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 .
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.
Claim 8 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to non-statutory subject matter. Computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs, are not physical “things.” They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program’s functionality to be realized. In contrast, a claimed non-transitory computer-readable medium encoded with a computer program is a computer element which defines structural and functional interrelationships between the computer program and the rest of the computer which permit the computer program’s functionality to be realized, and is thus statutory. See Lowry, 32 F.3d at 1583-84, 32 USPQ2d at 1035.
Claim 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to non-statutory subject matter. The claimed medium can be interpreted to be in the form of a signal, such as a transmission medium or carrier wave in view of the instant disclosure and the knowledge of one of ordinary skill in the art. Such transmission medium or carrier wave claims are ineligible for patent protection because they do not fall within any of the four statutory classes of § 101.
Claim(s) 1-13 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite the following:
1. A method for detecting a production error of an assembly in a manufacturing facility, comprising:
providing sensor data having at least two dimensions, wherein a respective dimension of the sensor data comprises measurement data relative to the assembly;
performing a dimensional reduction of the sensor data, wherein at least one feature is extracted based on the at least two dimensions of the sensor data;
reconstructing the dimension-reduced sensor data based on the at least one extracted feature to provide reconstructed sensor data;
determining a reconstruction error based on a comparison of the sensor data with the reconstructed sensor data; and
detecting the production error of the assembly based on the determined reconstruction error.
2. The method according to claim 1, wherein determining the reconstruction error comprises:
calculating a Euclidean distance between the sensor data and the reconstructed sensor data to determine the reconstruction error based on the calculated Euclidean distance.
3. The method according to claim 1 wherein detecting the production error comprises:
defining a threshold value for the reconstruction error, and
comparing the reconstruction error with the defined threshold value to detect the production error of the assembly.
4. The method according to claim 1, further comprising using a machine learning model.
5. The method according to claim 4, wherein the machine learning model was trained based on the following steps:
providing reference data having at least two dimensions, wherein a respective dimension of the reference data comprises measurement data relative to an assembly without production errors,
performing a dimensional reduction of the reference data, wherein at least one reference feature is extracted from the reference data based on the at least two dimensions of the reference data,
reconstructing the dimension-reduced reference data based on the at least one extracted reference feature, and
determining a reconstruction error based on a comparison of the reference data with the reconstructed reference data,
wherein the steps of performing the dimensional reduction of the reference data, reconstructing the dimension-reduced reference data, and determining the reconstruction error are performed until the reconstruction error passes below a defined boundary value.
6. The method according to claim 1, wherein:
the assembly is a radar sensor, and
the measurement data of the respective dimension of the sensor data results from measurements by the radar sensor and/or on the radar sensor.
7. The method according to claim 1, wherein the method is carried out as part of a quality test.
8. A computer program comprising instructions that, when the computer program is executed by a computer, cause the computer to carry out the method according to claim 1.
9. A device for data processing which is configured to carry out the method according to claim 1.
10. A computer-readable storage medium comprising instructions which, when executed by a computer, cause it to carry out the steps of the method according to claim 1.
11. The method according to claim 1, further comprising using a neural network.
12. The method according to claim 1, further comprising an auto-encoder.
13. The method according to claim 1, wherein the method is carried out as part of an end-of-line test of manufacturing.
The limitations above, as drafted, is a process or function that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting by a computer/program, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer/program language, providing data, performing dimensional reduction, reconstructing, determining error and detecting error, in the context of this claim encompasses a user thinking about or manually providing data, performing dimensional reduction, reconstructing, determining error and detecting error. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional element(s) – computer/program. The computer/program is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of computer/program amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Pub. No. 2023/0236586 to Cheon.
Cheon teaches:
1. A method for detecting a production error of an assembly in a manufacturing facility (Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226), comprising:
providing sensor data having at least two dimensions, wherein a respective dimension of the sensor data comprises measurement data relative to the assembly (paragraph 45-49, 178, 197, 214, 222);
performing a dimensional reduction of the sensor data, wherein at least one feature is extracted based on the at least two dimensions of the sensor data (paragraph 178, 197, 214, 222);
reconstructing the dimension-reduced sensor data based on the at least one extracted feature to provide reconstructed sensor data (Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226);
determining a reconstruction error based on a comparison of the sensor data with the reconstructed sensor data (Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226); and
detecting the production error of the assembly based on the determined reconstruction error (Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226).
2. The method according to claim 1, wherein determining the reconstruction error comprises: calculating a Euclidean distance between the sensor data and the reconstructed sensor data to determine the reconstruction error based on the calculated Euclidean distance (paragraphs 164-167, 176-178, 189-191, 201-204, 214).
3. The method according to claim 1 wherein detecting the production error comprises: defining a threshold value for the reconstruction error, and comparing the reconstruction error with the defined threshold value to detect the production error of the assembly (Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226).
4. The method according to claim 1, further comprising using a machine learning model (paragraph 5-7, 39-40, 44, 50, 58, 64-68, 71-82, 92-105, 111-121, 133-142, 149, 154, 158, 163, 166, 172-173, 181, 187, 197-199, 220-226).
5. The method according to claim 4, wherein the machine learning model was trained based on the following steps: providing reference data having at least two dimensions, wherein a respective dimension of the reference data comprises measurement data relative to an assembly without production errors, performing a dimensional reduction of the reference data, wherein at least one reference feature is extracted from the reference data based on the at least two dimensions of the reference data, reconstructing the dimension-reduced reference data based on the at least one extracted reference feature, and determining a reconstruction error based on a comparison of the reference data with the reconstructed reference data, wherein the steps of performing the dimensional reduction of the reference data, reconstructing the dimension-reduced reference data, and determining the reconstruction error are performed until the reconstruction error passes below a defined boundary value (paragraph 5-7, 39-40, 44, 50, 58, 64-68, 71-82, 92-105, 111-121, 133-142, 149, 154, 158, 163, 166, 172-173, 181, 187, 197-199, 220-226; Abstract, paragraphs 5-7, 44, 178, 197-200, 214, 218-222, 226).
6. The method according to claim 1, wherein: the assembly is a radar sensor, and the measurement data of the respective dimension of the sensor data results from measurements by the radar sensor and/or on the radar sensor (paragraph 46; (paragraph 45-49, 178, 197, 214, 222).
7. The method according to claim 1, wherein the method is carried out as part of a quality test (claim 2, 10, 17; paragraphs 34-35, 38-46, 85, 156-159, 166, 168, 172, 185-192, 199).
8. A computer program comprising instructions that, when the computer program is executed by a computer, cause the computer to carry out the method according to claim 1 (paragraph 74, 83, 133, 228-237).
9. A device for data processing which is configured to carry out the method according to claim 1 (paragraph 74, 83, 133, 228-237).
10. A computer-readable storage medium comprising instructions which, when executed by a computer, cause it to carry out the steps of the method according to claim 1 (paragraph 74, 83, 133, 228-237).
11. The method according to claim 1, further comprising using a neural network (paragraph 68, 77, 100, 107, 142).
12. The method according to claim 1, further comprising an auto-encoder (paragraph 96, 173, 197, 218-222).
13. The method according to claim 1, wherein the method is carried out as part of an end-of-line test of manufacturing (paragraph 3, Figs. 5E-5G, paragraphs 182-201).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEAN P SHECHTMAN whose telephone number is (571)272-3754. The examiner can normally be reached 9:30am-6:00pm, M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Kraig can be reached at 571-272-8660. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sean Shechtman/Primary Examiner, Art Unit 2896
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