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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 23, 2026 has been entered.
Claim Status
Claims 1-20 were previously pending and subject to a final rejection dated November 24, 2025. In the RCE, submitted on March 23, 2026, claims 1 and 19-20 were amended. Therefore, claims 1-20 are currently pending and subject to the following final rejection.
Response to Arguments
Applicant’s Remarks on Pages 12-17 of the RCE, regarding the previous rejections of the claims under 35 U.S.C. 101 have been fully considered but are not found persuasive.
On Page 12 of RCE, Applicant states “amended claim 1 recites a specific computer-implemented architecture and control technique that uses a machine learning model to govern construction and dynamic modification of a connection table data structure in a manner that reduces memory storage requirements and processing load imposed on processing hardware. Even assuming, arguendo, that certain aspects of amended claim 1 could be characterized as involving mathematical relationships or demand forecasting, the claim as a whole integrates any alleged judicial exception into a practical application.”
Examiner respectfully disagrees and notes that Para. [0142] of the Specification discloses that “preemptive setting of limits may reduce amount of output for reduced memory storage requirement and processing load imposed on processing hardware for more efficient utilization of computing resources. According to further aspects, such code may provide an absolute upper limit for connecting times. In an example, the limit values may be hard-coded. However, aspects of the present disclosure are not limited thereto, such that the limit values may be a variable value that may be adjusted or updated based on a machine learning model or based on monitoring of flight schedules.” That is, the reduction in “memory storage requirements and processing load imposed on processing hardware” is result of a “reduce[d] amount” of data to process, rather than in a practical application of the machine learning model itself.
On Pages 12-13 of the Response, in arguing that the claims recite a specific technical implementation and not mere “apply it” computer usage, Applicant states “claim 1 now recites more than the use of a generic processor to perform demand forecasting. In particular, claim 1 recites generating, in memory, a connection table data structure configured to store connection records corresponding to candidate itineraries; storing one or more limit values, including a variable upper limit value for total connecting time, where the variable upper limit value is adjusted or updated based on a machine learning model; generating exception rules via the machine learning model through iterative processing of input data; inserting connection records into the connection table data structure only when those records comply with air service restriction limits, comply with the machine learning generated exception rules, and have a total connecting time within the variable upper limit value; and, in response to updating the variable upper limit value or the exception rules, dynamically modifying the connection table data structure by deleting stored connection records that no longer comply. These limitations define a specific software/data structure mechanism by which the machine learning model is used to control what data is admitted to, retained in, and deleted from the connection table data structure. The amended claim therefore no longer recites only abstract forecasting logic plus generic computer implementation. Instead, the claim recites a particular technical solution in which machine learning generated controls are applied to a working in-memory data structure to constrain combinatorial growth and to reduce downstream computer burdens.”
Examiner respectfully disagrees and notes “generating… a connection table…configured to store connection records corresponding to candidate itineraries; storing one or more limit values, including a variable upper limit value for total connecting time, where the variable upper limit value is adjusted or updated… generating exception rules … through iterative processing of input data; inserting connection records into the connection table…only when those records comply with air service restriction limits, comply with the…generated exception rules, and have a total connecting time within the variable upper limit value; and, in response to updating the variable upper limit value or the exception rules, dynamically modifying the connection table….by deleting stored connection records that no longer comply” are limitations that recite the abstract idea of a certain method of organizing human activity (e.g., commercial interaction).
The high-level recitation of a connection table data structure (See Figs. 7A-7B; and Paras. 140-142) and a machine learning model (See Para. 14) amount to “apply it.” (See MPEP 2106.05(f)). For example, nothing in the claims explains how the machine learning generates “controls….to constrain combinatorial growth” beyond reciting “updating…the variable upper limit value is adjusted or updated based on a machine learning model” and that “at least one of the variable upper limit value or the one or more exception rules based on an output of the machine learning model”. That is, the claims and specification fail to disclose details of how the machine learning functions to adjust or update the variable upper limit value, and how the machine learning functions to update at least one of the variable upper limit value or the one or more exception rules based on an output of the machine learning model, such that “the claim recites a particular technical solution in which machine learning” functions. Thus, Applicant’s arguments are not found persuasive.
On Page 13 of the Response, in arguing that “The Specification Expressly Describes the Claimed Technical Problem and Technical Solution”, Applicant states “Paragraph 3 of the Specification explains that existing forecasting approaches may impose burdens on airline computing systems, including significant resource burdens on aging airline systems, and that a more tailored approach is desirable. Thus, the application does not frame the invention merely as a business prediction, but as an approach that addresses technical burdens arising in computerized airline-demand processing.”
