DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending.
Drawings
3. Applicant’s Drawings submitted October 9, 2024 are acceptable.
Priority
4. No Foreign Priority has been asserted by Applicant.
5. Pursuant to Applicant’s June 17, 2026 Amendment, the previous rejection is withdrawn.
Claim Rejections - 35 USC § 102
6. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
7. Claims 1-4, 6, 8-10, 13, 15-17, 19, and 20 are rejected under 35 U.S.C. 102(a) as being anticipated by Mandavilli et al., US 2019/0191214.
Regarding claim 1, Mandavilli discloses a system for bilaterally matching sets of artificial intelligence (Al) based information to configure in-vehicle entertainment systems for passengers onboard a vehicle (providing personalized programming on an in-vehicle entertainment system by matching upcoming media programming with user (passenger) preferences (bilaterally), e.g. [0044, 0047], comprising:
a client agent that is configured to, using a first trained model, select a set of passenger-specific features representing a passenger onboard a vehicle (a trained neural network model/artificial intelligence/machine learning technique (client agent using a first trained model) provides automatic channel selection to turn
on a user's desired programming or provide a list of sports-related programming options to the user (set of passenger-specific features) of an in-vehicle entertainment system (onboard a vehicle), e.g. [0038, 0039, 0044, 0047], wherein the set of passenger-specific features are selected in response to a service request made by the
passenger via an in-vehicle entertainment system (the list of programming options provided to the user based on the user identifying themselves using a fingerprint sensor (service request made) at an in-vehicle entertainment system; e.g. [0041, 0047]),
and wherein the set of passenger-specific features includes at least one feature associated with a passenger action prior to the passenger being onboard the vehicle (the list of programming options is based on historical times and dates (prior to being onboard the vehicle) that the user watched TV (passenger action); e.g. Fig. 7, [0039, 0107];a service agent that is configured to, using a second trained model, determine service interaction information for a plurality of vehicle services that are available to passengers onboard the vehicle (the trained neural network model/artificial intelligence/machine learning algorithms (service agent using a second trained model) to determine the probability of the user watching one of channels 1-4 of upcoming
media programming (services available) based on historical times and dates that the user watched TV (service interaction information) of the in-vehicle entertainment system; e.g. Fig 9, [0047, 0080, 0085, 0107], wherein the plurality of vehicle services are identified based on querying a service data server that manages available services onboard one or more vehicles including the vehicle (retrieving (querying) TV channel viewing data from the cloud on a remote server (service data server) from the artificial
intelligence/machine learning algorithms of the smart TV/in-vehicle entertainment; e.g. [0047, 0060, 0113]; and a bilateral matching engine deployed on a cloud computing platform that is remote from the vehicle (the artificial intelligence/learning algorithms (bilateral matching engine) determine when upcoming media programming matches the user's preferences are stored remotely in the cloud; e.g. [0043, 0060], the bilateral matching engine communicatively coupled to both the client agent and the service agent and configured to generate a response to the service request to be provided via the in-vehicle entertainment system (the artificial intelligence/learning algorithms that match upcoming media programming with uscr's preferences transmits instructions to local devices to alert the user to upcoming media programming (generate a response to the service request) on the in-vehicle entertainment system; e.g. [0043, 0047], note: in order to match the upcoming programming with the user preferences, the Al/learning algorithm must be coupled to the Al/learning algorithms for determining the sports-related programming options andhistorical times and dates the user watched TV of the in-vehicle entertainment system), wherein the response is generated based on a matching between the set of passenger-specific features selected using the first trained model and the service interaction information determined using the second model (the alert (response) is based on matching upcoming media programming with user's desired programming Al and historical dates and times AI; e.g. Fig. 9, [0038, 0039, 0044, 0047, 0080, 0085, 0107].
Regarding claim 2, Mandavilli discloses wherein the client agent is configured to select the at least one feature that captures the passenger action prior to the passenger being onboard the vehicle based on the client agent being communicably coupled to a personal electronic device operated by the passenger (the neural network/AI model provides the list of programming options based on historical times and dates
(passenger action prior to being onboard the vehicle) that the user watched TV (passenger action) on a smart TV (personal electronic device); e.g. Fig 7, [0037, 0039, 0107]).
