DETAILED ACTION
This is in response to the applicant’s communication filed on 7/19/24, wherein:
Claims 1-20 are currently pending.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites a method and therefore, falls into a statutory category. Similar independent claims 16 and 19 recite a system and a computer-readable storage medium, and therefore, also fall into a statutory category. Claim 1 is used as the exemplary claim.
Step 2A – Prong 1 (Is a Judicial Exception Recited?): The following underlined limitations identify the abstract limitations which are considered mental processes
using at least one computer hardware processor to perform, prior to completion of the inspector's inspection of the vehicle:
obtaining first information about the vehicle, the first information about the vehicle comprising a vehicle identifier;
obtaining second information about the vehicle using the vehicle identifier;
identifying one or more potential vehicle defects by using one or more trained machine learning (ML) defect detection models, the trained ML defect detection models being trained to detect vehicle defects of different types and including a first trained ML model trained to detect vehicle defects of a first type, the identifying comprising:
generating a first set of features using the first information about the vehicle and/or the second information about the vehicle;
processing the first set of features using the first trained ML model to obtain a first likelihood that the vehicle has a defect of the first type; and
identifying, based on the first likelihood, the defect of the first type as a first potential vehicle defect for the vehicle; and
notifying the inspector of the identified one or more potential vehicle defects, the notifying comprising notifying the inspector of the first potential vehicle defect.
These limitations constitute inspecting a vehicle by providing the inspector with information about potential vehicle defects (Specification ¶4), which are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting that the method involves at least one computer hardware processor and one or more trained machine learning (ML) defect detection models, nothing in the claim elements precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Alternatively, the following underlined limitations identify the abstract limitations which are considered certain methods of organizing human activity
using at least one computer hardware processor to perform, prior to completion of the inspector's inspection of the vehicle:
obtaining first information about the vehicle, the first information about the vehicle comprising a vehicle identifier;
obtaining second information about the vehicle using the vehicle identifier;
identifying one or more potential vehicle defects by using one or more trained machine learning (ML) defect detection models, the trained ML defect detection models being trained to detect vehicle defects of different types and including a first trained ML model trained to detect vehicle defects of a first type, the identifying comprising:
generating a first set of features using the first information about the vehicle and/or the second information about the vehicle;
processing the first set of features using the first trained ML model to obtain a first likelihood that the vehicle has a defect of the first type; and
identifying, based on the first likelihood, the defect of the first type as a first potential vehicle defect for the vehicle; and
notifying the inspector of the identified one or more potential vehicle defects, the notifying comprising notifying the inspector of the first potential vehicle defect.
These limitations constitute inspecting a vehicle by providing the inspector with information about potential vehicle defects (Specification ¶4), which is often used as part of vehicle buying and/or selling or vehicle repair (Specification ¶2-3), all of which are processes that, under their broadest reasonable interpretation, are considered certain methods of organizing human activity – commercial or legal interactions (including agreements in the form of contracts and marketing or sales activities or behaviors) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of at least one computer hardware processor and one or more trained machine learning (ML) defect detection models, both of which are considered computer components. The computer components are recited at a high-level of generality (i.e., as a generic processing device performing generic computer functions), such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the obtaining and notifying limitations may be considered insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claim is directed to an abstract idea.
The limitations reciting using one or more trained machine learning (ML) defect detection models, the trained ML defect detection models being trained to detect vehicle defects of different types and including a first trained ML model trained to detect vehicle defects of a first type provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the computers are invoked merely as a tool to perform existing processes (using one or more trained machine learning (ML) defect detection models, the trained ML defect detection models being trained to detect vehicle defects of different types and including a first trained ML model trained to detect vehicle defects of a first type). See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the steps of the abstract idea amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the obtaining and notifying limitations) as WURC (see 2106.05(d), identifying receiving or transmitting data over a network as WURC, as recognized by Symantec and identifying presenting offers as WURC, as recognized by OIP Techs). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent claims 2-9, 11-15, and 17 merely recite further embellishments of the abstract idea of independent claim 1 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claim 1; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits.
Claims 10, 18, and 20 further recite the additional element of a second trained ML model trained to detect vehicle defects of a second type different from the first type, which are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Even in combination, this additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea.
Notice
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 103
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Corda et al. (US 20230054982), in view of Li (US 20020072808).
