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 .
The action is responsive to amendment filed on 3/18/2026.
The Amendment to The Specification has been considered and entered.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to the rejections previously made and the amended claims filed on 3/18/2025 have been fully considered. In view of the claim amendments, the rejections are being updated accordingly.
35 USC 101 Rejections
Applicant’s arguments have been fully considered.
In response to the arguments, it is submitted that the claims are remained rejected, and the reason is set forth in the updated rejection. See rejections below for detail.
35 USC 112 Rejections
In view of the amendment filed, the rejections as set forth in the previous office action are thereby withdrawn.
35 USC 103 Rejections
Applicant’s arguments--which are primary directed the amended limitations recited in the independent claims--have been fully considered.
In response to the arguments, it is submitted that in view of the amendments filed, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made to address the amended claims, and that the claims have been properly addressed; see rejections below for detail.
Furthermore, it is submitted that all limitations in pending claims--including those not specifically argued--are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
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.
Claims 1 & 5, 8 and 15 each recites a process with mathematical relationships of generating semantic embeddings in an embedding space, generating a preservation prototype, identifying embedding according to a within a similarity threshold of the preservation prototype by comparing the semantic embeddings to the preservation prototype, and pruning one or more images correspond to embeddings. Such process is corresponding to one of the abstract ideas groups as set forth by Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance with utilizations of mathematical calculations involving comparing embeddings, similarity determinations via comparing distances, embedding data identification by compare with a similarity threshold, subtracting via pruning data information.
In addition, the limitations recited in claims 3, 11,18 are directed to additional mathematical relationships of similarity calculation and usage of the determined similarity values, which further emphasize the abstract idea of mathematical relationships being claimed.
Additionally, claims 6 and 18 are directed to additional mathematical relationships involving usage of threshold similarity in selecting semantic embedding, and grouping of semantic embedding within the threshold similarity via neighborhoods, which are also being recited in claims 10 & 17. These steps further emphasize the abstract idea of mathematical relationships being claimed.
Plus, the steps of extract embedding and combining embedding as recited in claims 2, 4, 13 & 19, generating cluster and identifying embedding as recited in claims 5, updating parameters as recited in claims 9 & 16, receiving user interaction and combining the embedding as recited in claim 12, and saving or preserving a digital image as recited in claims 7 & 14 & 20 are directed to insignificant extra-solution activities at Step 2A Prong Two, and also would be well-understood, routine, and conventional at Step 2B. The displaying does not provide any integration into a practical application. Rather, the limitations appear to be mere data outputting and applying the abstract idea.
Further, the additional elements (e.g. images, preservation prototype, captions, template strings) are directed to different types of data information, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims.
Hence, the claims do not include elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea.
Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such as a computer readable medium, memory, processor) in the claims amount to no more than usage of a generic computing system having a generic computing components, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known.
Thus, for at least the reasonings above, the claims 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 (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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Stanley et al (Pub No. US 2021/0232620, hereinafter Stanley) in view of Gupta et al (Patent No. US 12,518,560, hereinafter Gupta).
Stanley and Gupta are cited in the previous office action.
With respect to claim 1, Stanley discloses a computer-implemented method (abstract) comprising:
generating, within an embedding space, semantic embeddings from a plurality of digital images within a database ([0052], [0066], [0069]: generate embeddings from the image within a database);
generating a preservation prototype corresponding to a text input describing a semantic concept to preserve within the database ([0052] generate a preservation prototype correspond to a text input describing a concept to preserve in view of one or more concepts or content that are not compliant and thus not being preserved, and/or in view of a prototype of an edit preserved version, as further described in [0055]);
identifying, by comparing the semantic embeddings in the embedding space to the preservation prototype, a preservable embedding according similarity with preservation prototype indicating the semantic concept to preserve within the database ([0052], [0070], [0076]: identify a preservable embedding by comparing the semantic embeddings and identify one ore more complaint image with a respective preservable embedding and one or more images with respective that are not compliant with a respective embedding, which result in a compliant image with a respective preservable embedding is being identified and maintained with no further processing); and
generating a modified database by preserving a digital image of the plurality of digital images corresponding to the preservable embedding and pruning one or more digital images corresponding to semantic embeddings other than the preservable embedding from the database ([0055], [0082]: generate a modified data by preserving an image correspond to the preservable embedding and delete/remove one mor more images other than the preservable embedding represented by the non-compliant images from the database).
