DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/12/2026 has been entered.
Claim Status
Claims 1, 3-9, and 11-19 are pending for examination in the application filed as a supplemental amendment on 02/12/2026. Claims 1, 11, and 19 are currently amended, claims 1, 8, 11, 13, and 18 were amended in the request for continued examination filed 01/12/2026, claim 19 was newly added in the request for continued examination filed 01/12/2026, and claims 2 and 10 have been previously cancelled.
Priority
Acknowledgement is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent application JP2021-065015 filed on 04/06/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/22/2025 has been considered by the examiner.
Response to Arguments and Amendments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument, as facilitated by the newly added amendments.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries 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.
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.
Claims 1, 5-9, 11-13, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuo (WO2020259179A1) in view of Misawa (US20110002678A1).
Regarding claim 1, Zhuo teaches an image processing apparatus (electronic device 110) comprising: one or more processors ([pg. 13 para. 4] As shown in FIG. 9, the electronic device includes a processor and a memory connected through a system bus. [pg. 3 para. 14] The electronic device 110 may not be limited to various mobile phones, tablet computers, wearable devices, and the like);
and a memory storing instructions which, when the instructions are executed by the one or more processors, cause the image processing apparatus to ([pg. 13 para. 4] As shown in FIG. 9, the electronic device includes a processor and a memory connected through a system bus. Among them, the processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program may be executed by a processor to implement a focusing method provided in the following embodiments):
set a priority setting, in which one of a plurality of subject classifications is set, wherein each subject classification includes at least one of a plurality of types of subjects; ([pg. 4 para. 7] The electronic device can preset priorities corresponding to different categories, and the electronic device selects a target subject from multiple subjects according to the priorities corresponding to different categories);
detect the plurality of subject types for an input image, each of the plurality of subject types being in their own detection region ([pg. 3 para. 14] Specifically, the electronic device 110 may collect a preview image through the camera 120, perform subject detection on the preview image, and obtain the categories and regions corresponding to the multiple subjects contained in the preview image);
and determine, in a case where detection regions of the plurality of types of subjects overlap one another, one subject type corresponding to the overlapped region from the plurality of subject types based on the set priority setting ([pg. 4 para. 8] The electronic device can obtain the subject corresponding to the category with the highest priority as the target subject; when there is a subject whose area overlaps with the subject with the highest priority or whose distance is within a certain range, the electronic device will be the subject whose overlap or distance is within a certain range The subject with the highest priority is the target subject).
Zhuo does not explicitly teach set a priority setting, according to an input of a user.
Misawa, in the same field of endeavor of image subject detection, teaches set a priority setting, according to an input of a user ([0058] The priority subject determining unit 8 determines a priority subject with the highest priority among the subjects detected by the subject detection unit 7. In this embodiment, the user sets priority conditions in advance, and the result of setting is stored in the ROM 15C, which will be described later. Then, the priority subject determining unit 8 determines the priority subject by referencing the conditions).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Misawa to set a priority setting according to an input of the user because "The user determines the priority subject using the setting screen 40. FIG. 5 shows a case where the pet priority is selected, and further the center priority is selected. Then, the subjects detected by the subject detection unit 7 are labeled to distinguish between the priority subject and subjects other than the priority subject" [Misawa 0059] and "When the two or more types of subjects are detected from the image, one priority subject is determined among the two or more types of subjects. The aperture and the optical system are adjusted so that the priority subject is focused and the subject other than the priority subject is within a depth of field of the photographing unit" [Misawa Abstract].
Regarding claim 5, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein execution of the stored instructions further causes the image processing apparatus to, after acquiring detection results of a plurality of preset subject types in the input image ([pg. 3 para. 14] Specifically, the electronic device 110 may collect a preview image through the camera 120, perform subject detection on the preview image, and obtain the categories and regions corresponding to the multiple subjects contained in the preview image), perform processing for determining a main subject corresponding to the input image based on the priority setting ([pg. 4 para. 7] In operation 206, a target subject is selected from multiple subjects according to the priorities corresponding to different categories. The electronic device can preset priorities corresponding to different categories, and the electronic device selects a target subject from multiple subjects according to the priorities corresponding to different categories. For example, the priority of the category may be reduced in order of people, animals, and plants).
