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 .
Status of Claims
Claims 1-28 are pending.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the details of the flowcharts in Figure 2 and Figure 3 as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: paragraph 116 states "output image 112".
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 and 27 are objected to because of the following informalities. Claim 1 (similarly claim 27) recites “one or more images of an evaluated MI…” should be “one or more images of a MI…”.
Appropriate corrections are required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpretated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are, “relevancy determination unit …” “MI evaluation unit …” and “multi-level hierarchical unit…” in claims 1-28.
Because of these claim limitations being interpretated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim 19 has been given the following interpretation under broadest reasonable interpretation in light of the specification. Claim 19 recites an arbitrary number for the fraction of the plurality of MI states. The specification does not provide the significance of the percentage provided of the fraction of the plurality of MI states, and merely recites the number as drafted in the claim, paragraph 88. Therefore, the value presented in the claim is not a required design choice, but an arbitrary percentage. For purposes of searching for prior art, examiner interpretates this claim as any amount that is part of the plurality of MI states
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-28 are rejected under 35 U.S.C. 112(b). Specifically, as noted above, claim limitations “relevancy determination unit …” “MI evaluation unit …” and “multi-level hierarchical unit…” invokes 35 U.S.C. 112(f). The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or actions to the function. Applicant’s specification is devoid of any structures that perform the functions in the claims. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b).
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f);
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to function recited in the claim, without introducing new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)).
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 1-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 27 and 28 recite the term “relevant” is a relative term which renders the claim indefinite. The term “relevant” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In such a method, anything related to a manufactured item can be considered relevant. For examination purposes, examiner has interpretated the term “relevant” according to Merriam-Webster definition of "relating in an appropriate way to something under consideration: having significant and demonstrable bearing on or relation to the matter at hand". Claims 2-26, dependent on claim 1, are similarly rejected.
Additionally, claims 1, 27 and 28 recite the term “respective fraction” is a relative and unbounded term which renders the claim indefinite. The term “respective fraction” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It can mean an infinitesimally small portion or a substantial portion, e.g., 49%. Claims 2-26, dependent on claim 1, are similarly rejected.
Claim 10 recites “without detection of objects that are below a predefined number of pixels” is indefinite as the claim contains subject matter not described in the specification which fails to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Paragraph 43-44 of the specification does state “a small element may be of a minimal size to be detected…”. However, this description does not clearly describe the elements of claim 10, and therefore are indefinite. For examination purposes, examiner interprets this claim limitation as a requirement of the size of the image/patch of a manufactured item
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first
paragraph, as failing to comply with the written description requirement. The claims
contain subject matter which was not described in the specification in such a way as to
reasonably convey to one skilled in the relevant art that the inventor or a joint inventor,
or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the
application was filed, had possession of the claimed invention. Specifically, as noted in
the previous sections above, claim “relevancy determination unit …” “MI evaluation unit …” and “multi-level hierarchical unit…” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s specification is devoid of any corresponding structures that perform the functions in the claims. Accordingly, Claims 1-28 are also rejected under 35 U.S.C. 112(a) for lack of written description.
Claim 10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 10 recites "without detection of objects that are below a predefined number of pixels" the specification and drawings do not provide further explanation on the relevancy determination unit determining the relevant one or more narrow AI agents "without detection of objects that are below a predefined number of pixels".
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 28 are rejected under 35 U.S.C. 101, based on abstract idea. The claims recite a system and method of an ensemble of AI agents to detect and analyze a manufactured item for anomalies. With respect to independent system claim 1:
STEP 1: Do the claims fall within one of the statutory categories?
YES. Claim 1 is directed to a method i.e., a process.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
The limitation “provide an MI related evaluation” as drafted, recite an abstract idea, such as a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind of a person, i.e., concepts performed in the human mind (including observation, evaluation, judgement, opinion).
As such, a person can evaluate a manufactured item or an object with a degree of error or lack thereof either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a processor (e.g., processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
The additional elements of “obtaining one or more images of an evaluated MI” as recited is mere data gathering, which may not be considered as an element which integrates the above-listed identified abstract idea into practical application per MPEP 2106.05(g).