Stanley does not explicitly disclose the preservable embedding is identified within a similarity threshold of the preservation prototype as claimed.
However, Gupta discloses identifying, by comparing the semantic embeddings in the embedding space to the preservation prototype, a preservable embedding within a similarity threshold of the preservation prototype indicating the semantic concept to preserve within the database (Col. 4, lines 20-30, Col. 7, lines 27-30: compare embedding represented by vectors to identify at least one preservable embedding that is within a similarity threshold represented by a predetermined threshold of the preservation prototype represented by a key frame that indicating a semantic concept—which is merely a concept or category—to preserve in a database represented by a storge unit, such that the embedding not within a similarity threshold is being discarded/filtered).
Since both Stanley and Gupta are from the same field of endeavor as both are directed to using semantic embedding to generate a modified database by pruning image(s), which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings to incorporate the similarity threshold of the preservation prototype of Gupta into Stanley Lim for modified database generation as claimed. The motivation is to enhance searchability and discoverability with efficient storing, analyzing as well as searching images (Stanley, [0003]; Gupta, Col. 1, lines 63-67).
With respect to claim 2, the combined teachings of Stanley and Gupta further disclose
generating the preservation prototype by:
extracting a plurality of text embeddings from captions describing digital images (Stanley, [0048], [0065-0066]; Gupta, Col. 3, lines 38-40, Fig 1 & 5: extract text embedding corresponding to the textual information); and
combining the plurality of text embeddings into the preservation prototype (Stanley, [0054-0055], [0071]; Gupta, Col. 4, lines 10-30, Col. 7, lines 1-32, Fig 4-5: combine the text embedding into a preserved prototype with different compliant embedded contextual information, such as a stored content being used to identify complaint and non-complaint that determine whether the image is being preserved or not via prototype).
With respect to claim 3, the combined teachings of Stanley and Gupta further disclose
wherein identifying the preservable embedding comprises:
determining similarity scores between the preservation prototype and the semantic embeddings extracted from the plurality of digital images within the database (Stanley, [0066], [0078]; Gupta, Col. 6, lines 40-50, Fig 3: determine similarity scores between the images via similarity identification in view of at least the distance); and
selecting the preservable embedding based on comparing the similarity scores (Stanley, [0053-0055], [0071]; Gupta, Col. 3, lines 9-11 & 35-48, Col. 6, lines 40-55, Fig 3: select the preservable embedding based on the similarity scores, such that the images with scores similar to the non-compliant ones are not being kept while the ones with scores similar to the compliant one are being preserved).
With respect to claim 4, the combined teachings of Stanley and Gupta further disclose generating the preservation prototype by combining text embeddings extracted from template strings describing protected demographic groups (the “template strings describing protected demographic groups” are merely directed to types of descriptive data; Stanley, [0048-0049]; Gupta, Col. 4, lines 10-30, Col. 7, lines 1-32, Fig 4-5: combine the text embedding extracted from different strings describing protected groups, such that racist comment is considered non-compliant).
With respect to claim 5, the combined teachings of Stanley and Gupta further disclose generating embedding clusters from the semantic embeddings extracted from the plurality of digital images in the embedding space (Stanley, [0078-0080]]; Gupta, Col. 6, lines 49-55, Col. 7, lines 1-30: generate clusters for the embedding extracted from the images in the space); and
identifying the preservable embedding within an embedding cluster from among the embedding clusters based on comparing distances from the preservation prototype to one or more semantic embeddings within the embedding cluster (Gupta, Col. 6, lines 43-50: identify the embedding in a cluster based on the distances comparison from an embedding of the preservation prototype to one or more of the embeddings to determine the similarities among the embeddings).
With respect to claim 6, the combined teachings of Stanley and Gupta further disclose
determining, within the embedding space, a duplicate neighborhood defining semantic embeddings within a threshold distance of a sample semantic embedding (Stanley, [0066], [0078]; Gupta, Col. 4, lines 20-30, Col. 6, lines 43-50, Col. 7, lines 25-30: determine a duplication neighborhood represented by a cluster or group in a similarity score threshold correspond to the threshold distance of a sample semantic embedding representing by the training data); and
selecting the preservable embedding from the duplicate neighborhood as a semantic embedding that satisfies a threshold similarity relative to the preservation prototype (Stanley, [0066], [0078]; Gupta, Col. 6, lines 43-50, Col. 7, lines 25-30: select the preservable embedding as a semantic embedding which is merely an embedding when threshold similarity is met relative the prototype).