Regarding claim 6, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein a priority is set for each subject type in each priority setting ([pg. 4 para. 7] In operation 206, a target subject is selected from multiple subjects according to the priorities corresponding to different categories).
Regarding claim 7, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein execution of the stored instructions further causes the image processing apparatus to, in a case where detection results of the plurality of types of subjects overlap one another, determined a subject having a highest priority in the set priority setting as the subject type corresponding to the overlapped region ([pg. 4 para. 8] The electronic device can obtain the subject corresponding to the category with the highest priority as the target subject; when there is a subject whose area overlaps with the subject with the highest priority or whose distance is within a certain range, the electronic device will be the subject whose overlap or distance is within a certain range The subject with the highest priority is the target subject).
Regarding claim 8, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein execution of the stored instructions further causes the image processing apparatus to, in a case where detection results of the plurality of subject types having a same priority overlap, determine a subject having a highest reliability as the subject type corresponding to the overlapped region ([pg. 4 para. 8] The electronic device can obtain the subject corresponding to the category with the highest priority as the target subject; when there is a subject whose area overlaps with the subject with the highest priority or whose distance is within a certain range, the electronic device will be the subject whose overlap or distance is within a certain range The subject with the highest priority is the target subject. Optionally, the electronic device may also combine one or more of the location of the region corresponding to the subject, the area of the region corresponding to the subject, and the confidence level of the subject's corresponding category to determine the target subject).
Regarding claim 9, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein execution of the stored instructions further causes the image processing apparatus to, normalize reliability according to the subject types and determines the subject type by using the normalized reliability ([pg. 9 para. 9] Specifically, the electronic device can input the preview image and the center weight map into the subject detection model, and perform the detection to obtain the subject area confidence map. The subject area confidence map contains the confidence values of each pixel for different subject categories. For example, the confidence that a certain pixel belongs to a person is 0.8, the confidence of a flower is 0.1, and the confidence of a dog is 0.1. [pg. 10 para. 1] The electronic device can output the region and the corresponding category of each subject according to the magnitude of the confidence value of each pixel in the subject region confidence map in different categories).
Regarding claim 11, Zhuo teaches a method for controlling an image processing apparatus ([pg. 3 para. 3] FIG. 1 is an application environment diagram of a focusing method in one or more embodiments), the method comprising:
setting a priority setting, in which one of a plurality of subject classifications is set, wherein each subject classification includes at least one of a plurality of types of subjects ([pg. 4 para. 7] The electronic device can preset priorities corresponding to different categories, and the electronic device selects a target subject from multiple subjects according to the priorities corresponding to different categories);
and detecting the plurality of subject types for an input image, each of the plurality of subject types being in their own detection region ([pg. 3 para. 14] Specifically, the electronic device 110 may collect a preview image through the camera 120, perform subject detection on the preview image, and obtain the categories and regions corresponding to the multiple subjects contained in the preview image);
and determining, in a case where detection regions of the plurality of types of subjects overlap one another, one subject type corresponding to the overlapped region from the plurality of subject types based on the set priority setting ([pg. 4 para. 8] The electronic device can obtain the subject corresponding to the category with the highest priority as the target subject; when there is a subject whose area overlaps with the subject with the highest priority or whose distance is within a certain range, the electronic device will be the subject whose overlap or distance is within a certain range The subject with the highest priority is the target subject).
Zhuo does not explicitly teach setting a priority setting, according to an input of a user.
Misawa, in the same field of endeavor of image subject detection, teaches setting a priority setting, according to an input of a user ([0058] The priority subject determining unit 8 determines a priority subject with the highest priority among the subjects detected by the subject detection unit 7. In this embodiment, the user sets priority conditions in advance, and the result of setting is stored in the ROM 15C, which will be described later. Then, the priority subject determining unit 8 determines the priority subject by referencing the conditions).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Zhuo with the teachings of Misawa to set a priority setting according to an input of the user because "The user determines the priority subject using the setting screen 40. FIG. 5 shows a case where the pet priority is selected, and further the center priority is selected. Then, the subjects detected by the subject detection unit 7 are labeled to distinguish between the priority subject and subjects other than the priority subject" [Misawa 0059] and "When the two or more types of subjects are detected from the image, one priority subject is determined among the two or more types of subjects. The aperture and the optical system are adjusted so that the priority subject is focused and the subject other than the priority subject is within a depth of field of the photographing unit" [Misawa Abstract].
