Detailed Action
This is a Final Office action in response to communications received on 5/18/2026. Claims 1, 10 and 17 were amended. Claims 1-20 are pending and are examined.
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 .
Response to Arguments
Applicant’s amendments, filed 5/18/2026, to claim(s) 17 correcting the claim to recite “non-transitory” are sufficient to overcome the rejection to the aforementioned claim(s). Accordingly, the rejection of claim(s) 17-20 under 101, as filed in (4) of the Non-Final Office action filed 2/27/2026, is withdrawn.
Applicant’s arguments regarding the rejection under 35 U.S.C. 102 of the claims under Kaidi (US 20240176902 A1) have been considered, but are moot because the new ground of rejection necessitated from amending the independent claims 1, 10 and 17. The instant rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically argued in the Applicant's response.
Consequently, the rejection of the claims under 35 U.S.C. 103 is presented as below.
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, 5-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaidi (US 20240176902 A1), in view of Verma (US 20240370580 A1).
Regarding claim 1, Kaidi teaches the limitations of claim 1 substantially as follows:
A computer-implemented method for securing content data contained in a file segment, comprising: (Kaidi; Abstract: Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file)
inferring security separation boundaries in a file to define file segments of content data, (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., inferring security separation boundaries in a file))
wherein the inferring is performed by one or more artificial intelligence (AI) models trained to infer the security separation boundaries of the file segments based on an inferred importance of the content data contained within the file segments; (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., the inferring is performed by one or more artificial intelligence (AI) models trained))
generating content security scores for the file segments, wherein the generating is performed by the one or more AI models trained to generate the content security scores based at least in part on the inferred importance of the content in the file segments; (Kaidi; [0017]: the data scanning system may independently classify different sections of the data file based on the content included within the different sections (i.e., content security scores for the file segments))
assigning security tiers to the file segments that correspond to the content security scores generated for the file segments, the security tiers specifying security protocols for storing the file segments; and (Kaidi; [0059]: the tagging module may associate corresponding sensitivity level tags to the respective paragraphs . The different tags may correspond to different sensitivity levels (i.e., assigning security tiers to the file segments that correspond to the content security scores generated for the file segments))
Kaidi does not teach the limitations of claim 1 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment.
However, in the same field of endeavor, Verma discloses the limitations of claim 1 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment. (Verma; [0014]: The data ingestion engine may store each data segment that is assigned a security clearance level in the data silo that corresponds to the security clearance level assigned to the data segment. For example, a data segment may be assigned a level 2 security clearance level. As such, the data segment may be stored at a data silo that is assigned a level 2 security clearance level)
Verma is combinable with Kaidi because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kaidi to incorporate storage of file segments in corresponding security clearance level silos as in Verma in order to improve the security of the system by ensuring that protected information in correspondingly secure locations.
Regarding claim 3, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma teach the limitations of claim 3 as follows:
The computer-implemented method of claim 1, further comprising generating metadata for a file segment to include information selected from the group consisting of: a content security score; a last assessment timestamp; and file segment offsets for the file segment. (Kaidi; [0059]: the tags may be implemented within the metadata of the data file (i.e., a content security score))
Regarding claim 5, Kaidi and Verma teach the limitations of claim 3.
