DETAILED ACTION
The Action is responsive to Applicant’s Application and Claims filed August 7, 2025.
Please note claims 2-21 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Drawings
The drawings, filed April 29, 2025 are considered in compliance with 37 CFR 1.81 and accepted.
Information Disclosure Statement
The information disclosure statements filed June 13, 2025 are in compliance with 37 CFR 1.97(c) and therein have been considered. Its corresponding PTO-1449 has been electronically signed as attached.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2, 12, 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 21, 29 of U.S. Patent No. 11,907,995. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Instant Application 19/193406
US Patent No. 11,907,995
2. A system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and
configured to execute instructions to cause the system to:
determine that a mobile device of a user is at or approaching a location
associated with an edge computing device, wherein the edge computing device is separate from a cloud computing system and provides data storage services to additional devices in a proximity to the location;
predict an interest of the user associated with a transaction;
determine, using a machine learning (ML) model and based on one or more
activities of the user that are associated with the location and the interest, data usable to process the transaction at the location, wherein the data is sharcable with the edge computing device to reduce a latency to load the data on the mobile device when processing the transaction at the location; and
transfer the data from the cloud computing system to the edge computing
device to facilitate processing of the transaction.
1. A service provider system comprising:
a non-transitory memory; and
one or more hardware processors coupled to the non-transitory memory and
configured to read instructions from the non-transitory memory to cause the service provider system to perform operations comprising:
detecting, a user within a geofenced proximity associated with a merchant location; determining that a merchant device at the merchant location utilizes data associated with users to provide services to the users at the merchant location; determining an edge computing system, separate from the merchant device, having an edge computing storage node within a distance to a geographic area corresponding to the merchant location, wherein the edge computing storage node comprises a cloud computing node for a cloud computing service on a
network is separate from the merchant device, and wherein the edge computing
storage node is further separate from a central storage utilized by the user for the
cloud computing service on the network;
determining a user profile for the user with the service provider system;
identifying one or more activities previously performed by the user within the geofenced proximity;
determining, using a machine learning (ML) model associated with the
merchant location based on the user profile and the one or more activities, a data storage action between the edge computing storage node and the central storage, wherein the data storage action moves at least a portion of the data from the edge computing storage node to the central storage or moves at least a portion of the data from the central storage to the edge computing storage node; and
executing the data storage action between the edge computing storage node and the central storage.
12. A method comprising:
determining that a user is at or approaching a location associated with an edge storage component of a distributed data storage network that further includes a centralized cloud storage, wherein the centralized cloud storage stores user data for the user;
identifying a behavior of the user when at or approaching the location based on one
or more activities of the user;
predicting that the user will engage in a transaction at the location based on the
behavior;
determining, using a machine learning (ML) model and based on at least one of the behavior or the transaction, a portion of the user data to be transferred from the centralized cloud storage to the edge storage component that reduces a latency to load the portion of the user data to a checkout flow for the transaction while the user is at the location, wherein the portion of the user data is usable to complete the checkout flow by the user or a merchant associated with the transaction; and
transferring the portion of the user data from the centralized cloud storage to the
edge storage component over the distributed data storage network for completion of the checkout flow at the location.
21. A method comprising:
detecting, by a service provider system, a user within a geofenced proximity
associated with a merchant location;
determining services provided by a merchant device to users at the merchant location using data associated with the users; determining an edge computing system, independent from the merchant device, having an edge computing
storage node corresponding to a geographic area including the merchant location, wherein the edge computing storage node comprises a cloud computing node for a cloud computing service on a network is separate from the
merchant device, and wherein a central storage utilized by the user for the cloud computing service on the network is separate from the edge computing storage node;
determining a user profile for the user with the service provider system;
identifying one or more activities previously performed by the user within the geofenced proximity;
determining, using a machine learning (ML) model associated with the merchant
location based on the user profile and the one or more activities, a data storage action between the edge computing storage node and the central storage, wherein the data storage action moves at least a portion of the data from the edge computing storage node to the central storage or moves at least a portion of the data from the central storage to the edge
computing storage node; and
executing the data storage action between the edge computing storage node and the
central storage.
21. A non-transitory machine-readable medium having stored thereon machine-
readable instructions executable to cause a machine to perform operations comprising:
detecting a user at or approaching a physical location associated with an edge storage node of a distributed storage network having a plurality of edge storage nodes, wherein the edge storage node is separate from a cloud storage component of the distributed storage
network and provides data storage services to in association with the physical location at a
lower latency than other ones of the plurality of edge storage nodes;
predicting a transaction processable by the user in association with the physical
location;
determining, using a machine learning (ML) model and based on one or more
activities of the user that are associated with the physical location and the transaction predicted, data usable to process the transaction at the physical location, wherein the data is
shareable with the edge computing device to reduce a latency to load the data on the mobile device when processing the transaction at the physical location; and
transferring the data from the cloud computing system to the edge computing device to facilitate processing of the transaction.
29. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
detecting, by a service provider system, a user within a geofenced proximity
associated with a merchant location;
determining that a merchant device at the merchant location provides services to
users at the merchant location using data associated with the users; determining an edge computing system that does not include the merchant device, wherein the edge computing system includes an edge computing storage node providing data storage services for a geographic area including the merchant location, wherein the edge computing storage node comprises a cloud computing node for a
cloud computing service on a network is-separate from the merchant device, and wherein the edge computing includes a central storage utilized by the user for the cloud computing
service on the network independent from the cloud computing storage node;
determining a user profile for the user with the service provider system;
identifying one or more activities previously performed by the user within the geofenced proximity;
determining, using a machine learning (ML) model associated with the merchant
location based on the user profile and the one or more activities, a data storage action between the edge computing storage node and the central storage, wherein the data storage action moves at least a portion of the data from the edge computing storage node to the central storage or moves at least a portion of the data from the central storage to the edge
computing storage node; and
executing the data storage action between the edge computing storage node and the central storage.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-6, 10-15, 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Montague (US Pat. No. 11,023,957) further in view of Zhu et al. (US Pub. No. 2018/0041557).
Regarding claim 2, Montague teaches a system comprising:
‘a non-transitory memory’ (Col. 13, Lines 1-8)
‘one or more hardware processors coupled to the non-transitory memory and configured to execute instructions (Col. 13, Lines 1-8) to cause the system to:
determine that a mobile device of a user is at or approaching a location
associated with an edge computing device, wherein the edge computing device is
separate from a computing system and provides data storage services to additional devices in a proximity to the location’ as detecting when a user device is within proximity of a geo-fence of a merchant device, wherein the merchant device is part of a network of devices separate from the server (Col. 2, Liens 37-55; Fig. 1; Col. 5, Line 57-Col. 6, Line 34)
‘predict an interest of the user associated with a transaction’ as a determination of user interest being predictive associated with transactions (Col. 3, Lines 13-15, Col. 14, Lines 51-64)
‘determine, using a machine learning (ML) model and based on one or more activities of the user that are associated with the location and the interest, data usable to process the transaction at the location, wherein the data is shareable with the edge computing device’ as one or more machine learning algorithms based on historic customer and market trends and merchant historic decisions (Col. 19, Lines 14-22)
‘transfer the data from the computing system to the edge computing
device to facilitate processing of the transaction’ (Col. 19, Line 1-Col. 21, Line 21)
Montague fails to explicitly teach:
‘a cloud computing system’
‘to reduce a latency to load the data on the mobile device when processing the transaction at the location’
Zhu teaches:
‘a cloud computing system’ as a cloud server system (¶0013, 22)
‘to reduce a latency to load the data on the mobile device when processing the transaction at the location’ as reducing latency perceived by mobile client computing device (¶0031)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Zhu’s would have allowed Montague’s to improve reliability in mobile computing devices and networks (¶0003)
Regarding claim 3, Montague teaches wherein, prior to determining the data, executing the instructions further causes the system to:
‘determine a likelihood for the user to process the transaction at the location, wherein determining the data is further based on the likelihood’ as using external databases to determine customer and merchant likelihood to visit a particular location (Col. 18, Line 58-Col. 19, Line 6)
Regarding claim 4, Montague teaches ‘wherein determining the data includes selecting, by the ML model, the data from at least one of user data or financial data available for the user based on the data improving a speed by which the transaction is processable at the location’ as an interface to allow for financial information to authorize payment by a remote financial institution for checkout (Col. 3, Lines 21-34; Col. 7, Lines 12-35)
Regarding claim 5, Montague teaches wherein executing the instructions further causes the system to:
‘generate a user profile of the user at the edge computing device based on at least the one or more activities and the interest’ as generation of a user profile regarding behaviors and preferences stored in a network (Col. 4, Lines 15-24)
‘update the user profile at the edge computing device based on one or more additional activities and whether the transaction was processed’ as tracking a user by maintaining a profile, including past merchant visits (Col. 16, Lines 10-17)
‘provide the updated user profile to the cloud computing system for association with another user profile of the user’ as aggregating a number of users by location or user characteristic with reference to a user profile stored in the user database (Col. 16, Lines 58-62)
Regarding claim 6, Montague teaches wherein executing the instructions further causes the system to:
‘predict one or more additional activities to the user at the location based on one or more incentives applicable to the transaction’ as updating user profiles to reflect incentivized options (Col. 37m Lines 9-35)
‘transfer additional data associated with the one or more additional activities to the edge computing device accessible by the mobile device of the user’ as tracking a user by maintaining a profile, including past merchant visits (Col. 16, Lines 10-17)
