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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/11/2026 has been entered.
Claims 1, 4, 12, 15 and 20 have been amended. Claims 2-3 and 13-14 are canceled. Claims 1, 4-12, and 15-20 are pending.
Response to Amendment
Applicant’s amendments and arguments have been considered. However, the 101 rejection remains.
Response to Argument
With respect to the 101 arguments, Applicant argues that the claims are not directed to a judicial exception, such that the “independent claims cannot practically be performed in the human mind or with pen and paper, and therefore do not fall within the mental process grouping” (See Remarks at pg. 17). Specifically, Applicant argues that the “operations are performed on machine-maintained graph data structures over a collection of assets and cannot be carried out in the human mind or with pen and paper” (See Remarks at pg. 18). However, Examiner respectfully disagrees. Examiner first notes that the claims recite limitations that recite both certain methods of organizing human activity and mental processes. Certain methods of organizing human activity recite the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer (for example a user interacting with a device by submitting queries and receiving results) may fall within the “certain methods of organizing human activity” grouping. Also, the method of observing and evaluating insights utilizing a metadata graph representative of assets of an organization are analyses that are capable of being performed by humans and therefore a mental process. As an example, the metadata graphs in Figs. 2A-2D are clear examples of manual illustrations of evaluating a collection of data. Accordingly, the claims recite mental processes and certain methods of organizing human activity.
Further with respect to the 101 arguments, Applicant argues that the claimed invention is statutory by attempting to analogize the claimed invention to the claims analyzed and found eligible in the Federal Circuit’s Enfish decision, specifically by alleging that the amended claims are drawn to “a specific, technical implementation, defined by a particular ordered combination” that improves “how the computer stores, retrieves, and updates query results” (See Remarks at pgs. 19-20). In response, however, the Examiner emphasizes that, while the claims in Enfish were directed toward addressing problems related to configuring a computer memory in accordance with a self-referential table, the claimed solution represents improvements to software including “benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements.” Therefore, Enfish’s claims are distinguishable from Applicant's claims because the practice of organizing and evaluating observe insights in k-partite graphs to provide an organizational user with a modified query result is not rooted in, or reasonably understood as encompassing, a software-based invention that improves the performance of the computer system itself. The generation of a metadata graph to map insight-pertinent assets impacted by a user adjustment to derive a new metadata subgraph and obtain new insights from the new subgraph are means of an evaluative analysis. The claimed evaluative analysis manipulates data to obtain modified queries and insights, such that it recites a mental process operable on general-purpose computer components.
Also, with respect to the 101 arguments, Applicant argues that the claimed invention is statutory by attempting to analogize the claimed invention to the claims analyzed and found eligible in Example 47, specifically by alleging the amended claimed limitations do not merely apply generic machine learning, but use a machine learning algorithm in a specific way that amounts to an improvement in the technical field (See Remarks at pgs. 20-21). However, Examiner respectfully disagrees. In response, however, the Examiner emphasizes that, while the claims in Example 47 were directed toward addressing problems related to implementing machine learning algorithms, the claimed solution recites a specific physical integrated circuit for implementing an artificial neural network to detect network intrusions to enhance security. Therefore, Example 47 claims are distinguishable from Applicant’s claims because the practice of identifying an optimal machine learning model to apply the evaluative metadata graph is not rooted in, or reasonably understood as encompassing, a specific hardware circuit to implement a machine learning algorithm that improves network security. Applicant’s Specification, ¶0068, recites that any “existing rule-based, machine learning, and/or deep learning classification algorithms” can employ the insight inferring model. With respect to Applicants Specification, the claimed automated machine learning service values the judicial exception in the manner of “apply it,” such that any generic machine learning model can be applied to implement the model. See the updated 101 rejection below.
Also, with respect to the 101 rejection, “the claims include significantly more than that which the Examiner contends is abstract” (See Remarks at pgs. 17-18). Specifically, Applicant argues that the “evidence addresses only the general-purpose hardware on which the invention may run; it does not address the ordered combination of the targeted subgraph re-derivation and re-inference steps recited in the amended claims” (See Remarks at pg. 12). However, Examiner first notes that Step 2B addresses whether the additional elements amount to significantly more than the abstract idea. The amended claim limitations reciting elements of the targeted subgraph re-derivation and re-inference do not amount to additional elements, rather these steps recite a mental process, such that they further narrow the evaluative process of utilizing a metadata graph to interpret insights. The additional elements recited in the claims are considered to be well‐understood, routine, and conventional and do not amount to significantly more. See the additional elements and evidence provided in the updated 101 rejection below.