Kaidi and Verma teach the limitations of claim 5 as follows:
The computer-implemented method of claim 3, further comprising: storing the metadata for the file segment in the file segment according to the security tier assigned to the file segment. (Kaidi; [0059]: the tags may be implemented within the metadata of the data file (i.e., metadata for the file segment in the file segment according to the security tier assigned to the file segment))
Regarding claim 6, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma teach the limitations of claim 6 as follows:
The computer-implemented method of claim 1, further comprising monitoring a dedicated file directory and, responsive to detecting that the file has been placed in the dedicated file directory, securing the content data contained in the file. (Kaidi; [0014]: scan data files stored in one or more data repositories associated with an organization, and automatically classify the data files based on the content of the data file. The data files stored in the one or more data repositories can be of different file types (i.e., responsive to detecting that the file has been placed in the dedicated file directory, securing the content data contained in the file))
Regarding claim 7, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma teach the limitations of claim 7 as follows:
The computer-implemented method of claim 1, further comprising: determining that a file segment of the file is tagged with a description identifier that indicates, at least in part, an importance of content data contained in the file segment; and generating a content security score for the file segment based at least in part on the importance of the content data indicated by the description identifier. (Kaidi; [0015]-[0017]: the data scanning system may tag a corresponding sensitivity level to each section of the data file based on the content of the corresponding section of the data file)
Regarding claim 8, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma teach the limitations of claim 8 as follows:
The computer-implemented method of claim 1, further comprising: determining that content data in a file segment has been modified; generating an updated content security score for the file segment, wherein responsive to a determination that the updated content security score corresponds to a security tier that is different from a currently assigned security tier, reassigning the file segment to the security tier that corresponds to the updated content security score; and (Kaidi; [0027]: when the sensitivity level of a section in the source data file is changed, the data scanning system may automatically use the link associated with the section to identify locations of corresponding sections (having the same content) in other data files (e.g., the target data file) to update the sensitivity levels of the corresponding sections)
storing the file segment according to the security tier that corresponds to the updated content security score. (Kaidi; [0014]: scan data files stored in one or more data repositories associated with an organization, and automatically classify the data files based on the content of the data file. The data files stored in the one or more data repositories can be of different file types (i.e., storing the file segments according to the security tiers assigned to the file segments) (Determining and assigning access level to a storage location based on classification of data within is equivalent to “storing… according to security tiers))
Regarding claim 9, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma teach the limitations of claim 9 as follows:
The computer-implemented method of claim 1, wherein inferring an importance of a file segment comprises making inferences for the features selected from the group consisting of: a content value of the file segment, a risk level based on a current security protocol implemented for the file segment, an impact cost of a data exfiltration and/or a data loss event, and a data cost for creating and/or obtaining the content data contained in the file segment. (Kaidi; [0017]: the data scanning system may independently classify different sections of the data file based on the content included within the different sections (i.e., content value of the file segment))
Regarding claim 10, Kaidi teaches the limitations of claim 10 substantially as follows:
A computer system comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more storage media to cause the processor set to perform operations comprising: (Kaidi; [0089]: The computer system performs specific operations by the processor and other components by executing one or more sequences of instructions contained in the system memory component)
inferring security separation boundaries in a file to define file segments of content data, (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., inferring security separation boundaries in a file))
wherein the inferring is performed by one or more artificial intelligence (AI) models trained to infer the security separation boundaries of the file segments based on an inferred importance of the content data contained within the file segments; (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., the inferring is performed by one or more artificial intelligence (AI) models trained))
generating content security scores for the file segments, wherein the generating is performed by the one or more AI models trained to generate the content security scores based at least in part on the inferred importance of the content in the file segments; (Kaidi; [0017]: the data scanning system may independently classify different sections of the data file based on the content included within the different sections (i.e., content security scores for the file segments))
assigning security tiers to the file segments that correspond to the content security scores generated for the file segments, the security tiers specifying security protocols for storing the file segments; and (Kaidi; [0059]: the tagging module may associate corresponding sensitivity level tags to the respective paragraphs . The different tags may correspond to different sensitivity levels (i.e., assigning security tiers to the file segments that correspond to the content security scores generated for the file segments))
Kaidi does not teach the limitations of claim 10 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment.
However, in the same field of endeavor, Verma discloses the limitations of claim 10 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment. (Verma; [0014]: The data ingestion engine may store each data segment that is assigned a security clearance level in the data silo that corresponds to the security clearance level assigned to the data segment. For example, a data segment may be assigned a level 2 security clearance level. As such, the data segment may be stored at a data silo that is assigned a level 2 security clearance level)
Verma is combinable with Kaidi because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kaidi to incorporate storage of file segments in corresponding security clearance level silos as in Verma in order to improve the security of the system by ensuring that protected information in correspondingly secure locations.