Regarding claim 12, Zhuo and Misawa teach the method of claim 11. Zhuo further teaches calculating detection reliability for the subjects detected ([pg. 6 para. 3] the electronic device may determine the pre-selected subject as the target subject in combination with one or more of the area, location, and confidence of the category of the pre-selected subject. The confidence of the category refers to the degree of credibility that the subject belongs to the category. Generally, the larger the area of the region corresponding to the pre-selected subject, the smaller the distance between the position and the center of the preview image, and the higher the confidence of the category, the preselected subject is considered more suitable as the target subject), wherein the determination of a subject type is in the same region based on the reliability ([pg. 4 para. 8] Optionally, the electronic device may also combine one or more of the location of the region corresponding to the subject, the area of the region corresponding to the subject, and the confidence level of the subject's corresponding category to determine the target subject).
Regarding claim 13, Zhuo and Misawa teach the method of claim 11. Zhuo further teaches a non-transitory computer-readable storage medium storing a program for causing a computer to execute the method for controlling an image processing apparatus ([pg. 13 para. 4] The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program may be executed by a processor to implement a focusing method provided in the following embodiments. The internal memory provides a cached operating environment for the operating system computer program in the non-volatile storage medium).
Regarding claim 15, Zhuo and Misawa teach the apparatus of claim 1. Zhuo further teaches wherein execution of the stored instructions further configures the image processing apparatus to set one of a plurality of settings, the plurality of settings including a setting for setting priority one of the subject classifications ([pg. 4 para. 7] The electronic device can preset priorities corresponding to different categories, and the electronic device selects a target subject from multiple subjects according to the priorities corresponding to different categories),
and a setting for setting the subject classifications to the same priority regardless of the subject classifications ([pg. 5 para. 5] In one embodiment, when there are multiple subjects corresponding to the category with the highest priority in the preview image, the electronic device may obtain the depth image corresponding to the preview image, calculate the depth information of each subject in the preview image according to the depth image, and change Multiple subjects with the highest priority category in the preview image and depth information within a preset range are used as target subjects).
Regarding claim 19, Zhuo teaches an image processing apparatus (electronic device 110) comprising: one or more processors ([pg. 13 para. 4] As shown in FIG. 9, the electronic device includes a processor and a memory connected through a system bus. [pg. 3 para. 14] The electronic device 110 may not be limited to various mobile phones, tablet computers, wearable devices, and the like);
a memory storing instructions which, when the instructions are executed by the one or more processors, cause the image processing apparatus to (]pg. 13 para. 4] As shown in FIG. 9, the electronic device includes a processor and a memory connected through a system bus. Among them, the processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory may include a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program may be executed by a processor to implement a focusing method provided in the following embodiments):
set a priority setting, in which one of a plurality of subject classifications is set, wherein each subject classification includes at least one of a plurality of types of subjects; ([pg. 4 para. 7] The electronic device can preset priorities corresponding to different categories, and the electronic device selects a target subject from multiple subjects according to the priorities corresponding to different categories);
detect the plurality of subject types for an input image, each of the plurality of subject types being in their own detection region and belonging to any one of the plurality of subject classifications ([pg. 3 para. 14] Specifically, the electronic device 110 may collect a preview image through the camera 120, perform subject detection on the preview image, and obtain the categories and regions corresponding to the multiple subjects contained in the preview image);
and determine one subject type corresponding to an overlapped region in which the plurality of subject types overlap one another, from the plurality of subject types based on the set priority setting ([pg. 4 para. 8] The electronic device can obtain the subject corresponding to the category with the highest priority as the target subject; when there is a subject whose area overlaps with the subject with the highest priority or whose distance is within a certain range, the electronic device will be the subject whose overlap or distance is within a certain range The subject with the highest priority is the target subject).
