Response to an Amendment
This office action is a response to a communication made on 11/18/2025.
Claims 1, 4, 6, 8, 11, 13-14, 17 and 19 are currently amended.
Claims 1-20 are pending for this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see remarks on page 7-8, filed 11/18/2025, with respect to the rejection(s) of claim(s) 1, 8 and 14 under 103 have been considered and regarding the amended feature of “wherein the intelligent API comprises an artificial intelligence (AI) engine” are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Masputra et al. (US20130201996A1) in view of Karp (US 2015/0372833), and further in view of White et al. (US 2024/0289696).
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Masputra et al. (US20130201996A1), hereinafter “Masputra” in view of Karp (US 2015/0372833), and further in view of White et al. (US 2024/0289696), hereinafter “White”.
With respect to claim 1, Masputra discloses a system for grouping and filtering of electronic data using an intelligent application programming interface, the system comprising:
a processing device (Fig. 16, teaches processing system 2320);
a non-transitory storage device containing instructions when executed by the processing device (¶0223, teaches the processing system 2320 may retrieve instruction(s) from the memory 2330), causes the processing device to perform the steps of:
analyzing one or more data packets using a data grouping module of an intelligent application programming interface (‘API’) (¶0050, teaches a packet classifier 202 for classifying packets (i.e. analyzing data packets), an API 203 for interfacing with applications 201),
based on analyzing the data packets, appending one or more data tags to the each of the one or more data packets (¶0110, teaches the module performing packet classification 202 also associates (i.e. appending) one or more tags with the packet, in order to assist the rest of the system in identifying the type or flow of the packet);
based on the one or more data tags, filtering the one or more data packets into one or more categories using a data finalizer module of the intelligent API (¶0050, teaches a packet classifier 202 for classifying packets (i.e. data packets), an API 203 for interfacing with applications 201, ¶0097, teaches the system assigns network control packets to the highest priority classification, wherein highest priority is the filtering packets into one or more categories, ¶0110, teaches the module performing packet classification 202 also associates one or more tags with the packet);
based on filtering the one or more data packets (¶0097, teaches the system assigns network control packets to the highest priority classification, wherein highest priority is the filtering packets into one or more categories), wherein the one or more data packets are ordered within the data processing queue according to the one or more data tags and one or more categories (¶0010, teaches the packet service classifications specifying a relative priority for packets (i.e. data packets are ordered) stored within each respective queue, ¶0097, teaches the system assigns network control packets to the highest priority classification, wherein highest priority is the filtering packets into one or more categories, ¶0110, teaches the module performing packet classification 202 also associates one or more tags with the packet); and
executing transmission of the one or more data packets according to an order of the one or more data packets within the data processing queue (¶0010 and ¶0042, teaches scheduling packets for transmission from each of the transmit queues, wherein packets are scheduled for transmission according to the packet service classifications and wherein network control packets are prioritized for transmission above all other packet service classifications…the network stack queues and schedules the packet for transmission).
However, Masputra remain silent on generating a data processing queue using a data organizer module of the intelligent API.
Karp discloses generating a data processing queue using a data organizer module of the intelligent API (¶0099, ¶0109, ¶0121-¶0123, ¶0232, teaches the APIs 90 allow applications (i.e. data organizer module) executed by the third parties to initiate specific data processing tasks that are executed by the central server…smart device communication and/or control via an application accessing an API…an audible queue might be to “Turn on the heat.” In such a scenario, the commands provided to the thermostat 10A would set the thermostat one degree Celsius above the current ambient temperature…Data in the data model may be organized hierarchically).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Masputra’s API and a queue simply consists of one or more packets with generating a data processing queue using a data organizer module of the intelligent API of Karp, in order to improve latency, accuracy and cost efficiency in the system and ensure data items are handled in the correct sequence (Karp).
Masputra ¶0050, teaches a packet classifier 202 for classifying packets (i.e. analyzing data packets), an API 203 for interfacing with applications 201. However, Masputra in view of Karp remain silent on wherein the intelligent API comprises an artificial intelligence (AI) engine; packets using the intelligent API comprises an artificial intelligence (AI) engine; the intelligent API comprising the AI engine.