Regarding claim 12, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma teach the limitations of claim 12 as follows:
The computer system of claim 10, wherein the operations further comprise: generating metadata for a file segment to include information selected from the group consisting of: a content security score; a last assessment timestamp; and file segment offsets for the file segment. (Kaidi; [0059]: the tags may be implemented within the metadata of the data file (i.e., a content security score))
Regarding claim 13, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma teach the limitations of claim 13 as follows:
The computer system of claim 10, wherein the operations further comprise: monitoring a dedicated file directory and, responsive to detecting that the file has been placed in the dedicated file directory, securing the content data contained in the file. (Kaidi; [0014]: scan data files stored in one or more data repositories associated with an organization, and automatically classify the data files based on the content of the data file. The data files stored in the one or more data repositories can be of different file types (i.e., responsive to detecting that the file has been placed in the dedicated file directory, securing the content data contained in the file))
Regarding claim 14, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma teach the limitations of claim 14 as follows:
The computer system of claim 10, wherein the operations further comprise: determining that a file segment of the file is tagged with a description identifier that indicates, at least in part, an importance of content data contained in the file segment; and generating a content security score for the file segment based at least in part on the importance of the content data indicated by the description identifier. (Kaidi; [0015]-[0017]: the data scanning system may tag a corresponding sensitivity level to each section of the data file based on the content of the corresponding section of the data file)
Regarding claim 15, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma teach the limitations of claim 15 as follows:
The computer system of claim 10, wherein the operations further comprise: determining that content data in a file segment has been modified; generating an updated content security score for the file segment, wherein responsive to a determination that the updated content security score corresponds to a security tier that is different from a currently assigned security tier, reassigning the file segment to the security tier that corresponds to the updated content security score; and (Kaidi; [0027]: when the sensitivity level of a section in the source data file is changed, the data scanning system may automatically use the link associated with the section to identify locations of corresponding sections (having the same content) in other data files (e.g., the target data file) to update the sensitivity levels of the corresponding sections)
storing the file segment according to the security tier that corresponds to the updated content security score. (Kaidi; [0014]: scan data files stored in one or more data repositories associated with an organization, and automatically classify the data files based on the content of the data file. The data files stored in the one or more data repositories can be of different file types (i.e., storing the file segments according to the security tiers assigned to the file segments) (Determining and assigning access level to a storage location based on classification of data within is equivalent to “storing… according to security tiers))
Regarding claim 16, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma teach the limitations of claim 16 as follows:
The computer system of claim 10, wherein inferring an importance of a file segment comprises making inferences for the features selected from the group consisting of: a content value of the file segment, a risk level based on a current security protocol implemented for the file segment, an impact cost of a data exfiltration and/or a data loss event, and a data cost for creating and/or obtaining the content data contained in the file segment. (Kaidi; [0017]: the data scanning system may independently classify different sections of the data file based on the content included within the different sections (i.e., content value of the file segment))
Regarding claim 17, Kaidi teaches the limitations of claim 17 substantially as follows:
A computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more storage media to perform operations comprising: (Kaidi; [0089]: The computer system performs specific operations by the processor and other components by executing one or more sequences of instructions contained in the system memory component)
inferring security separation boundaries in a file to define file segments of content data, (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., inferring security separation boundaries in a file))
wherein the inferring is performed by one or more artificial intelligence (AI) models trained to infer the security separation boundaries of the file segments based on an inferred importance of the content data contained within the file segments; (Kaidi; [0056]: the scanning manager may divide the data file into multiple sections, where each section may correspond to a distinct portion of the data file (e.g., different paragraphs, different sentences, different pages, etc.) The scanning manager may provide the subject matter derived for the data file, and the content from each section to the machine learning model one at a time to determine whether each section of the data file includes sensitive data and the sensitivity level of the data in each section. (i.e., the inferring is performed by one or more artificial intelligence (AI) models trained))
generating content security scores for the file segments, wherein the generating is performed by the one or more AI models trained to generate the content security scores based at least in part on the inferred importance of the content in the file segments; (Kaidi; [0017]: the data scanning system may independently classify different sections of the data file based on the content included within the different sections (i.e., content security scores for the file segments))
assigning security tiers to the file segments that correspond to the content security scores generated for the file segments, the security tiers specifying security protocols for storing the file segments; and (Kaidi; [0059]: the tagging module may associate corresponding sensitivity level tags to the respective paragraphs . The different tags may correspond to different sensitivity levels (i.e., assigning security tiers to the file segments that correspond to the content security scores generated for the file segments))
Kaidi does not teach the limitations of claim 17 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment.