Regarding claim 3, Mandavilli discloses wherein the service request indicates a type of media content to be provided via the in-vehicle entertainment system (using the fingerprint sensor integrated into asmartphone, the user can upload fingerprint data to be used as a basis for a user profile that recognizes scheduled media programming of interest to the user, for example, sports (type); e.g. [0039, 0062-3], and wherein the service interaction information determined by the service agent using the second
trained model includes similarity information between different media content of the indicated type that are available on the vehicle (the trained neural network model/artificial intelligence/machine learning algorithms (service agent using a second trained model) uses the historical times and dates the user watched TV, and scans channel listings for sporting event programming to present a list of sports-related
programming options (similarity between different media content of the indicated type); e.g. [0039, 0041, 0047, 0107].
Regarding claim 4, Mandavilli discloses wherein the service interaction information describes at least one of historical use of the plurality of vehicle services on the one or more vehicles including the vehicle, or similarities between the plurality of vehicle services (the trained neural network model/artificial intelligence/machine learning algorithms (service agent using a second trained model) uses the historical
times and dates the user watched TV, and scans channel listings for sporting event programming to present a list of sports-related programming options (similarity between different media content of the indicated type); e.g. [0039, 0041, 0047, 0107]).
Regarding claim 6, Mandavilli discloses wherein the bilateral matching engine is configured to perform the matching using a third trained model implemented on the cloud computing platform (the artificial intelligence/learning algorithms (bilateral matching engine) determine when upcoming media programming matches the user's preferences, are stored remotely in the cloud; paras [0043, 0060].
Regarding claim 8, Mandavilli discloses wherein the service agent comprises a customization module configured to learn from the passenger's selection of a vehicle service subsequent to the in-vehicle entertainment system providing the response to adapt the available services onboard the one or more vehicles (the trained neural network/AI/ML algorithms recognize and analyze new entries in the user profile or user behavior database (learn from the passenger's selection) when the user is alerted that the desired TV show is playing (subsequent to providing the response to adapt the available services) on the in-vehicle entertainment system; paras [0058, 0060].
Regarding claim 9, Mandavilli discloses wherein the bilateral matching engine is configured to provide the response to the in-vehicle entertainment system via a terrestrial network connection (the Al/learning algorithms matching upcoming programming to user preferences transmits personalized programming to the in-vehicle entertainment systema according terrestrial trunked radio TETRA (satellite-based); e.g.
[0035, 0044, 0047].
Regarding claim 10, Mandavilli discloses wherein the client agent is configured to select the set of passenger-specific features from sensor data obtained from one or more human-machine interfaces 'HMI' deployed in the vehicle or included in the in-vehicle entertainment system (smartphone sensors are turned on (HMI) to detect the presence of a particular user within the device vicinity, and based on the detected
user, AI/ML algorithms analyze the user's profile to select a program in the in-vehicle entertainment system; paras [0047, 0050, 0060, 0061]), and wherein the client agent is configured to implement an HMI scheme determined using the first trained model, the HMI scheme specifying which HMI interfaces are relevant for monitoring to identify the passenger-specific features (tracking a user's TV watching behavior (monitor passenger specific features) on a smart TV and implementing the neural network model to add channels to a favorite channel list (identify set of preference features); the smart TV is an electronic device with user interfaces such as a touchscreen, microphone and camera (HMI interfaces) therefore, the neural network model is configured to implement an HMI scheme specifying which HMI interfaces are relevant for monitoring; paras [0037, 0038, 0123].
