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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 8, 2025 has been entered.
Response to Amendment
Claims 1, 11-13, 16, and 18 have been amended. Claims 4-10, 17, and 19-20 have not been modified. Claims 2-3 and 14-15 have been cancelled. Claims 1, 4-13, and 16-20 are pending and are provided to be examined upon their merits.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 17, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to Remarks filed on December 8, 2025 have been considered but are not fully persuasive. Response has been provided below.
Applicant argues 35 U.S.C. §103 Rejection, starting pg. 8 of Remarks:
Examiner acknowledges Applicant arguments and withdraws the prior art rejection.
Applicant argues 35 U.S.C. §101 Rejection, starting pg. 19 of Remarks:
Regarding A, Applicant argues that the claims recite a specific data structure (N-ontology space for an N-dimensional ontology), specific transformation (transforming unstructured text into ontological vectors), and a specific process (using an optimization algorithm) similar to Example 48 claims 2 and 3. Examiner respectfully disagrees.
Unlike Example 48 claim 2, which provides a specific, technical improvement to the field of speech-separation (“provides an improvement over existing speech-separation methods by providing a particular speech-separation technique that solves the problem of separating speech from different speech sources belonging to the same class, while not requiring prior knowledge of the number of speakers or speaker-specific training”), the instant application applies mathematical calculations (claim 1, “forming an ontological vector”, “generating an N-dimensional ontology space”) to identify mathematical relationships between vectors (claim 1, “similar or related ontological terms are closer together”, “grouping the ontological vectors”, “weighting each group of ontological vectors at least in part based on the frequency of occurrence of the ontological vectors”, “assigning an initial ranking to each of the initial hypotheses”) to improve upon the abstract idea of combining observations that become newly available for study ([0011], “Thus, a need exists to combine uncertain information based on early observations with new observations as disease spreads to new areas to avoid being misled and surprised.”).
The mathematical nature of the data processing is supported by [0043] of Applicant specification “The populated ontology space 346 is a geometric representation of possible events that are encoded by that particular corpus of data 214 according to that particular ontology 324.” [0047] of Applicant specification further recites: “it is a moderately well- defined optimization problem that can be solved using an iterative optimization algorithm (such as coordinate or gradient descent) or a heuristic optimization algorithm (such as simulated annealing, a Monte Carlo-based algorithm, a genetic algorithm, etc.).”
As noted by [0010] of Applicant specification, generating hypotheses can be performed by human analysts (“Project Argus made observations of disease or potential disease incidents, enabling human analysts to form hypotheses regarding the correct interpretation of such events on an ad hoc basis.”). Thus, the claimed mathematical process of transforming initial data into ontological vectors mapped to an N-dimensional ontology space and using an optimization algorithm to identify highest weighted clusters is only applied to improve upon the abstract idea of generating and ranking hypotheses. An improvement to the abstract idea does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Additionally, although the algorithm is considered part of the abstract idea of mathematical processes and is not an additional element, [0047] of Applicant specification admits that using an optimization algorithm to rank hypotheses by weight is known (“The computer processor(s) 160 executing the hypothesis generation module 260 identify and rank the hypotheses 268 by identifying the clusters of highest weights in the ontology space 346. Identifying that set of clusters in the ontology space 346 is not a trivial problem for ontologies 324 of significant size and structure. However, it is a moderately well- defined optimization problem that can be solved using an iterative optimization algorithm (such as coordinate or gradient descent) or a heuristic optimization algorithm (such as simulated annealing, a Monte Carlo-based algorithm, a genetic algorithm, etc.).”).
Regarding B, Applicant argues that the dependent claims reflect an improvement in the technical field of pandemic infection modeling, like claim 3 of Example 47. Examiner respectfully disagrees.
The improvement of claim 3 of Example 47 is specific to proactive prevention of network intrusions that is beyond the abstract idea of detecting one or more anomalies in network traffic (“According to the background section, existing systems use various detection techniques for detecting potentially malicious network packets and can alert a network administrator to potential problems. The disclosed system detects network intrusions and takes real-time remedial actions, including dropping suspicious packets and blocking traffic from suspicious source addresses. The background section further explains that the disclosed system enhances security by acting in real time to proactively prevent network intrusions… Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background.”).
Unlike Example 47 claim 3, the improvement of the instant application is instead directed to the abstract idea of pandemic infection modeling, which is a human activity routinely performed by epidemiologists (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”).