However, in the same field of endeavor, Verma discloses the limitations of claim 17 as follows:
extracting and storing the file segments to datastores having defined security tiers that correspond to the assigned security tiers, where each file segment is stored to a datastore that has a security protocol that corresponds to a security tier assigned to the file segment. (Verma; [0014]: The data ingestion engine may store each data segment that is assigned a security clearance level in the data silo that corresponds to the security clearance level assigned to the data segment. For example, a data segment may be assigned a level 2 security clearance level. As such, the data segment may be stored at a data silo that is assigned a level 2 security clearance level)
Verma is combinable with Kaidi because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kaidi to incorporate storage of file segments in corresponding security clearance level silos as in Verma in order to improve the security of the system by ensuring that protected information in correspondingly secure locations.
Regarding claim 18, Kaidi and Verma teach the limitations of claim 17.
Kaidi and Verma teach the limitations of claim 18 as follows:
The computer program product of claim 17, wherein the operations further comprise: generating metadata for a file segment to include information selected from the group consisting of: a content security score; a last assessment timestamp; and file segment offsets for the file segment. (Kaidi; [0059]: the tags may be implemented within the metadata of the data file (i.e., a content security score))
Regarding claim 19, Kaidi and Verma teach the limitations of claim 17.
Kaidi and Verma teach the limitations of claim 19 as follows:
The computer program product of claim 17, wherein the operations further comprise: determining that content data in a file segment has been modified; generating an updated content security score for the file segment, wherein responsive to a determination that the updated content security score corresponds to a security tier that is different from a currently assigned security tier, reassigning the file segment to the security tier that corresponds to the updated content security score; and (Kaidi; [0027]: when the sensitivity level of a section in the source data file is changed, the data scanning system may automatically use the link associated with the section to identify locations of corresponding sections (having the same content) in other data files (e.g., the target data file) to update the sensitivity levels of the corresponding sections)
storing the file segment according to the security tier that corresponds to the updated content security score. (Kaidi; [0014]: scan data files stored in one or more data repositories associated with an organization, and automatically classify the data files based on the content of the data file. The data files stored in the one or more data repositories can be of different file types (i.e., storing the file segments according to the security tiers assigned to the file segments) (Determining and assigning access level to a storage location based on classification of data within is equivalent to “storing… according to security tiers))
Regarding claim 20, Kaidi and Verma teach the limitations of claim 17.
Kaidi and Verma teach the limitations of claim 20 as follows:
The computer program product of claim 17, wherein the operations further comprise: generating metadata for a file segment to include information selected from the group consisting of: a content security score; a last assessment timestamp; and file segment offsets for the file segment. (Kaidi; [0059]: the tags may be implemented within the metadata of the data file (i.e., a content security score))
Claims 2, 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kaidi (US 20240176902 A1), in view of Verma (US 20240370580 A1), as applied to independent claims, in view of Crabtree (US 20250267296 A1).
Regarding claim 2, Kaidi and Verma teach the limitations of claim 1.