Regarding claim 10, Montague teaches wherein executing the instructions further causes the system to:
‘detect the one or more activities of the user while the user is at or approaching the location’ as detecting customer activity coinciding with travel routes close to a geo-fence (Col. 20, Lines 50-54)
Regarding claim 11, Montague teaches ‘wherein the one or more activities are detected based on at least one of a biometric received, an interaction or an activity on the mobile device, or via a merchant device associated with the transaction’ as an interaction from a mobile device or a merchant device associated with a transaction (Col. 32, Lines 14-23)
Regarding claim 12, Montague teaches a method comprising:
‘determining that a user is at or approaching a location associated with an edge storage component of a distributed data storage network’ as detecting when a user device is within proximity of a geo-fence of a merchant device, wherein the merchant device is part of a network of devices separate from the server (Col. 2, Liens 37-55; Fig. 1; Col. 5, Line 57-Col. 6, Line 34)
‘identifying a behavior of the user when at or approaching the location based on one or more activities of the user’ as identifying an interaction from the user within proximity of a merchant (Col. 16, Line 66-Col. 17, Line 7)
‘predicting that the user will engage in a transaction at the location based on the behavior’ as predicting interest of a user in a merchant and its products for purchase (Col. 18, Lines 16-22)
‘determining, using a machine learning (ML) model and based on at least one of the behavior or the transaction, a portion of the user data to be transferred to the edge storage component to a checkout flow for the transaction while the user is at the location, wherein the portion of the user data is usable to complete the checkout flow by the user or a merchant associated with the transaction’ as one or more machine learning algorithms based on historic customer and market trends and merchant historic decisions (Col. 19, Lines 14-22) and as a customer device able to communicate with the merchant device and a central server to make a purchase (Col. 7, Lines 1-45)
‘transferring the portion of the user data to the edge storage component over the distributed data storage network for completion of the checkout flow at the location’ as transferring user payment data to the merchant or server to complete the purchase (Col. 7, Lines 1-45; Col. 8, Lines 40-53)
Montague fails to explicitly teach:
‘that further includes a centralized cloud storage, wherein the centralized cloud storage stores user data for the user’
‘from the centralized cloud storage’
‘that reduces a latency to load the portion of the user data’
Zhu teaches:
‘that further includes a centralized cloud storage, wherein the centralized cloud storage stores user data for the user’ as a cloud server system (¶0013, 22)
‘from the centralized cloud storage’ (¶0013, 22)
‘that reduces a latency to load the portion of the user data’ as reducing latency perceived by mobile client computing device (¶0031)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Zhu’s would have allowed Montague’s to improve reliability in mobile computing devices and networks (¶0003)
Regarding claim 13, Montague teaches ‘wherein the predicting that the user will engage in the transaction comprises determining a likelihood for the user to process the transaction using the ML model or another ML model’ as using machine learning algorithms and external databases to determine customer and merchant likelihood to visit a particular location (Col. 18, Line 58-Col. 19, Line 6)
Regarding claim 14, Montague teaches ‘wherein the determining the portion of the user data comprises identifying at least one of personal information or financial information for the user that may be entered to the checkout flow to improve a speed by which the transaction is processable at the location’ as an interface to allow for financial information to authorize payment by a remote financial institution for checkout (Col. 3, Lines 21-34; Col. 7, Lines 12-35)
Regarding claim 15, Montague teaches further comprising:
‘updating user profile of the user stored by the centralized cloud storage based on additional data, stored by the edge storage component, that is associated with at least one of the user visiting the location or processing the transaction at the location’ as tracking a user by maintaining a profile, including past merchant visits (Col. 16, Lines 10-17)
Regarding claim 16, Montague teaches further comprising:
‘transferring additional data associated with at least one of the transaction, the behavior of the user, or the location to the edge storage component for delivery to a mobile device of the user when the user is at or approaching the location’ as tracking a user by maintaining a profile, including past merchant visits (Col. 16, Lines 10-17)
Regarding claim 19, Montague teaches ‘wherein the identifying the behavior comprises detecting one or more activities of the user while the user is at or approaching the location’ as detecting customer activity coinciding with travel routes close to a geo-fence (Col. 20, Lines 50-54)
Regarding claim 20, Montague teaches ‘wherein the one or more activities comprise at least one of a biometric received, an interaction or an activity on a mobile device of the user, or transaction data from a merchant device that processed the transaction at the location’ as an interaction from a mobile device or a merchant device associated with a transaction (Col. 32, Lines 14-23)
Regarding claim 21, Montague teaches a non-transitory machine-readable medium having stored thereon machine- readable instructions executable to cause a machine to perform operations comprising:
‘detecting a user at or approaching a physical location associated with an edge storage node of a distributed storage network having a plurality of edge storage nodes, wherein the edge storage node provides data storage services to in association with the physical location’ as detecting when a user device is within proximity of a geo-fence of a merchant device, wherein the merchant device is part of a network of devices separate from the server (Col. 2, Liens 37-55; Fig. 1; Col. 5, Line 57-Col. 6, Line 34)