The additional elements “determining, by a relevancy determination unit and based on the one or more images, one or more relevant narrow AI agents of the ensemble that are relevant to a processing of the one or more images; wherein the ensemble is relevant to a first plurality of MI states; processing the one or more images, by the one or more relevant narrow AI agents, to provide one or more narrow AI agent MI related outputs; wherein each narrow AI agent is relevant to a respective fraction of the first plurality of MI states; and processing, by a MI evaluation unit, the one or more narrow AI agent MI related outputs decisions” are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). See also MPEP 2106.04(a)(2)(III) with respect to Mental Processes: “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer”. See also MPEP 2106.04(a)(2)(III)(C)(3) Using a computer as tool to perform a mental process and MPEP 2106.04(a)(2)(III)(D) as well as the case law cited therein.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO,
The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional step/element/limitation amounts to no more than an abstract idea performed on a computer. The additional elements are simply appending well-understood routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) per MPEP 2106.05(d) and 2106.07(a)(III). Therefore, claim 1 is not patent eligible.
In addition, the elements of claims 27 and 28 are analyzed in the same manner as claim 1. The additional element recited in claims 27, i.e., “non-transitory computer readable medium” recited at a high level of generality and merely equate to “apply it”, and refer to the use of computers as a tool to perform the abstract idea. Therefore independent claims 1, 27 and 28 are not patent eligible, either.
Similar analysis is made for the dependent claims 2-26, under their broadest reasonable interpretation are identified as: being either directed towards mere data gathering or an abstract idea, mental process and mathematical calculation, and not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
For all of the above reasons, claims 1-28 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claims 1-28 are not eligible subject matter under 35 U.S.C 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 8-18, 20, and 23-28 are rejected under 35 U.S.C. 103 as being unpatentable over Riquelme Ruiz et al. (WO2022251717A1) in view of Tobin Jr. et al. (US 5,982,920).
Regarding claim 28, Riquelme teaches “A system for operating an ensemble of narrow AI agents (Riquelme paragraph [0017] "The neural network 100 is an example of a system")
an ensemble of narrow AI agents (Riquelme paragraph [0028] "an expert module is a component of a network block that is configured to process a strict subset of the elements, i.e., less than all of the elements, of the block input to the network block")
a relevancy determination unit that is configured to determined, based on one or more images (Riquelme paragraph [0018] "The neural network 100 is configured to process an input image 102" and paragraph [0045] "B is the number of images in a batch of input images 102 that are being processed by the neural network 100"), one or more relevant narrow AI agents of the ensemble that are relevant to a processing of the one or more images (Riquelme paragraph [0037] "After determining the patches of the block input 112, the
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network block 110j can assign, using a routing module 120, each patch to one or more of the expert modules 130a-e of the network block 110j");
wherein the ensemble is relevant (Riquelme paragraph [0028] "an expert module is a component of a network block that is configured to process a strict subset of the elements, i.e., less than all of the elements, of the block input to the network block") to a first plurality of MI states (Riquelme paragraph [0043] "That is, in some implementations, at least some of the expert modules 130a-e can "specialize" in certain types of patches, e.g., patches that depict a particular semantic object or a category of semantic objects");
wherein the one or more relevant narrow AI agents are configured to process the one or more images, by the, to provide one or more narrow AI agent MI related outputs (Riquelme paragraph [0037] "The
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network block can then process each image patch using the corresponding assigned expert modules 130a-e to generate respective expert module outputs, and combine the expert module outputs of the expert modules 130a-e using a combination module 140 to generate the block output 114 for the
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network block 110j");
wherein each narrow AI agent is relevant to a respective fraction (Riquelme paragraph [0043] "That is, in some implementations, at least some of the expert modules 130a-e can "specialize" in certain types of patches, e.g., patches that depict a particular semantic object or a category of semantic objects") of ; and
a MI evaluation unit, that is configured to process the one or more narrow AI agent MI related outputs decisions (Riquelme paragraph [0037] "combine the expert module outputs of the expert modules 130a-e using a combination module 140 to generate the block output 114 for the
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network block 110j") to provide an
However, Riquelme does not explicitly teach the manufactured item and related manufactured item states.
