g. to receive, store, or transmit data … does not integrate a judicial exception into a practical application”), … a historic data from one or more sources for a predefined time period, wherein the historic data corresponds to a historical open platform communications (OPC) data from the one or more sources and an input data from at least one OPC client (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity; MPEP 2106.05(h)(vi), limiting the data collection and analysis to a particular field of use does not integrate the abstract idea into a practical application);
… the historic data (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity) …
… using [the] one or more artificial intelligence/machine learning (AI/ML) models (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) … one or more events in the historic data (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
… one or more patterns associated with the identified one or more events (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity) …
… one or more root causes associated with each of the one or more patterns identified (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity) …
the identified one or more patterns with the identified one or more root causes (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
generating, …, a database (MPEP 2106.05(g), mere data gathering is considered insignificant extra-solution activity; MPEP 2106.05(f)(2), “using a computer in its ordinary capacity … e.g. to receive, store, or transmit data … does not integrate a judicial exception into a practical application”; MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) comprising the correlated one or more patterns and the one or more root causes (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
one or more anomalies and faults associated with the historic data, based at least on the correlation (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
rendering, …, a user interface comprising a patterns block, a root cause block, a recommendation field, and a real-time status field, wherein the user interface is configured to display the one or more patterns via the patterns block and display the one or more root causes via the root cause block (MPEP 2106.05(g), the display and output of data is considered insignificant extra-solution activity; MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity);
and initiating, …, performance of: (i) one or more control and optimization actions for one or more industrial processes or equipment associated with at least one of the one or more sources based on the database comprising the correlated one or more patterns and one or more root causes or (ii) one or more mitigation actions configured to prevent recurrence of the one or more anomalies and faults (MPEP 2106.05(d), well-understood, routine, and conventional activity does not integrate an abstract idea into a practical application; MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application).
It is well-understood, routine, and conventional to take action to control, mitigate, or optimize in relation to root cause analysis of a fault, as shown by the following:
Mdini et al., "Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks," in IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 547-561, March 2020
Soualhia et al., "Automated Traces-based Anomaly Detection and Root Cause Analysis in Cloud Platforms," 2022 IEEE International Conference on Cloud Engineering (IC2E), CA, USA, 2022, pp. 253-260
Vo et al., "Root-Cause Problem Solving in an Industry 4.0 Context," in IEEE Engineering Management Review, vol. 48, no. 1, pp. 48-56, March 2020
Step 2B: Significantly More Analysis
The additional elements of the claim do not integrate the abstract ideas into a practical application. The claims simply state mental processes with mere instructions to perform these abstract ideas on a generic computer (MPEP 2106.05(f)(3)). The computer is cited at such a high level of generality that it cannot be determined to be a particular machine (MPEP 2106.05(b)) and is simply linking the judicial exception to a particular technology (MPEP 2106.05(h)). The claim recites only the idea of a solution, but fails to recite details as to how the solution to the problem is accomplished, because it leaves a majority of the analysis to the generic computer (MPEP 2106.05(f)(1)).
Claims 12 and 20
Independent claims 12 and 20 repeat the limitations of Claim 1 while reciting additional generic hardware of the generic computer upon which the abstract ideas are applied (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application).
Claims 2-5, 8-10, 13-14, and 17-19
Claims 2-5, 8-10, 13-14, and 17-19 recite limitations further specifying the type of data or source to be manipulated (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity). Claims 5 and 14 also further specify the field of use (MPEP 2106.05(h)(vi), limiting the data collection and analysis to a particular field of use does not integrate the abstract idea into a practical application).
Claims 6 and 15
Claims 6 and 15 recite that the model used to apply the abstract ideas on the computer (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) may comprise mathematics (MPEP 2106.04(a)(2)(I), mathematical relationships, formulas/equations, and calculations are abstract ideas), such as XGBoost or a random forest, or a mental process such as making an analysis based on rules or observable data (MPEP 2106.04(a)(2)(III)(A), “observations, evaluations, judgments, and opinions,” and identifying patterns are mental processes).
Claim 7 and 16
Claims 7 and 16 recite:
wherein the NLP (the ability to process natural language in a human-like manner is seen as a mental process with respect to MPEP 2106.04(a)(2)(III)(A)) model is configured to (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application) associate (MPEP 2106.04(a)(2)(III)(A), “observations, evaluations, judgments, and opinions,” and identifying patterns are mental processes) one or more log messages from the historic data with one or more issues associated with the one or more zones based at least on the analysis of the historic data, wherein the one or more issues comprise at least one of an unauthorized action, a resource access, a file modification, and a process creation (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity). Please note that although a specific model is selected, NLP and other machine learning models are capable of providing solutions for a large number of problems, and thus the limitation is still seen as a generic solution upon a generic computer. Additionally, the claims use the computer as a tool to perform the existing process of detecting an issue by analyzing data.