Examiner respectfully disagrees and notes that Paragraph [0003] does not address the technical problems with the load/data processing capabilities of aa computing system. Rather, the cited paragraph addresses the need for “a more tailored prediction of flight demand per airline and per flight” and not different processing/load capabilities of a computing system.
On Pages 13-14 of the RCE, Applicant further argues “Paragraph 142 of the Specification is particularly significant. It states that code may be utilized ‘to limit output in the connection table,’ and that preemptive setting of limits may reduce the amount of output for reduced memory storage requirement and processing load imposed on processing hardware for more efficient utilization of computing resources. Paragraph 142 further explains that such code may provide an upper limit for connecting time and, importantly, that the limit values may be variable values that are adjusted or updated based on a machine learning model. Thus, the present amendment directly tracks the disclosed technical mechanism of using machine learning updated limit values to manage the contents of the connection table. Paragraph 144 of the Specification provides additional direct support for the amended claim language. That paragraph explains that exception rules may be built to ensure connections are valid and to limit the number of combinations that are to be built, and further explains that such exception rules may be generated by a machine learning model via iterative processing of input data and updated periodically or in real time to add, adjust, or delete rules. This is precisely the role assigned to the exception rules in amended claim 1.”
Examiner respectfully disagrees and as discussed above, the cited portions of the specification fail to disclose details of how the machine learning updates limit values to manage the contents of the connection table, or how the machine learning model generates exception rules by iterative processing of input data. Furthermore, as noted in Recentive Analytics “the requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.” Therefore, the high-level recitation of “a machine learning mode” in both the claims and specification fails to disclose a “Technical Problem and Technical Solution” as alleged.
On Page 14 of the RCE, Applicant further argues “Paragraph 128 explains that rules for avoiding invalid connections may be set or modified via a machine learning algorithm and that, through multiple iterations, the model may recognize which rules result in invalid connections and autonomously remove such rules. That disclosure directly supports the amended claim language requiring machine-learning-generated exception rules that are iteratively updated and that control the insertion and retention of connection records.”
Examiner notes that a careful reading of Paragraph 128 states that “the rules applied may be set or modified manually or via a machine learning algorithm.” That is, nothing in Paragraph 128 describes the use of the machine learning algorithm to recognize which set rule results in creation of an invalid connection and therefore remove such rules, beyond amounting to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)). See also Credit Acceptance Corp. v. Westlake, “Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase” does not show an improvement in computer-functionality.
On Page 14 of the Response, Applicant argues “Finally, paragraphs 122 and 123 explain that the machine learning model may be updated based on the determined flight share information and agency gap values, and that the updated model may be used to predict the number of seats expected and the corresponding aircraft type for a target segment. Thus, the Specification ties the machine learning-driven control of the connection building subsystem to the ultimate predictive output of the claimed system.”
Examiner respectfully disagrees and as discussed above, the cited portions of the specification fail to disclose details of how the machine learning model is updated – such that the machine learning model being updated would amount to more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
On Page 14 of the RCE, in arguing that “Amended Claim 1 Integrates Any Alleged Judicial Exception Into a Practical Application”, Applicant argues “claim 1 integrates any alleged judicial exception into a practical application. The claim does so by applying machine learning to control operation of a specific in-memory data structure in a way that reduces memory and processing burdens. The machine learning model in amended claim 1 is not merely invoked as an abstract analytical tool or a field-of-use label. Rather, outputs of the machine learning model are used to generate and update the variable upper limit value and exception rules that determine whether connection records are stored in the connection table data structure and whether previously stored connection records are deleted from that data structure. This is a concrete technical application. The amended claim requires a dynamic, machine learning-driven modification of stored data records within a specific working data structure. That is a meaningful limit on the claim and reflects an improvement in the manner in which the computer system constructs, stores, and manages candidate-itinerary connection data. As explained in paragraph 142 of the Specification, this control reduces memory storage requirements and processing load imposed on processing hardware.”