White discloses wherein the intelligent API comprises an artificial intelligence (AI) engine (See Fig. 1. Cloud hosted applications (i.e. API or application programming interface), Fig. 4 and ¶0076-¶0077, teaches process flow 400 is performed in accordance with information processing system environment 100 (i.e., multicloud edge platform) in conjunction with data characterization engine 120 using ML classification sub-system 210…detects a source application associated with data obtained from execution of at least one of a plurality of applications in an information processing system, wherein the plurality of applications comprise services associated with multiple different policies. Step 404 classifies the data to determine an intent associated with the data, wherein classifying comprises utilizing a machine learning classification (i.e. Artificial intelligence) process) ;
packets using the intelligent API comprises an artificial intelligence (AI) engine (Fig. 1. Cloud hosted applications (i.e. API or application programming interface), Fig. 4 and ¶0076-¶0077, teaches a process flow 400 for data detection (i.e. data packets) and classification according to an illustrative embodiment. process flow 400 is performed in accordance with information processing system environment 100 (i.e., multicloud edge platform) in conjunction with data characterization engine 120 using ML classification sub-system 210…detects a source application associated with data obtained from execution of at least one of a plurality of applications in an information processing system, wherein the plurality of applications comprise services associated with multiple different policies. Step 404 classifies the data to determine an intent associated with the data, wherein classifying comprises utilizing a machine learning classification (i.e. Artificial intelligence) process;
the intelligent API comprising the AI engine (See Fig. 1. Cloud hosted applications (i.e. API or application programming interface), Fig. 4 and ¶0076-¶0077, teaches process flow 400 is performed in accordance with information processing system environment 100 (i.e., multicloud edge platform) in conjunction with data characterization engine 120 using ML classification sub-system 210…detects a source application associated with data obtained from execution of at least one of a plurality of applications in an information processing system, wherein the plurality of applications comprise services associated with multiple different policies. Step 404 classifies the data to determine an intent associated with the data, wherein classifying comprises utilizing a machine learning classification (i.e. Artificial intelligence) process).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Masputra’s APIs with an artificial intelligence (AI) engine of White in order to function as a responsive, self-learning component, improving overall system resilience and performance (White).
For claim 8, it is a non-transitory computer readable medium claim corresponding to the system of claim 1. Therefore claim 8 is rejected under the same ground as claim 1.
For claim 14, it is a method claim corresponding to the system of claim 1. Therefore claim 14 is rejected under the same ground as claim 1.
With respect to claims 2, 9 and 15, Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 1, 8 and 14, wherein filtering the one or more data packets comprises assigning a priority value to each of the one or more data packets (Masputra, ¶0010 and ¶0097, teaches a packet service classification assigned to network control packets being assigned a highest priority relative to the other transmit queues…the system assigns network control packets to the highest priority classification, wherein highest priority is the filtering packets into one or more categories).
With respect to claims 3, 10, and 16, Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 2, 9 and 15, wherein the one or more data packets are further ordered within the data processing queue based on the priority value assigned to each of the one or more data packets (Masputra, ¶0010, teaches the packet service classifications specifying a relative priority for packets (i.e. data packets are ordered) stored within each respective queue... a packet service classification assigned to network control packets being assigned a highest priority relative to the other transmit queues).
With respect to claims 4, 11 and 17, Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 1, 8 and 14, wherein analyzing the one or more data packets using the data grouping module comprises analyzing data and metadata associated with each of the one or more data packets (Masputra, ¶0050, teaches a packet classifier 202 for classifying packets (i.e. analyzing data packets), an API 203 for interfacing with applications 201, Karp, ¶0091, ¶0251 teaches analyze the data and/or to generate statistics based on the analysis or as part of the analysis…the device service 84 responds with a data object including data objects from the data model (e.g., a metadata data object), wherein the metadata comprises at least one of file size, creation time, storage location, file name, priority, or file format (Maputra, ¶0010, teaches relative priority for packets stored within each respective queue, wherein priority is a metadata, Karp, ¶0176, teaches the name data value (i.e. metadata as file name) may be displayed in user interface labels ).
With respect to claims 5, 12 and 18, Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 1, 8 and 14, wherein the one or more data tags comprises at least one of an empty tag, a junk tag, a duplicate tag, a priority tag, an event-oriented tag, or a time-oriented tag (Masputra, ¶0110, ¶0125, ¶0176-¶0177, teaches the module performing packet classification 202 also associates one or more tags with the packet…retransmitting already sent data might create duplicate copies of the same packet in the interface queue…events related to the interface are sent from the networking stack 102 to the attached scheduler 116 and queue management logic 115, ).
With respect to claims 6, 13 and 19, Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 1, 8 and 14, wherein the one or more categories comprises a high priority category, a low priority category, or a discarded category (Masputra, ¶0097, teaches the system assigns network control packets to the highest priority classification, wherein highest priority is the filtering packets into one or more categories).
With respect to claims 7 and 20 Masputra in view of Karp, and further in view of White discloses the system, the non-transitory computer readable medium, and the method of claims 1, 8 and 14, wherein executing transmission of the one or more data packets further comprises encrypting the one or more data packets and transmitting the one or more data packets using a zero-trust mechanism (Masputra, ¶0010 and ¶0042, teaches scheduling packets for transmission from each of the transmit queues, wherein packets are scheduled for transmission according to the packet service classifications and wherein network control packets are prioritized for transmission above all other packet service classifications…the network stack queues and schedules the packet for transmission, Karp, ¶0083, teaches the notification signals sent by the away-service robots to the activity sensing systems are authenticated and encrypted (i.e. zero trust mechanism) such that the notifications cannot be learned and replicated by a potential burglar).
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 GOLAM MAHMUD whose telephone number is (571)270-0385. The examiner can normally be reached Mon-Fri 8.00-5.00pm.
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/G.M/Examiner, Art Unit 2458
/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458