Claim 11
Claim 11 recites:
storing (MPEP 2106.05(g), mere data gathering is considered insignificant extra-solution activity; MPEP 2106.05(f)(2), “using a computer in its ordinary capacity … e.g. to receive, store, or transmit data … does not integrate a judicial exception into a practical application”), via the at least one processor (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application), the correlated one or more patterns with the one or more root causes (MPEP 2106.05(g), “selecting a particular data source or type of data to be manipulated” is considered insignificant extra-solution activity) in a memory communicatively coupled to the at least one processor (MPEP 2106.05(f), mere instructions to apply an abstract idea on a generic computer is not enough to integrate the claim into a practical application).
Claim Interpretation
Multiple claims use “/”. This is interpreted by the examiner as an “or”. The claims should be revised if this differs from the intended interpretation.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-6, 8, 12-15, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Sayyarrodsari et al. (U.S. Publication No. 2021/0096551 A1), hereinafter referred to as Sayyarrodsari.
With regards to Claim 1, Sayyarrodsari teaches:
A method comprising:
receiving, via at least one processor (Paragraph 0004; Paragraphs 0030 and 0057), a historic data from one or more sources (Paragraphs 0040-0041, data historians and industrial devices and controllers) for a predefined time period (Paragraph 0038), wherein the historic data corresponds to a historical open platform communications (OPC) data from the one or more sources (Paragraph 0085, OPC data) and an input data from at least one OPC client (Paragraphs 0048 and 0056, specifies which types of data are to be analyzed);
analyzing, via the at least one processor, the historic data using one or more artificial intelligence/machine learning (AI/ML) models to identify one or more events in the historic data (Paragraph 0042);
identifying, via the at least one processor, one or more patterns associated with the identified one or more events using the one or more AI/ML models (Paragraphs 0042 and 0086);
identifying, via the at least one processor, one or more root causes associated with each of the one or more patterns identified using the one or more AI/ML models (Paragraphs 0042 and 0086);
correlating, via the at least one processor, the identified one or more patterns with the identified one or more root causes (Paragraphs 0086 and 0117);
generating, via the at least one processor, a database comprising the correlated one or more patterns and the one or more root causes (Paragraphs 0086, 0108, and 0117-0118);
predicting, via the at least one processor, one or more anomalies and faults associated with the historic data, based at least on the correlation (Paragraph 0117);
rendering, via the at least one processor, a user interface comprising a patterns block, a root cause block, a recommendation field, and a real-time status field, wherein the user interface is configured to display the one or more patterns via the patterns block and display the one or more root causes via the root cause block (Paragraphs 0119, 0039, 0056, 0122, 0112, 0109, 0115, and 0097);
and initiating, via the at least one processor, performance of: (i) one or more control and optimization actions for one or more industrial processes or equipment associated with at least one of the one or more sources based on the database comprising the correlated one or more patterns and one or more root causes or (ii) one or more mitigation actions configured to prevent recurrence of the one or more anomalies and faults (Paragraphs 0048 and 0119).
With regards to Claim 2, Sayyarrodsari teaches the method of Claim 1 as referenced above. Sayyarrodsari further teaches:
wherein the one or more sources comprise at least one of a scale, a remote terminal unit (RTU), a distributed control system (DCS), a programmable logic controller (PLC), or an analyzer (Paragraph 0037).
With regards to Claim 3, Sayyarrodsari teaches the method of Claim 1 as referenced above. Sayyarrodsari further teaches:
wherein the predefined time period comprises at least one of a day, time, season, months, or years (Paragraph 0038; Paragraph 0116).
With regards to Claim 4, Sayyarrodsari teaches the method of Claim 1 as referenced above. Sayyarrodsari further teaches:
wherein the historical OPC data comprise at least one of the one or more events, one or more error messages, one or more keywords, or one or more log messages associated with one or more zones (Paragraphs 0066 and 0044, various zones, such as gas and oil, automotive, and power generation; Paragraph 0116, zones such as Industrial IoT; Paragraph 0086; Paragraph 0038; Paragraph 0131; Paragraph 0089), and wherein the input data comprises at least one of an input request from the OPC client corresponding to reading and/or writing the historical OPC data (Paragraphs 0048 and 0056, specifies which types of data are to be analyzed).
With regards to Claim 5, Sayyarrodsari teaches the method of Claim 4 as referenced above. Sayyarrodsari further teaches:
wherein the one or more zones comprise at least one of a manufacturing plant, a power generation facility, an oil and gas refinery, a smart grid, or a transportation system of an industrial control system/Industrial Internet of Things (ICS/ IIoT) environment (Paragraphs 0066 and 0044, various zones, such as gas and oil, automotive, and power generation; Paragraph 0116, zones such as Industrial IoT).
