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 .
Election/Restrictions
Claims 7-8, 15-16, and 19-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 28 July 2026.
Claim Objections
Applicant is advised that should claim 2 be found allowable, claim 3 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. Similarly, Applicant is advised that should claim 10 be found allowable, claim 11 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Allowable Subject Matter
Claim 4-6 and 12-14 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. Reasons for allowance will be provided in the event the application becomes in condition for allowance.
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.
Claim(s) 1-3, 9-11, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Fitzgerald (U.S.P.G. Pub. No. 2023/0351782) in view of Alagirusamy (“Process control in blowroom and carding operations”, copy provided, see PTO-892).
Regarding claim 1, Fitzgerald (U.S.P.G. Pub. No. 2023/0351782) discloses:
A model training method, comprising:
Obtain a set of documents (paragraph [0083], the system is configured to receive checks);
extracting a handwritten area from each document in the set (paragraph [0084], the payee line is extracted from a check);
classifying the handwritten area to obtain a handwritten digit image block and a handwritten text image block (paragraphs [0087]-[0087], the neural network determines the likelihood of the handwriting corresponding to a number 0-9 or one of the text letters upper case A-Z or lower case a-z);
constructing a digit recognition model of different handwritten digit categories based on the handwritten digit image block, to extract a target digit from a new document (paragraphs [0087], [0088], a digit character is recognized); and
constructing a text recognition model of different handwritten text categories based on the handwritten text image block, to extract a target text from the new document (paragraphs [0087]-[0088], an upper case letter A-Z or lower case letter a-z is recognized; the model is trained to recognize the different text categories)
Fitzgerald does not explicitly disclose:
Wherein the set of documents obtained are a historical process flow card set for spinning process;
wherein digits and text on the card are used to construct a process flow database for the spinning process.
Alagirusamy (“Process control in blowroom and carding operations”, copy provided, see PTO-892) discloses:
obtaining a historical process flow card set for spinning process (pages 169, 204, various testing/QA metrics for the spinning process can be recorded);
wherein digits and text on the card are used to construct a process flow database for the spinning process (pages 169, 204, the recorded metrics are used to modify parameters related to QA/QC for the spinning process)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system of Alagirusamy with the system of Fitzgerald such that the system of Fitzgerald was applied to documents such as a historical process flow card set, containing QA/QC information for a spinning spinning process as described in Alagirusamy. The suggestion/motivation would have been in order to implement a system capable of “ensur[ing] production of yarns with the desired quality at the highest possible speed and with minimum waste” (page 191 of the Alagirusamy reference).
Regarding claim 2, Fitzgerald additionally discloses:
wherein constructing different handwritten digit categories, comprises:
segmenting the handwritten digit image block into first sub-image blocks respectively corresponding to cells in the historical process flow card by taking the cells as segmentation units (paragraph [0084], the clustering algorithm identifies a cluster of non-white pixels that may be a handwritten character);
performing a feature extraction operation on each first sub-image block to obtain a first feature vector of each first sub-image block (paragraph [0087], the cluster is provided to a character classification model that classifies the cluster based on its features); and
performing cluster analysis on the first feature vector of each first sub-image block to obtain different handwritten digit categories (paragraph [0087], the classification model provides the likelihood that the cluster is one of a set of different characters from different categories)
Regarding claim 3, Fitzgerald additionally discloses:
wherein constructing different handwritten digit categories, comprises:
segmenting the handwritten digit image block into second sub-image blocks respectively corresponding to cells in the historical process flow card by taking the cells as segmentation units (paragraph [0084], the clustering algorithm identifies a cluster of non-white pixels that may be a handwritten character);
performing a feature extraction operation on each second sub-image block to obtain a second feature vector of each second sub-image block (paragraph [0087], the cluster is provided to a character classification model that classifies the cluster based on its features); and
performing cluster analysis on the second feature vector of each second sub-image block to obtain different handwritten digit categories (paragraph [0087], the classification model provides the likelihood that the cluster is one of a set of different characters from different categories)
Regarding claim 9, Fitzgerald discloses:
An electronic device, comprising:
at least one processor (see paragraphs [0004], [0021]-[0022]); and
a memory connected in communication with the at least one processor; wherein the memory stores an instruction executable by the at least one processor, and the instruction, when executed by the at least one processor (see paragraphs [0004], [0021]-[0022]), enables the at least one processor to execute:
Obtain a set of documents (paragraph [0083], the system is configured to receive checks);
extracting a handwritten area from each document in the set (paragraph [0084], the payee line is extracted from a check);
classifying the handwritten area to obtain a handwritten digit image block and a handwritten text image block (paragraphs [0087]-[0087], the neural network determines the likelihood of the handwriting corresponding to a number 0-9 or one of the text letters upper case A-Z or lower case a-z);
constructing a digit recognition model of different handwritten digit categories based on the handwritten digit image block, to extract a target digit from a new document (paragraphs [0087], [0088], a digit character is recognized); and
constructing a text recognition model of different handwritten text categories based on the handwritten text image block, to extract a target text from the new document (paragraphs [0087]-[0088], an upper case letter A-Z or lower case letter a-z is recognized; the model is trained to recognize the different text categories)
Fitzgerald does not explicitly disclose:
Wherein the set of documents obtained are a historical process flow card set for spinning process;
wherein digits and text on the card are used to construct a process flow database for the spinning process.
Alagirusamy (“Process control in blowroom and carding operations”) discloses:
obtaining a historical process flow card set for spinning process (pages 169, 204, various testing/QA metrics for the spinning process can be recorded);
wherein digits and text on the card are used to construct a process flow database for the spinning process (pages 169, 204, the recorded metrics are used to modify parameters related to QA/QC for the spinning process)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system of Alagirusamy with the system of Fitzgerald such that the system of Fitzgerald was applied to documents such as a historical process flow card set, containing QA/QC information for a spinning spinning process as described in Alagirusamy. The suggestion/motivation would have been in order to implement a system capable of “ensur[ing] production of yarns with the desired quality at the highest possible speed and with minimum waste” (page 191 of the Alagirusamy reference).
Regarding claim 10, Fitzgerald additionally discloses:
wherein constructing different handwritten digit categories, comprises:
segmenting the handwritten digit image block into first sub-image blocks respectively corresponding to cells in the historical process flow card by taking the cells as segmentation units (paragraph [0084], the clustering algorithm identifies a cluster of non-white pixels that may be a handwritten character);
performing a feature extraction operation on each first sub-image block to obtain a first feature vector of each first sub-image block (paragraph [0087], the cluster is provided to a character classification model that classifies the cluster based on its features); and
performing cluster analysis on the first feature vector of each first sub-image block to obtain different handwritten digit categories (paragraph [0087],the classification model provides the likelihood that the cluster is one of a set of different characters from different categories)
Regarding claim 11, Fitzgerald additionally discloses:
wherein constructing different handwritten digit categories, comprises:
segmenting the handwritten digit image block into second sub-image blocks respectively corresponding to cells in the historical process flow card by taking the cells as segmentation units (paragraph [0084], the clustering algorithm identifies a cluster of non-white pixels that may be a handwritten character);
performing a feature extraction operation on each second sub-image block to obtain a second feature vector of each second sub-image block (paragraph [0087], the cluster is provided to a character classification model that classifies the cluster based on its features); and
performing cluster analysis on the second feature vector of each second sub-image block to obtain different handwritten digit categories (paragraph [0087], the classification model provides the likelihood that the cluster is one of a set of different characters from different categories)
Regarding claim 17, arguments analogous to claim 1 are applicable. The computer readable medium is explicitly taught by paragraph [0022] of Fitzgerald.
Regarding claim 18, arguments analogous to claim 2 are applicable. The computer readable medium is explicitly taught by paragraph [0022] of Fitzgerald.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN R WALLACE whose telephone number is (571)270-1577. The examiner can normally be reached Monday-Friday from 8:30-5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached at 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN R WALLACE/ Primary Examiner, Art Unit 2682
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