With respect to claim 7, the combined teachings of Stanley and Gupta further disclose
wherein generating the modified database comprises preserving the digital image corresponding to the preservable embedding for storage within the modified database (Stanley, [0055]; Gupta, Col. 3, lines 9-11, Col. 7, lines 25-31: generate the modified data by preserving digital and removing duplicate and non-compliant images correspond to the preservable embedding).
With respect to claim 8, Stanley discloses a non-transitory computer readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations (Abstract, Fig 2) comprising:
generating, within an embedding space, semantic embeddings from a plurality of digital images within a database ([0052], [0066], [0069]: generate embeddings from the image within a database);
identifying, from among the semantic embeddings in the embedding space, a preservable embedding ([0052], [0070], [0076]: identify a preservable embedding) by:
generating a preservation prototype from a combination of template strings describing a semantic concept to preserve within the database ([0052] generate a preservation prototype correspond to a text input describing a concept to preserve in view of one or more concepts or content that are not compliant and thus not being preserved, and/or in view of a prototype of an edit preserved version, as further described in [0055]); and
selecting the preservable embedding based on comparing distances from the preservation prototype to the semantic embeddings in the embedding space ([0066], [0078]: select the preservable embedding indirectly by compare distances from the preservation prototype embedding in the embedding space that indicate similarities between embeddings as the images with non-compliant content are being identified for removal/deletion as described in [0055]); and
generating a modified database by preserving a digital image of the plurality of digital images corresponding to the preservable embedding and pruning one or more digital images corresponding to semantic embeddings other than the preservable embedding from the database ([0055], [0082]: generate a modified data by preserving an image correspond to the preservable embedding and delete/remove one mor more images other than the preservable embedding represented by the non-compliant images from the database).
Stanley does not explicitly disclose the preservable embedding is selected within a similarity threshold as claimed.
However, Gupta discloses selecting the preservable embedding based on comparing distances from the preservation prototype to the semantic embeddings in the embedding space within a similarity threshold (Col. 4, lines 32-36. Col. 6, lines 43-50, Col. 7, lines 27-30: select at least one preservable embedding that is within a similarity threshold represented by a predetermined threshold in the embedding vector space to preserve in a database represented by a storge unit, such that the embedding not within a similarity threshold is being discarded/filtered).
Since both Stanley and Gupta are from the same field of endeavor as both are directed to using semantic embedding to generate a modified database by pruning image(s), which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings to incorporate the similarity threshold of Gupta into Stanley Lim for modified database generation as claimed. The motivation is to enhance searchability and discoverability with efficient storing, analyzing as well as searching images (Stanley, [0003]; Gupta, Col. 1, lines 63-67).
With respect to claim 9, the combined teachings of Stanley and Gupta further disclose wherein the operations further comprise updating parameters of a vision-language neural network using the modified database (Stanley, [0050]; Gupta, Col. 6, lines 60-67: update parameters of a vision-language neural network--which is merely a neural network—using the modified/updated database with update being updated via learned process).
With respect to claim 10, the combined teachings of Stanley and Gupta further disclose wherein the operations further comprise:
generating a plurality of preservation prototypes from combinations of template strings describing semantic concepts to preserve within the database (Stanley, [0054-0055], [0071]; Gupta, Col. 2, lines 49-67, Fig 1 & 4-5: generating preservation prototypes represented by novel images from combinations of template strings which are merely types of data represented by the combinations of multi-feature information of the images preserved in the database);
determining a duplicate neighborhood for a selected semantic embedding from among the semantic embeddings in the embedding space (Stanley, [0078]; Gupta, Col. 6, lines 43-50, Col. 7, lines 25-30: determine a duplication neighborhood represented by a nearest neighborhood, cluster or group in the embedding space); and
identifying the preservable embedding from the duplicate neighborhood by determining a semantic embedding within the duplicate neighborhood that is closest to a least represented preservation prototype from among the plurality of preservation prototypes (Stanley, [0066], [0078]; Gupta, Col. 3, lines 17-23, Col. 6, lines 43-50: identify the preservable embedding indirectly from the cluster or group via distance determination including but not limited to usage of nearest neighborhood/cluster via non-compliant embedding identification, such that visually similar key frames are being clustered together and hence non-selected embedding is correspond to the identified preservable embedding for preserving in the database).
