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 .
DETAILED ACTION
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 21, 23, and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 21 recites:
“A machine learning model that generates a correction value for measuring position information of a measurement target in a first direction, wherein the machine learning model is configured to generate the correction value based on a feature quantity which is related to a second direction different from the first direction and is obtained from image data of the measurement target, and the correction value is an estimated error amount of the position information of the measurement target in the first direction obtained from the image data.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Similar limitations comprise the abstract ideas of Claims 23 and 24.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 21: A machine learning model that generates a correction value for measuring position information of a measurement target in a first direction;
In Claim 23: A method of processing in a computer that generates a machine learning model to be used in a measurement apparatus for measuring position information of a measurement target in a first direction; obtaining, from image data generated by capturing an image of the measurement target by a scope of the measurement apparatus;
In Claim 24: A computer that generates a machine learning model to be used in a measurement apparatus for measuring position information of a measurement target in a first direction; obtaining, from image data generated by capturing an image of the measurement target by a scope of the measurement apparatus.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a machine learning model (Claim 21) and a computer (Claim 24) are examples of generic computer equipment (components) that are generally recited and not meaningful and, therefore, are not qualified as particular machines to indicate a practical application. The limitations that generically recite using a measurement target (Claim 21) or a measurement apparatus for measuring position information of a measurement target in a first direction… obtaining, from image data generated by capturing an image of the measurement target by a scope of the measurement apparatus (Claims 23 and 24) represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
These independent claims, therefore, are not patent eligible.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is 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.
The limitation “…the correction value is an estimated error amount of the position information of the measurement target in the first direction obtained from the image data” is indefinite as it is unclear how the correction values in first and second directions may be related as the directions are different.
The Specification is silent about it.
For the purpose of a compact prosecution, the Examiner interpreted this limitation as error value (alignment value) dependency in the second direction is learned from the first direction data.
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 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 1-4, 21, 22, 25, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Kenta Saiki et al. (US 2016/0098837), hereinafter ‘Saiki’ in view of KANATANI YUHO et al. (JP 2005197483), hereinafter ‘Yuho’, in view of Hideyuki Hashimoto (JP WO2005036620), hereinafter ‘Hashimoto’ and in view of Tsutomu Takenaka (US 9348241), hereinafter ‘Takenaka’.
With regards to Claim 1, Saiki discloses
A measurement apparatus that measures position information of a measurement target in a first direction (The substrate inspection apparatus … The stage moves in a first moving direction in a horizontal plane and a second moving direction perpendicular to the first moving direction in the horizontal plane [0010]), comprising:
a scope configured to capture an image of the measurement target and generate image data corresponding to the measurement target (the camera photographs a target indicating the position of the stage [0005]); and
a processor (The present disclosure may be realized by providing a computer [0071]) configured to obtain, based on the image data, the position information of the measurement target in the first direction (as illustrated in FIG. 8, measured are positions of two points B and C in a coordinate system of the photographed image 28 …In the image coordinate system, positions are measured in pixels [0065]; also Fig.7),
wherein the processor is configured to determine the position information of the measurement target in the first direction (First, a position of the target mark 26 in the photographed image 28 is measured in pixels. The position of the target mark 26 is corrected by using the rotation correcting angles θ1 to θ4 according to the position of the target mark 26 [0059]; also, [0060]) based on:
provisional position information of the measurement target in the first direction obtained from the image data (the position of the target mark 26 in a photographed image 28 is registered as a registration position P (FIG. 6A) [0049]; In the image coordinate system, positions are measured in pixels [0065]; registered position along the X axis and the Y axis in the coordinate system of the image photographed by the camera after moving the stage, Claim 3), and
a correction value (a rotation correcting angle is calculated for each quadrant in a coordinate system of the photographed image 28. Specifically, as illustrated in FIG. 7, when assuming that the registration position P is the coordinate origin in the coordinate system of the photographed image 28, a rotation correcting angle θ1 of a first quadrant is calculated by the following Equation (1) [0054]).
