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
Priority
Acknowledgment is made of applicant's claim for foreign priority based on Japanese patent document 2022040380 filed on March 15, 2022.
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.
1. Claims 1 - 18 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. The claims are directed to system, device and non-transitory computer readable recording medium of concealing and analysis of data.
Step 1: The claim (claim 1) recite a system for converting and concealing data and then analyzing the data by evaluating a degree of influence of data between manufacturing and product data which recites a series of system of determining influence. Thus, the claims are directed to a process and machine, which is one of the statutory categories of invention.
Step 2A Prong 1: Abstract ideas have been identified by the courts by way of example, including fundamental economic practices, certain methods of organization of human activities, an idea 'of itself,' and mathematical relationships/formulas. Alice Corp., 134 S. Ct. at 2355 - 56. Claim 1 recites limitations of: -concealing first data; -analyzing the data; -determining the degree of influence of the concealed data. The analysis and evaluation limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic manufacturing system and a product itself. That is, other than reciting “first manufacturing system” and “second product” nothing in the claim precludes the determining steps from practically being performed in the human mind. For example, but for the “first product manufactured in a first manufacturing system” and “second product manufactured” language, the claim encompasses concealing data, analysis of data, and determine a degree of influence that are mental processes. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim only recites evaluating a degree of influence in the conclusion section. The steps of data analysis and evaluating a degree of influence are recited at a high level of generality. These limitations are no more than mere instructions to apply the exception using an unknown structure and these steps could be performed as a mental process. Accordingly, the elements of manufacturing system and a first product do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea. Accordingly, this additional elements do 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.
Step (2B): The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea and do not provide an inventive concept. In this instance, the claims do 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 of evaluating a degree of influence is no more than mere mental step to apply the exception using unknown or generic manufacturing components. Mere generalities of data analysis and evaluation to apply an exception using a generic components cannot provide an inventive concept. The claim is not patent eligible.
Thus the claim is not drawn to patent eligible subject matter as it is directed to the same abstract idea without significantly more.
For Claim 2, the elements of: wherein the second data is data concealed by a second data conversion device; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 3, the elements of: a third data conversion device configured to conceal third data on a third product manufactured in a third manufacturing system and output the third data as third concealed data, wherein the data analysis device selects an element of the first concealed data and the third concealed data that affects the second data by evaluating a degree of influence between the second data and data including the first concealed data and the third concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 4, the elements of: a first optimization device configured to adjust a parameter in a manufacturing step of the first product based on an element of the first data corresponding to the selected element of the first concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 5, the elements of: a third optimization device configured to adjust a parameter in a manufacturing step of the third product based on an element of the third data corresponding to the selected element of the third concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 6, the elements of: wherein data on each product includes elements and values for each of the elements, and concealment of the data includes at least one of element name masking, replacement between elements, standardization of values of elements, and dimension compression; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 7, the elements of: adding a fourth data conversion device, does not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 8, the elements of: a first optimization device configured to adjust a parameter in a manufacturing step of the first product based on an element of the first data corresponding to the selected element of the first concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 9, the elements of: data on each product includes elements and values for each of the elements, and concealment of the data includes at least one of element name masking, replacement between elements, standardization of values of elements, and dimension compression; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 10, the elements of: wherein a third data conversion device configured to conceal third data on a third product manufactured in a third manufacturing system and output the third data as third concealed data, wherein the data analysis device selects an element of the first concealed data and the third concealed data that affects the second data by evaluating a degree of influence between the second data and data including the first concealed data and the third concealed data do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 11, the elements of: a fourth data conversion device configured to conceal fourth data on a fourth product manufactured in a fourth manufacturing system using the first product and output the fourth data as fourth concealed data, wherein the data analysis device selects an element of the first concealed data that affects the second data and the fourth concealed data by evaluating the degree of influence between the first concealed data and the second data and a degree of influence between the first concealed data and the fourth concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 12, the elements of: a first optimization device configured to adjust a parameter in a manufacturing step of the first product based on an element of the first data corresponding to the selected element of the first concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 13, the elements of: a third optimization device configured to adjust a parameter in a manufacturing step of the third product based on an element of the third data corresponding to the selected element of the third concealed data; do not add significantly more than the abstract idea and are also rejected under 35 USC 101.