With respect to claim 11, the combined teachings of Stanley and Gupta further disclose wherein the operations further comprise:
iteratively sampling the semantic embeddings in the embedding space to generate duplicate neighborhoods of one or more semantic embeddings within a threshold distance of a sampled embedding (Stanley, [0066], [0078]; Gupta, Col. 6, lines 40-66: iteratively sampling the semantic embeddings in the space to generate clusters within a threshold distance via neural network that trained interactively with samples);
determining, at each iteration, a preserved embedding for a respective duplicate neighborhood according to similarity relative to one or more preservation prototypes (Stanley, [0050], [0078]; Gupta, Col. 6, lines 40-66: determine a preserved embedding correspond to respective image at each iteration according to the similarity determination); and
generating a running average similarity for the database by iteratively updating similarity scores between iteratively selected preserved embeddings and the one or more preservation prototypes (Stanley, [0050], [0078]; Gupta, Col. 6, lines 40-66: generate similarity score correspond to the running average similarity which is merely a similarity by iteratively updating the scores via a neural learning processing).
With respect to claim 12, the combined teachings of Stanley and Gupta further disclose
wherein the operations further comprise generating the preservation prototype by:
receiving, from a client device, a user interaction defining a template string for a preservation factor (Stanley, [0054]; Gupta, Col. 7, lines 51-62: receive user interaction with input and/or feedback defining a template string which is merely a type of data for a preservation factor, which is another type of data); and
combining a text embedding extracted from the template string with one or more additional text embeddings extracted from additional template strings representing the semantic concept to preserve within the database (Stanley, [0050], [0054-0055]; Gupta, Col. 4, lines 10-30, Col. 7, lines 1-32, Fig 4-5: combine a text embedding extract from the string with additional data such as into different embedded contextual information represent a concept/subject to preserve or maintain in the database).
With respect to claim 13, the combined teachings of Stanley and Gupta further disclose
wherein the operations further comprise generating the preservation prototype by: extracting text embeddings from template strings describing protected demographic groups (Stanley, [0048-0049]; Gupta, Col. 3, lines 38-40, Fig 1 & 5: extract text embedding corresponding to the textual information); and
combining the text embeddings into the preservation prototype (Stanley, [0054-0055], [0071]; Gupta, Col. 4, lines 10-30, Col. 7, lines 1-32, Fig 4-5: combine the text embedding into a prototype or content with different embedded contextual information, such as a stored content being used to identity duplicates).
With respect to claim 14, the combined teachings of Stanley and Gupta further disclose
wherein generating the modified database comprises preserving a digital image corresponding to the preservable embedding for storage within the modified database (Stanley, [0055]; Gupta, Col. 3, lines 9-11, Col. 7, lines 25-31: generate the modified data by preserving digital and removing duplicate via embedding).
With respect to claim 15, Stanley discloses a system (abstract) comprising:
one or more memory devices (Fig 2); and
one or more processors coupled to the one or more memory devices (Fig 2), the one or more processors configured to cause the system to:
extract semantic embeddings from a plurality of digital images within a database ([0053], [0065-0066]: extract semantic embedding represented by the feature embeddings from database images);
generate embedding clusters from the semantic embeddings extracted from the plurality of digital images in an embedding space ([0053]: generate embedding cluster via classification and/or segmentation, including but not limited to splitting the embeddings into segment or bucket as described in [0069]);
generate a preservation prototype corresponding to a text input describing a semantic concept to preserve within the database ([0052] generate a preservation prototype correspond to a text input describing a concept to preserve in view of one or more concepts or content that are not compliant and thus not being preserved, and/or in view of a prototype of an edit preserved version, as further described in [0055]);
identify, within an embedding cluster from among the embedding clusters, a preservable embedding based on comparing distances from the preservation prototype to one or more semantic embeddings in the embedding cluster ([0066], [0078]: identify ta preservable embedding indirectly by compare distances from the preservation prototype within the cluster of embedding space that indicate similarities between embeddings as the images with non-compliant content are being identified for removal/deletion as described in [0055]); and
generate a modified database by preserving a digital image of the plurality of digital images corresponding to the preservable embedding and pruning one or more digital images corresponding to semantic embeddings other than the preservable embedding from the database ([0055], [0082]: generate a modified data by preserving an image correspond to the preservable embedding and delete/remove one mor more images other than the preservable embedding represented by the non-compliant images from the database).