Zhuo does not explicitly teach set a priority setting, according to an input of a user.
Misawa, in the same field of endeavor of image subject detection, teaches set a priority setting, according to an input of a user ([0058] The priority subject determining unit 8 determines a priority subject with the highest priority among the subjects detected by the subject detection unit 7. In this embodiment, the user sets priority conditions in advance, and the result of setting is stored in the ROM 15C, which will be described later. Then, the priority subject determining unit 8 determines the priority subject by referencing the conditions).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Misawa to set a priority setting according to an input of the user because "The user determines the priority subject using the setting screen 40. FIG. 5 shows a case where the pet priority is selected, and further the center priority is selected. Then, the subjects detected by the subject detection unit 7 are labeled to distinguish between the priority subject and subjects other than the priority subject" [Misawa 0059] and "When the two or more types of subjects are detected from the image, one priority subject is determined among the two or more types of subjects. The aperture and the optical system are adjusted so that the priority subject is focused and the subject other than the priority subject is within a depth of field of the photographing unit" [Misawa Abstract].
Claims 3-4, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhuo in view of Misawa and Choi (US20220004805A1).
Regarding claim 3, Zhuo and Misawa teach the apparatus of claim 1. Choi, in the same field of endeavor of image analysis for object recognition, teaches wherein the memory further stores a plurality of dictionary data that has completed learning based on a neural network for each subject type, and wherein the dictionary data includes different network parameters ([0092] In addition, the processor 2200 may train the plurality of recognition models by using different training datasets, wherein types and numbers of classes, which may be classified by the plurality of recognition models, are different from each other. For example, the processor 2200 may train a first recognition model and a second recognition model, wherein the first recognition model provides a greater number of classifiable classes than the second recognition model although providing lower recognition accuracy than the second recognition model. [0058] As used herein, the term “recognition model” may refer to, but is not limited to, an artificial intelligence model including one or more neural networks, which are trained to receive an image of an object as input data and obtain object information by performing object recognition on one or more objects in the image).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Choi to train multiple neural networks for different subject types to employ multiple "second recognition models" which are "trained to more finely recognize objects belonging to a certain category to obtain more accurate object information than the first recognition model" [Choi 0149].
Regarding claim 4, Zhuo, Misawa and Choi teach the apparatus of claim 3. Choi teaches wherein execution of the stored instructions further causes the image processing apparatus to switch between at least part of the plurality of types of dictionary data based on the set priority setting ([0092] The processor 2200 according to an embodiment of the disclosure may generate a plurality of recognition models for recognizing objects. The processor 2200 may generate a recognition model by receiving a training dataset from a data server via the communication interface 2100. In addition, the processor 2200 may train the plurality of recognition models by using different training datasets, wherein types and numbers of classes, which may be classified by the plurality of recognition models, are different from each other. [0154] In another embodiment of the disclosure, the model generation server 2000 may determine the second recognition model by additionally using the priority information 820 of FIG. 8. For example, when, although characteristic information of a second subset space is determined to be representatively “electronic products,” “groceries” are also present in a number or more in the second subset space, the model generation server 2000 may determine the second recognition model, which is to be allocated to the second subset space, to be a plurality of second recognition models, that is, both the “second recognition model E,” which recognizes only electronic products, and the “second recognition model F,” which recognizes only groceries);
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Choi to use different training data for different subject types and choosing the data based on the priority type to employ a "second recognition model" which is "trained to more finely recognize objects belonging to a certain category to obtain more accurate object information than the first recognition model" [Choi 0149].
Regarding claim 14, Zhuo, Misawa, and Choi teach the apparatus of claim 4. Choi teaches wherein, execution of the stored instructions further causes the image processing apparatus to, in a case where an arbitrary region of the input image is specified by a user operation, selects a switching sequence that switches between all of detectable dictionaries regardless of the set priority setting ([0154] In another embodiment of the disclosure, the model generation server 2000 may determine the second recognition model by additionally using the priority information 820 of FIG. 8. [0163] In addition, in operation S1110, the electronic device 1000 may check whether there is a change in space information, and may determine that there may be a need to update the spatial map, the first object information, the subset space information, or the like. For example, after the generation of the subset spaces and the determination of the second recognition models are completed, the electronic device 1000 may obtain the second object information while moving through the space (operation S360 of FIG. 3 or operation S1170 of FIG. 11). During the process of performing object recognition by the electronic device 1000, a structural change in the space, or the like may be sensed by the sensor(s) 1100, or information indicating that the characteristic of the space has been changed by an input by a user).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Choi to switch between dictionaries when a region is specified by a user because "When an update is needed due to a change in the space information, the electronic device 1000 may update the space information by performing operation S1120 again" [Choi 0163].
