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 .
Claim(s) 1-4, 7-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Novoselsky et al. 20200271756 herein Novoselsky.
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.
Claim(s) 1-4, and 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Novolesky in view of Kashi Vishvanathan et al. 20240134828 herein Kashi
Per claim 1, Novoselsky discloses: A data network architecture comprising at least one equipment configured to perform the function specified by the user and/or manufacturer, (fig. 2) at least one source providing data to the equipment , (fig. 2, 200; ¶0337; a radar unit 200 can track an airborne object and transfer data indicative of RCS measurement to target identification system 210, which then conducts machine learning for the series of the target aspect angles and then classifies the series of target RCS measurements within e.g. 1-2 seconds or some other time interval short enough to facilitate a tactical response) and at least one live data storage that is connected to the source and enables the storing of live data (L) provided directly by the source characterized by at least one control unit that is connected to the source (3), (fig. 2. 255) at least one meaningful data storage that is connected to the control unit and enables the storing of meaningful data (M) generated by processing the live data (L) that is transmitted by the source to the control unit (fig. 2, 230; ¶0209; The memory (230) can be configured to, for example, store various data used in computation, ¶0321; Classification unit 275 can utilize machine learning model 285 to classify radar plot data (for example: received from radar unit 220),).
Novolesky does not specifically disclose: checks the live data that comes from the source and are located in the live data storage and the meaningful data that are located in the meaningful data storage simultaneously with the data flow; compares the live data in the live data storage with the meaningful data (M) in the meaningful data storage updates the meaningful data (M) in the meaningful data storage according to the live data (L) in the live data storage and transfers meaningful data from the meaningful data storage ( to the equipment and/or source , thus ensures that the source and/or equipment operates almost completely unaffected by the data interruption in the event of an interruption in the data flow transmitted from the source to the live data storage the control unit feeds the equipment and/or source with current meaningful data from the meaningful data storage to ensure operation of the source and/or equipment.
However, Kashi discloses: checks the live data that comes from the source and are located in the live data storage and the meaningful data that are located in the meaningful data storage simultaneously with the data flow; (fig. 2, ¶0155; a primary resource on the source file system is called application, and an auxiliary (or secondary) source on the target file system is called an application target. When a source object and a target object are created, they have a single replication relationship. Both objects can only be updated from the source side, including changing compartments, editing or deleting details. When a user wants to delete the target side, the replication can be deleted by itself.) compares the live data in the live data storage with the meaningful data (M) in the meaningful data storage updates the meaningful data (M) in the meaningful data storage according to the live data (L) in the live data storage and transfers meaningful data from the meaningful data storage to the equipment and/or source , thus ensures that the source and/or equipment operates almost completely unaffected by the data interruption in the event of an interruption in the data flow transmitted from the source to the live data storage the control unit feeds the equipment and/or source with current meaningful data from the meaningful data storage to ensure operation of the source and/or equipment (fig. 2, ¶0155; .. For a planned failover, the source side can be deleted, and both the source side and target replication are deleted. For an unplanned failover, the source side is not available, so only the target replication can be deleted. In other words, there are two resources for a single replication, and they should be kept in sync. There are various workflows for updating metadata on both the source and target sides. Additionally, retries, failure handling, and cross-region APIs for failover are also part of the cross-region communication process; the examiner interprets the limitation as synchronizing the live and meaningful storages and failing over the meaningful storage).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Novolesky and Kashi because Kashi enables efficient replication and snapshot during cross region replication. Kashi improves replication efficiency (¶0009).
Per claim 2, Novoslesky discloses: characterised by the control unit in which meaningful data (M) is created using a recurrent neural network-based machine learning algorithm by providing data from the source in a volume determined by the user or manufacturer (¶0321; Classification unit 275 can utilize machine learning model 285 to classify radar plot data (for example: received from radar unit 220); ¶0315; Processing and Memory Circuitry 220 can include a machine learning model 285. Machine learning model 285 can be any kind of suitable machine learning model, such as, for example,—a neural network, support vector machines etc. In some embodiments, machine learning model 285 is a neural network with one input layer, one output layer, and at least one hidden layer).
Per claim 3, Novoslesky discloses: characterised by the control unit that allows the live data storage and the meaningful data storage to have a low storage volume by being directly connected to the source (3), (fig. 2a, 255; ¶0214; aircraft RCS database (255) includes table entries which map the direction of the radar beam in the aircraft body frame (for example: represented as azimuth and elevation angles) to, for example, previously observed RCS values for particular aircraft. Aircraft RCS database (255) can be utilized, for example, by RCS estimation unit (270) in its estimation of RCS series for candidate aircraft; the examiner is not certain how the a storage volume is related to a directly connected source and therefor interprets the claim as the live data being stored in a local memory ). thus allowing the meaningful data (M) to be created by providing less data from the source (¶0206; Radar Unit (200) can be, for example, any kind of stationary or mobile radar unit that can supply radar data (for example: radar plot timestamps, range, elevation and azimuth angles, SNR, Doppler velocity etc.) to target identification system (210)).