Stanley does not explicitly disclose the preservation is identified within a similarity threshold as claimed.
However, Gupta discloses identify, within an embedding cluster from among the embedding clusters, a preservable embedding based on comparing distances from the preservation prototype to one or more semantic embeddings in the embedding cluster within a similarity threshold (Col. 4, lines 32-36, Col. 6, lines 43-50, Col. 7, lines 27-30: identify an embedding correspond to the preservable embedding--which merely an embedding--that is within a similarity threshold represented by a predetermined threshold based on the distances comparison from an embedding of the preservation prototype to one or more of the semantic embeddings in the space. The distances indicate similarities which determine embedding not within a similarity threshold relative to the preservation prototype is being discarded/filtered).
Since both Stanley and Gupta are from the same field of endeavor as both are directed to using semantic embedding to generate a modified database by pruning image(s), which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify and combine their teachings to incorporate the similarity threshold of Gupta into Stanley Lim for modified database generation as claimed. The motivation is to enhance searchability and discoverability with efficient storing, analyzing as well as searching images (Stanley, [0003]; Gupta, Col. 1, lines 63-67).
With respect to claim 16, the combined teachings of Stanley and Gupta further disclose
wherein the one or more processors are further configured to cause the system to determine, from a repository of digital images, a selected digital image utilizing a vision-language neural network comprising parameters learned from the modified database (Stanley, [0050]; Gupta, Col. 6, lines 60-67: determine a selected image which is merely an image using a vision-language neural network--which is merely a neural network--with parameter learned from the modified/updated database with duplicates and non-compliant image being removed ).
With respect to claim 17, the combined teachings of Stanley and Gupta further disclose
wherein the one or more processors are further configured to cause the system to generate, within an embedding cluster of the embedding clusters, a set of duplicate neighborhoods corresponding to the semantic embeddings (Stanley, [0054], [0069], [0078]; Gupta, Col. 3, lines 17-40, Fig 1: generate duplication neighborhoods represented by similar neighborhoods, or categories or sub- group having similar content correspond to the embeddings in a within an embedding clusters/segments).
With respect to claim 18, the combined teachings of Stanley and Gupta further disclose
wherein the one or more processors are further configured to cause the system to generate the set of duplicate neighborhoods by:
selecting a semantic embedding from among the one or more semantic embeddings in the embedding cluster (Stanley, [0054], [0078], [0083]; Gupta, Col. 3, lines 17-40: select a semantic embedding which is merely an embedding among the embeddings in a cluster) and
designating, as a duplicate neighborhood from among the set of duplicate neighborhoods, a set of semantic embeddings within a threshold similarity of the semantic embedding within the embedding cluster (Stanley, [0054], [0069], [0078]; Gupta, Col. 6, lines 43-50, Col. 7, lines 25-30: designate a set of embeddings corresponds to the images within a similarity threshold).
With respect to claim 19, the combined teachings of Stanley and Gupta further disclose wherein the one or more processors are further configured to cause the system to generate the preservation prototype by:
extracting a plurality of text embeddings from captions describing digital images (Stanley, [0048-0049]; Gupta, Col. 3, lines 38-40, Fig 1 & 5: extract text embedding corresponding to the textual information); and
combining the plurality of text embeddings into the preservation prototype Stanley, [0054-0055], [0071]; Gupta, Col. 4, lines 10-30, Col. 7, lines 1-32, Fig 4-5: combine the text embedding into a prototype or content with different embedded contextual information, such as a stored content being used to identity duplicates).
With respect to claim 20, the combined teachings of Stanley and Gupta further disclose
wherein the one or more processors are further configured to cause the system to generate the modified database by preserving a digital image corresponding to the preservable embedding for storage within the modified database (Stanley, [0055]; Gupta, Col. 3, lines 9-11, Col. 7, lines 25-31: generate the modified data by preserving digital and removing duplicate via embedding).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Owyang whose telephone number is (571)270-1254. The examiner can normally be reached Monday-Friday, 8am-6pm EST.
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/MICHELLE N OWYANG/Primary Examiner, Art Unit 2168
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