Kaidi and Verma do not teach the limitations of claim 2 as follows:
The computer-implemented method of claim 1, wherein storing the file segments according to the security tiers further comprises encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments.
However, in the same field of endeavor, Crabtree discloses the limitations of claim 2 as follows:
The computer-implemented method of claim 1, wherein storing the file segments according to the security tiers further comprises encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments. (Crabtree; [0333]: dynamic content encryption and filtering engine employs a content segmentation subsystem that divides incoming data stream into discrete units. These units can be video frames, audio segments, or data packets. Each unit is then analyzed by AI algorithm to determine its sensitivity level or classification. Based on this analysis, an appropriate encryption algorithm is applied to units requiring protection (i.e., encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments))
Crabtree is combinable with Kaidi and Verma because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Kaidi and Verma to incorporate segment specific encryption of data as in Crabtree in order to improve the security of the system by providing a means by which information may be secured via encryption.
Regarding claim 4, Kaidi and Verma teach the limitations of claim 2.
Kaidi and Verma do not teach the limitations of claim 4 as follows:
The computer-implemented method of claim 3, further comprising: encrypting the metadata for the file segment and storing the metadata separate from the file segment.
However, in the same field of endeavor, Crabtree discloses the limitations of claim 4 as follows:
The computer-implemented method of claim 3, further comprising: encrypting the metadata for the file segment and storing the metadata separate from the file segment. (Crabtree; [0273], [0333]: dynamic content encryption and filtering engine employs a content segmentation subsystem that divides incoming data stream into discrete units. These units can be video frames, audio segments, or data packets. Each unit is then analyzed by AI algorithm to determine its sensitivity level or classification. Based on this analysis, an appropriate encryption algorithm is applied to units requiring protection; This content data includes raw video and audio files, metadata, and other relevant information needed for streaming (i.e., encrypting the metadata for the file segment))
Crabtree is combinable with Kaidi and Verma because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Kaidi and Verma to incorporate segment specific encryption of data as in Crabtree in order to improve the security of the system by providing a means by which information may be secured via encryption.
Regarding claim 11, Kaidi and Verma teach the limitations of claim 10.
Kaidi and Verma do not teach the limitations of claim 11 as follows:
The computer system of claim 10, wherein storing the file segments according to the security tiers further comprises encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments.
However, in the same field of endeavor, Crabtree discloses the limitations of claim 11 as follows:
The computer system of claim 10, wherein storing the file segments according to the security tiers further comprises encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments. (Crabtree; [0333]: dynamic content encryption and filtering engine employs a content segmentation subsystem that divides incoming data stream into discrete units. These units can be video frames, audio segments, or data packets. Each unit is then analyzed by AI algorithm to determine its sensitivity level or classification. Based on this analysis, an appropriate encryption algorithm is applied to units requiring protection (i.e., encrypting file segments assigned to a security tier that specifies an encryption technique for securing the file segments))
Crabtree is combinable with Kaidi and Verma because all are from the same field of endeavor of data segment classification. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified system of Kaidi and Verma to incorporate segment specific encryption of data as in Crabtree in order to improve the security of the system by providing a means by which information may be secured via encryption.
Prior Art Considered But Not Relied Upon
Manville (US 20160292429 A1) which teaches objects stored in a file system, such as files, is stored in a cloud-based object store. In some embodiments, files may be segmented into a plurality of segments or “chunks”, each of which is stored in a corresponding location in the cloud-based object store. Segment locations may be based on, for example, security attributes.
Redlich (US 20240111880 A1) which teaches processing data with an extraction module which segments the OSD. Segmented data, sometimes called extracted data in contrast to remainder data (data remaining in the stripped-down OSD), is sometimes processed into secure storage modules.
Conclusion
For the above-stated reasons, claims 1-20 are rejected.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
Accordingly, THIS ACTION IS MADE FINAL. 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.
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/BLAKE I NARRAMORE/Examiner, Art Unit 2438
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