DETAILED ACTION
Claims 1-4 are pending in this application and have been examined under the priority date of 01/08/2021 in accordance with the applicants’ claim for foreign priority.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/19/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-4 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding claim 1; claim 1 is directed to an abstract idea, mental process or steps of mere data gathering without significantly more. The claim recites the following limitations which could practically be performed as a human mental process or step of mere data gathering;
“An inspection image acquisition unit that acquires an inspection image that is a captured image of an object to be inspected; (Mere data gathering of collecting an image)
and a prediction unit that applies the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data, (Mental process, where the model constitutes abstract math)
and performs prediction whether the object to be inspected is a normal product (Mental process of making a decision),
and prediction of a defect type in a case where the object to be inspected is a defective product, (Mental process of making a decision)
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, (Mental process of making a decision regarding whether or not data appears normal)
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types, (Mental process of making a decision regarding classification of data)
and a weight for each of the defect type labels to a learning image of a defective product. (Mental process by which a person could look at an image and arbitrarily rank the image to assign a weight)”
The above limitations are steps which could practically be performed as a human mental process or step of mere data gathering performed by a human, under step 2A prong 1 (MPEP 2106). Under step 2A prong 2, the claim recites the additional elements of “A defect inspection device”, “an inspection image acquisition unit”, “a prediction unit”, and “a prediction model”, which fail to integrate the claimed steps into practical application, or significantly more. Further, under step 2B, the claim does not include any limitations which amount to significantly more than an abstract idea.
Dependent claim 2 does not add limitations that further meaningfully translate the abstract ideas into practical application or amount to significantly more.
Regarding claim 2, claim 2 recites the limitations;
“Wherein the training data is configured by assigning a maximum weight to the normal label of the normal product ground truth label, (Mental process by which a person could look at an image and arbitrarily rank the image to assign a weight)
and assigning a weight less than the maximum weight to each of the plurality of defect type labels of the defective product ground truth label. (Mental process by which a person could look at an image and arbitrarily rank the image to assign a weight)”
The above limitations are recited with a high level of generality, and are drawn to mental processes or steps of mere data gathering without significantly more.
Regarding claim 3; claim 1 is directed to an abstract idea, mental process or steps of mere data gathering without significantly more. The claim recites the following limitations which could practically be performed as a human mental process or step of mere data gathering;
“a first step of acquiring an inspection image that is a captured image of an object to be inspected by an inspection image acquisition unit of a computer; (Mere data gathering of collecting an image)
and a second step of applying the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data, (Mental process, where the model constitutes abstract math)
and performing prediction whether the object to be inspected is a normal product, (Mental process of making a decision)
and prediction of a defect type in a case where the object to be inspected is a defective product by a prediction unit of the computer, (Mental process of making a decision)
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, (Mental process of making a decision regarding whether or not data appears normal)
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types, (Mental process of making a decision regarding classification of data)
and a weight for each of the defect type labels to a learning image of a defective product, (Mental process by which a person could look at an image and arbitrarily rank the image to assign a weight)”
The above limitations are steps which could practically be performed as a human mental process or step of mere data gathering performed by a human, under step 2A prong 1 (MPEP 2106). Under step 2A prong 2, the claim recites the additional elements of “A defect inspection device”, “an inspection image acquisition unit”, “a prediction unit”, “a prediction model” and a “computer” containing one or more of the units. The recited additional elements fail to integrate the claimed steps into practical application, or significantly more. Further, under step 2B, the claim does not include any limitations which amount to significantly more than an abstract idea.
Regarding claim 4; claim 1 is directed to an abstract idea, mental process or steps of mere data gathering without significantly more. The claim recites the following limitations which could practically be performed as a human mental process or step of mere data gathering;
“a first step of inputting training data to which a ground truth label is assigned by a training data input unit of a computer; (Mere data gathering of collecting an image)
and a second step of applying the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data, (Mental process, where the model constitutes abstract math)
and performing prediction whether the object to be inspected is a normal product, (Mental process of making a decision)
and prediction of a defect type in a case where the object to be inspected is a defective product by a prediction unit of the computer, (Mental process of making a decision)
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, (Mental process of making a decision regarding whether or not data appears normal)
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types, (Mental process of making a decision regarding classification of data)
and a weight for each of the defect type labels to a learning image of a defective product, (Mental process by which a person could look at an image and arbitrarily rank the image to assign a weight)”
The above limitations are steps which could practically be performed as a human mental process or step of mere data gathering performed by a human, under step 2A prong 1 (MPEP 2106). Under step 2A prong 2, the claim recites the additional elements of “A defect inspection device”, “an inspection image acquisition unit”, “a prediction unit”, “a prediction model” and a “computer” containing one or more of the units. The recited additional elements fail to integrate the claimed steps into practical application, or significantly more. Further, under step 2B, the claim does not include any limitations which amount to significantly more than an abstract idea.
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.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses 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 limitation(s) are:
Inspection image acquisition unit in claim 1, 3, and 4.
Prediction unit in claim 1, 3, and 4
Prediction model in claim 1, 3, and 4
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) 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 Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim(s) 1,3 and 4 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He (US 20190073568 A1).