Tobin teaches “a manufactured item (MI) (Tobin column 3 lines 20-24 "automated system for emulating the ability of an expert process engineer to
view a picture of a wafer map, perform visual grouping and shape analysis, and then determine the health of the manufacturing process")”, “the MI (Tobin column 3 lines 54-58 "The initial focus of wafer map signature analysis is to reduce the data set to simpler, non-overlapping (or nearly non-overlapping) sets that can be individually analyzed and finally classified to a user-defined class")”, “first plurality of MI states (Tobin column 3 lines 59-60 "four subgroups, into which a majority of all signature types fall")”, and “provide an MI related evaluation (Tobin column 3 lines 54-58 "The initial focus of wafer map signature analysis is to reduce the data set to simpler, non-overlapping (or nearly non-overlapping) sets that can be individually analyzed and finally classified to a user-defined class").”
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
ensemble of expert modules as taught by Riquelme to include processing of a manufactured item, such as a semi-conductor wafer as taught by Tobin.
The suggestion/motivation for doing so would have been that there is a need in
the field of anomaly detection in manufactured items, "current manual method of wafer
map evaluation is due to the limited number of wafers that can be manually evaluated in a given period of time and to the lack of objective and repeatable conclusions which can
be reached in the decision making process by human inspectors. In general, while there are numerous data gathering techniques, the tools for analyzing the data need improvement in ways that will increase throughput and efficiently diagnose process limiting yield issues" as noted by the Tobin disclosure in column 2, lines 12-20. The use of the system as taught by Riquelme also provides the suggestion/motivation of combining Riquelme and Tobin “Using techniques described in this specification, a system can process images using a feedforward neural network by selectively activating subsets of the parameters of the neural network based on the network input, significantly improving the time and computational efficiency of the processing of the image” as disclosed by Riquelme in paragraph 7.
Therefore, it would have been obvious to combine the disclosure of Riquelme with the Tobin disclosure to obtain the invention as specified in claim 28 as there is a
reasonable expectation of success and/or because doing so merely combines prior art
elements according to known methods to yield predictable results.
Claim 1 recites a method with steps corresponding to the system elements
recited in claim 28. Therefore, the recited steps of this claim are mapped to the
proposed combination in the same manner as the corresponding elements of system
claim 28. Additionally, the rationale and motivation to combine the Riquelme and Tobin
references, presented in rejection of claim 28 apply to this claim.
Regarding claim 2, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein each relevant narrow Al agent is relevant to a dedicated class out of multiple classes (Riquelme paragraph [0043] "That is, in some implementations, at least some of the expert modules 130a-e can "specialize" in certain types of patches, e.g., patches that depict a particular semantic object or a category of semantic objects"), wherein at least some of the multiple classes are different classes of MI anomalies (Tobin column 4 lines 1-16 "G(x,y)-global distribution-a logical grouping of unclustered single events associated with, for example, particle contamination in the manufacturing process; C(x,y)----curvilinear distribution----curvilinear and radial clustered events associated with wafer spinning processes, mechanical surface damage, polishing anomalies, etc; A(x,y)-amorphous distribution-nonlinear, tightly grouped cluster events arising from various processes, such as lithography, etch, watermarks, or particle stains; and M(x,y)-micro-structure-a collection of distributed single-pixel events which contain linear distributions of defects. These defect distributions reside on the image sub-pixel level and are related to small, fine micro-scratching from chemical and mechanical polishing for wafer planarization").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 2. Finally the method recited in claim 2 is met by Riquelme and Tobin.
Regarding claim 3, the combination of Riquelme and Tobin teaches “The method according to claim 2 wherein each class is defined by at least a part of one or more MI states (Tobin column 3 lines 59-60 "four subgroups, into which a majority of all signature types fall"), wherein the at least part of the one or more MI states are a fraction of the first plurality of MI states (Tobin column 4 lines 1-16 "G(x,y)-global distribution-a logical grouping of unclustered single events associated with, for example, particle contamination in the manufacturing process; C(x,y)----curvilinear distribution----curvilinear and radial clustered events associated with wafer spinning processes, mechanical surface damage, polishing anomalies, etc; A(x,y)-amorphous distribution-nonlinear, tightly grouped cluster events arising from various processes, such as lithography, etch, watermarks, or particle stains; and M(x,y)-micro-structure-a collection of distributed single-pixel events which contain linear distributions of defects. These defect distributions reside on the image sub-pixel level and are related to small, fine micro-scratching from chemical and mechanical polishing for wafer planarization").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 3. Finally the method recited in claim 3 is met by Riquelme and Tobin.