Additionally, each of the dependent claims fail to integrate the judicial exception into a practical application for the following reasons:
Claims 6 and 18: These claims recite the method further comprising: using the initial data to train a machine learning module to generate a predictive model of a pandemic infection, the predictive model generating an initial prediction of how a disease will spread in one or more locations; updating the predictive model based on the comparison of the updated hypotheses and the initial hypotheses; and using the updated data and the updated predictive model to generate an updated prediction of how the disease will spread; which teaches an abstract idea of certain methods of organizing human activity, such as predicting how a disease will spread in one or more locations, which is a human activity typically performed by epidemiologists. This claim further teaches iterative training at a high level of generality, such that no specific, technical improvements are being made to the field of machine learning.
Claims 7 and 19: These claims recite wherein the predictive model generates the initial prediction based on predictor variables, identified in the initial data by the machine learning module, and associations, identified by the machine learning module, between the identified predictor variables and the spread of the disease; which only serves to limit the data that is used to perform the abstract idea of generating the initial prediction.
Claims 8 and 20: These claims recite wherein the machine learning module adjusts the predictive model by learning additional predictor variables and/or adjusted associations between the identified predictor variables and the spread of the disease; which teaches an abstract idea of identifying additional predictor variables and/or adjusted associations between variables and spread of disease, which is a human activity routinely performed by epidemiologists. This claim further teaches training the machine learning at a high level of generality, such that no specific, technical improvements are made to how machine learning models are trained.
Claim 9: This claim recites wherein the associations used by the predictive model comprise weights or Bayesian probabilities; which only serves to limit the type of associations. This claim further teaches an abstract idea of mathematical processes.
Claim 10: This claim recites wherein the predictor variables used by the predictive model comprise numerical values or Boolean conditions; which only serves to limit the variables. This claim further teaches an abstract idea of mathematical processes.
Regarding the prior art rejection
Based on prior art search results, the prior art deemed closest to the instant claims are:
Ophir (US 20150235138), which teaches methods for identifying an ontology, generating an N-dimensional ontology space, generating and ranking initial hypotheses by extracting ontological terms, grouping ontological vectors, weighting each group, and assigning an initial ranking. However, Ophir fails to teach or render obvious the specific combination of claimed elements, such as receiving updated data, assigning an updated ranking to each of the updated hypotheses, and comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses corresponding to the same group of ontological vectors in the manner claimed.
Teng (US 5222197), which teaches methods for receiving updated data, using the data to generate updated hypotheses, and assigning an updated ranking. However, Teng fails to teach or render obvious the specific combination of claimed elements, such as comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses corresponding to the same group of ontological vectors in the manner claimed, as Teng teaches introducing new hypotheses that was not present in the initial hypotheses.
Kipersztok (US 20070018953), which teaches updating relationships between hypotheses as new information is added to a changing corpus of text ([0058]). However, Kipersztok uses an unspecified model to perform text retrieval based on predicted hypotheses, which does not support generating hypotheses from input text or comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses corresponding to the same group of ontological vectors in the manner claimed.
Setti (Setti; Leonardo, Airborne Transmission Route of COVID-19: Why 2 Meters/6 Feet of Inter-Personal Distance Could Not Be Enough, Int J Environ Res Public Health. 2020 Apr 23;17(8):2932), which teaches supporting an updated hypothesis based on new information about COVID-19. However, Setti fails to teach or render obvious the specific combination of claimed elements, such as generating an ontological vector space, assigning an updated ranking to each of the updated hypotheses, and comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses corresponding to the same group of ontological vectors in the manner claimed, as Setti introduces a new hypothesis that was not initially present.
Applicant’s arguments filed on December 8, 2025 are incorporated by reference as further reasons for withdrawing the prior art rejection.
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, 4-13, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1, 4-13, and 16-20). Accordingly, claims 1, 4-13, and 16-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis, and is similar to system claim 13.