Regarding claim 13, Mandavilli discloses a of improving specificity of in-vehicle entertainment systems to passengers onboard a vehicle (improving user experience by providing personalized programming on an in-vehicle entertainment system; paras [0044, 0047]), comprising: selecting, via a first machine learning 'ML' model, a set of preference features associated with a passenger onboard a vehicle (a trained neural network model/artificial intelligence/machine learning technique (first ML model) provides automatic channel selection to turn on a user's desired programming or provide a list of sports-related programming options to the user (set of preference features) of an in-vehicle entertainment system (onboard a vehicle); e.g. [0038, 0039, 0044, 0047], in connection to a service request available to the passenger via an in-vehicle entertainment system (the list of programming options provided to the user based on the user identifying themselves using a fingerprint sensor (service request) at
an in-vehicle entertainment system; e.g. [0041, 0047], wherein the set of preference features includes at least feature associated with a passenger action prior to the passenger being onboard the vehicle (the list of programming options is based on historical times and dates (prior to being onboard the vehicle) that the user watched TV (passenger action); e.g. Fig. 7, [0039, 0107]; determine, via a second ML model, service interaction information for a plurality of vehicle services that are available to passengers onboard the vehicle, wherein the service interaction information includes at least one of a historical usage of the plurality of vehicle services by a group of passengers or comparisons between the plurality of vehicle services (the trained neural network model/artificial intelligence/machine learning algorithms (second ML model) to determine the probability of the user watching one of channels 1-4 of upcoming media programming (services available) based on historical times and dates (service
interaction information) of one or more users (group of passengers) of the in-vehicle entertainment system, e.g. Fig. 9, [0047, 0080, 0085, 0107, 0116], and operating a matching engine deployed on a cloud computing platform that is remote from the vehicle, the matching engine being configured to (the artificial intelligence/learning algorithms (matching engine) determine when upcoming media programming matches the user's preferences are stored remotely in the cloud; e.g. [0043, 0060]): generate a passenger-specific set of vehicle services [0043, 0044, 0047]) and cause the in-vehicle entertainment system to indicate the passenger-specific set of vehicle services
in response to the passenger selecting the service request via the in-vehicle entertainment system (the in-vehicle entertainment system presenting (indicate) personalized programming (passenger-specific) comprising a list of sports-related programming options (set of vehicle services) based on user provided input to open an application (selecting the service request); paras [0039, 0044, 0047, 0061].
Regarding claim 15, Mandavilli discloses wherein the plurality of vehicle services includes any one or more of a plurality of media content available for playback via the in-vehicle entertainment system, a plurality of consumable items, and a plurality of products available for remote purchase via the in-vehicle entertainment system (providing internet-based TV, online interactive media and on-demand media
streaming, e.g. [0036].
Regarding claim 16, Mandavilli discloses wherein the first ML model is implemented via a client agent that is configured to monitor particular activity on a personal electronic device associated the passenger, the particular activity being identified by the passenger and being used as input to the first ML model to
identify the set of preference features (tracking a user's TV watching behavior (monitor particular activity) on a smart TV (personal electronic device) and implementing (input) the neural network model to add channels to a favorite channel list (identify set of preference features); e.g. [0037, 0038].
Regarding claim 17, Mandavilli discloses wherein the matching engine is configured to generate the passenger-specific set of vehicle services using a third ML model that is trained to optimize the matching between the set of preference features and the service interaction information (AI/ML techniques (third model) provide personalized (passenger-specific) programming; the AI/ML techniques comprising an
inbuilt optimizer trained to match the user's preferences to upcoming media programming; e.g. [0038, 0043, 0044, 0106].
Regarding claim 19, Mandavilli discloses wherein the matching engine is configured to transmit the passenger-specific set of vehicle services to the in-vehicle entertainment system via one of a ground-based connection or a satellite-based connection (the Al/learning algorithms matching upcoming programming to
user preferences transmits personalized programming to the in-vehicle entertainment systema according to LTE (ground-based) or terrestrial trunked radio TETRA (satellite-based), e.g. [0035, 0044, 0047].
Regarding claim 20, Mandavilli discloses a computing system that is remote to a vehicle (remote server, e.g. [0060]), the computing system comprising:
at least one processor, e.g. [0129]; and at least one memory storing instructions that, when executed by the at least one processor, cause the computing system to (instructions stored in memory executed by the processor to implement methods; e.g. [0129]: receive, from a client agent configured to collect activity information for a passenger that is onboard a vehicle, a set of preference features associated with the passenger (a trained neural network model/artificial intelligence/machine learning technique (client agent) provides automatic channel selection to turn on a user's desired programming or provide a list of sports-related programming options to the user (set of
preference features) of an in-vehicle entertainment system (onboard a vehicle) based on analysis of a user profile (activity information); e.g. [0038, 0039, 0044, 0047]),
the set of preference features including at least one feature associated with a passenger action prior to the passenger being onboard the vehicle (the list of programming options is based on historical times and dates (prior to being onboard the vehicle) that the user watched TV (passenger action); e.g. Fig. 7, [0039, 0107],
obtain, from a services agent, service interaction information that identifies a plurality of vehicle services that are available in the vehicle for the passenger and that further includes at least one of a historical usage of the plurality of vehicle services by other passengers or comparisons between the plurality of vehicle services (the trained neural network model/artificial intelligence/machine learning algorithms (services agent) to determine the probability of the user watching one of channels 1-4 of upcoming media
programming (services available) based on historical times and dates that one or more users watched TV (service interaction information) of the in-vehicle entertainment system; e.g. Fig. 9, [0047, 0080, 0085, 0107, 0116], generate a passenger-specific set of vehicle services based on using a machine learning 'ML' model to perform a matching between the set of preference features received from the client agent and the service interaction information obtained from the services agent (the artificial intelligence/learning algorithms that match upcoming media programming with uscr's preferences determines personalized programming (passenger-specific set of vehicle services) on the in-vehicle entertainment system; paras [0043, 0044, 0047], and cause an in-vehicle entertainment system to indicate the passenger-specific set of vehicle services in response to a service request by the passenger via the in-vehicle entertainment system (the in-vehicle entertainment system presenting (indicate) personalized programming (passenger-specific) comprising a list of sports-related programming options (set of vehicle services) based on user provided input to open an
application (selecting the service request); e.g. [0039, 0044, 0047, 0061].