Regarding claim 1 He discloses; A defect inspection device (He, [0010] the defect inspection system has a computer and subsystems for executing a program), comprising:
an inspection image acquisition unit that acquires an inspection image that is a captured image of an object to be inspected (He, [0043] the system has image capturing devices such as cameras (image acquisition unit) to capture images, [0028] defects are classified in the images at the pixel level);
and a prediction unit that applies the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data (He, [0029] the system is configured to train a neural network (prediction model) to classify and annotate defects at the pixel level of an image, where [0030] the system takes in one or more images of the specimen to be reviewed, and detects and classifies defects on it, [0082] the model is trained on defect and non-defect images (training data)),
and performs prediction whether the object to be inspected is a normal product (He, [0030] the system is trained to review images and detect whether defects are present, [0082] the system is trained on defective and non-defective images to determine whether it can differentiate between the two),
and prediction of a defect type in a case where the object to be inspected is a defective product (He, [0080] the system may be trained to determine the defect type or defect pattern after a defect is detected),
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product (He, [0082] the model is trained on defect and non-defect images (training data), [0083] where images without defects are only labeled as being defect free),
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types (He, [0080] the defective products are labeled as defective, as well as labeled with defect types),
and a weight for each of the defect type labels to a learning image of a defective product (He, [0101] weights are obtained after training the second part of the network which classified defect type, [0105] the defect labels are used to generate the weights, which can be used to train other networks or fine tune the network to detect defect types, indicating the defect types each have a weight).
Regarding claim 3 He discloses;
A defect inspection method (He, [0012] computer implemented defect classification method), comprising:
a first step of acquiring an inspection image that is a captured image of an object to be inspected by an inspection image acquisition unit of a computer (He, [0043] the system has image capturing devices such as cameras (image acquisition unit) to capture images, [0028] defects are classified in the images at the pixel level);
and a second step of applying the inspection image acquired by the inspection image acquisition unit to a prediction model trained by using training data (He, [0029] the system is configured to train a neural network (prediction model) to classify and annotate defects at the pixel level of an image, where [0030] the system takes in one or more images of the specimen to be reviewed, and detects and classifies defects on it, [0082] the model is trained on defect and non-defect images (training data)),
and performing prediction whether the object to be inspected is a normal product (He, [0030] the system is trained to review images and detect whether defects are present, [0082] the system is trained on defective and non-defective images to determine whether it can differentiate between the two),
and prediction of a defect type in a case where the object to be inspected is a defective product by a prediction unit of the computer (He, [0080] the system may be trained to determine the defect type or defect pattern after a defect is detected),
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product (He, [0082] the model is trained on defect and non-defect images (training data), [0083] where images without defects are only labeled as being defect free),
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types (He, [0080] the defective products are labeled as defective, as well as labeled with defect types),
and a weight for each of the defect type labels to a learning image of a defective product (He, [0101] weights are obtained after training the second part of the network which classified defect type, [0105] the defect labels are used to generate the weights, which can be used to train other networks or fine tune the network to detect defect types, indicating the defect types each have a weight).
Regarding claim 4 He discloses; A prediction model generation method (He, [0012] computer implemented defect classification method), comprising:
a first step of inputting training data to which a ground truth label is assigned by a training data input unit of a computer (He, [0029] the system is configured to train a neural network (prediction model) to classify and annotate defects at the pixel level of an image, where [0030] the system takes in one or more images of the specimen to be reviewed, and detects and classifies defects on it, [0082] the model is trained on defect and non-defect images (training data));
and a second step of performing machine learning processing by a prediction model generation unit of the computer by using the training data input by the training data input unit to generate a prediction model that outputs a prediction result as to whether an object to be inspected is a normal product (He, [0030] the system is trained to review images and detect whether defects are present, [0082] the system is trained on defective and non-defective images to determine whether it can differentiate between the two),
and a prediction result of a defect type in a case where the object to be inspected is a defective product when an inspection image that is a captured image of the object to be inspected is input (He, [0080] the system may be trained to determine the defect type or defect pattern after a defect is detected),
wherein the training data is configured by assigning a normal product ground truth label that does not include a label indicating a possibility of correspondence to a defective product and includes only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product (He, [0082] the model is trained on defect and non-defect images (training data), [0083] where images without defects are only labeled as being defect free),
and by assigning a defective product ground truth label that does not include a normal label indicating a possibility of correspondence to the normal product and includes a plurality of defective type labels indicating a possibility of correspondence to a plurality of defect types (He, [0080] the defective products are labeled as defective, as well as labeled with defect types),
and a weight for each of the defect type labels to a learning image of a defective product (He, [0101] weights are obtained after training the second part of the network which classified defect type, [0105] the defect labels are used to generate the weights, which can be used to train other networks or fine tune the network to detect defect types, indicating the defect types each have a weight).
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
2. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over He (US 20190073568 A1) in view of Kooman (US 20220342316 A1).
Regarding claim 2 He does not teach; The defect inspection device according to claim 1, wherein the training data is configured by assigning a maximum weight to the normal label of the normal product ground truth label,
and assigning a weight less than the maximum weight to each of the plurality of defect type labels of the defective product ground truth label.
However, in the same field of endeavor of defect detection, Kooman teaches;
wherein the training data is configured by assigning a maximum weight to the normal label of the normal product ground truth label (Kooman, [0213] the weights can be assigned to map out the defects, where a weight or value of 1 is assigned to no defect images, and a weight of 0 corresponds to a non-defective pixel or image),
and assigning a weight less than the maximum weight to each of the plurality of defect type labels of the defective product ground truth label (Kooman, [0213] the weights can be assigned to map out the defects, where a weight or value of 1 is assigned to no defect images, and a weight of 0 corresponds to a non-defective pixel or image).
The combination of He and Kooman would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. He and Kooman both teach defect detection, however Kooman teaches weighting according to defect. The addition of this feature would be advantageous because assigning a higher weight to non-defective images or pixels can be used to favor non-defective predictions in the model and assure accuracy when making parameter adjustments. (Kooman, [0213] and [0215])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous prior art, please see the attached PTO 892 Notice of References Cited form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666