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 .
Response to Amendment and Arguments
Applicant’s amendment filed on July 15, 2026 has been entered and made of record. Claims 1-20 are pending and are being examined in this application.
In light of Applicant’s amendments to the claims, the 102 rejection is withdrawn.
Applicant’s arguments with respect to the 102 rejections have been considered, but are moot in view of the new ground(s) of rejection provided below.
Allowable Subject Matter
Claims 5-7, 12-14, 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Claims 1-4, 8-11, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Pisner (US Pub. 20240161017) in view of Phan et al. (US Pub. 20200057956).
Referring to claim 1, Pisner discloses A computer system comprising a memory communicatively coupled to a processor system, wherein the processor system is operable to perform processor system operations to predict … in a target domain (TD) dataset [fig. 16; system 1602, random access memory 1615 / non-transitory machine-readable storage media 1618, processing devices 1604], the processor system operations comprising:
training a model to perform an … prediction task on a TD [abstract; par. 15; a transfer learning process trains a connectome ensemble predictive model (CEPM) to leverage knowledge from one or more source domains to solve a related but different problem in a target domain];
wherein the training includes applying a transfer learning operation [abstract; par. 15; note the transfer learning process] that includes learning to predict … based at least in part on a first source domain (SD) precision matrix computed from a first SD [abstract; pars. 15, 18-23, 48, and 159-163; the source domains are represented by source connectome ensemble representations (sCERs); the sCERs are generated by sampling a plurality of source Connectome Graphical Model (sCGMs) representing connections between nodes in respective source domains; Connectome Graphical Models (CGMs) based on covariance include Gaussian Graphical Models (GGMs), where the most common GGM is the inverse of the covariance matrix, also called the precision matrix]; wherein the first SD is different from the TD and is a domain of a different but related task relative to the … prediction task on the TD [abstract; par. 15; note the related but different problems between the source domains and the target domain]; and
wherein the first SD precision matrix is computed using source-domain data of the first SD [abstract; pars. 15, 18-23, 48, and 159-163; note the sCGM (i.e., source domain precision matrix) representing connections between nodes in a source domain] and is used by the transfer learning operation to train the model for the … prediction task on the TD [abstract; pars. 15, 18-23, 48, and 159-163; the transfer learning process transfers the sCER to the target domain].
Pisner does not appear to explicitly disclose that the prediction task is an anomaly prediction task.
However, Phan discloses that the prediction task is an anomaly prediction task [title; pars. 2, 17, 18, 20, and 33; a sparsity-constrained model for anomaly detection/prediction is trained using transfer learning from a Gaussian graphical models].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the transfer learning process taught by Pisner so that the target domain problem is anomaly detection as taught by Phan, with a reasonable expectation of success. The motivation for doing so would have been to enhance productivity and reduce costs across different application domains related to industrial or manufacturing processes [Phan, pars. 2-4 and 17].
Referring to claim 2, Pisner discloses The computer system of claim 1, wherein learning to predict the anomaly is further based at least in part on a second SD precision matrix computed from a second SD that is different from the first SD [abstract; pars. 15, 18-23, 48, and 159-163; note the plurality of sCGMs representing connections between nodes in respective source domains].
Referring to claim 3, Pisner discloses The computer system of claim 2, wherein learning to predict the anomaly is further based at least in part on a first SD mean vector computed from the first SD [par. 164; the transfer learning process is refined based on an evaluated quality of domain adaptation and alignment; the quality is evaluated by comparing embedded source Connectome Feature Vectors (sCFVs) with embedded target Connectome Feature Vectors (tCFVs) using one or more metrics such as Maximum Mean Discrepancy (MMD)].
Referring to claim 4, Pisner discloses The computer system of claim 3, wherein learning to predict the anomaly is further based at least in part on a second SD mean vector computed from the second SD [abstract; pars. 15, 18-23, 48, and 159-164; note the plurality of the source domains, which would have respective sCFVs that are evaluated using the one or more metrics such as MMD].
Referring to claim 8, see the rejection for claim 1, which incorporates the claimed method.
Referring to claim 9, see the rejection for claim 2.
Referring to claim 10, see the rejection for claim 3.
Referring to claim 11, see the rejection for claim 4.
Referring to claim 15, see at least the rejection for claim 1. Pisner further discloses A computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor to perform processor system operations comprising: the claimed steps [fig. 16; system 1602, random access memory 1615 / non-transitory machine-readable storage media 1618, processing devices 1604].
Referring to claim 16, see the rejection for claim 2.
Referring to claim 17, see the rejection for claims 3 and 4.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRACE PARK whose telephone number is (571)270-7727. The examiner can normally be reached M-F 8AM-5PM.
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/Grace Park/Primary Examiner, Art Unit 2144
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