Claim Rejections - 35 USC § 103
8. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
9. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0191214 to Mandavilli and Official Notice,
Regarding claim 12, Mandavilli discloses wherein the bilateral matching engine comprises a model that is used to process text-based or audio-based utterances included in the set of passenger-specific features (automatic speech recognition for recognizing the user's voice (process audio-based utterances) to provide personalized (passenger-specific) media programming-related notifications to the user; e.g. [0044, 0068, 0080].
Mandavilli fails to explicitly disclose a large language model.
Official Notice is taken that to providing a large language model is common knowledge in the art, as it was known in the art that automatic speech recognition commonly use large language models
To have provided a large language model for Mandavilli would have been obvious to one of ordinary skill in the art before the priority date of Applicant’s Application, as to use a large language model, as motivation for doing so would have been to identify a device user by voice recognition without relying on a small set of specific words.
10. Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over
Mandavilli, US 2019/0191214, to claims 1-4, 6, 8-10, 13, 15-17, 19, and 20 above, and further in view of Moynihan, WO 2023/167758.
Regarding claim 5, Mandavilli discloses the bilateral matching engine (the artificial intelligence/learning algorithms (bilateral matching engine) determine when upcoming media programming matches the user's preferences, are stored remotely in the cloud; e.g. [0043].
Mandavilli fails to explicitly disclose generating the response according to a rule that the response includes a particular vehicle service that i: has not been previously consumed by the passenger according to the set of passenger-specific features and ii: is similar, according to the service interaction information, to other vehicle services that the passenger prefers according to the passenger-specific features.
Moynihan discloses recommending relevant content items (abstract) and generating the response according to a rule that the response includes a particular vehicle service that (automatically provide (generate the response) content items in a vehicle computer system (particular vehicle service) based on unique rules; page 7, lines 3-10; page 11, line 29-page 12, line 4, has not been previously consumed by the passenger according to the set of passenger-specific features (creating an entirely new content item (has not been previously consumed) for the user; page 22, lines 32-34; page 23, lines 6-9) and is similar, according to the service interaction information, to other vehicle services that the passenger prefers according to the passenger-specific features (the new content items are relevant to information contained in the user profile including user preferences (passenger-specific features); page 20, lines 25-26;
page 23, lines 6-9, 19-26).
To have provided this for Mandavilli, as modified by Moynihan, would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, as to modify Mandavilli to include cloud AI-based information one would have a reasonable expectation of success, as the motivation for doing such is to provide a workable information exchange capable of being implemented in various languages.with the generating the response according to a rule of Moynihan, for the purpose of presenting relevant content items to users during a meeting, e.g. Moynihan, page 1, lines 21-29.
Regarding claim 14, Mandavilli discloses the method of claim 13.
Mandavilli fails to explicitly disclose wherein the passenger-specific set of vehicle services is generated according to a rule that the passenger-specific set includes a particular vehicle service that i: has not been previously consumed by the passenger and ii: is similar, according to the service interaction information, to
other vehicle services that the passenger prefers according to the set of preference features associated with the passenger.
Moynihan teaches wherein the passenger-specific set of vehicle services is generated according to a rule that the passenger-specific set includes a particular vehicle service that (automatically provide (generate the response) content items in a vehicle computer system (particular vehicle service) based on unique rules;
page 7, lines 3-10; page 11, line 29-page 12, line 4), has not been previously consumed by the passenger according to the set of passenger-specific features (creating an entirely new content item (has not been previously consumed) for the user; page 22, lines 32-34; page 23, lines 6-9) and is similar, according to the service interaction information, to other vehicle services that the passenger prefers according to the set of preference features associated with the passenger (the new content items
are relevant to information contained in the user profile including user preferences (passenger-specific features); page 20, lines 25-26; page 23, lines 6-9, 19-26).