For Claim 14, the elements of: data on each product includes elements and values for each of the elements, and concealment of the data includes at least one of element name masking, replacement between elements, standardization of values of elements, and dimension compression; do not add significantly more than the abstract idea and are also rejected under 35 USC 101
Similar independent device claim 15, method claim 17, and non-transitory computer-readable media 18 and associated dependent claims also do not add anything significantly more and are also rejected under 35 USC 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.
Claims 1, 2, 12, 15, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US PG Pub. No. 202300412091), herein “Park,” in view of Japanese patent document Yamamura Satoshi (JP 2006155394 A), herein “Satoshi.”
Regarding claim 1,
Park teaches a data analysis system comprising: (Abstract: “A quality data analysis apparatus and method for reducing time for product quality analysis and the quality cost by reducing the occurrence of product defects, the apparatus includes an input configured to obtain quality data on a product for process factors occurring in a production of the product, a data pre-processor to pre-process the quality data by encoding the process factors for each data types and setting the process factors that are lost, to a preset value, a determiner configured to determine whether the product is acceptable based on the process factors using machine learning…”) a first data conversion device configured to conceal first data (encode process factor(s)) on a first product (process system for the product) manufactured in a first manufacturing system and output the first data as first concealed data; (Abstract: “by encoding the process factors for each data types and setting the process factors that are lost, to a preset value, a determiner configured to determine whether the product is acceptable based on the process factors using machine learning…” Par. 0068: “Example categorical data may include a target factor indicating whether or not a field claim has occurred against the product. For instance, the encoding process for the target factor indicates a case where no field claim occurs against the product…” Par. 0067: “The data pre-processing unit 104 may perform an encoding process of converting the categorical process factor into an embedding value suitable for an inference model.” Par. 0135: “With categorical data, the data pre-processing unit 104 performs an encoding process of converting the same data into an embedding value…” See also Par. 0211. Examiner’s Note – Similar to Satoshi, Park teaches both the process of making the product (first product) and the product itself (second product). Also on point with the instant application of identify defects of makers (Par. 0009), Park teaches defect analysis in the product process. See paragraph 0006.)
Satoshi teaches a data analysis device (Par. 0032: “…executed by the analysis presentation PC 13 or 25 by reading a program recorded on the hard disk into a processor and executing it.”) configured to select an element of the first concealed data (first factor 102(b)) that affects second data (second factor 102(c)) on a second product manufactured using the first product by evaluating a degree of influence between the first concealed data and the second data. (Satoshi Par. 0025: “The present invention can be applied to a data mining system that analyzes manufacturing history information collected for various management items in the course of manufacturing a product and analyzes product failure factors based on the strength of association between the management items. In this embodiment, a data mining system that evaluates a relationship between two types of management items using a correlation coefficient will be described.” Par. 0033: “The history information holding unit 101 is a functional block that holds manufacturing history information to be analyzed in the data mining system in a data structure shown in FIG. The prediction information holding unit 102 is a functional block in which prediction information indicating a combination of management items for which a correlation is calculated in a correlation calculation unit 103 described later is recorded. The prediction information is registered in the prediction information holding unit 102 in advance before being analyzed by the correlation calculation unit 103. FIG. 4 is a diagram illustrating a data structure of prediction information. Prediction information includes each information of combination number 102a, first factor 102b, second factor 102c, and prediction class 102d. The first factor 102b and the second factor 102c indicate combinations of analysis candidates using management item IDs. The prediction class 102d is strength information indicating the strength of correlation expected in the combination of the first factor 102b and the second factor 102c, and all combinations included in the prediction information are arranged in the order of expected correlation strength. The class is divided into 5 levels by 20% from the top. The value of the prediction class 102d indicates that the correlation is predicted to be stronger as the numerical value is larger.” Par. 0016, 0027, and 0043.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system that encodes process data in the process of making or process factors to a product that helps detect defects in the process and/or product as in Park with a system and method that teaches analysis to determine the correlation of data between manufacturing and the product itself as in Satoshi in order to predict and determine defects from a causal relationship and/or correlation of the manufacturing process and the product. (Abstract and Par. 0034)
Regarding claim 2,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Park also teaches that the second data is data concealed by a second data conversion device. (Par. [0066]: “The data pre-processing unit 104 performs an appropriate encoding process for each data type of the process factors and sets lost data that occurred in the data collection process to an appropriate value. [0067] The data pre-processing unit 104 may perform an encoding process of converting the categorical process factor into an embedding value suitable for an inference model. [0068] Example categorical data may include a target factor indicating whether or not a field claim has occurred against the product. For instance, the encoding process for the target factor indicates a case where no field claim occurs against the product as 0 and a case where a field claim occurs as 1. Accordingly, encoding for such a target factor may be a process of generating a label for analysis toward the quality analysis based on an inference model.”)