Per claim 4, Novoslesky discloses: characterised by at least one buffer that is connected to the source , is the unit where data is temporarily stored to ensure that the data coming from the source is stored in the live data storage and/or the meaningful data storage , and enables the data storing order to be given to the live data storage and/or the meaningful data storage (fig. 2a, 255; ¶0214; aircraft RCS database (255) includes table entries which map the direction of the radar beam in the aircraft body frame (for example: represented as azimuth and elevation angles) to, for example, previously observed RCS values for particular aircraft. Aircraft RCS database (255) can be utilized, for example, by RCS estimation unit (270) in its estimation of RCS series for candidate aircraft).
Per claim 7, Novoslesky discloses: characterised by at least one communication tool (9) that provides data transmission between the equipment (2), the control unit (5) and the source (3) and has a data transmission interface determined by the user (fig. 2a, ¶0215; Display unit (290) can be, for example, any kind of display system or monitor suitable for depiction of, for example, radar tracking and aircraft identification data. By way of non-limiting example, display unit (290) can be a computer monitor, laptop computer, tablet, mobile phone etc; ¶0216; Display unit (290) can be, for example, operably connected to target identification system (210) via, for example, a conventional electronics communication connection of an appropriate type).
Per claim 8, Novoslesky discloses: characterised by the control unit (5) in which meaningful data (M) is generated using a recurrent neural network-based learning algorithm prior to the flight of the aircraft and/or spacecraft (fig. 2a, ¶0315; a neural network, support vector machines etc. In some embodiments, machine learning model 285 is a neural network with one input layer, one output layer, and at least one hidden layer; ¶0319; machine learning model 285, after training, can be used to classify features derived from, for example, a RCS time series derived from radar monitoring (including real-time monitoring) of an airborne object, and determine a classification label indicating an identification of the airborne object with an aircraft type).
Per claim 9, Novoslesky discloses: characterised by the equipment (2) that is located on the aircraft and/or spacecraft (fig. 1 and fig.2a, ¶0198; Radar/Tracker/Identification/Display System (100) additionally detects and records data indicative of the radar cross section (RCS) of the airborne object).
Per claim 10, Novoslesky discloses: characterised by a source (3) that is located on the aircraft and/or spacecraft (fig. 1 and fig.2a, ¶0198; Radar/Tracker/Identification/Display System (100) additionally detects and records data indicative of the radar cross section (RCS) of the airborne object).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Novolesky in view of Clymer 20230004799 herein Clymer.
Per claim 5, Novoslesky does not specifically disclose: characterised by at least one external storage (8) that is connected to the second storage (6), ensures that the meaningful data (M) in the meaningful data storage (6) is controlled and updated by the control unit (5) and that the updated meaningful data (M) is stored before being transferred to the equipment (2) and/or source (3), and has high storage volume (fig. 1 & 5, cloud storage; ¶0094; The cloud-based remote access can be accessed by a smartphone, a desktop computer, a tablet, or any other client computing systems, anytime and/or anywhere. The cloud-based remote access is coded to engage in 1) the request and response cycle from all web browser based applications, 3) the request and response cycle from a dedicated on-line server, 4) the request and response cycle directly between a native application resident on a client device and the cloud-based remote access to another client computing system, and 5) combinations of these; the examiner interprets the claims as an external storage connected to the system).
It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to combine the teachings of Novoslesky and Clymer’s cloud based neural network to handle complex neural network. Clymer’s distributed network allows for scalabilty (¶0093).
Per claim 6, Novoslesky discloses: characterised by the control unit (5) enabling the performing of the process steps of: processing the live data (L) or data series transmitted by the source (3) with the recurrent neural network-based learning algorithm and creating meaningful data (M) (101), checking the live data (L) in the live data storage (4) according to the meaningful data (M) in the meaningful data storage (6) simultaneously with the data flow according to the meaningful data created and updating the meaningful data (M) (102), feeding the source (3) and/or equipment (2) with updated meaningful data (M) (103), (¶0320; Processing and Memory Circuitry 220 can include training unit 265. Training unit 265 can receive RCS estimation data from the RCS estimation unit 270 and prepare training data for the machine learning model 285. An example process for preparing training data is described below with reference to FIG. 8.)
Novoslesky does not specifically disclose: and long-term storing of meaningful data (M) in external storage (8) (104).
However, Clymer discloses: and long-term storing of meaningful data (M) in external storage (8) (104) (fig. 1 & 5, cloud storage; ¶0094; The cloud-based remote access can be accessed by a smartphone, a desktop computer, a tablet, or any other client computing systems, anytime and/or anywhere. The cloud-based remote access is coded to engage in 1) the request and response cycle from all web browser based applications, 3) the request and response cycle from a dedicated on-line server, 4) the request and response cycle directly between a native application resident on a client device and the cloud-based remote access to another client computing system, and 5) combinations of these;).
Response to Arguments
Applicant’s arguments, filed 12/28/25, with respect to the rejection(s) of claim(s) 1 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kashi.
Remark
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
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 BABOUCARR FAAL whose telephone number is (571)270-5073. The examiner can normally be reached M-F 8:30-5:30 EST.
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BABOUCARR . FAAL
Primary Examiner
Art Unit 2138
/BABOUCARR FAAL/Primary Examiner, Art Unit 2138
Prosecution Insights