Regarding claim 4, the combination of Riquelme and Tobin teaches “The method according to claim 2 wherein the different classes of MI anomalies comprise an MI corner chip off class (Tobin column 4 lines 4-6 "C(x,y)----curvilinear distribution----curvilinear and radial 5 clustered events associated with wafer spinning processes, mechanical surface damage, polishing anomalies, etc").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 4. Finally the method recited in claim 4 is met by Riquelme and Tobin.
Regarding claim 5, the combination of Riquelme and Tobin teaches “The method according to claim 2 wherein the different classes of MI anomalies comprise one or more MI scratch classes (Tobin column 4 lines 10-15 "M(x,y)-micro-structure-a collection of distributed single-pixel events which contain linear distributions of defects. These defect distributions reside on the image sub-pixel level and are related to small, fine micro-scratching from chemical and mechanical polishing for wafer planarization").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 5. Finally the method recited in claim 5 is met by Riquelme and Tobin.
Regarding claim 8, the combination of Riquelme and Tobin teaches “The method according to claim 2 wherein the different classes of MI anomalies comprise one or more MI watermark classes (Tobin column 4 lines 7-9 "A(x,y)-amorphous distribution-nonlinear, tightly grouped cluster events arising from various processes, such as lithography, etch, watermarks, or particle stains").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 8. Finally the method recited in claim 8 is met by Riquelme and Tobin.
Regarding claim 9, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein the ensemble of narrow AI agents comprises hierarchical structure of AI agents, and wherein the relevancy determination unit is a multi-level hierarchical unit (Riquelme paragraph [0028] "an expert module is a component of a network block that is configured to process a strict subset of the elements, i.e., less than all of the elements, of the block input to the network block. An expert module can be configured to process the strict subset of the elements of the block input using one or more neural network layers to generate an updated representation of the strict subset of elements").”
Regarding claim 10, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein the determining, by the relevancy determination unit, of one or more relevant narrow AI agents of the ensemble (Riquelme paragraph [0037] "After determining the patches of the block input 112, the
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network block 110j can assign, using a routing module 120, each patch to one or more of the expert modules 130a-e of the network block 110j"), is executed without detection of objects that are below a predefined number of pixels (Riquelme paragraph [0032] "each patch of the block input 112 is the same size").”
Regarding claim 11, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein the relevancy determination unit is
trained to classify images to classes (Riquelme paragraph [0078] "the neural network 100 can be configured to generate a classification output that includes a respective score corresponding to each of multiple categories. The score for a category indicates a likelihood that the image belongs to the category"), wherein each class is at least a part of one or more MI states, the one or more MI states are a fraction of the first plurality of MI states (Tobin column 3 lines 59-60 "four subgroups, into which a majority of all signature types fall").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 11. Finally the method recited in claim 11 is met by Riquelme and Tobin.
Regarding claim 12, the combination of Riquelme and Tobin teaches “The method according to claim 11 comprising receiving, by the relevancy determination unit a definition of at least some of the classes before training (Riquelme paragraph [0078] "the neural network 100 can be configured to generate a classification output that includes a respective score corresponding to each of multiple categories. The score for a category indicates a likelihood that the image belongs to the category").”
Regarding claim 13, the combination of Riquelme and Tobin teaches “The method according to claim 11 comprising defining, by the relevancy determination unit at least some of the classes (Tobin column 4 lines 1-16 "G(x,y)-global distribution-a logical grouping of unclustered single events associated with, for example, particle contamination in the manufacturing process; C(x,y)----curvilinear distribution----curvilinear and radial clustered events associated with wafer spinning processes, mechanical surface damage, polishing anomalies, etc; A(x,y)-amorphous distribution-nonlinear, tightly grouped cluster events arising from various processes, such as lithography, etch, watermarks, or particle stains; and M(x,y)-micro-structure-a collection of distributed single-pixel events which contain linear distributions of defects. These defect distributions reside on the image sub-pixel level and are related to small, fine micro-scratching from chemical and mechanical polishing for wafer planarization").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 13. Finally the method recited in claim 13 is met by Riquelme and Tobin.