Examiner respectfully disagrees for the reasons discussed above. Furthermore, as reiterated above, the claims and specification fail to disclose how the “dynamic, machine learning-driven modification of stored data” occurs beyond merely claiming the “wherein the variable upper limit value is adjusted or updated based on a machine learning model”, “the one or more exception rules are generated by the machine learning model via iterative processing of…data” and “updating…at least one of the variable upper limit value or the one or more exception rules based on an output of the machine learning model.” As discussed in MPEP 2106.04(d)(1), “a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art… the examiner should not determine that the claim improves technology or a technical field.” Thus, Applicant’s arguments are not found persuasive.
On Page 15 of the RCE, in arguing that “The Amended Claim is Consistent with the Reasoning of Ex Parte Desjardins”, Applicant states “Similar to the claims found eligible in Desjardins, amended claim 1 does not merely recite an abstract mathematical model running on generic hardware. Instead, the claim recites a specific machine learning-driven improvement in computer operation. In particular, the amended claim uses machine learning to generate and update control parameters and rules that govern the contents of a working data structure and to dynamically delete stored records from that data structure when updated machine learning outputs indicate those records should no longer be retained. That is analogous to the type of machine learning- based technical improvement recognized in Desjardins, where the focus was not merely on use of machine learning in the abstract, but on how the claimed machine learning implementation improved the functioning of the computer system itself. Here, the claimed machine learning model improves operation of the connection building subsystem by limiting the number of stored combinations and reducing memory storage and processing complexity, as expressly described in paragraphs 128, 142, 144 of the Specification.”
Examiner respectfully disagrees and notes in Desjardins, the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the Appeals Review Panel evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Here, as discussed at length above, the specification does not identify improvements as to how the machine learning model itself operates such that the present claims and specification are analogous to those in Desjardins. Thus, Applicant’s arguments are not found persuasive.
On Page 15 of the RCE, in arguing that “The Amended Claim Also Parallels USPTO Eligibility Examples Involving Technical Data Reduction and System Control”, Applicant argues “Amended claim 1 is also consistent with USPTO eligibility examples recognizing that claims are eligible when they use defined criteria to control what data is collected, stored, or processed in order to improve system performance. For example, amended claim 1 parallels USPTO Subject Matter Eligibility Example 40 (Adaptive Monitoring of Network Traffic Data), which recognizes eligibility where defined criteria are used to control what data is collected, stored, or processed in order to improve system performance. Here, the claim uses machine learning-generated rules and machine learning-updated limit values to determine which connection records may be inserted into the connection table data structure and which stored records should be deleted from that structure. In this way, the amended claim controls the size and content of the working data structure itself, thereby reducing system burdens. This is not insignificant extra solution activity but instead is the core technical mechanism by which the claimed system operates more efficiently.”
Examiner respectfully disagrees and notes, eligible claim 1 of Example 40, limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. Here, nothing in the claims or specification recites a similar technical improvement. Rather, “when the increased speed comes solely from the capabilities of a general-purpose computer” (See FairWarning) it is not sufficient to show an improvement in computer-functionality. Thus, for the reasons discussed above and herein, Applicant’s arguments are not found persuasive.
Lastly on Page 16 of the RCE, in arguing “In the Alternative, Amended Claim 1 Recites Significantly More Than Any Alleged Abstract Idea”, Applicant states “Even if the Office were to maintain that amended claim 1 is ‘directed to’ an abstract idea at Step 2A, the amended claim nonetheless recites significantly more under Step 2B. The claim does not merely recite generic computer components performing generic storage and processing. Instead, it recites a particular arrangement and use of a connection table data structure, machine learning-generated exception rules, machine learning-updated variable upper limit values, and dynamic deletion of stored connection records in response to updated machine learning outputs. These limitations amount to a specific, non-generic technological implementation that improves computer operation by limiting the number of records retained and processed. The Specification expressly identifies these technical benefits in paragraph 142, and further supports the machine learning-based generation and updating of rules in paragraphs 128 and 144. For at least the reasons set forth above, amended claim 1 is not directed merely to an abstract idea. Rather, the claim recites a specific machine learning-driven, data structure-based technical solution that integrates any alleged judicial exception into a practical application and improves the operation of the computer system itself.”
Examiner respectfully disagrees and notes it is unclear what “particular arrangement and use of a connection table data structure, machine learning-generated exception rules, machine learning-updated variable upper limit values, and dynamic deletion of stored connection records in response to updated machine learning outputs” is being claimed beyond the high-level recitation of “wherein the variable upper limit value is adjusted or updated based on a machine learning model”, “the one or more exception rules are generated by the machine learning model via iterative processing of…data” and “updating…at least one of the variable upper limit value or the one or more exception rules based on an output of the machine learning model”. As noted in Recentive Analytics, claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Lastly, as discussed above, the “improve[ment in] computer operation” is result of a “reduce[d] amount” of data to process, rather than in an improvement of the computer itself. Thus, Applicant’s arguments are not found persuasive for the reasons discussed above and herein.