Referring to claim 1:
Corda discloses a method for assisting an inspector to inspect a vehicle by providing the inspector with information about potential vehicle defects via a mobile device used by the inspector, the method comprising: using at least one computer hardware processor to perform, prior to completion of the inspector's inspection of the vehicle {Corda [0065]-[0068][0071]; The method 20 may, for example, be performed by the system 2 described above, for example it may be partially performed by the server 7, e.g. by the processor 9 of the server 7 [0071] and In the illustrated embodiment, the user input device is a driver input device 13 associated with the vehicle 1, which in turn comprises a driver output terminal 15. The driver output terminal 15 is used to provide indications of calculated likely defects to a driver, as well as recommended actions . . . The driver input device 13 may be remote from the server 7, and may be a driver’s smart phone [0066]}:
identifying one or more potential vehicle defects by using one or more trained machine learning (ML) defect detection models, the trained ML defect detection models being trained to detect vehicle defects of different types and including a first trained ML model trained to detect vehicle defects of a first type {Corda [0072][0073]; the algorithm is trained on the historical data using known machine learning techniques such as those described in Collaborative Deep Learning for Recommender Systems by Wang et al to recognize correlations in the historical data [0072] and The historical data may contain data associated with vehicles of a common type, optionally of a common manufacturer, or of a common model [0073]}, the identifying comprising:
generating a first set of features using the first information about the vehicle and/or the second information about the vehicle {Corda [0067] [0095]; Each vehicle 1 in the fleet of vehicles 8 also comprises a plurality of the sensors 10, which are configured to generate data across the fleet relating to the condition of one or more vehicle components [0067]};
processing the first set of features using the first trained ML model to obtain a first likelihood that the vehicle has a defect of the first type {Corda [0015][0095][0115][0117]; The checklist for the inspection report data may be a prioritised checklist, with checklist items relating to more likely and/or more severe possible defects being presented first [0015] and At step 28, the algorithm calculates whether there are any likely defects which may be present on the vehicle 1 based on the current vehicle sensor data 6b [0095]}; and
identifying, based on the first likelihood, the defect of the first type as a first potential vehicle defect for the vehicle {Corda [0097]; If likely defects are identified in either or both of steps 28 and 32, then an indication is provided and/or a suggested action is provided at step 33 [0097]}; and
notifying the inspector of the identified one or more potential vehicle defects, the notifying comprising notifying the inspector of the first potential vehicle defect {Corda [0066]; The driver output terminal 15 is used to provide indications of calculated likely defects to a driver, as well as recommended actions [0066]}.
Corda discloses a system for identifying likely defects with an asset within a fleet (abstract). Corda does not disclose obtaining first information about the vehicle, the first information about the vehicle comprising a vehicle identifier; and obtaining second information about the vehicle using the vehicle identifier.
However, Li discloses a similar system for vehicle-specific service (abstract). Li discloses obtaining first information about the vehicle, the first information about the vehicle comprising a vehicle identifier; and obtaining second information about the vehicle using the vehicle identifier {Li [0061][0062] and Fig. 17; a service associate can specify a particular vehicle via keypunching the VIN number or via VIN wireless bar code scanner that prepopulate these data fields as shown by reference numeral 300 [0061]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Corda to incorporate obtaining information as taught by Li because this would provide a manner for populating data fields associated with the vehicle (Li [0061]), thus aiding the user by providing desired information about the vehicle.
Referring to claim 2:
Corda, as modified by Li, discloses wherein notifying the inspector of the first potential vehicle defect comprises: providing the inspector with information indicating the first potential vehicle defect and the first likelihood that the vehicle has the defect of the first type {Corda [0015] [0066][0115][0117]; The checklist for the inspection report data may be a prioritised checklist, with checklist items relating to more likely and/or more severe possible defects being presented first [0015]}.
Referring to claim 3:
Corda, as modified by Li, discloses wherein notifying the inspector of the first potential vehicle defect comprises: providing the inspector with instructions indicative of one or more actions for the inspector to take to confirm whether the first potential vehicle defect is present in the vehicle {Corda [0015][0036]; Each checklist item may be considered to be a suggested action, with the suggested action being to perform an inspection of the asset component corresponding to the checklist item [0015] and an inspection report of defective steering may be input which prompts the algorithm to indicate that there is a likely defect with the tires and suggest that the tires are checked. Upon checking the tires, the driver will input whether or not they were defective [0036]}.
Referring to claim 4:
Corda, as modified by Li, discloses wherein the first potential vehicle defect of the first type is an engine defect, an exhaust smoke defect, a transmission defect, a drivetrain defect, a frame rot defect, a frame damage defect, a vehicle title defect, a vehicle modification defect, a drivability defect, and/or a hail damage defect {Corda [0074] and Table 1; where Table 1 shows vehicle components which may be defective, including exhaust, engine/clutch, engine coolant, engine oil, etc.}.
Referring to claim 5:
Corda, as modified by Li, discloses wherein notifying the inspector of the identified one or more potential vehicle defects comprises: providing the inspector with information indicating: (1) a plurality of potential vehicle defects, including the first potential vehicle defect; and (2) a ranking of the plurality of potential vehicle defects, the ranking of potential vehicle defects being based on respective likelihoods of the vehicle defects being present in the vehicle {Corda [0015][0095][0115][0117]; The checklist for the inspection report data may be a prioritised checklist, with checklist items relating to more likely and/or more severe possible defects being presented first [0015]}.