However, Saiki does not disclose using a correction value which is output from a machine learning model by inputting, in the machine learning model, a feature quantity, of the image data, related to a second direction different from the first direction, the alignment process of aligning the original and the shot region of the substrate to be performed using the position information of the measurement target in the first direction determined by the processor.
Yuho discloses using a correction value which is output from a model by inputting, in the model, a feature quantity, of the image data, related to a second direction different from the first direction (The second method uses the fact that the position information Xi of the alignment mark AM1 is proportional to the position Yi of the measurement range 26d in the non-measurement direction VS, and at a plurality of locations in the non-measurement direction VS in the measurement field of view. The position information Xi of the alignment mark AM1 and the position Yi of the measurement range 26d are obtained and substituted into a model expression “Xi = RE .Math. Yi + C” … When the signal processing is performed at 27, the position information of the alignment mark AM1 may be corrected using the rotation error RE and the position of the alignment mark AM1 in the non-measurement direction …The rotation error RE is calculated by statistically calculating the model formula, for example, by the method of least squares, p.9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho to determine the position information of the measurement target in the first direction using a correction value which is output from a model by inputting, in the model, a feature quantity, of the image data, related to a second direction different from the first direction to estimate an error and thus improve accuracy (In order to perform high-precision rotation adjustment, it is necessary to accurately measure the rotation error of the image sensor with respect to the alignment mark in advance, and not based on visual observation, and based on the measurement result of the rotation error, Yuho, p.2).
Hashimoto discloses inputting, in the machine learning model, a feature quantity (the corresponding correction value group … acquired in advance by the first learning controller is sequentially input to the object stage control system as the correction value of the position deviation by the control device, p.6).
Takenaka discloses alignment process of aligning the original and the shot region of the substrate to be performed using the position information of the measurement target in the first direction determined by the processor (alignment process of aligning the original and the shot region of the substrate to be performed using the position information of the measurement target in the first direction determined by the processor, Col.6, Lines 21-24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, and Takenaka to input, in the machine learning model, a feature quantity related to position deviation, to establish a correction value to improve accuracy, as discussed above, and based on the correction, to perform an alignment process between the original and the shot region of the substrate using the position information of the measurement target in the first direction using the correction value.
With regards to Claims 2-4, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claimed invention as discussed in Claim 1 (3).
However, Saiki does not specifically disclose wherein the processor is configured to obtain the correction value by inputting, in the machine learning model, a feature quantity, of the image data, related to the first direction and the feature quantity, of the image data, related to the second direction, a storage configured to store the machine learning model, and a machine learning controller configured to generate the machine learning model by machine learning.
Yuho discloses using a correction value which is output from a model by inputting, in the model, a feature quantity, of the image data, related to a second direction different from the first direction as discussed in Claim 1.
Hashimoto discloses inputting, in the machine learning model, a feature quantity as discussed in Claim 1 while using a learning controller (that implies operating using “machine learning model”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, and Takenaka to input, in the machine learning model, to obtain the correction value by inputting, in the machine learning model (controller), a feature quantity, of the image data, related to the first direction and the feature quantity, of the image data, related to the second direction that both define a horizontal plane.
Hashimoto also discloses a storage configured to store the machine learning model (iterative learning control in advance for each of the movement operations in the first mode, the second mode, and the third mode described above. Groups are acquired and stored in the corresponding buffer memory, p.21), and a machine learning controller configured to generate the machine learning model by machine learning (the control device stores a first correction value group acquired by iterative learning control in the first learning controller and a second correction value group acquired by iterative learning control in the second learning controller as discussed above, p.6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, and Takenaka to store the machine learning model generated by machine learning to use the learned correction value (corresponding to an operation condition selected from a plurality of correction value groups that asymptotically approach a position deviation that is a difference between the target position of the object stage and the current position, Hashimoto, p.30).