‘predicting a transaction processable by the user in association with the physical location’ as predicting interest of a user in a merchant and its products for purchase (Col. 18, Lines 16-22)
‘determining, using a machine learning (ML) model and based on one or more activities of the user that are associated with the physical location and the transaction predicted, data usable to process the transaction at the physical location, wherein the data is shareable with the edge computing device when processing the transaction at the physical location’ as one or more machine learning algorithms based on historic customer and market trends and merchant historic decisions (Col. 19, Lines 14-22) and as a customer device able to communicate with the merchant device and a central server to make a purchase (Col. 7, Lines 1-45)
‘transferring the data from the cloud computing system to the edge computing device to facilitate processing of the transaction’ as transferring user payment data to the merchant or server to complete the purchase (Col. 7, Lines 1-45; Col. 8, Lines 40-53)
Montague fails to explicitly teach:
‘is separate from a cloud storage component of the distributed storage network and’
‘at a lower latency than other ones of the plurality of edge storage nodes’
‘to reduce a latency to load the data on the mobile device’
Zhu teaches:
‘is separate from a cloud storage component of the distributed storage network and’ as a cloud server system (¶0013, 22)
‘from the centralized cloud storage’ (¶0013, 22)
‘at a lower latency than other ones of the plurality of edge storage nodes’ as reducing latency perceived by mobile client computing device (¶0031)
‘to reduce a latency to load the data on the mobile device’ (¶0031)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Zhu’s would have allowed Montague’s to improve reliability in mobile computing devices and networks (¶0003)
Claim(s) 7-9, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Montague (US Pat. No. 11,023,957) Zhu et al. (US Pub. No. 2018/0041557) further in view of Caine et al. (US Pub. No. 2017/0208436)
Regarding claim 7, Montague and Zhu fail to explicitly teach wherein executing the instructions further causes the system to:
‘receive a request for the data at the edge computing device’
‘request an authentication of the user at the edge computing device’
‘process the authentication, wherein the data is provided to the edge computing device responsive to the request for the data’
Caine teaches:‘receive a request for the data at the edge computing device’ as initiating a request for content (¶0034)
‘request an authentication of the user at the edge computing device’ as identifying the user by authentication (¶0036)
‘process the authentication, wherein the data is provided to the edge computing device responsive to the request for the data’ and after authentication, allowing a user to access content on their assigned devices at certain locations (¶0036)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Caine’s would have allowed Montague and Zhu’s to maintain policies for information security (¶0002)
Regarding claim 8, Montague and Caine teaches wherein executing the instructions further causes the system to:
‘determine a sub-location of the location that is associated with the transaction’ as a sub-location affiliated with the location (Caine ¶0035)
‘generate an interactive display for an application that enables the user to navigate to the sub-location within the location, wherein the interactive display further provides additional information associated with the interest of the user’ as an interactive display that enables navigation and additional information about a user (Montague Col. 26, Lines 37-47; Col. 37, Line 54- Col. 38, Line 5) including locations and sub-locations (Caine ¶0035)
‘transfer the interactive display to the edge computing device accessible by the mobile device of the user’ as transferring data to the user (Col. 7, Lines 1-45; Col. 8, Lines 40-53)
Regarding claim 9, Caine teaches wherein, prior to transferring the data, executing the instructions further causes the system to:
‘determine at least one of identification information or authentication information associated with the user that enables processing the transaction using the data’ as identifying the user by authentication (¶0036)
‘transfer the at least one of the identification information or the authentication information to the edge computing device prior to processing the transaction’ and after authentication, allowing a user to access content on their assigned devices at certain locations (¶0036)
Regarding claim 17, Caine teaches further comprising:
‘requesting an authentication by the user for access to the portion of the user data’ as identifying the user by authentication (¶0036)
‘determining whether to deliver the portion of the user data to a device based on a response to the authentication requested’ and after authentication, allowing a user to access content on their assigned devices at certain locations (¶0036)
Regarding claim 18, Montague and Caine teach further comprising:
‘determining an account of the user that is usable to process a payment for the transaction’ as allowing for intake of user payment (Montague Col. 3, Lines 21-34)
‘determining at least one of identification information or authentication information associated with the user that enable processing the transaction using the account’ as identifying the user by authentication (Caine ¶0036)
‘transferring the at least one of the identification information or the authentication information to the edge storage component’ as after authentication, allowing a user to access content on their assigned devices at certain locations (¶0036)
Examiner’s Note
Examiner has cited particular columns/paragraphs and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
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/VAN H OBERLY/ Primary Examiner, Art Unit 2166