Claim 1:
A method for identifying hypotheses regarding a pandemic infection in initial data and testing the identified hypotheses using updated data, the method comprising:
identifying an ontology having N elements, each element having a plurality of ontological terms, each combination of ontological terms from each of the N elements of the ontology forming an ontological vector generating an N-dimensional ontology space, wherein:
each of the N dimensions corresponds to one of the N elements of the identified ontology; and
similar or related ontological terms are closer together in the N-dimensional ontology space than dissimilar and unrelated ontological terms
receiving initial data from a plurality of data sources;
using the initial data to generate and rank initial hypotheses regarding a pandemic infection by:
extracting ontological terms from the initial data and generating a plurality of ontological vectors by mapping the extracted ontological terms from the initial data to the N-dimensional ontology space;
grouping the ontological vectors to form groups of ontological vectors, each group of ontological vectors forming an initial hypothesis;
weighting each group of ontological vectors at least in part based on the frequency of occurrence of the ontological vectors in the initial data; and
assigning an initial ranking to each of the initial hypotheses by using an optimization algorithm to rank the groups of ontological vectors identified in the initial data in accordance with the weight of each group of ontological vectors;
receiving updated data;
using the updated data to generate updated hypotheses by identifying the groups of ontological vectors in the updated data and ranking the ontological vectors identified in the updated data; and
comparing the updated rankings of the updated hypotheses to the initial rankings of the initial hypotheses by:
identifying an updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vectors; or
identifying an initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same group of ontological vectors.
These claims recite an abstract idea of: mathematical processes. The claim recites “generating an N-dimensional ontology space,…”, “generating a plurality of ontological vectors by mapping the extracted ontological terms from the initial data to the N-dimensional ontology space”, “weighting each group of ontological vectors at least in part based on the frequency of occurrence of the ontological vectors…”, “assigning an initial ranking to each of the initial hypotheses by using an optimization algorithm…”, and “assigning an updated ranking to each of the updated hypotheses…”. Generating dimensional spaces is a mathematical representation of the relationships between data points, as supported by [0043] of Applicant specification “The populated ontology space 346 is a geometric representation of possible events that are encoded by that particular corpus of data 214 according to that particular ontology 324.” [0047] of Applicant specification recites: “it is a moderately well- defined optimization problem that can be solved using an iterative optimization algorithm (such as coordinate or gradient descent) or a heuristic optimization algorithm (such as simulated annealing, a Monte Carlo-based algorithm, a genetic algorithm, etc.).” which teaches wherein the optimization algorithm comprises several different mathematical processes to optimize data.
These above limitations, under their broadest reasonable interpretation, also cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards generating and ranking the ontological vectors identified in the data, which [0011] of Applicant’s specification is in order “to combine uncertain information based on early observations with new observations as disease spreads to new areas to avoid being misled and surprised”. Managing observations of people in the form of ranking, which provides a recommended target of study, modifies the personal behaviors of the people involved in relevant research fields.
Accordingly, the claim recites at least one abstract idea.
Claim 13 is abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
A machine learning module (6-8,18-20); a predictive model (6-10,18-20); one or more computer networks (11); a system (13,16-20); a data collection module (13); hypothesis generation module (13,16); a hypothesis space difference evaluation module (13)
Any computing devices (system) would be able to perform the method and the associated software modules that are used within the computing environment (data collection, hypothesis generation, hypothesis space difference evaluation modules) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. [0021] of Applicant specification recites: “FIG. 1, the architecture 100 may include a server 120 that communicates with client devices 180, for example via one or more networks 130 such as the Internet. The server 120 includes one or more hardware computer processors 160 and non-transitory computer readable storage media 140. The server 120 receives data 212 from data sources 110. The server 120 may be any suitable computing device including, for example, an application server or a web server.” [0022] of Applicant specification recites: “FIG. 2 is a block diagram illustrating the system 200, which is realized by software modules executed by the hardware computer processor(s) 160, generating and distributing initial hypotheses 268 and an initial prediction 246 according to an exemplary embodiment.” No specific, technical improvements are being made to computing devices as generic devices with software modules are simply being used to perform the abstract idea.
Machine learning (machine learning module, predictive model) is also taught at a high level of generality. [0031] of Applicant specification recites: “The machine learning module 240 may utilize any or all supervised, unsupervised, or semi- supervised learning approaches. The machine learning module 240 may utilize approaches that include classification, regression, regularization, decision-tree, Bayesian, clustering, association, neural networks, deep learning algorithms, etc. Deep learning algorithms may include recurrent models, convolutional models, transformer models with or without attention, etc. The machine learning module 240 may employ various machine learning algorithms known in the art, for instance pre-train transformers (used as global data), one or more final layers (trained while maintaining previous layers for localization),18 etc. “ No specific, technical improvements are being made to the field of machine learning as any generic machine learning algorithm applied to perform the abstract idea of predicting spread of disease.
