DETAILED ACTION
This office action is a response to a communication made on 12/04/2025.
Claims 1-6 are pending and 7-22 are withdrawn.
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 .
Election/Restrictions
Claims 1-6 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected 7-22, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/04/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2023 and 12/09/2025 were filed before the mailing date of the non- final action on 02/23/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Edgar (US 2020/0097832 A1).
With respect to claim 1, Edgar discloses a method for management of neural data, the method comprising:
obtaining one or more samples of neural data (¶0010, teaches network data is organized and sequenced with individual data records/frames. The record/frame is the basic storage unit and it captures a data event, ¶0220, teaches the sampling consist of 1000 Carbon Monoxide sensors (i.e. samples of neural data) producing IoT data frames at various intervals over the course of 1 minute); and
processing the one or more samples of neural data according to protocol specifying formatting of neural data records for storage and transmission, to generate formatted neural data records (¶0010 and ¶0026, teaches the record/frame is the basic storage unit and it captures a data event. a data event occurs when a data producer, such as sensor (i.e. neural data), at some specific time produces a data record/frame. Each data record/frame consists of a number of data fields. The data fields contain all the information about the data event, such as its timestamp, name, measurement, type etc, wherein timestamp and measurement are protocol …a neural synchronization algorithm for optimizing data record transmission and processing through the use of thalamic motion encoded in a protocol, referred to as motion signal protocol (MSP) (i.e. protocol buffer ), that has the structure (i.e. format) to seamlessly integrate with any data record/frame protocol., ¶0073, teaches the neural synchronization process can use any form of transmission format and run on any form of computer or neural network, ¶0087, teaches generate any type of protocol, data structuring, or Standards-based format regardless of its original format, ¶0220, teaches the sampling consist of 1000 Carbon Monoxide sensors producing IoT data frames at various intervals over the course of 1 minute).
With respect to claim 2, Edgar discloses the method of claim 1, further comprising:
storing the formatted neural data records in a database (Edgar, ¶0143, teaches datasets are most commonly used for storing multiple records once they have been retrieved from a database server, ¶0228, teaches motion is derived by measuring data states of time-series data at a fixed time interval. ¶0234, teaches the data frame 112 is interpreted as depicted in FIG. 5 where it is organized (i.e. formatted) into a dataset containing a linear sequence of Motion Signal Protocol (MSP) ordered first-in first-out (FIFO)).
With respect to claim 3, Edgar discloses the method of claim 2, wherein storing the formatted neural data records in a database comprises:
storing the formatted neural data records in a time-series database (Edgar, ¶0073, teaches the neural synchronization process can use any form of transmission format and run on any form of computer or neural network, ¶0076, teaches Stream 58 will be composed of information encoded in some repetitive computer data format (protocol) produced and encoded according to some linear time standard or interval, ¶0130, teaches timestamps, sequence numbers, counters, and indexes all show motion. Their associated data fields change every iteration within the time-series data stream, ¶0143, teaches datasets are most commonly used for storing multiple records once they have been retrieved from a database server).
With respect to claim 4, Edgar discloses the method of claim 1, wherein processing the one or more samples of neural data according to the protocol buffer definitions comprises:
arranging the one or more samples of neural data in timestamped measurement sequences comprising a measurement name field, a tag field, and a value field to hold a value derived from the one or more samples of the neural data (¶0010, teaches each data record/frame consists of a number of data fields. The data fields contain all the information about the data event, such as its timestamp, name, measurement, type etc, ¶0117, teaches each data record/frame 58 is wrapped with a unique identifier (i.e. tag filed) and a set of command codes that provides instructions for applying the thalamic motion data, ¶0130, teaches for example, with a time field (i.e. value field), if the first value received was 12:00:00 and the second value was 12:00:10, then the increment of ten (10) seconds would be sent, ¶0220, teaches the sampling consist of 1000 Carbon Monoxide sensors (i.e. samples of neural data) producing IoT data frames at various intervals over the course of 1 minute).
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) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Edgar in view of Ehlinger et al. (US 10511515 B1), hereinafter “Ehlinger”. Ehlinger cited in applicant IDS filed 12/19/2023.
With respect to claim 5, Edgar discloses the method of claim 1, however, Edgar remain silent on further comprising: establishing communication links with network nodes of different, non-related, networks, wherein each of the networks is configured to execute respective different applications configured to process the formatted neural data records; and transmitting to at least one of the networks nodes of the different, non-related, networks one or more of the formatted neural data record for downstream processing.
Ehlinger discloses further comprising: establishing communication links with network nodes of different, non-related, networks, wherein each of the networks is configured to execute respective different applications configured to process the formatted neural data records (Col-4, II. 54-60, teaches a computing device (e.g., an avionics computing device or a non-avionics computing device) having a protocol engine and an application stored in a non-transitory computer-readable medium. By executing the protocol engine and/or the application, the computing device may be configured to send and receive messages as protocol buffer data, Col-8, II. 40-43, teaches based at least on execution of the protocol engine 602, the processor 114-1 may be configured to encode structured data (i.e. formatted neural data records) as protocol buffer data, Col-15, II. 26-28, teaches the secure server router computing device 112-8 may be configured to establish secure wireless connections to a non-avionics computing device 140); and
transmitting to at least one of the networks nodes of the different, non-related, networks one or more of the formatted neural data record for downstream processing (Col-8, II. 40-43, teaches based at least on execution of the protocol engine 602, the processor 114-1 may be configured to encode structured data (i.e. formatted neural data records) as protocol buffer data, Col-15, II. 26-28, teaches the secure server router computing device 112-8 may be configured to establish secure wireless connections to a non-avionics (i.e. non-related) computing device 140, Col-16, II. 67-Col-17, II. 1-4, teaches the non - avionics computing device 140 may be configured to receive filtered avionics data via a wireless connection ( e.g. , a secure wireless connection ) from one of the avionics computing devices ( e.g. , the secure server router computing device 112-8 )).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Edgar’s generate any type of protocol, data structuring, or Standards-based format regardless of its original format with establishing communication links with network nodes of different, non-related, networks, wherein each of the networks is configured to execute respective different applications configured to process the formatted neural data records; and transmitting to at least one of the networks nodes of the different, non-related, networks one or more of the formatted neural data record for downstream processing of Ehlinger, in order to interact across independent systems and send the prepared neural data to one or more of those networks (Ehlinger).
With respect to claim 6, Edgar in view of Ehlinger disclose the method of claim 5, wherein a first network from the different, non-related networks is implemented on a computing platform different from another computing platform implementing another of the different, non-related networks (Ehlinger, Col-4, II. 35-38, teaches the computing device may be configured to send a message as protocol buffer data to a destination (e.g., another application, an application of another computing device, and/or a file system). For example, based at least on execution of the protocol engine, the computing device may be configured to encode structured data as protocol buffer data, where the protocol buffer data is packed data readable by the destination, Col-15, II. 26-28, teaches the secure server router computing device 112-8 may be configured to establish secure wireless connections to a non-avionics (i.e. non-related) computing device 140, Col-16, II. 67-Col-17, II. 1-4, teaches the non - avionics computing device 140 may be configured to receive filtered avionics data via a wireless connection ( e.g. , a secure wireless connection ) from one of the avionics computing devices ( e.g. , the secure server router computing device 112-8 )).
Conclusion
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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/GOLAM MAHMUD/Examiner, Art Unit 2458