Referring to claim 6:
Corda, as modified by Li, discloses wherein the first information about the vehicle further comprises an odometer reading from the vehicle {Li [0061]; Other information regarding the vehicle, such as, but not limited to the odometer reading of the vehicle can also be displayed [0061]}.
Referring to claim 7:
Corda, as modified by Li, discloses wherein the second information about the vehicle further comprises information selected from the group consisting of: a year of manufacture of the vehicle, a make and model of the vehicle, an age of the vehicle at time of inspection, an engine displacement volume of the vehicle, a longitude coordinate of an inspection location, a latitude coordinate of the inspection location, a Koppen climate code associated with the inspection location, a drive train type of the vehicle, a fuel type of the vehicle, engine description keywords, a US state code associated with the inspection location, a Carfax® alert associated with the vehicle, and a National Highway Traffic Safety Administration (NHTSA) recall profile associated with the vehicle {Li [0049][0050]; The vehicle quality feedback module 60 also includes a recall module 62 for determining whether the vehicle is under recall based on the known issues database 95. The user is notified whether the vehicle is under recall by recall module 62 [0050]}.
Referring to claim 16:
Claim 16 is rejected on a similar basis to claim 1, with the following additions:
Corda discloses a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method for assisting an inspector to inspect a vehicle by providing the inspector with information about potential vehicle defects via a mobile device used by the inspector {Corda [0065]-[0068][0071]; The server 7 may comprise a computer readable medium having an algorithm stored thereon [0065] and The method 20 may, for example, be performed by the system 2 described above, for example it may be partially performed by the server 7, e.g. by the processor 9 of the server 7 [0071] and In the illustrated embodiment, the user input device is a driver input device 13 associated with the vehicle 1, which in turn comprises a driver output terminal 15. The driver output terminal 15 is used to provide indications of calculated likely defects to a driver, as well as recommended actions . . . The driver input device 13 may be remote from the server 7, and may be a driver’s smart phone [0066]}.
Referring to claim 19:
Claim 19 is rejected on a similar basis to claim 16.
Claims 8, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Corda et al. (US 20230054982), in view of Li (US 20020072808), and further in view of Brunda et al. (US 20230063381).
Referring to claim 8:
Corda, as modified by Li, discloses a system for identifying likely defects with an asset within a fleet (Corda abstract). Corda, as modified by Li, does not disclose wherein the first trained ML model is trained to detect an engine noise defect by processing the first set of features to obtain the first likelihood that the vehicle has the engine noise defect.
However, Brunda discloses a similar system for providing assistance with vehicle emissions testing compliance (abstract). Brunda discloses wherein the first trained ML model is trained to detect an engine noise defect by processing the first set of features to obtain the first likelihood that the vehicle has the engine noise defect {Brunda [0075]-[0078]; By the same token, a large quantity of data from many users may be collected to build machine learning models having broad applicability to the year/make/model/engine of the vehicle 30, thus improving the sound or vibration-based symptomatic diagnostics capability of the app 100 for all users [0076]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Corda and Li to incorporate training a ML model to detect an engine noise defect as taught by Brunda because this would provide a manner for improving sound or vibration-based symptomatic diagnostics capability (Brunda [0076]), thus aiding the user by providing desired information about the vehicle.
Referring to claim 9:
Claim 9 is rejected on a similar basis to claim 7.
Referring to claim 17:
Claim 17 is rejected on a similar basis to claim 8.
Claims 10-14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Corda et al. (US 20230054982), in view of Li (US 20020072808), and further in view of Li ‘877 (US 20190095877).
Referring to claim 10:
Corda, as modified by Li, discloses wherein the identifying comprises: generating a [second] set of features using the first information about the vehicle and/or the second information about the vehicle {Corda [0067] [0095]; Each vehicle 1 in the fleet of vehicles 8 also comprises a plurality of the sensors 10, which are configured to generate data across the fleet relating to the condition of one or more vehicle components [0067] where the portions of the claim in brackets are not taught by Corda, and are addressed below}; processing the [second] set of features using [the second] trained ML model to obtain a [second] likelihood that the vehicle has a defect of the second type {Corda [0015][0095][0115][0117]; The checklist for the inspection report data may be a prioritised checklist, with checklist items relating to more likely and/or more severe possible defects being presented first [0015] and At step 28, the algorithm calculates whether there are any likely defects which may be present on the vehicle 1 based on the current vehicle sensor data 6b [0095]}; and identifying, based on the [second] likelihood, the defect of the [second] type as a [second] potential vehicle defect {Corda [0097]; If likely defects are identified in either or both of steps 28 and 32, then an indication is provided and/or a suggested action is provided at step 33 [0097]}, and wherein the notifying comprises: notifying the inspector of the [second] potential vehicle defect {Corda [0066]; The driver output terminal 15 is used to provide indications of calculated likely defects to a driver, as well as recommended actions [0066]}.
