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 .
Preliminary amendment filed on 11/26/24 has been received and entered. Claims 1-20 are pending.
Claim Objections
Claims 1 and 2 are objected to because of the following informalities: The term “tiny” is relative and does not specify a measurement that would ascertain the scope of the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zainal Abidin et al. (US Pub. No. 20240202404 A1).
As to claim 1, Bin Zainal Abidin et al. discloses A computer-implemented method for detection of flange abnormalities, the method comprising:
obtaining, with one or more hardware processors, sensor data associated with at least one flange, wherein the sensor data is captured by at least one sensor communicatively coupled with an embedded device on a mesh network (See paragraph 0042);
executing, with the one or more hardware processors, a trained tiny machine learning model at the embedded device, wherein the sensor data is input to the trained tiny machine learning model and the trained tiny machine learning model predicts a state of the at least one flange (See paragraph 0059); and
transmitting, with the one or more hardware processors, the state to a master node across the mesh network, wherein further actions are performed responsive to the state of the at least one flange (See paragraph 0044).
Claims 8 and 15 are independent directed to an apparatus and system (respectively) with similar limitations in scope and therefore rejected under the same ground.
As to claim 2, Bin Zainal Abidin et al. discloses the computer implemented method of claim 1, wherein the tiny trained machine learning model is built from a machine learning model trained using a training dataset comprising raw sensor data and synthesized sensor data (See paragraph 0059).
Claims 9 and 16 are similar in scope and therefore rejected under the same ground.
As to claim 3, Bin Zainal Abidin et al. discloses The computer implemented method of claim 1, wherein the further actions comprise an inspection of the at least one flange (See paragraph 0113).
Claims 10 and 17 are similar in scope and therefore rejected under the same ground.
As to claim 4, Bin Zainal Abidin et al. discloses The computer implemented method of claim 1, wherein the master node comprises a machine learning algorithm that is retained using sensor data from embedded devices on the mesh network, wherein the retrained machine learning algorithm is used to update the trained tiny machine learning model (See paragraph 0035, 0059).
Claims 11 and 18 are similar in scope and therefore rejected under the same ground.
As to claim 5, Bin Zainal Abidin et al. discloses The computer implemented method of claim 1, wherein the master node controls the embedded device using commands propagated from the master node, to a root node, and to a node comprising the embedded device (See paragraph 0045, 0056, wherein the information is centralize and transmitted among network nodes, well known method of process control and monitoring).
Claims 12 and 19 are similar in scope and therefore rejected under the same ground.
As to claim 6, Bin Zainal Abidin et al. discloses The computer implemented method of claim 1, wherein the state of the at least one flange is normal or abnormal (see paragraph 0113, wherein the detection includes fault/error of components including flange).
Claims 13 and 20 are similar in scope and therefore rejected under the same ground.
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.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Bin Zainal Abidin et al. (US Pub. No. 20240202404 A1) in view of Rama Subba Reddy Gorla Probabilistic structural and thermal analysis of a gasketed flange.
As to claim 7, Bin Zainal Abidin et al. discloses The computer implemented method of claim 1, but does not show the probability distribution (although its is a well-known and obvious method of mathematical presentation).
Rama Subba Reddy Gorla teaches wherein the state of the flange is a probability distribution that one or more abnormalities is present (see page 538, section 5, page 539, section 6, general probably distribution function with design choice).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify Bin Zainal Abidin et al.’s to clearly show mathematical distribution of data for accountability and efficient calculation.
Claim 14 is similar in scope and therefore rejected under the same ground.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure. See PTO 892 for list of completed relevant cited prior art.
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/NEVEEN ABEL JALIL/Supervisory Patent Examiner, Art Unit 2152 February 11, 2026