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 .
DETAILED ACTION
Response to Amendment
Applicant’s “Amendment” filed on 08/06/2026 has been considered.
Claims 1, 2, 7-9, and 15-20 are amended. Claims 1-20 remain pending in this application and an action on the merits follow.
Applicant’s response by virtue of amendment to claims 1-20 has not overcome the Examiner’s rejection under 35 USC § 101.
Applicant’s response by virtue of amendment to claims 2, 9, and 16 has overcome the Examiner’s rejection under 35 USC § 112.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 recites the limitation "ML model in generating" in page 4. There is insufficient antecedent basis for this limitation in the claim.
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 USC 101. The claimed invention is directed to non-statutory subject matter because claims 1, 8, and 15 are directed to an abstract idea without significantly more. Claims 2-7, 9-14, and 16-20 fail to remedy these deficiencies.
The claims 1, 8, and 15 recite receiving an image of an object of interest, identifying a location of the object of intertest, identifying an object of interest having a label that is missing or damaged using a machine learning model, cropping the image to remove objects other than the object of interest, performing optical character recognition to the cropped image to read any visible text, generating an initial label exception based on results of performing the OCR, assigning a confidence score to the initial label exception based on results of performing the OCR, classifying the initial label exception according to a type of issue with the label, creating customized label check instructions, updating the initial label exception with the customized label check instructions to create an enhanced label exception, presenting the enhanced label exception; receiving user feedback indicating whether the enhanced label exception accurately identified the label as missing or damaged, and retraining the machine learning model using the user feedback to reduce false positives.
The Claims 1, 8, and 15 recite identifying, cropping, performing, generating, assigning, classifying, creating, updating, and presenting steps as drafted, are processes that under broadest reasonable interpretation, cover performance of managing personal behavior and cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “a processor, a non-transitory computer-readable medium storing instructions that are operative upon execution by the processor, an image capture device of a robotic device, a user interface device, and one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations”, nothing in the claim element precludes the steps from practically being performed by organizing human activity and being performed in the mind. For example, but for the “the processor, the one or more computer storage devices, the image capture device of the robotic device, the user interface device, and the computer” in the context of these claims encompasses a person manually identifies a location of the object of interest based on a location label, crops the image to remove objects, reads any visible text from the cropped image, generates an initial label exception/anomaly, assigns a confidence score to the initial label exception/anomaly, classifies the initial label exception/anomaly to either a missing label type or a damaged label type, creates customized label check instructions to guild and correct the label, updates the initial label exception to create an enhanced label exception, and presents/displays the enhanced label exception on a paper/board/interface to instruct/locate/correct the issue associated with the label. The optical character recognition (OCR) is utilized as a tool to perform reading/recognizing any text on the cropped images, which is a generic data processing. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing personal behavior but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” and the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The limitation of identifying and retraining steps as drafted, are processes that under broadest reasonable interpretation, cover performance of the limitation by utilizing mathematical algorithms/functions but for the recitation of generic computer components. That is, other than reciting “the processor, the one or more computer storage devices, the image capture device of the robotic device, the user interface device, and the computer”, nothing in the claim element precludes the steps from practically being performed by utilizing mathematical algorithms. For example, but for the “the processor, the one or more computer storage devices, the image capture device of the robotic device, the user interface device, and the computer” language, identifying and retraining steps in the context of these claims encompasses a user manually applies the mathematical algorithms/machine learning model to identify the object of interest having a label that is missing or damaged and to retrain the machine learning model. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by utilizing mathematical algorithms/functions but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because receiving steps are recited at a high level of generality (i.e., as a general means of receiving images and user feedbacks step) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of receiving, identifying, cropping, reading, generating, assigning, classifying, creating, updating, presenting, and retraining steps in a computer environment. The claimed computer components such as the processor, the one or more computer storage devices, the image capture device of the robotic device, the user interface device, and the computer are recited at a high level of generality and are merely invoked as tools to perform receiving, identifying, cropping, reading, generating, assigning, classifying, creating, updating, presenting, and retraining steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims 1, 8, and 15 are directed to an abstract idea.
The claims 1, 8, and 15 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 elements of using the processor, the one or more computer storage devices, the image capture device of the robotic device, the user interface device, and the computer to perform receiving, identifying, cropping, reading, generating, assigning, classifying, creating, updating, presenting, and retraining steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 1, 8, and 15 are not patent eligible.