Corda, as modified by Li, discloses a system for identifying likely defects with an asset within a fleet (Corda abstract). Corda, as modified by Li, does not disclose wherein the one or more trained ML defect detection models include a second trained ML model trained to detect vehicle defects of a second type different from the first type.
However, Li ‘877 discloses a similar system for vehicle damage detection (abstract). Li ‘877 discloses wherein the one or more trained ML defect detection models include a second trained ML model trained to detect vehicle defects of a second type different from the first type {Li ‘877 [005][0027][0028] [0038][0051]; Each of the sets of images 106.sub.i and 108.sub.i depict a distinct type of vehicle damage as shown in portions of the images 102.sub.i and 104.sub.i. The image sets 106.sub.i and 108.sub.i depicting distinct types of damage may be used as positive training sets in training the machine learning model [0027] and Although described for simplicity herein with respect to one machine learning model, it should be understood that multiple such models may be trained and thereafter used (e.g., an ensemble of trained machine learning models) [0028] and In one embodiment, the machine learning model may be trained using positive training sets comprising sets of images extracted from images depicting damaged vehicles, with each such extracted training set depicting a different type of damage [0038]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Corda and Li to incorporate a second ML model as taught by Li ‘877 because this would provide a manner for using multiple machine learning models (Li ‘877 [0028][0038]), thus aiding the user by providing separate models for different types of damage, thereby improving the models.
Referring to claim 11:
Corda, as modified by Li and Li ‘877, discloses wherein the first set of features are different from the second set of features {Li ‘877 [0027]-[0030]; For example, the extracted images 106.sub.i and 108.sub.i may include images depicting the following types of vehicle damage: dents in the bodies of the vehicles and scratches on the vehicles. Detection of other types of vehicle damage is also contemplated [0027] where differently trained models will generate different features, and further, different types of damage are identified as different features}.
Referring to claim 12:
Corda, as modified by Li and Li ‘877, discloses wherein the first set of features comprise at least one feature obtained from the first information and at least one feature obtained from the second information {Corda [0067] [0095]; Each vehicle 1 in the fleet of vehicles 8 also comprises a plurality of the sensors 10, which are configured to generate data across the fleet relating to the condition of one or more vehicle components [0067] where each sensor provides information, so multiple sensors provide at least first and second information}.
Referring to claim 13:
Corda, as modified by Li and Li ‘877, discloses wherein the second trained ML model is trained to detect a transmission defect by processing the second set of features to obtain the second likelihood that the vehicle has the transmission defect {Corda [0074] and Table 1; where Table 1 addresses Transmission Control System Malfunction}.
Referring to claim 14:
Claim 14 is rejected on a similar basis to claim 7.
Referring to claim 18:
Claim 18 is rejected on a similar basis to claim 10.
Referring to claim 20:
Claim 20 is rejected on a similar basis to claim 10.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Corda et al. (US 20230054982), in view of Li (US 20020072808), in view of Li ‘793 (US 20180260793), and further in view of Chowdhury et al. (US 20220222440).
Referring to claim 15:
Corda, as modified by Li, discloses a system for identifying likely defects with an asset within a fleet (Corda abstract). Corda, as modified by Li, does not disclose wherein the first trained ML model is a trained random forest model.
However, Li ‘793 discloses a similar system for assessment of damage and repair costs in vehicles (Li ‘793 abstract). Li ‘793 discloses wherein the first trained ML model is a trained random forest model {Li ‘793 [0249]-[0251]; Structured Random Forest (SRF) [0249]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Corda and Li to incorporate a random forest model as taught by Li ‘793 because this would provide a manner for enhancing the boundaries of images used (Li ‘793 [0251]), thus aiding the user by providing desired information about the vehicle.
Corda, as modified by Li and Li ‘793, discloses a system for identifying likely defects with an asset within a fleet (Corda abstract). Corda, as modified by Li and Li ‘793, does not disclose having between 0.5 million and 17 million parameters.
However, Chowdhury discloses a related system for assessing risk associated with a machine learning model (abstract). Chowdhury discloses having between 0.5 million and 17 million parameters {Chowdhury [0055]; the machine learning model may include thousands, tens of thousands, hundreds of thousands, at least one million, millions, tens of millions, or hundreds of millions of parameters [0055]}.
It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Corda, Li, and Li ‘793 to incorporate having between .5 million and 17 million parameters as taught by Chowdhury because this would provide a manner for including parameters needed for the machine learning model (Chowdhury [0055]), thus aiding the user by providing desired information about the vehicle using the machine learning model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARRIE S GILKEY/Primary Examiner, Art Unit 3626