With regards to Claim 6, Sayyarrodsari teaches the method of Claim 4 as referenced above. Sayyarrodsari further teaches:
training, via the at least one processor, the one or more AI/ML models using one or more AI/ML techniques, based at least on the received historic data (Paragraphs 0040-0043, data used in AI/ML model) wherein the one or more AI/ML techniques comprise at least one of a supervised learning, an unsupervised learning, a rule based AI model, a natural language processing (NLP) model, an AI keyword search, a random forest, an eXtreme Gradient Boosting (XGBoost), or an ensembling technique (Paragraph 0071, rules submitted by user used in analytic model; Paragraph 0086, clustering).
With regards to Claim 8, Sayyarrodsari teaches the method of Claim 1 as referenced above. Sayyarrodsari further teaches:
wherein the one or more events comprise at least one of communication lost with controller, access to remote server, station failure, calibration error, calibration cleared, channel hardware failure, configuration changed, device firmware mismatch, firmware downgraded, device duplicate address, rogue node connected, over temperature alert, sensor alert, short circuit detected, abrupt shutdown, parameter access lock changed, or controller CPU 90 percent (%) (Paragraph 0083, various alerts; Paragraph 0086, temperature event; Paragraph 0149, temperature alert triggered).
With regards to Claim 12, Sayyarrodsari teaches:
A system comprising:
a memory (Paragraph 0057);
and at least one processor communicatively coupled to the memory, wherein the at least one processor is configured to (Paragraph 0057): …
Please see the above rejection of Claim 1 for citations of the remaining limitations of Claim 12.
With regards to Claim 13, Sayyarrodsari teaches the system of Claim 12 as referenced above. Sayyarrodsari further teaches the limitations of Claim 13. Please see the above rejections of Claims 2 and 3 regarding these limitations.
With regards to Claim 14, Sayyarrodsari teaches the system of Claim 12 as referenced above. Sayyarrodsari further teaches the limitations of Claim 14. Please see the above rejections of Claims 4 and 5 regarding these limitations.
With regards to Claim 15, Sayyarrodsari teaches the system of Claim 14 as referenced above. Sayyarrodsari further teaches the limitations of Claim 15. Please see the above rejection of Claim 6 regarding these limitations.
With regards to Claim 17, Sayyarrodsari teaches the system of Claim 12 as referenced above. Sayyarrodsari further teaches the limitations of Claim 17. Please see the above rejection of Claim 8 regarding these limitations.
With regards to Claim 20, Sayyarrodsari teaches:
A non-transitory machine-readable information storage medium comprising one or more instructions which when executed by at least one processor cause the at least one processor to (Paragraph 0057; Paragraph 0170): …
Please see the above rejection of Claim 1 for citations of the remaining limitations of Claim 20.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 7, 9-10, 16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sayyarrodsari in view of Mishra et al. (I.N. Patent Publication No. 2023/11064273, as attached by Applicant with the IDS), hereinafter referred to as Mishra.
With regards to Claim 7, Sayyarrodsari teaches the method of Claim 6 as cited above. Sayyarrodsari further teaches:
wherein the … model is configured to associate one or more log messages from the historic data (Paragraph 0038, log) with one or more issues associated with the one or more zones (Paragraph 0066, zones) based at least on the analysis of the historic data (Paragraphs 0086 and 0101, cluster data based on type of industrial device) …
Mishra teaches the following limitations not explicitly taught by Sayyarrodsari:
… NLP (Paragraph 0033) … wherein the one or more issues comprise at least one of an unauthorized action, a resource access, a file modification, and a process creation (Paragraph 0061, failed authentication).
Therefore, it would have been obvious to one of ordinary skill in the art in which said subject matter pertains to, prior to the effective filing date of the claimed invention, add security issues to the types of issues that can be analyzed by the method of Sayyarrodsari, as taught by Mishra. Since Sayyarrodsari already teaches that security features, including those that limit access of certain data to authorized users only (Paragraph 0179; Paragraph 0162), by expanding the method’s analysis of issues to include security issues such as fraudulent logins, the system’s security features will become more robust by being able to handle issues with said security system that may arise (Mishra, Paragraph 0066).
With regards to Claim 9, Sayyarrodsari teaches the method of Claim 1 as cited above.
Mishra teaches the following limitations not explicitly taught by Sayyarrodsari:
wherein the one or more patterns comprise at least one of too many login failure event, an unauthorized elevated privilege event, a firmware version changed/downgraded event, a device index change event, or an erase master boot records and clear logs, backup and restore service stopped event (Paragraph 0061, multiple failed authentication).
