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 .
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 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.
DETAILED ACTION
This office action is in response to application 18/568115 filed on 12/07/23.
Claims 1-20 are remain pending in the application.
Oath/Declaration
The oath/declaration filed on December 07th, 2023 is acceptable.
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.
Claims 1-6 and 11-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaushal et al. (U.S. Patent No. 2012/0209798).
As to claims 1 the prior art teaches a computer implemented method for diagnosing a system comprising a plurality of modules, the method comprising:
receiving a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of the plurality of modules system, wherein each module is characterized by one or more signals (see fig 1-4 and 10-12 paragraph 0044-0048);
and (ii) edges connected between the plurality of nodes, the edges representing propagation of performance between modules (see fig 1-4 and 10-13 paragraph 0047-0052);
generating a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules (see fig 3-5 and 10-14 paragraph 0050-0056);
obtaining a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module (see fig 3-6 and 10-14 paragraph 0054-0059);
and using, by a hardware computer system, the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour of the system (see fig 3-6 and 10-14 paragraph 0058-0065).
As to claims 2 and 16 the prior art teaches wherein each node of the causal graph is associated with a first score quantifying a probability that the module of the system, represented by the node in the causal graph, is behaving out-of-specification and out-of-population (see fig 1-4 and 10-12 paragraph 0044-0047);
and a second score quantifying a probability that the module of the system, represented by the node in the causal graph, is out-of- population without behaving out-of-specification (see fig 1-4 and 10-12 paragraph 0047-0050).
As to claims 3 and 17, the prior art teaches wherein each of the edges of the causal graph is assigned a weight quantifying the probability to which abnormal behaviour propagates from one module to another (see fig 9-12 paragraph 0102-0106).
As to claim 4 and 18, the prior art teaches wherein the health metric defines a probability that the module behaves out-of-population (see fig 5-10paragraph 0070-0074).
As to claim 5 and 19, the prior art teaches wherein obtaining the health metric for each module comprises comparing the one or more sensor signals mapped to the respective module to sensor data of at least one corresponding module of a system that is not exhibiting abnormal behaviour (see fig 6-11 paragraph 0088-0094).
As to claim 6 and 20, the prior art teaches further comprising generating a list of modules of the system that are potentially a root cause of the behavior of the system, and a confidence value for each module in the list defining a confidence that the respective module is the root cause of the abnormal behaviour, wherein the identified module has the highest confidence value (see fig 3-6 and 10-14 paragraph 0068-0073).
As to claim 7, the prior art teaches wherein the reasoning tool comprises a network comprising: first type nodes each representing that an associated module of the system is behaving out-of-specification, and first type edges indicating a probability of out-of- specification behaviour propagating between two first type nodes (see fig 3-5 and 10-14 paragraph 0051-0054);
and second type nodes each connected to a corresponding first type node by a second edge indicating at least one probability relating to the module, associated with the corresponding first type node, behaving out-of-population (see fig 3-5 and 10-14 paragraph 0053-0058).
As to claim 8, the prior art teaches wherein the at least one probability comprises: a probability that the module associated with the corresponding first type node is behaving out-of-specification and out-of-population(see fig 3-5 and 10-14 paragraph 0055-0060);
and a probability that the module associated with the corresponding first type node is behaving out-of-population without behaving out-of-specification(see fig 3-5 and 10-14 paragraph 0059-0066).
As to claim 9, the prior art teaches further comprising wherein the method comprises assigning the each health metric to a corresponding second type node of the second type nodes (see fig 3-5 and 10-14 paragraph 0050-0056).
As to claim 10, the prior art teaches wherein the network is a Bayesian network (see fig 3-5 and 10-14 paragraph 0050-0056).
As to claim 11 the prior art teaches wherein the method further comprising comprises outputting, to a user, an indication of the module of the system that is the most likely root cause of the behavior of the system (see fig 10-15 paragraph 0076-0080).
As to claim 12, the prior art teaches wherein the system is a lithographic machine (see fig 10-15 paragraph 0102-0106 and background).
As to claim 13, the prior art teaches wherein the system is component of a lithographic machine (see fig 10-15 paragraph 0104-0107 and background).
As to claim 14 the prior art teaches a computer-readable storage medium for diagnosing a system comprising a plurality of modules, the computer readable storage medium comprising stored instructions which, when executed by a processor of a computing device, are configured to cause the computing device to at least:
receive a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of a plurality of modules of a system, wherein each module is characterized by one or more signals (see fig 1-4 and 10-12 paragraph 0044-0048);
and (ii) edges connected between the plurality of nodes, the edges representing propagation of performance between modules (see fig 1-4 and 10-13 paragraph 0047-0052);
generate a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules (see fig 3-5 and 10-14 paragraph 0050-0056);
obtain a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module (see fig 3-6 and 10-14 paragraph 0054-0059);
and use the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour of the system (see fig 3-6 and 10-14 paragraph 0058-0065).
As to claim 15 the prior art teaches a computing device for diagnosing a system comprising a plurality of modules, the computing device comprising a processor configured to:
receive a causal graph, the causal graph defining (i) a plurality of nodes each representing a module of the plurality of modules system, wherein each module is characterized by one or more signals (see fig 1-4 and 10-12 paragraph 0044-0048);
and (ii) edges connected between the plurality of nodes, the edges representing propagation of performance between modules(see fig 1-4 and 10-13 paragraph 0047-0052);
generate a reasoning tool by augmenting the causal graph with diagnostics knowledge based on historically determined relations between performance, statistical and causal characteristics of at least one module out of the plurality of modules (see fig 3-5 and 10-14 paragraph 0050-0056);
obtain a health metric of the at least one module, wherein the health metric is associated with the one or more signals associated with the at least one module (see fig 3-6 and 10-14 paragraph 0054-0059);
and use the health metric as an input to the reasoning tool to identify a module that is the most likely cause of the behaviour of the system (see fig 3-6 and 10-14 paragraph 0058-0065).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BINH C TAT whose telephone number is 571 272-1908. The examiner can normally be reached on flex 7:00Am-8PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jack Chiang can be reached on 571 272-7483. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BINH C TAT/Primary Examiner, Art Unit 2851
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