Regarding claim 12,
The previously cited reference(s) teach the limitations of claim 1 which claim 2 depends. Park also teaches a first optimization device configured to adjust a parameter in a manufacturing step of the first product based on an element of the first data corresponding to the selected element of the first concealed data. (Par. 0028: “In another general aspect, there is provided a non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause a processor to perform: selecting adjustment process factors from main process factors using a User Interface (UI), and obtaining adjustment factor values for the adjustment process factors, wherein the main process factors are previously selected in a training for an inference model; generating a determination based on the adjustment process factors using the inference model, wherein the determination indicates a probability of a product having okay or no good quality; selecting the adjustment factor values as an optimal factor values for the adjustment process factors, when the probability of the no good product is less than a preset reference probability; and changing the quality control standard for the adjustment process factors based on the optimal factor values.” See also Par. 0010 and 0020.)
Regarding claim 15, it is directed to a device to implement the system or apparatuses set forth in claim 1. Park and Satoshi teach the claimed system in claim 1. Satoshi also teaches the acquisition means in Par. 0008: (“data mining device for obtaining an association between prediction information acquisition means for acquiring prediction information indicating at least one combination of management items predicted to have no association, and indicated by the prediction information It is characterized by comprising a relation calculation means for obtaining a relation between management items, excluding the combination.” Both Park and Satoshi teach the selection element in numerous instances. Therefore, Park and Satoshi teach the device, to implement the system, in claim 15.
Regarding claim 17, it is directed to a method to implement the system or apparatuses set forth in claim 1. Park and Satoshi teach the claimed system in claim 1. Satoshi also teaches the acquisition means in Par. 0008: (“data mining device for obtaining an association between prediction information acquisition means for acquiring prediction information indicating at least one combination of management items predicted to have no association, and indicated by the prediction information It is characterized by comprising a relation calculation means for obtaining a relation between management items, excluding the combination.” Both Park and Satoshi teach the selection element in numerous instances. Therefore, Park and Satoshi teach the method, to implement the system, in claim 17.
Regarding claim 18, it is directed to a non-transitory computer readable recording medium to implement the system or apparatuses set forth in claim 1. Park and Satoshi teach the claimed system in claim 1. Satoshi also teaches the acquisition means in Par. 0008: (“data mining device for obtaining an association between prediction information acquisition means for acquiring prediction information indicating at least one combination of management items predicted to have no association, and indicated by the prediction information It is characterized by comprising a relation calculation means for obtaining a relation between management items, excluding the combination.” Both Park and Satoshi teach the selection element in numerous instances. Therefore, Park and Satoshi teach the non-transitory computer readable recording medium, to implement the system, in claim 18.
claims 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Park in view of Satoshi in further view of Foo et al. (US PG Pub. No. 20180315233), herein “Foo.”