Regarding claim 14, the combination of Riquelme and Tobin teaches “The method according to claim 13 wherein the defining comprises performing an unsupervised training (Riquelme paragraph [0074] "a training system can pre-train the neural network 100 in an unsupervised or self-supervised manner").”
Regarding claim 15, the combination of Riquelme and Tobin teaches “The method according to claim 11 wherein the at least part of one or more MI states is at least one out of (a) one or more factors of a MI state (Tobin column 4 lines 14-16 "micro-scratching from chemical and mechanical polishing for wafer planarization"),
(b) one or more element of a MI state (Tobin column 4 lines 47-50 "a scratch will generate an elongated defect where the wafer was damaged. It will also scatter small particles of wafer material around the scratch"),
(c) one or more parameters of a MI state (Tobin column 4 lines 31-35 "some shallow, short scratches will have a low density of defects per unit area, but will still appear as several connected pixels pro-ducing an elongated shape on the wafer map"),
and (d) one or more variables of a MI state (Tobin column 4 lines 21-23 "original density image, p(x,y), is initially parsed into two categories based on defect density values: low-density, potentially random events and higher-density").”
The proposed combination as well as the motivation for combining Riquelme and Tobin references presented in the rejection of claim 28, applies to claim 15. Finally the method recited in claim 15 is met by Riquelme and Tobin.
Regarding claim 16, the combination of Riquelme and Tobin teaches “The method according to claim 11 wherein each narrow AI agent is associated with a dedicated class (Riquelme paragraph [0078] "the neural network 100 can be configured to generate a classification output that includes a respective score corresponding to each of multiple categories. The score for a category indicates a likelihood that the image belongs to the category") and the method comprises training each narrow AI agent to output a narrow AI agent MI related output associated with the dedicated class (Riquelme paragraph [0028] "an expert module is a component of a network block that is configured to process a strict subset of the elements, i.e., less than all of the elements, of the block input to the network block").”
Regarding claim 17, the combination of Riquelme and Tobin teaches “The method according to claim 16 wherein the training comprises training each narrow AI agent using images (Riquelme paragraph [0074] "a training system can pre-train the neural network 100 in an unsupervised or self-supervised manner using unlabeled images, e.g., to train the neural network 100 to segment the unlabeled images into different classes based on their similarity or to train the neural network 100 to perform unsupervised semantic segmentation of the unlabeled images") of the dedicated class (Riquelme paragraph [0043] "That is, in some implementations, at least some of the expert modules 130a-e can "specialize" in certain types of patches, e.g., patches that depict a particular semantic object or a category of semantic objects").”
Regarding claim 18, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein the narrow AI agents are end-to-end narrow AI agents (Riquelme paragraph [0025] "one or more of the network blocks110a-1 each determine a set of patches of the block input to the network block 101a-1 (which is an intermediate representation of the input image 102), and processes the determined patches to generate a block output for the network block 110a-1").”
Regarding claim 20, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein a number of narrow AI agents relevant to one of the first plurality of MI states differs from a number of narrow AI agents relevant to another of the first plurality of MI states (Riquelme paragraph [0041] "some patches can be assigned to a different number of expert modules").”
Regarding claim 23, the combination of Riquelme and Tobin teaches “The method according to claim 1 wherein at least some of the narrow AI agents comprise at least a portion of a neural network (Riquelme paragraph [0061] "the network block can first 110j process the block input using one or more self-attention neural network layers and/or one or more element-wise feedforward neural network layers that are configured to process each patch of the block input 102").”
Regarding claim 24, the combination of Riquelme and Tobin teaches “The method according to claim 1 comprising feeding, by the relevancy determination unit the one or more images to each one of the one or more relevant narrow AI agents (Riquelme paragraph [0041] "some patches can be assigned to a different number of expert modules" and paragraph [0045] "B is the number of images in a batch of input images 102 that are being processed by the neural network 100").”