Claims 2-7, 9-14, and 16-20, disclose insignificant helpful content to further describe content, such as label checks are performed for a plurality of labels within a retail facility, object exclusion/removal from inventory based on the text instructions on labels, resolving instructions for a missing label, a damaged label, and a partially damaged label issue, inventory system is updated based on the resolved issue, and using the feedback to generate the enhanced label exceptions, which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 2-7, 9-14, and 16-20 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 2-7, 9-14, and 16-20 further limit the abstract idea but not make it less abstract. Thus, the claims 2-7, 9-14, and 16-20 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 2-7, 9-14, and 16-20. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 2-7, 9-14, and 16-20 are not patent eligible.
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 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.
Claims 1-3, 6-10, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2025/0045548 to Golchha et al, in view of U.S. Patent Application Publication No. 2024/0220921 to Hekman et al., and further in view of Japan Patent Application Publication No. JP 2022/137805 to Yotsumoto.
With regard to claims 1, 8, and 15, Golchha discloses a system for label checking, the system comprising:
a processor (paragraph 4, The apparatus includes at least a processor and a memory); and
a non-transitory computer-readable medium storing instructions that are operative upon execution by the processor to (paragraph 4, a processor and a memory):
receive an image of an object of interest, the image generated by an image capture device of a robotic device (paragraphs 19 and 26, Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. A scanned label 120 may refer to the process of capturing or digitizing the information on a label using a scanning device, such as a barcode scanner or an optical character recognition (OCR) system.);
identify the object of interest having a label that is missing or damaged using a machine learning (ML) model that performs object detection and recognition on the image of the object of interest (paragraphs 26, 28, and 62, . A scanned label 120 may refer to the process of capturing or digitizing the information on a label using a scanning device, such as a barcode scanner or an optical character recognition (OCR) system. For example, a slide could be classified as flipped if two or more of the following conditions are met: label misplacement is more than 80% from the correct position. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. Examiner notes that the flipped label is unrecognizable which is considered as “a label that is damaged”.);
cropping the image to remove objects other than the object or the label from the image (paragraphs 26, 27, and 32, Still referring to FIG. 1, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into features.) ;
performing optical character recognition (OCR) to the cropped image to read any visible text included on the label (paragraphs 26, 27, and 32, With continued reference to FIG. 1, processor 104 is configured to generate a scanned label 120 using a text recognition module 124. As used in the current disclosure, a “text recognition module” is a software designed to automatically recognize and extract text from images or scanned document. The output of a text recognition module 124 may be the extracted text in a machine-readable format, which can be further processed, stored, or analyzed by other applications or systems. )
generate an initial label exception associated with the object of interest based on results of performing the OCR on the label (paragraphs 61 and 62, A label parameter assessment may analyze how well extracted data from the slides of profile 108 match expected patterns that correspond to correctly oriented slides. Extracted data may refer to data received using OCR processes as described above. Processor 104 may flag the profile 108 as potentially containing flipped digital slides in the digital representations and display the flag/alert through an interface or display device as described herein. The output of the orientation machine learning model may include orientation labels such as “correct orientation, “incorrect orientation,” “correct label positions,” “flipped,” and the like to indicate the orientation status. );
assign a confidence score to the initial label exception based on results of performing the OCR, the confidence score indicating a degree of confidence associated with the initial label exception (paragraphs 42-48 and 52, these variables may be used to compare a scanned label 120 and a historically scanned labels to determine the confidence score 136 specific to the scanned label 120.);
classify the initial label exception according to a type of issue with the label responsive to the assigned confidence score exceeding a threshold score, the type of the issue comprising a missing label type of issue or a damaged label type of issue (paragraph 62, Similarly, anomaly detection thresholds may trigger alerts when unusual patterns in contrast or label positions are detected. Training data may include a plurality of thresholds categorized to an orientation status/label. For example, training data may include a flipped orientation threshold, a 90 degree orientation threshold, a correct orientation threshold, and the like. For example, a slide could be classified as flipped if two or more of the following conditions are met: color deviation exceeds 20% from the norm; contrast inversion is more than 50% from the expected gradient; label misplacement is more than 80% from the correct position. Processor 104 may flag the profile 108 as potentially containing flipped digital slides in the digital representations and display the flag/alert through an interface or display device as described herein.);
receive, via the user interface device, user feedback indicating whether the enhanced label exception accurately identified the label as missing or damaged (paragraph 62, processor 104 may also provide options for manual verification in uncertain cases); and
retrain the machine learning model using the user feedback to reduce false positives (paragraph 62, To enhance reliability, processor 104 may also provide options for manual verification in uncertain cases and continuously adjust its thresholds and criteria based on new data and expert feedback to improve accuracy.).