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 an abstract idea without significantly more.
Step 1
Claims 1-18 are directed to a method (i.e., a process), claim 19 is directed to a system comprising a processor, and claim 20 is directed to a non-transitory computer readable medium (i.e., a machine). Therefore the claims all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claims 1, 19 and 20 recite a series of steps/functions of: acquiring and aggregating raw data; parsing the acquired raw data and identifying and aggregating a plurality of segments of location pairs; generating a connection table configured to store connection records corresponding to candidate itineraries derived from the plurality of segments of location pairs; storing one or more limit values utilized to limit output in the connection table, including a variable upper limit value for total connecting time, wherein the variable upper limit value is adjusted or updated based on a first model; prior to inserting connection records, generating one or more exception rules configured to ensure that connections are valid and to limit a number of combinations that are to be built, wherein the one or more exception rules are generated by the first model via iterative processing of input data, with the one or more exception rules being updated periodically to add new exception rules, adjust existing exception rules, or delete outdated exception rules; building, via a model, a plurality of connections based on the aggregated plurality of segments of location pairs, wherein each of the plurality of segments of location pairs is serviced by an aircraft of at least one of a plurality of airlines, wherein building the plurality of connections includes inserting a connection record into the connection table if the connection record complies with one or more air service restriction limits, complies with the one or more exception rules, and has a total connecting time of no more than the variable upper limit value, and otherwise excluding the connection record from storage in the connection table to limit connection table output by limiting a number of connection records stored in the connection table; updating at least one of the variable upper limit value or the one or more exception rules based on an output of the first model; in response to updating the variable upper limit value or the one or more exception rules, dynamically modify the connection table by deleting from the connection table one or more stored connection records having a total connecting time greater than the updated variable upper limit value or failing an updated exception rule; for each of the plurality of segments of location pairs, generating a quality of service index (QSI) coefficient for each of the plurality of airlines; determining a connection window for one or more connection flights based on the aggregated plurality of segments of location pairs, wherein the connection window includes minimum and maximum connect times with categorized dwell time penalties within a predetermined range applied based on total connection time ranges; generating for each of the plurality of segments of location pairs, a circuity curve based on a distance of a segment of a location pair, wherein the circuitry curve implements a three-tier penalty system with differing factors applied based on distance-based circuity thresholds wherein itineraries above a highest circuitry curve are automatically removed
before insertion into the connection table and excluded from further storage and downstream evaluation; determining flight share information at an airline level for a target segment of location pair based on a corresponding QSI coefficient and a corresponding circuity curve; determining agency gap values at the airline level for the target segment of location pair based on the flight share information; and updating the model based on the flight share information and the agency gap values for predicting at least one of a number of seats expected for the target segment of location pair for a target airline and a corresponding aircraft type for the target segment of location pair.
Examiner notes that “to reduce memory storage requirement and processing load imposed on processing hardware” is functional claim language.
The claim as a whole recites a certain method of organizing human activity. The limitations recited above– (under broadest reasonable interpretation) recite the abstract idea of a certain method of organizing human activity, (e.g., commercial interaction). Therefore, the claims recite an abstract idea.
The mere recitation of generic computer components ((i) a communication network (claims 1, 19, and 20), (ii) one or more servers (claims 1, 19, and 20), (iii) a processor operably coupled to memory; memory (claims 1 and 19), (iv) a computer model (claims 1, 19, and 20), (v) a non-transitory computer readable storage medium that stores a computer program that is executed by a processor (claim 20), (vi) first model is a machine learning model (claims 1, 19, and 20), (vii) processor evaluation (claims 1, 19, and 20), and (viii) updating in real-time (claims 1, 19, and 20)) recited at a high-level of generality, does not take the claims out of the certain methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 19 and 20 as a whole amount to: “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The claims recite the additional elements of: (i) a communication network (claims 1, 19, and 20), (ii) one or more servers (claims 1, 19, and 20), (iii) a processor operably coupled to memory; memory (claims 1 and 19), (iv) a computer model (claims 1, 19, and 20), (v) a non-transitory computer readable storage medium that stores a computer program that is executed by a processor (claim 20), (vi) first model is a machine learning model (claims 1, 19, and 20), (vii) processor evaluation (claims 1, 19, and 20), and (viii) updating in real-time (claims 1, 19, and 20).