Regarding claim 18, Zhuo and Misawa teach the apparatus of claim 1. Choi teaches wherein execution of the stored instructions further causes the image processing apparatus to, based on the set priority setting, use deep learning models for detecting a priority subject type more frequently than deep learning models for detecting other subject types for a plurality of input images ([0004] Machine learning refers to an algorithm technology for self-classifying/learning features of a plurality of pieces of input data, and element technologies include technical fields such as linguistic understanding, visual understanding, inference/prediction, knowledge representation, and motion control, which use machine learning algorithms such as deep learning. [0149] For example, the plurality of second recognition models (for example, a second recognition model A, a second recognition model B, . . . , and a second recognition model K) may be recognition models suitable to recognize electronic products or groceries. In this case, the second recognition model A may recognize “electronic products” and “groceries,” and may be a recognition model trained by using a training dataset that includes “electronic products” and “groceries” in a ratio of 7:3, during the generation of the recognition model. In addition, the second recognition model C may recognize “electronic products,” and may be a recognition model trained by using a training dataset that includes only “electronic products,” during the generation of the recognition model).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Choi to detect objects in the priority setting more frequently by employing a "second recognition model" which is "trained to more finely recognize objects belonging to a certain category to obtain more accurate object information than the first recognition model" [Choi 0149].
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Zhuo in view of Misawa and Peled (US20190325584A1).
Regarding claim 16, Zhuo and Misawa teach the apparatus of claim 15. Peled, in the same field of endeavor of image object classification, teaches wherein the plurality of settings includes a setting for setting all of the plurality of subject types as unadopted ([0046] the resulting neural network (i.e., Subject detection neural network 130) only recognizes Subject 190 and ignores or fails to classify other Subjects. For example, the general classification of ‘Subject’ can be manipulated such that only Subject 190, as learned from the initialization video data 182, is classified as ‘Subject’. Under such training, all other Subjects will be classified as unknown or irrelevant objects).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Peled to set subject types as unadopted so that "Subject 190 will be thus be the only Subject possibly detected within a given frame" [Peled 0046].
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Zhuo in view of Misawa and Xie (CN110490146A).
Regarding claim 17, Zhuo and Misawa teach the apparatus of claim 1. Xie, in the same field of endeavor of image classification, teaches wherein the plurality of subject types include at least one of Bird, Dog, Cat, Motorcycle and Automobile, and the Bird, the Dog, the Cat are classified as Animal, and the Motorcycle and the Automobile are classified as Vehicle ([pg. 11 para. 5] the basis of step S2 to obtain deep learning algorithm, it can be for synchronous real-time recognition and alarm to all intrusion in the target scene, identifying the type comprises a human, bicycle, car, motorcycle, truck, airplane, truck, bird, dog, cat and other common intrusion target, and equine, ovine, bovine, elephant, bear, zebra, long neck deer very visible to alarm and identify the intrusion target. the user can be freely selected and set for all alarm types. alarm type can be roughly divided into human, vehicle, non-motor vehicle, animal and airplane, convenient for fast classification by the user).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the apparatus of Zhuo with the teachings of Xie to include at least one of bird, dog, cat, motorcycle and automobile, and the bird, the dog, the cat are classified as animal, and the motorcycle and the automobile are classified as vehicle because the "algorithm may also accurately identify the type and risk probability level of intrusion object, provides the user with additional auxiliary information" [Xie pg. 12 para 4].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jacqueline R Zak whose telephone number is (571)272-4077. The examiner can normally be reached M-F 9-5. 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, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACQUELINE R ZAK/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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