However, Golchha does not disclose identify a location of the object of interest based on a location label detected by the robotic device; present the identified location via a user interface device, create customized label check instructions… and present the enhanced label exception.
However, Hekman teaches create customized label check instructions based on the type of the issue associated with the label, wherein the customized label check instructions guide a user in locating the object of interest and correcting the type of the issue associated with the label (The planogram reference data include an arrangement of the display including the locations for each product denoted by SKU/UPC, wherein detecting SKUs in incorrect places and/or detecting SKUs which do not appear in the reference planogram data should generate an exception. In providing the final inventory report, only the exceptions which are actionable should be reported and the rest removed. An “actionable” item includes one which, even if some feature identification process is erroneous, the output can be verified by a human on site. As an example, an item detected adjacent to a price label for a different item could be the result of: (i) erroneous character detection (i.e., reading) of the price label. (ii) erroneous detection of the item from its imaged features, or (iii) a misplaced item, wherein a human may view the image captured to rapidly determine if case (iii) applies, which requires an action to move the misplaced item. paragraphs 125 and 130);
update the initial label exception with the customized label check instructions to create an enhanced label exception, the enhanced label exception comprising the customized label check instructions and a real-time image of the object of interest (In some cases, such as misplaced products or low occurrence exceptions may be actioned via displaying an image of the identified product for human review and possible corrective action, paragraph 160); and
present the enhanced label exception with the customized label check instructions via a user interface device enabling improved efficiency locating and correcting the issue associated with the label (In some cases, such as misplaced products or low occurrence exceptions may be actioned via displaying an image of the identified product for human review and possible corrective action. The other two cases may take a mere matter of seconds to verify the product as humans are naturally strong pattern matchers. It is appreciated that the largest time cost for human store associates tasked with finding and correcting misplaced items is finding them to begin with amongst possibly thousands of items and product displays which is now handled entirely by the robot 102 and server 202 automatically., paragraphs 130 and 160).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Golchha to include, create customized label check instructions… and present the enhanced label exception, as taught in Hekman, in order to identify exceptions in feature detection analytics (Hekman, paragraph 3).
However, Yotsumoto teaches identify a location of the object of interest based on a location label detected by the robotic device (The bookshelf determination unit 31e is based on the identification information of the bookshelf attached to the image by the image acquisition unit 31b, the information stored in the bookshelf DB 32b of the storage unit 32, and the characters of the label recognized by the character recognition unit 31d. Then, a process of determining whether or not the book is stored in an appropriate bookshelf is performed. page 6, lines 1-13); present the identified location via a user interface device (The bookshelf determination unit 31e determines that the book is stored in an appropriate bookshelf when both pieces of identification information match, and determines that the book is not stored in an appropriate bookshelf when both pieces of identification information do not match. The notification processing unit 31f performs processing for notifying the terminal device 5 of the determination result of the bookshelf determination unit 31e. For example, when the bookshelf determination unit 31e determines that a book is not stored in an appropriate bookshelf, the notification processing unit 31f transmits information about the book to the terminal device 5, thereby notifying the terminal device 5 of the information to the user. the ID of the bookshelf captured in the image, the position of the books stored in the inappropriate place or the label of the book, the inappropriate place, and so on. "Classification symbol" obtained from the label of the book stored in the , the ID of the appropriate bookshelf that should store the book with this "classification symbol", and the position of the book or its label whose label characters could not be recognized and other information to the terminal device 5., Fig. 12, page 6, lines 1-22 and page 12, lines 1-5).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Golchha to include, identify a location of the object of interest based on a location label detected by the robotic device; present the identified location via a user interface device, as taught in Yotsumoto, in order to recognize a label attached to a book stored in a library shelf. (Yotsumoto, technical-field).