Computer networks are also taught at a high level of generality. [0021] of Applicant specification recites: “As shown in FIG. 1, the architecture 100 may include a server 120 that communicates with client devices 180, for example via one or more networks 130 such as the Internet.” No specific, technical improvements are being made to the field of computer networking as the Internet is applied to perform the insignificant extra-solution activity of transmitting data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 4 and 16: These claims recite wherein using the optimization algorithm to rank the weighted groups of ontological vectors comprises: using a first optimization function to perform a coarse ranking of the weighted groups of ontological vectors and identify a subset of the highest ranked groups; and using a second optimization function to perform a precise ranking of the subset of groups ranked highest by the first optimization function; which further limits usage of the algorithms to perform the abstract idea of ranking the ontological vectors.
Claims 5 and 17: These claims recite wherein the optimizing algorithm includes a heuristic optimization function or an iterative optimization function; which only serves to further limit the type of algorithm.
Claims 6 and 18: These claims recite the method further comprising: using the initial data to train a machine learning module to generate a predictive model of a pandemic infection, the predictive model generating an initial prediction of how a disease will spread in one or more locations; updating the predictive model based on the comparison of the updated hypotheses and the initial hypotheses; and using the updated data and the updated predictive model to generate an updated prediction of how the disease will spread; which teaches an abstract idea of certain methods of organizing human activity, such as predicting how a disease will spread in one or more locations, which is a human activity typically performed by epidemiologists. This claim further teaches iterative training at a high level of generality, such that no specific, technical improvements are being made to the field of machine learning.
Claims 7 and 19: These claims recite wherein the predictive model generates the initial prediction based on predictor variables, identified in the initial data by the machine learning module, and associations, identified by the machine learning module, between the identified predictor variables and the spread of the disease; which only serves to limit the data that is used to perform the abstract idea of generating the initial prediction.
Claims 8 and 20: These claims recite wherein the machine learning module adjusts the predictive model by learning additional predictor variables and/or adjusted associations between the identified predictor variables and the spread of the disease; which teaches an abstract idea of identifying additional predictor variables and/or adjusted associations between variables and spread of disease, which is a human activity routinely performed by epidemiologists. This claim further teaches updating training the machine learning at a high level of generality, such that no specific, technical improvements are made to how machine learning models are trained.
Claim 9: This claim recites wherein the associations used by the predictive model comprise weights or Bayesian probabilities; which only serves to limit the type of associations. This claim further teaches an abstract idea of mathematical processes.
Claim 10: This claim recites wherein the predictor variables used by the predictive model comprise numerical values or Boolean conditions; which only serves to limit the variables. This claim further teaches an abstract idea of mathematical processes.
Claim 11: This claim recites the method further comprising: outputting, for transmittal via one or more computer networks: the updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vectors; or the initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same groups of ontological vectors; which only serves to limit the hypothesis output. This claim further teaches the computer network(s) at a high level of generality, such that they are only applied to perform the insignificant extra-solution activity of transmitting data.
Claim 12: This claim recites wherein: the updated hypothesis having a higher updated ranking than the initial ranking of the initial hypothesis corresponding to the same group of ontological vector represents a potential new insight regarding the pandemic infection; or the initial hypothesis having a higher initial ranking than the updated ranking of the updated hypothesis corresponding to the same group of ontological vectors represents a previous assumption regarding the pandemic infection; which only serves to narrow the abstract idea of the ranking of the hypotheses.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 4-12 and 16-20 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 4-12 and 16-20, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 4-12 and 16-20, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1, 4-13, and 16-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Caudill (US 20020129015): [0052], “The vectorization unit has two configurations, a document vectorization unit 130 and a query vectorization unit 134. Each of these configurations converts ontologically parsed text into vector representations. The document vectorization unit 130 converts the set of predicate structures derived from ontologically parsing a document into one or more large-dimensioned numerical vectors.” [0148], “The single winning neurode represents the best match between the input signal and the currently organized network's set of weight vectors. In n-dimensional weight-space, the input vector and the weight vector of the winning neurode most nearly point in the same direction (i.e., have the maximum cosine of the angle between the two vectors)” [0072], “Relevancy ranking, the second major component of the relevancy ranking and clustering method and system, is a process that produces a set of documents sorted or ranked, according to certain criteria. The relevancy ranking process, according to the present invention, uses a similarity comparison algorithm to determine the relevance of a document to a query… Each query predicate structure is compared with each document predicate structure to determine a matching degree, represented by a real number.” [0122], “Although the neural network structure specified herein is illustrative of the type of neural network architecture and learning algorithm that may be used in this component, the scope of the present invention is not intended to be limited”
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684