With regards to Claim 21, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claim limitations as discussed in Claim 1.
However, Saiki does not specifically disclose the correction value is an estimated error amount of the position information of the measurement target in the first direction obtained from the image data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, and Takenaka the correction value is (based on) an estimated error amount of the position information of the measurement target in the first direction obtained from the image data learned from factual/measurement data related to the first direction.
With regards to Claim 22, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claim limitations as discussed in Claim 1.
With regards to Claims 25 and 28, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claim limitations as discussed in Claims 1/22.
In addition Saiki discloses extracting or calculating the feature quantity related to the second direction from the image data (calculate in each quadrant divided by the X axis and the Y axis … a deviation of the Y axis in the rotational direction with respect to the second moving direction, [0011]).
Yuho discloses a processor that calculates the feature quantity related to the second direction from the image data, and to obtain the correction value by inputting, in the model, the feature quantity related to the second direction as discussed in Claim 1.
Claims 5, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Saiki in view of Yuho, Hashimoto, and Takenaka, in further view of Hiromichi Gido et al. (US 20220138983), hereinafter ‘Godo’.
With regards to Claim 5, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claimed invention as discussed in Claim 4 including the machine learning controller performs machine learning by using the feature quantity as input data of the machine learning model as discussed in Claim 1.
However, Saiki does not specifically disclose using, as supervised data, a difference between position information of the measurement target measured by an external inspection apparatus and the position information determined by the processor.
Godo discloses using, as supervised data, a difference between position information of the measurement target measured by an external inspection apparatus and the position information determined by the processor (a difference between the inspection image and the image output from the neural network, which have been subjected to the smoothing processing, is obtained, whereby an abnormal portion included in the inspection image is detected [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, Takenaka, and Godo to use, as supervised data, a difference between position information of the measurement target measured by an external inspection apparatus and the position information determined by the processor to automatically sense an abnormality included in an inspection image with high accuracy, Godo, [0077].
With regards to Claim 23, Saiki in view of Saiki in view of Yuho, Hashimoto, Takenaka, and Godo discloses the claim limitations as discussed in Claims 1 and 5.
With regards to Claim 24, Saiki in view of Saiki in view of Yuho, Hashimoto, Takenaka, and Godo discloses the claim limitations as discussed in Claims 1 and 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Saiki in view of Yuho, Hashimoto, and Takenaka, in further view of Kohei Matsuda (US 20210302155), hereinafter ‘Matsuda’.
With regards to Claim 6, Saiki in view of Yuho, Hashimoto, and Takenaka discloses the claimed invention as discussed in Claim 4.
However, Saiki does not specifically disclose the machine learning is performed by using at least one of Gaussian process regression, Bayesian inference, a multilayer perceptron, a multiple regression analysis, and a decision tree.
Matsuda discloses the machine learning is performed by using at least one of Gaussian process regression, Bayesian inference, a multilayer perceptron, a multiple regression analysis, and a decision tree (using a machine learning model such as a decision tree may be used [0062]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Saiki in view of Yuho, Hashimoto, Takenaka, and Matsuda to use in machine learning at least one of Gaussian process regression, Bayesian inference, a multilayer perceptron, a multiple regression analysis, and a decision tree as known techniques in the art of machine learning.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1 and 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 22, 24, and 25 of U.S. Patent No. 12148181. Although the claims at issue are not identical, they are not patentably distinct from each other because it appears that independent claims 1, 22, 24, and 25 of US Patent No. 12148181 are claims which are narrower than the instant independent claims 1 and 22. Therefore, they disclose the features of the latter claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Satoru Oishi (US 2006/0092420) discloses a function that is generated to correct a shift between the true value and the actual position of the mark element using a waveform feature value.
KUDO YOSHIHIKO (JP 2006147989) discloses obtaining a one-dimensional signal by integrating in a non-measurement direction orthogonal to a predetermined measurement direction and performing correction processing and necessary calculation processing sent to the control unit as final alignment mark position information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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