The above additional elements are recited at a high-level of generality such that, when viewed as whole/ordered combination, they amount to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (See Fig. 1), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than reciting the words “apply it” (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; or generally link the use of the judicial exception to a particular technological environment. The same analysis applies here in 2B, i.e., reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements discussed above do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Claims 3-18 recite details in the claim limitations which merely narrow the previously recited abstract idea limitiaitions. For these reasons, described above with respect to claim 1, these judicial exceptions, when viewed as a whole/ordered combination, are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 3-18 are ineligible.
Dependent claim 2 describes details of transmitting, to a target airline, at least one of the number of seats expected for the target segment of location pair and the corresponding type of aircraft for the target segment of location pair; and assigning the corresponding type of aircraft for the target segment of location pair, and thus further describe the abstract idea. The claim adds the additional element of a computer of the target airline, which, when viewed as a whole/ordered combination, fails to integrate the abstract idea into a practical application or amount to significantly more because the computer merely serve as generic computing components implementing the abstract idea.
Allowable over the Prior Art
Claims 1-20 are allowable over the prior art, because the prior art fails to disclose, teach, or suggest, all the limitations of the independent claims in their entirety. However the claims are subject to the above rejections under 35 U.S.C. 101.
Prior Art
The closest prior art includes:
“The Route Network Development Problem based on QSI Models” by Idrissi et al., dated 2017, (hereinafter “Idrissi”). Idrissi discloses that airlines have to choose flight schedules by considering demand, passengers preferences and competitors. The problem of allocating a new flight involves the route network development, and consists to determine a set of (OriginDestination) pairs to serve and then choose flight schedules with respect to the Quality of Service Index (QSI).
U.S. Patent Application Publication No. 2015/0371245 to Bental et al. (hereinafter “Bental”). Bental discloses QSI is a metric that quantifies the value of travel itineraries to passengers for a given carrier in a given market and is readily known in the airline industry. QSI may be derived from various factors including but not limited to number of stops by the carrier between an origin and a destination, the type of aircraft (e.g., widebody jet, narrowbody jet, turboprop, etc.), flight frequency (how many different flights in a day), travel time, and time-of-day (during business hours v. outside of business hours).
U.S. Patent No. 12,248,897 to Deng et al. (hereinafter “Deng”). Deng discloses a deep learning-based demand forecasting system for revenue management systems (flights, airlines).
U.S. Patent Application Publication No. 2015/0149234 to Rasheed et al. (hereinafter “Rasheed”). Rasheed discloses obtaining market information for the airline that defines at least one market for the airline, determining a plurality of aircraft types (priority groupings) for the airline within the at least one market to define an airline fleet model, and determining deployment priorities for the plurality of aircraft types within the at least market.
U.S. Patent Application Publication No. 2022/0230108 to Bollapragada et al. (hereinafter “Bollapragada”). Bollapragada discloses providing an airline recovery scheduling solution that reduces airline cost and improves crew and passenger satisfaction that would benefit airlines and passengers alike when such disruptions occur.
“The airline seat capacity allocation problem: An expected marginal profit approach” by Abdelghany et al., dated 2023 (hereinafter “Abdelghany”). Abdelghany discloses establishing optimal flight capacity allocation per fleet type, with the ultimate goal of maximizing the overall profitability of the airline's daily schedule.
“Connections Analysis FAQs” by OAG Analytics, dated September, 2017 (hereinafter “OAG Analytics”). OAG Analytics discloses that circuity is a term which reflects how far from the Great Circle Distance between the origin and destination is the actual routing when two or more flights connect. A large circuity value applies where the hub airport at which two flights connect is further from the great circle route between the origin and destination of the route
The following is prior art not cited but considered relevant:
"Quality of Service Index (QSI)" by Airport Council International, dated January, 2018 (hereinafter “Airport Council International”). Airport Council International discloses developing logical assumptions to account for share “premium” or “gap” variances, and the make adjustments to QSI weighting factors.
U.S. Patent No. 11,948,111 to Deng et al. (hereinafter “Deng”). Deng discloses a method of training a neural network to approximate a forecasting error of a passenger-demand forecasting model.
Conclusion
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/RUPANGINI SINGH/
Examiner, Art Unit 3628