With regard to claims 2, 9, and 16, the combination of references discloses assign a score for each initial label exception in a plurality of initial label exceptions associated with a plurality of objects within a retail facility (Golchha, paragraph 62); identify a set of high confidence initial label exceptions in the plurality of initial label exceptions using the score assigned to each initial label exception (Golchha, paragraph 62); and generate the customized label check instructions for checking labels on a set of objects associated with the set of high confidence initial label exceptions, wherein the customized label check instructions are presented to at least one user via the user interface device (Hekman, paragraphs 125, 130, and 160).
With regard to claims 3, 10, and 17, the combination of references discloses the instructions are further operative to: identify a plurality of confidence scores associated with a plurality of label exceptions having confidence scores within a threshold range (Golchha, paragraph 62); select initial label exceptions having a confidence score within the threshold range ((Golchha, paragraph 62, if the deviation in color distribution from the expected pattern exceeds a predetermined threshold, the slide may be flagged as potentially misoriented.); and update each selected initial label exception with a classification of the type of issue associated with the label and customized label check instructions for resolving the type of the issue associated with each label (Hekman, paragraphs 125, 130, and 160, As discussed above, the customer report 716 may have filters to include or exclude certain exceptions based on the customer's need to know the exception or whether the exception is actionable by the customer. The same exception, however, may be actionable for corporate management of the store to update their catalog and could be reported if requested by the management. In some cases, such as misplaced products or low occurrence exceptions may be actioned via displaying an image of the identified product for human review and possible corrective action, however some environments may not desire human review.).
With regard to claims 6 and 20, the combination of references discloses update an inventory system using information associated with a replaced label responsive to receiving an indication that the issue associated with the enhanced label exception is resolved (Hekman, paragraph 202-203, A resolution feedback signal corresponds to the user of the device 208 indicating that the issue which generated the exception has been resolved. If the exception has been resolved, the entry is removed from the customer report 716 in block 1114.).
With regard to claim 7, Golchha discloses prompt a user to provide feedback regarding the enhanced label exception, wherein the feedback comprises an indication whether the issue associated with the enhanced label exception is a correct label exception accurately identifying a label issue or a false positive (paragraph 62, To enhance reliability, processor 104 may also provide options for manual verification in uncertain cases and continuously adjust its thresholds and criteria based on new data and expert feedback to improve accuracy.); and using the feedback associated with a plurality of enhanced label exceptions to retrain ML model in generating the enhanced label exceptions (paragraph 4, wherein training the confidence machine-learning model includes retraining the confidence machine-learning model with feedback from previous iterations of the confidence machine-learning model and determining the confidence score using the trained confidence machine-learning model, adjust the confidence score for accuracy, and display the confidence score using a display device.).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2025/0045548 to Golchha et al, U.S. Patent Application Publication No. 2024/0220921 to Hekman et al., and Japan Patent Application Publication No. JP 2022/137805 to Yotsumoto, and further in view of Korean Patent No. 100533143 to Yoshino.
With regard to claims 4,11, and 18, the combination of references substantially discloses the claimed invention, however, the combination of references does not disclose identify a selected object having a label with text instructions identifying the object as an object to be excluded from inventory using optical character recognition; and filter the identified object from a plurality of objects undergoing label check.
However, Yoshino teaches identify a selected object having a label with text instructions identifying the object as an object to be excluded from inventory using optical character recognition; and filter the identified object from a plurality of objects undergoing label check (a character recognition apparatus (2) for performing character recognition on handwriting data. The deletion instruction fields 14a to (c) correspond to field data for each product. For example, the deletion instruction field 14a corresponds to a product name, a quantity, a unit price, an amount of money, and the like for the product A. FIG. Now, when the user wants to erase the record for the product B, the user attaches X to the deletion designation column 14b. Upon character recognition of this sales slip, the computer 10 identifies whether or not an X is attached to the deletion designation field. Since the blank designation column 14b is marked with X, the computer 20 excludes from the recognition string a record composed of each field corresponding to this 14b (brand name, quantity, unit price amount, etc. of the product B). abstract, page 27, lines 13-21).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of references to include, identify a selected object having a label with text instructions identifying the object as an object to be excluded from inventory using optical character recognition; and filter the identified object from a plurality of objects undergoing label check, as taught in Yoshino, in order to provide a work management system capable of correctly and efficiently recognizing characters in a writing medium (Yoshino, page 4, lines 6-8).