Therefore, it would have been obvious to one of ordinary skill in the art in which said subject matter pertains to, prior to the effective filing date of the claimed invention, add security patterns to the types of patterns that can be analyzed, by the method of Sayyarrodsari as taught by Mishra. Since Sayyarrodsari already teaches that security features, including those that limit access of certain data to authorized users only (Paragraph 0179; Paragraph 0162), by expanding the method’s analysis of patterns to include security issues such as fraudulent logins, the system’s security features will become more robust by being able to handle issues with said security system that may arise (Mishra, Paragraph 0066).
With regards to Claim 10, Sayyarrodsari teaches the method of Claim 1 as cited above.
Mishra teaches the following limitations not explicitly taught by Sayyarrodsari:
wherein the one or more root causes comprise at least one of an unauthorized access, a privilege escalation, an unauthorized user/attacker trying to take advantage of vulnerable firmware, a possibility of intrusion/malware attack, or an intrusion and possibility of ransomware trying to stop backup (Paragraphs 0061-0064, assumes fraudulent/id theft activity if too many failed authentication/login attempts).
Therefore, it would have been obvious to one of ordinary skill in the art in which said subject matter pertains to, prior to the effective filing date of the claimed invention, add security issues to the types of issues that can be analyzed by the method of Sayyarrodsari, as taught by Mishra. Since Sayyarrodsari already teaches that security features, including those that limit access of certain data to authorized users only (Paragraph 0179; Paragraph 0162), by expanding the method’s analysis of issues to include security issues such as fraudulent logins, the system’s security features will become more robust by being able to handle issues with said security system that may arise (Mishra, Paragraph 0066).
With regards to Claim 16, Sayyarrodsari teaches the system of Claim 15 as referenced above. Sayyarrodsari in view of Mishra further teaches the limitations of Claim 16. Please see the above rejection of Claim 7 regarding these limitations, as well as the motivation to combine references in accordance with 35 U.S.C. 103.
With regards to Claim 18, Sayyarrodsari teaches the system of Claim 12 as referenced above. Sayyarrodsari in view of Mishra further teaches the limitations of Claim 18. Please see the above rejection of Claim 9 regarding these limitations, as well as the motivation to combine references in accordance with 35 U.S.C. 103.
With regards to Claim 19, Sayyarrodsari teaches the system of Claim 12 as referenced above. Sayyarrodsari in view of Mishra further teaches the limitations of Claim 19. Please see the above rejection of Claim 10 regarding these limitations, as well as the motivation to combine references in accordance with 35 U.S.C. 103.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Sayyarrodsari in view of Ramanujan et al. (U.S. Patent Publication No. 2022/0066852 A1), hereinafter referred to as Ramanujan.
With regards to Claim 11, Sayyarrodsari teaches the method of Claim 11 as referenced above. Sayyarrodsari further teaches:
storing, via the at least one processor, the correlated one or more patterns … in a memory communicatively coupled to the at least one processor (Paragraph 0074, store contextualized data; Paragraph 0060, store templates; Paragraph 0057, memory and processor). Note that Sayyarrodsari teaches the identification of root causes in Paragraph 0117, but not the explicit storing of the root causes with the pattern.
However, Ramanujan teaches:
storing, … the correlated one or more patterns with the one or more root causes (Paragraphs 0005 and 0021-0023, old tickets associated with a root cause can be stored for later analysis)
Therefore, it would have been obvious to one of ordinary skill in the art in which said subject matter pertains to, prior to the effective filing date of the claimed invention, store the patterns with the root causes in the method of Sayyarrodsari in order to improve future analysis as taught by Ramanujan (Paragraphs 0021-0023).
Response to Arguments
Applicant's arguments filed January 8th, 2026 have been fully considered but they are not persuasive.
The objection to Claim 4 is withdrawn due to the amendment.
Applicant argues that the claims should not be rejected under 35 U.S.C. 101. Examiner respectfully disagrees. As cited and analyzed above, additional elements are not the same as mental processes, and thus do not need to be capable of being done in the mind.
Applicant further argues that the claims recite an inventive concept and an improvement to technology. Examiner respectfully disagrees. Applicant argues that the claims are directed towards improving fault detection. The claims recite fault prediction based off of a general analysis and pattern correlation and identification, and the improvement is of the mental processes of analysis, identification, and correlation (Paragraph 00120). According to MPEP 2106.04(a)(II), an improvement to an abstract idea is still considered an abstract idea. Additionally, the invention is not specifically geared to fixing any one technological issue, and instead is described as general data analysis that can be applied to several fields and types of data (Paragraphs 0003-0004, 0087, and 00121; Please also see MPEP 2106.05(a)(II) MPEP 2106.05(h), and MPEP 2106.05(g)). Please see the above rejection and MPEP 2106 for further details.
Regarding arguments related to newly amended features not being taught by the art, Examiner respectfully disagrees. New citations have been added in the above rejections to show where these features are taught.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/G.K.S./Examiner, Art Unit 2113 /BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113