To have provided this for Mandavilli would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, as to modify Mandavilli with the generating the response according to a rule of Moynihan, for the purpose of presenting relevant content items to users during a meeting (Moynihan page 1, lines 21-29).
11. Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over
Mandavilli, US 2019/0191214, in view of Shah et al., US 2019/0115027.
Regarding claim 7, Mandavilli discloses the bilateral matching engine is configured to (the artificial intelligence/learning algorithms (bilateral matching engine) determine when upcoming media programming matches the user's preferences, are stored remotely in the cloud, e.g. [0043]) detect a passenger selection of a vehicle service subsequent to the in-vehicle entertainment system providing the
response (the user provides input to open an application (passenger selection) upon receipt of a notification that an application-provided program is scheduled to begin (subsequent to the response), e.g. [0060].
Mandavilli fails to explicitly disclose re-training the third trained model based on the passenger selection based on a reinforcement learning from human feedback 'RLHF' technique.
Mandavilli discloses turn-based reinforcement learning (abstract) and teaches re-training the third trained model based on the passenger selection based on a reinforcement learning from human feedback 'RLHF' technique (a plurality of turn-level training instances (re-training) training a reinforcement model based on turn-level user (human) feedback provided by a user (passenger selection); e.g. [0005, 0008].
It would have been obvious for one of ordinary skill in the art before the effective filing date with a reasonable expectation of success, to modify Mandavilli with the RLHF of Moynihan, for the purpose of training a dialog component of an automated assistant
(Moyhinan, e.g. [0005].
Regarding claim 18, Mandavilli discloses the matching engine (the artificial intelligence/learning algorithms (matching engine) determine when upcoming media programming matches the user's preferences, are stored remotely in the cloud; e.g. [0043] and a passenger selection of a vehicle service subsequent to passenger-specific set of vehicle services being indicated via the in-vehicle entertainment system (analyzing new entries in the user profile or user behavior database (passenger selection) when the user is alerted that the desired TV show is playing (subsequent to passenger-specific set of vehicle services being indicated) on the in-vehicle entertainment system; paras [0058, 0060].
Mandavilli fails to explicitly disclose re-training the third ML model using a reinforeement learninprocess based on a passenger selection.
Moynihan teaches re-training the third ML model using a reinforcement learning process based on a passenger selection (a plurality of turn-level training instances (re-training) training a reinforcement model based on turn-level user feedback provided by a user (passenger selection); e.g. [0005, 0008].
It would have been obvious for one of ordinary skill in the art before the effective filing date with a reasonable expectation of success Mandavilli with the reinforcement learning process of Moynihan, e.g. [0005, for the purpose of training a dialog component of an automated assistant.
12. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over
Mandavilli, US 2019/0191214, in view of Sobhany, US 2020/0238933.
Regarding claim 11, Mandavilli discloses the client agent transmitting the set of passenger-specific features to the bilateral matching engine (the AL/ML model that matches (bilateral matching engine) upcoming media programming with user's preferences (passenger specific features) learned by the smart TV's (client agent) neural network modelling, therefore the smart TV's neural network must provide
(transmitting) the user's preferences to the AL/ML matching model; e.g. [0037, 0043, 0044].
Mandavilli fails to explicitly disclose a data anonymizer that is configured to anonymize the set of passenger-specific features.
Sobhany discloses, e.g. Abstract, generating output actions for a vehicle such as entertainment features and teaches a data anonymizer that is configured to anonymize the set of passenger-specific features (intermediate data, comprising user state, user observational data and user drive style (passenger-specific features) is anonymized, e.g. [0050, 0051].
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s application, to modify Mandavilli with the data anonymizer of Sobhany, e.g. [0051], for the purpose of protecting the privacy of the vehicle's passengers, as the motivation for doing such is to provide a workable digital information exchange in a smart phone.
13. Applicant’s Information Disclosure Statement (IDS) submitted Jun 17, 2026 has been reviewed. Note the attached IDS.
14. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW JOSEPH RUDY whose telephone number is
571-272-6789. The examiner can generally be reached on Monday thru Friday from about 10am-6pm EST.
If attempts to reach the examiner by telephone are unsuccessful the examiner’s supervisor, Fadey Jabr, can be reached on 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANDREW JOSEPH RUDY/
Primary Examiner
Art Unit 3668
571-272-6789
Prosecution Insights