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 .
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 § 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Phan et al. (US Pub. 20200057956).
Referring to claim 1, Phan 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 an anomaly in a target domain (TD) dataset [pars. 6, 14, and 17; a system comprises a processor coupled to a memory device, the processor operable to detect anomalies in a sensor network], the processor system operations comprising:
training a model to perform an anomaly prediction task on a TD [pars. 14, 17, and 18; a probabilistic graphical model for detecting anomalies is trained via a transfer learning algorithm that learns and transfers connectivity pattern and dependency magnitude from a normal training phase (i.e., source domain) to a testing phase (i.e., target domain) for a dependency matrix];
wherein the training includes applying a transfer learning operation that includes learning to predict the anomaly based at least in part on a first source domain (SD) precision matrix computed from a first SD [pars. 14, 17, and 18; note the learning and transferring performed by the transfer learning algorithm; a sparsity level of the dependency matrix is selected to keep the magnitudes of precision matrix entries (that are learned and transferred from the normal training phase) to a manageable amount].
Referring to claim 8, see the rejection for claim 1, which incorporates the claimed method.
Referring to claim 15, see at least the rejection for claim 1. Phan 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 [pars. 6, 14, and 17; a system comprises a processor coupled to a memory device, the processor operable to detect anomalies in a sensor network].
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 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Phan in view of Pisner (US Pub. 20240161017).
Referring to claim 2, Phan does not appear to explicitly disclose 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.
However, 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 [pars. 18-23 and 48; multiple pre-trained models (i.e., source domains) are combined into an ensemble model, which is then adapted to a target domain; each model can be implemented as a graphical model such as a Gaussian Graphical Model, which is the inverse of the covariance matrix, also called the “precision” matrix; see also Phan, par. 18, disclosing a precision matrix for a source domain].
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 taught by Phan so that the transfer learning is applied to an ensemble model combining multiple source domains as taught by Pisner, with a reasonable expectation of success. The motivation for doing so would have been to form a more robust and powerful learning framework [Pisner, par. 18].
Referring to claim 9, see the rejection for claim 2.
Referring to claim 16, see the rejection for claim 2.
Claims 3, 4, 10, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Phan and Pisner in view of Krause et al. (US Pub. 20230120761).
Referring to claim 3, Phan and Pisner do not appear to explicitly disclose 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.
However, Krause 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 [pars. 17-19, 42, and 48-50; when anomalies indicate a quality defect or a faulty process, a neural network (e.g., an autoencoder) is trained with process signal data sets for a laser machining process the product or process of which has been classified as “OK” (i.e., a source domain); during training, the parameters mean vector is determined using defect-free or labeled data sets (i.e., the source domain)].
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 taught by the combination of Phan and Pisner so that the training is based on a mean vector of a source domain as taught by Krause, with a reasonable expectation of success. The motivation for doing so would have been to require as few instances of faults for training as possible [Krause, pars. 13 and 14].
Referring to claim 4, Pisner and Krause disclose 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 [Pisner: pars. 18-23 and 48; note the ensemble of source domains / Krause: pars. 17-19, 42, and 48-50; note the determining of the mean vector for the source domain].
Referring to claim 10, see the rejection for claim 3.
Referring to claim 11, see the rejection for claim 4.
Referring to claim 17, see the rejection for claims 3 and 4.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Grace Park/Primary Examiner, Art Unit 2144
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