Regarding claim 25, the combination of Riquelme and Tobin teaches “The method according to claim 1 comprising feeding, by the relevancy determination unit the one or more images to each one of the one or more relevant narrow AI agents (Riquelme paragraph [0041] "some patches can be assigned to a different number of expert modules" and paragraph [0045] "B is the number of images in a batch of input images 102 that are being processed by the neural network 100") and maintaining at least one irrelevant narrow AI agent in a low power mode in which a power consumption of the at least one irrelevant narrow AI agent is lower than a power consumption of a relevant narrow AI agent (Riquelme paragraph [0042] "for some input images 102, some expert modules 130a-e of the
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network block 110j are idle for the block input 112 generated from the input image 102. Allowing some expert modules 130a-e to be idle during some executions of the neural network 100 can improve the efficiency of the neural network 100 by reducing the number of computations required to generate a network output 108, as not every parameter of the neural network 100 is used to process each input image 102").”
Regarding claim 26, the combination of Riquelme and Tobin teaches “The method according to claim 1 comprising determining which part of the one or more images to send to each relevant narrow AI agent (Riquelme paragraph [0037] "After determining the patches of the block input 112, the
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network block 110j can assign, using a routing module 120, each patch to one or more of the expert modules 130a-e of the network block 110j").”
Regarding claim 27, it recites a computer readable medium including computer executable instructions corresponding to the elements of the system recited in claim 28. Therefore, the recited instructions of the computer readable medium of claim 27 are mapped to the proposed combination in the same manner as the corresponding elements of the system claim 28. Additionally, the rationale and motivation to combine Riquelme and Tobin presented in rejection of claim 28, apply to this claim.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Riquelme and Tobin, in view of Chen (US 2020/0005445 A1).
Regarding claim 6, the combination of Riquelme and Tobin teaches “The method according to claim 2 wherein the different classes of MI anomalies comprise MI scratch classes (Tobin column 4 lines 10-15 "M(x,y)-micro-structure-a collection of distributed single-pixel events which contain linear distributions of defects. These defect distributions reside on the image sub-pixel level and are related to small, fine micro-scratching from chemical and mechanical polishing for wafer planarization")”.
However, the combination of Riquelme and Tobin does not teach “differ from each other by an orientation of a scratch”.
Chen teaches the classes “differ from each other by an orientation of a scratch (Chen paragraph [0020] "The wafer is placed under a camera module for determining an orientation and/or dimensions of the scratch on the wafer").
It would have been obvious to a person having ordinary skill in the art before
effective filing date of the claimed invention of the instant application to combine an
ensemble of expert modules for processing of manufactured item images as taught by Riquelme and Tobin to include the classification of scratches based on orientation as taught by Chen.
The suggestion/motivation for doing so would have been “Being able to quickly and efficiently identify the transfer step wherein the scratching occurs increases the cost effectiveness of the wafer manufacturing process" as noted by the Chen disclosure in paragraph 19.
Therefore, it would have been obvious to combine the disclosure of Riquelme and Tobin with the Chen disclosure to obtain the invention as specified in claim 6 as there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Regarding claim 7, the combination of Riquelme, Tobin, and Chen teaches “The method according to claim 2 wherein the different classes of MI anomalies comprise a vertical MI scratch class and a horizontal MI scratch class (Chen paragraph [0020] "The wafer is placed under a camera module for determining an orientation and/or dimensions of the scratch on the wafer").”
The proposed combination as well as the motivation for combining Riquelme, Tobin and Chen references presented in the rejection of claim 6, applies to claim 7. Finally the method recited in claim 7 is met by Riquelme, Tobin and Chen.
Claims 19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Riquelme and Tobin, in view of Shazeer et al. ("Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer" - Published 2017).
Regarding claim 19, the combination of Riquelme and Tobin teaches the method of claim 1. However, the combination of Riquelme and Tobin does not teach “wherein for at least some of the narrow AI agents the respective fraction is smaller than one percent of the first plurality of MI states”.
Shazeer teaches “at least some of the narrow AI agents the respective fraction is smaller than one percent of the first plurality of MI states (Shazeer page 8 paragraph 3 "Even at 65536 experts (99.994% layer sparsity), computational efficiency for the model stays at a respectable 0.72 TFLOPS/GPU").”
It would have been obvious to a person having ordinary skill in the art before
effective filing da