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 therefore, subject to the
conditions and requirements of this title.
Claims 1, 4-12, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In accordance with Step 1, it is first noted that the claimed method in claims 1 and 4-11; the claimed non-transitory computer readable storage medium in claims 12 and 15-19 and the claimed system in claim 20 are directed to a potentially eligible category of subject matter (i.e., processes, machine etc.). Thus, Step 1 is satisfied with respect to claims 1, 4-12, and 15-20.
In accordance with Step 2A, Prong One, claims 1, 4-12, and 15-20, the claimed invention recites an abstract idea. Specifically, the independent claim(s) recite(s) (abstract idea recited in italics and additional elements recited in bold):
Claim 1:
A method for insight creation filtering, the method comprising:
Receiving, at an insight service, a transparent insight query comprising a query expression, wherein the transparent insight query is transmitted by a device of an organizational user and the query expression is a series of terms that represent a query posed by the organizational user;
Extracting one or more an expression keywords from the query expression;
obtaining a metadata graph representative of an asset catalog wherein the metadata graph is a connected graph that maps a data structure configured to maintain asset metadata that describes a collection of assets known to the insight service and wherein the metadata graph comprises nodes representative of the collection of assets and edges representative of relationships between the collection of assets;
filtering, based on the one or more expression keyword, the metadata graph to identify a node subset, wherein filtering comprises keyword matching the one or more expression keywords to the asset metadata and semantic similarity calculation between the one or more expression keywords and the asset metadata;
generating a k-partite metadata graph using the node subset;
creating an interactive query result based on the k-partite metadata graph, wherein the
interactive query result comprises:
an insight inferred to address the transparent insight query using an insight inferring model on an asset metadata corpus obtained from the k-partite metadata graph,
a manifest listing providing a set of insight-pertinent assets represents information providing transparency as to how the insight had been produced,
a model input snapshot listing inputs used to infer the insight, and
a model snapshot revealing a technique used to infer the insight, , wherein the insight inferring model is selected by an automated machine learning service that automatically identifies an optimal machine learning algorithm, constructs the insight inferring model from the optimal machine learning algorithm, and fits the insight inferring model to the asset metadata corpus obtained from the k- partite metadata graph;
transmitting the interactive query result to the device of the organization user, wherein the organizational user interacts with the interactive query result by adjusting the set of the insight-pertinent assets;
detecting a user interaction with the interactive query result on-demand telemetry from the device, wherein the user interaction comprises an adjustment to the manifest listing the set of insight-pertinent assets;
identifying, within an insight-pertinent metadata subgraph of the k-partite metadata graph, a subgraph node subset comprising one or more subgraph nodes that map to one or more insight-pertinent assets, of the set of insight-pertinent assets, impacted by the adjustment;
deriving a new insight-pertinent metadata subgraph by removing, from the insight- pertinent metadata subgraph, the subgraph node subset and any subgraph edge connected to the subgraph node subset;
obtaining a new asset metadata corpus from a new asset catalog subset of the asset catalog that maps to the new insight-pertinent metadata subgraph;
producing a new insight by applying the insight inferring model to the new asset metadata corpus; and
creating a new interactive query result comprising the new insight and a new manifest listing a new set of insight-pertinent assets that map to the new insight-pertinent metadata subgraph.