Claims 5, 12-14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2025/0045548 to Golchha et al, U.S. Patent Application Publication No. 2024/0220921 to Hekman et al., and Japan Patent Application Publication No. JP 2022/137805 to Yotsumoto, and further in view of U.S. Patent Application Publication No. 2024/0338711 to Friesen et al.
With regard to claims 5, 12-14, and 19, the combination of references substantially discloses the claimed invention, however, the combination of references does not disclose generate a first set of customized label check instructions for resolving a first type of issue associated with a missing label, wherein the first set of customized label check instructions includes instructions for creating a new label for the object which is missing the label; generate a second set of customized label check instructions for resolving a second type of issue associated with an unreadable label, wherein the second set of customized label check instructions includes instructions for replacing a damaged label, wherein text on the damaged label is unreadable; and generate a third set of customized label check instructions for resolving a third type of issue associated with a partially damaged label which is present and at least partially readable, wherein the partially damaged label is at least partially unreadable.
However, Friesen teaches generate a first set of customized label check instructions for resolving a first type of issue associated with a missing label, wherein the first set of customized label check instructions includes instructions for creating a new label for the object which is missing the label; generate a second set of customized label check instructions for resolving a second type of issue associated with an unreadable label, wherein the second set of customized label check instructions includes instructions for replacing a damaged label, wherein text on the damaged label is unreadable; and generate a third set of customized label check instructions for resolving a third type of issue associated with a partially damaged label which is present and at least partially readable, wherein the partially damaged label is at least partially unreadable (the server system 116 can determine that a new label should be printed for the item 326N to replace the unique identifier that was scanned by the employee or missing during the scanning. The server system 116 can generate a notification about needing a new label for the item 326N to the user device 120, which can then be presented in the GUI 324 as the notification 338. Examiner notes that a missing/ damaged/partially damaged label exception can be resolved by replacing a new label. paragraph 155).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of references to include, generate a first set of customized label check instructions for resolving a first type of issue associated with a missing label, wherein the first set of customized label check instructions includes instructions for creating a new label for the object which is missing the label; generate a second set of customized label check instructions for resolving a second type of issue associated with an unreadable label, wherein the second set of customized label check instructions includes instructions for replacing a damaged label, wherein text on the damaged label is unreadable; and generate a third set of customized label check instructions for resolving a third type of issue associated with a partially damaged label which is present and at least partially readable, wherein the partially damaged label is at least partially unreadable, as taught in Friesen, in order to provide an ability to coordinate drive-up orders with drive-up order return services through easy and user-friendly GUI displays (Friesen, paragraph 3).
Response to Arguments
Applicants' arguments filed on 08/06/2026 have been fully considered but they are not fully persuasive especially in light of the new art used in the rejections.
Applicants remark that “the combination of references does not disclose identifying a location of the object based on a location label detected by the robotic device; identifying the object having a label that is missing or unreadable using a machine learning (ML) model that performs object detection and recognition on the image of the object; cropping the image to remove objects other than the object or the label from the image ;performing optical character recognition (OCR) to the cropped image to read any visible text included on the label; assigning a confidence score to the initial label exception based on the results of performing the OCR, the confidence score indicating a degree of confidence associated with the initial label exception; receiving, via the user interface device, user feedback indicating whether the enhanced label exception accurately identified the label as missing or damaged; and retraining the machine learning model using the user feedback to reduce false positives”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “Claim 1 now requires features such as a robotic device capturing image and location data. Claim 1 further now requires features related to image processing, such as cropping images and performing optical character recognition. Claim 1 further requires features related to training a machine learning (ML) model used for object recognition to reduce occurrences of false positives. Accordingly, Applicant respectfully submits that claim 1, as amended, is patent eligible and not directed toward an abstract idea pursuant to step 2A prong 1.”
Examiner directs Applicants' attention to the office action above.
Conclusion
Please refer to form 892 for cited references.
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 extension fee 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.
Any inquiry concerning this communication from the examiner should be directed to Ariel Yu whose telephone number is 571-270-3312. The examiner can normally be reached on Monday-Friday 9:00am-5:00pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Fahd A can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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