Regarding claim 14,
The previously cited reference(s) teach the limitations of claim 1 which claim 14 depends. They do not teach that the concealment includes masking, or compression, etc. However, Foo teaches that data on each product includes elements and values for each of the elements, and concealment of the data (encoding) includes at least one of element name masking, replacement between elements, standardization of values of elements, and dimension compression. (Claim 20: “An integrated circuit definition dataset that, when processed in an integrated circuit manufacturing system, configures the integrated circuit manufacturing system to manufacture a decoder unit configured to decode a plurality of texels in accordance with a texel request, the plurality of texels being encoded across one or more blocks of encoded texture data each encoding a block of texels, the decoder unit comprising: a first set of one or more decoders, each of the first set of decoders being configured to decode n texels from a single received block of encoded texture data; a second set of one or more decoders, each of the second set of decoders being configured to decode p texels from a single received block of encoded texture data, where p<n; and control logic configured to allocate different blocks of encoded texture data to the decoders in accordance with the texel request.” Par. 0164: “Though the above example has been described with reference to ASTC encoding, it will be appreciated that this is merely for the purposes of illustration and that the decoder units described herein could be configured to decode texels encoded according to some other encoding scheme, such as for example: PVRTC; PVRTC2; ETC1; ETC2; EAC; S3TC; 3Dc; or BC1-BC5. Each of these compression schemes encodes texture data for a block of texels into data blocks, and thus a decoder configured in accordance with the examples herein that decodes texture data encoded by one of these schemes may benefit from an improved performance-to-hardware requirement tradeoff.” See also Par. 0004, 0091, and 0172 - that teaches encoded data for both the integrated circuit and the integrated circuit manufacturing system which is on point with the instant application.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have combined the system that encodes process data in the process of making or process factors to a product that helps detect defects in the process and/or product as in Park with a system and method that teaches analysis to determine the correlation of data between manufacturing and the product itself as in Satoshi with encoding data for both the integrated circuit and the integrated circuit manufacturing system by a compression technique as in Foo in order to improve performance of hardware. (Par. 0164)
Regarding claim 16, it is dependent on claim 15 and is directed to a device to implement the system or apparatuses set forth in claim 14. Park, Satoshi, and Foo teach the claimed system in claim 14. Therefore, Park, Satoshi, and Foo teach the device, to implement the system, in claim 16.
Allowable Subject Matter
Claims 3 - 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §101 rejections above. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose the elements of claims 1 and 2, and wherein a third data conversion device configured to conceal third data on a third product manufactured in a third manufacturing system and output the third data as third concealed data, wherein the data analysis device selects an element of the first concealed data and the third concealed data that affects the second data by evaluating a degree of influence between the second data and data including the first concealed data and the third concealed data. Claims 4 – 6 depend from claim 3, and are also objected to.
Claims 7 - 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §101 rejections above. Reasons for allowance will be held in abeyance pending final recitation of the claims. The prior art does not disclose the elements of claims 1 and 2, and a fourth data conversion device configured to conceal fourth data on a fourth product manufactured in a fourth manufacturing system using the first product and output the fourth data as fourth concealed data, wherein 5 the data analysis device selects an element of the first concealed data that affects the second data and the fourth concealed data by evaluating the degree of influence between the first concealed data and the second data and a degree of influence between the first concealed data and the fourth concealed data. Claims 8 and 9 depend from claim 7, and are also objected to.
Claims 10 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §101 rejections above. Claim 10 has similar limitations as objected to claim 3 and is also objected to. Claim 13 depends on claim 10 and is also objected to.
Claim 11 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims pending resolving all intervening issues such as the 35 U.S.C. §101 rejections above. Claim 11 has similar limitations as objected to claim 7 and is also objected to.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kronberg (US PG Pub. No. 20220247558) teaches a quantum key that encrypts a signal between a first processing device and second processing device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD G ERDMAN whose telephone number is (571)270-0177. The examiner can normally be reached Mon - Fri 7am - 3pm or 4pm EST..
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/CHAD G ERDMAN/Primary Examiner, Art Unit 2116
1 Examiner’s Note – Park was found using PE2E search and found in search string L53.
Prosecution Insights