Claim 12:
A non-transitory computer readable medium (CRM) comprising computer readable program code, which when executed by a computer processor, enables the computer processor to perform a method for insight creation filtering, the method comprising:
Receiving, at an insight service, a transparent insight query comprising a query expression, wherein the transparent insight query is transmitted by a device of an organizational user and the query expression is a series of terms that represent a query posed by the organizational user;
Extracting one or more an expression keywords from the query expression;
obtaining a metadata graph representative of an asset catalog, wherein the metadata graph is a connected graph that maps a data structure configured to maintain asset metadata that describes a collection of assets known to the insight service;
filtering, based on the one or more expression keyword, the metadata graph to identify a node subset, wherein filtering comprises keyword matching and semantic similarity calculations;
generating a k-partite metadata graph using the node subset;
creating an interactive query result based on the k-partite metadata graph, wherein the interactive query result comprises:
an insight inferred to address the transparent insight query using an insight inferring model on an asset metadata corpus obtained from the k-partite metadata graph,
a manifest listing providing a set of insight-pertinent assets represents information providing transparency as to how the insight had been produced,
a model input snapshot listing inputs used to infer the insight, and
a model snapshot revealing a technique used to infer the insight, the insight inferring model is selected by an automated machine learning service that automatically identifies an optimal machine learning algorithm, constructs the insight inferring model from the optimal machine learning algorithm, and fits the insight inferring model to the asset metadata corpus obtained from the k- partite metadata graph;
transmitting the interactive query result to the device of the organization user, wherein the organizational user interacts with the interactive query result by adjusting the set of the insight-pertinent assets;
detecting a user interaction with the interactive query result on-demand telemetry from the device, wherein the user interaction comprises an adjustment to the manifest listing the set of insight-pertinent assets;
identifying, within an insight-pertinent metadata subgraph of the k-partite metadata graph, a subgraph node subset comprising one or more subgraph nodes that map to one or more insight-pertinent assets, of the set of insight-pertinent assets, impacted by the adjustment;
deriving a new insight-pertinent metadata subgraph by removing, from the insight- pertinent metadata subgraph, the subgraph node subset and any subgraph edge connected to the subgraph node subset;
obtaining a new asset metadata corpus from a new asset catalog subset of the asset catalog that maps to the new insight-pertinent metadata subgraph;
producing a new insight by applying the insight inferring model to the new asset metadata corpus; and
creating a new interactive query result comprising the new insight and a new manifest listing a new set of insight-pertinent assets that map to the new insight-pertinent metadata subgraph.
.
Claim 20:
A system, the system comprising: a client device; and an insight agent operative connected to the client device, and comprising a computer processor configured to perform a method for insight creation filtering, the method comprising:
Receiving, at an insight service, a transparent insight query comprising a query expression, wherein the transparent insight query is transmitted by a device of an organizational user and the query expression is a series of terms that represent a query posed by the organizational user;
Extracting one or more an expression keywords from the query expression;
obtaining a metadata graph representative of an asset catalog wherein the metadata graph is a connected graph that maps a data structure configured to maintain asset metadata that describes a collection of assets known to the insight service;
filtering, based on the one or more expression keywords, the metadata graph to identify a node subset, wherein filtering comprises keyword matching and semantic similarity calculations;
generating a k-partite metadata graph using the node subset;
creating an interactive query result based on the k-partite metadata graph, wherein the
interactive query result comprises:
an insight inferred to address the transparent insight query using an insight inferring model on an asset metadata corpus obtained from the k-partite metadata graph,
a manifest listing providing a set of insight-pertinent assets represents information providing transparency as to how the insight had been produced,
a model input snapshot listing inputs used to infer the insight, and
a model snapshot revealing a technique used to infer the insight, the insight inferring model is selected by an automated machine learning service that automatically identifies an optimal machine learning algorithm, constructs the insight inferring model from the optimal machine learning algorithm, and fits the insight inferring model to the asset metadata corpus obtained from the k- partite metadata graph;
transmitting the interactive query result to the device of the organization user, wherein the organizational user interacts with the interactive query result by adjusting the set of the insight-pertinent assets;
detecting a user interaction with the interactive query result on-demand telemetry from the device, wherein the user interaction comprises an adjustment to the manifest listing the set of insight-pertinent assets;
identifying, within an insight-pertinent metadata subgraph of the k-partite metadata graph, a subgraph node subset comprising one or more subgraph nodes that map to one or more insight-pertinent assets, of the set of insight-pertinent assets, impacted by the adjustment;
deriving a new insight-pertinent metadata subgraph by removing, from the insight- pertinent metadata subgraph, the subgraph node subset and any subgraph edge connected to the subgraph node subset;
obtaining a new asset metadata corpus from a new asset catalog subset of the asset catalog that maps to the new insight-pertinent metadata subgraph;
producing a new insight by applying the insight inferring model to the new asset metadata corpus; and
creating a new interactive query result comprising the new insight and a new manifest listing a new set of insight-pertinent assets that map to the new insight-pertinent metadata subgraph.
The above-recited italicized limitations viewed as an abstract idea are certain methods of organizing
human activity (i.e., fundamental economic principles or practices (including hedging, insurance,
mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations); managing
personal behavior or relationships or interactions between people (including social activities,
teaching, and following rules or instructions)) and mental processes (i.e., concepts performed in the
human mind (including an observation, evaluation, judgment, opinion). The claimed invention is directed to observing and evaluating insights utilizing a metadata graph representative of assets of an organization. Specifically, the above claims recite data gathering steps to observe insights that are then evaluated. Accordingly, the claims recite mental processes and certain methods of organizing human activity.
According to Step 2A, prong two, this judicial exception is not integrated into a practical application because the use of bolded additional elements for receiving/transmitting data (e.g., receiving, at an insight service, a transparent insight query comprising a query expression, wherein the transparent insight query is transmitted by a device of an organizational user and the query expression is a series of terms that represent a query posed by the organizational user; obtaining a metadata graph representative of an asset catalog wherein the metadata graph is a connected graph that maps a data structure configured to maintain asset metadata that describes a collection of assets known to the insight service and wherein the metadata graph comprises nodes representative of the collection of assets and edges representative of relationships between the collection of assets; a model input snapshot listing inputs used to infer the insight, a model snapshot revealing a technique used to infer the insight; transmitting the interactive query result to the device of the organization user, wherein the organizational user interacts with the interactive query result by adjusting the set of the insight-pertinent assets; detecting a user interaction with the interactive query result on-demand telemetry from the device; etc.); processing data in the form of evaluating/observing (e.g., filtering, based on the expression keyword, the metadata graph to identify a node subset; generating a k-partite metadata graph using the node subset; creating an interactive query result based on the k-partite metadata graph; and creating a new interactive query result based on the user interaction; identifying, within an insight-pertinent metadata subgraph of the k-partite metadata graph, a subgraph node subset comprising one or more subgraph nodes that map to one or more insight-pertinent assets, of the set of insight-pertinent assets, impacted by the adjustment; deriving a new insight-pertinent metadata subgraph by removing, from the insight- pertinent metadata subgraph, the subgraph node subset and any subgraph edge connected to the subgraph node subset; obtaining a new asset metadata corpus from a new asset catalog subset of the asset catalog that maps to the new insight-pertinent metadata subgraph; producing a new insight by applying the insight inferring model to the new asset metadata corpus; and creating a new interactive query result comprising the new insight and a new manifest listing a new set of insight-pertinent assets that map to the new insight-pertinent metadata subgraph; etc.); storing data; displaying data and repeating steps is merely implementing the abstract idea steps of valuing an idea in the manner of “apply it”. The claim(s) does/do not include additional elements that are sufficient to practically apply the judicial exception because they, whether taken separately or as a whole, merely use conventional computer components or technology to receive, process, store and display data and thus do not provide an inventive concept in the claims. The additional elements, “the insight inferring model is selected by an automated machine learning service that automatically identifies an optimal machine learning algorithm, constructs the insight inferring model from the optimal machine learning algorithm, and fits the insight inferring model to the asset metadata corpus obtained from the k- partite metadata graph,” generically link machine learning technology to the judicial exception. Examiner notes that Applicant’s Specification, ¶0068, recites that any “existing rule-based, machine learning, and/or deep learning classification algorithms” can employ the insight inferring model. The claimed automated machine learning service values the judicial exception in the manner of “apply it,” such that the machine learning model is merely implicit, and any generic machine learning model can be applied to implement the model. Additionally, the additional elements, “detecting a user interaction with the interactive query result on-demand telemetry from the device;” and “extracting an expression keyword from the query expression;” are insignificant extra-solution activity. The detection of user interaction is a result of mere data gathering from a user’s interaction with a device. Also, the courts have recognized that extracting data from a document, such as extracting an expression keyword, is a well-understood, routine and conventional function (See Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014)). Therefore, this limitation fails to practically apply the abstract idea.
In accordance with Step 2B, the claims only recite the above bold additional elements. The additional elements are recited at a high level of generality (i.e., as a generic computer for evaluating organizational insights using a metadata graph) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, as evidence of generic computer implementation, generic machine learning implementation and an indication that the claimed invention does not amount to significantly more, it is first noted in the Applicant’s Specification, in ¶0085-0088, “the computing system (400) may include one or more computer processors (402), non-persistent storage (404) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (406) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (412) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), input devices (410), output devices (408), and numerous other elements (not shown) and functionalities. Each of these components is described below. In one embodiment disclosed herein, the computer processor(s) (402) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores or micro-cores of a central processing unit (CPU) and/or a graphics processing unit (GPU)… the computing system (400) may include one or more output devices (408), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device. One or more of the output devices may be the same or different from the input device(s). The input and output device(s) may be locally or remotely connected to the computer processor(s) (402), non-persistent storage (404), and persistent storage (406). Many different types of computing systems exist, and the aforementioned input and output device(s) may take other forms. Software instructions in the form of computer readable program code to perform embodiments disclosed herein may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments disclosed herein.” Also, Applicant’s Specification, ¶0068, recites that any “existing rule-based, machine learning, and/or deep learning classification algorithms.” As additional evidence of conventional computer implementation, it is noted in the MPEP, the courts have recognized that “receiving or transmitting data over a network, e.g., using the Internet to gather data” (See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (e.g., computer receives and providing query information over a network; “detecting a user interaction with the interactive query result on-demand telemetry from the device;”); extracting data from a document (See Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014)) (e.g. “extracting an expression keyword from the query expression;”); “performing repetitive calculations” (Flook, 437 U.S. at 594, 198 USPQ2d at 199 (implementing an optimal machine learning algorithm)) and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93” (e.g. obtaining a metadata graph from a catalog) to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (See MPEP 2106.05(d)). From the interpretation of the MPEP and the Specification, one would reasonably deduce that the additional elements are merely embodies generic computers and generic computing functions.
The dependent claims 4-11 and 15-19 recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. The dependent claims do not remedy these deficiencies.
Specifically, claims 4-5, 8 and 15 are merely descriptive of the insights and the interactive query result that is developed from evaluating the insights. These descriptions further expand on the above abstract idea and therefore, also recite mental processes and certain methods of organizing human activity.
Claims 8 and 17 recite the mathematical function of an inference forming algorithm for evaluating asset metadata, which is an abstract idea.
Claims 6-7, 9, 11, 14, 16, 17-19 further narrow evaluative steps of creating an interactive query result.
Claims 17-19 recite receiving and transmitting query information according to user interaction and displaying the result of a query following evaluation, which amounts to the insignificant extra-solution activity of mere selection of a particular type of data to be manipulated (See MPEP 2106.05(g)).
Claim 10 further narrows the extraction of a keyword from a query expression. As noted in Step 2A, prong two above, courts have recognized that extracting data from a document is a well-understood, routine and conventional function (See Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014)). Therefore, this limitation fails to practically apply the abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sobhy Deraz et al. (US 2019/0171777): In non-limiting examples of the present disclosure, systems, methods and devices for assisting with providing dataset insights associated with datasets are presented. A dataset and a query relating to the dataset may be received. The dataset may be processed to determine metadata that describes one or more properties of the dataset. The dataset, the determined metadata, and the user query may be provided to one or more modular recommendation elements for processing into an insight result that indicates a result from data analysis directed to the query. The insight result may be transferred in a portable format for use by the productivity application in displaying one or more insight objects based on the insight result.
Remis et al. (US 2019/0317965): Methods and apparatus to facilitate generation of database queries are disclosed. An example apparatus includes a generator to generate a global importance tensor. The global importance tensor based on a knowledge graph representative of information stored in a database. The knowledge graph includes objects and connections between the objects. The global importance tensor includes importance values for different types of the connections between the objects. The example apparatus further includes an importance adaptation analyzer to generate a session importance tensor based on the global importance tensor and a user query, and a user interface to provide a suggested query to a user based on the session importance tensor.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALLISON MICHELLE NEAL whose telephone number is (571)272-9334. The examiner can normally be reached 9-2pm ET, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 5712705389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALLISON M NEAL/Primary Examiner, Art Unit 3625
Prosecution Insights