DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-21 are currently being examined.
Specification
The Specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. MPEP § 608.01
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
Claims 1, 2, 4, 6-9, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leordeanu et al (EP 4195142), in view of Ohya et al (US Published Application No. 2022/0067917).
With respect to independent Claim 1, Leordeanu et al discloses the limitations of independent Claim 1 as follows:
A computer-implemented method for automatically detecting anomalies of boards on a board production line using artificial intelligence (AI), the method comprising: (See Abstract)
capturing a series of images of an area of the production line; (See Pars. 0012, 0014-0017, 0019; Figs. 2, 3; Ref. Numerals 1b(imaging devices), "production line"(production line)
identifying one or more boards in the captured images; (See Pars. 0012, 0014-0017, 0019; Figs. 2, 3; Ref. Numerals 1b(imaging devices), "production line"(production line), "wood boards"(boards)
performing automatic classification of attributes of the identified boards by inputting the captured images to an AI engine; (See Pars. 0012, 0014-0017, 0019, 0027, 0028; Figs. 4, 6, 9; Ref. Numerals 1b(imaging devices), 4(AI engine), 4b(data processing server)
based on the automatic classification, assessing the board as being a good piece or a bad piece; and (See Pars. 0012, 0014-0017, 0019, 0027, 0028, 0030, 0039-0041; Figs. 4, 6, 9; Ref. Numerals 1b(imaging devices), 4(AI engine), 4b(data processing server)
Leordeanu et al, however, does not disclose the limitations related to assessing the board as being a good piece or a bad piece. With respect to those limitations, Ohya et al teaches the following:
based on the automatic classification, assessing the board as being a good piece or a bad piece; and (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board)
performing an intervention on the production line to remove or move the board identified as a bad piece. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
It 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, to modify Leordeanu et al with the teachings of Ohya et al to assess whether the board is a good piece or a bad piece so that a bad piece can be removed from the production line so as to avoid processing a bad piece of board. A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, assessing and then removing a bad piece of board from the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 2, which depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 which are incorporated herein by reference. With respect to Claim 2, Ohya et al disclose as follows:
The computer-implemented method of claim 1 further comprising:
automatically stopping the production line when the board is classified as bad; and (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
resuming the production line flow when the board identified as a bad piece has been removed. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, stopping the production line and then removing a bad piece of board from the production line to allow continued operation of the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 4, which depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 which are incorporated herein by reference. With respect to Claim 4, Leordeanu et al discloses as follows:
The computer-implemented method of claim 1 further comprising not performing any intervention when the board is identified as a good piece. (See Pars. 0012, 0014-0017, 0019, 0027, 0028, 0030, 0039-0041; Figs. 4, 6, 9; Ref. Numerals 1b(imaging devices), 4(AI engine), 4b(data processing server)
With respect to Claim 6, which depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 which are incorporated herein by reference. With respect to Claim 6, Leordeanu et al discloses as follows:
The computer-implemented method of claim 1, the automatic classification using one or more of the following:
position of the board on the production line, (See Pars. 0019, 0020, 0028; Fig. 3; Ref Numerals 1d(position sensors)
integrality of the board,
alignment of the board,
straightness of the board and (See Par. 0043)
integrity of the board.
With respect to Claim 7, which depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 which are incorporated herein by reference. With respect to Claim 7, Ohya et al disclose as follows:
The computer-implemented method of claim 1, the intervention being a robotized intervention. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, stopping the production line and then using a robot to remove a bad piece of board from the production line to allow continued operation of the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 8, which ultimately depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 and Claim 7 which are incorporated herein by reference. With respect to Claim 8, Ohya et al disclose as follows:
The computer-implemented method of claim 7, the robotized intervention further comprising picking and placing the board identified as a bad piece. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, stopping the production line and then using a robot to pick and remove a bad piece of board from the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 9, which ultimately depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 and Claim 7 which are incorporated herein by reference. With respect to Claim 9, Ohya et al disclose as follows:
The computer-implemented method of claim 7, the robotized intervention further comprising removing from the production line the board identified as a bad piece. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, stopping the production line and then using a robot to pick and remove a bad piece of board from the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to independent Claim 16, Leordeanu et al discloses the limitations of independent Claim 16 as follows:
A computer-implemented method for automatically monitoring line flow of a board production line using artificial intelligence (AI), the method comprising: (See Abstract)
capturing a series of images of a flow of boards of the production line; (See Pars. 0012, 0014-0017, 0019; Figs. 2, 3; Ref. Numerals 1b(imaging devices), "production line"(production line)
identifying the boards in the flow in the captured images; (See Pars. 0012, 0014-0017, 0019; Figs. 2, 3; Ref. Numerals 1b(imaging devices), "production line"(production line), "wood boards"(boards)
performing automatic classification of the flow of the identified boards by inputting the captured images to an AI engine; (See Pars. 0012, 0014-0017, 0019, 0027, 0028; Figs. 4, 6, 9; Ref. Numerals 1b(imaging devices), 4(AI engine), 4b(data processing server)
based on the automatic classification, assessing the flow of boards as being a normal flow or as comprising anomalies; and (See Pars. 0012, 0014-0017, 0019, 0027, 0028, 0030, 0039-0041; Figs. 4, 6, 9; Ref. Numerals 1b(imaging devices), 4(AI engine), 4b(data processing server)
Leordeanu et al, however, does not disclose the limitations related to performing an intervention on the production line when the flow is classified as having an anomaly. With respect to that limitation, Ohya et al teaches the following:
performing an intervention on the production line when the flow is classified as having an anomaly. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
It 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, to modify Leordeanu et al with the teachings of Ohya et al to assess whether the board has a defect (ie, anomaly) and should be removed from the production line so as to avoid processing a board with a defect. A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, removing a board with a defect (anomaly)from the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 17, which depends from independent Claim 16, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 16 which are incorporated herein by reference. With respect to Claim 17, Ohya et al disclose as follows:
The computer-implemented method of claim 16, the intervention being a robotized intervention. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, using a robot to remove a board with a defect from the production line to prevent a board with a defect from being processed) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 18, which ultimately depends from independent Claim 16, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 16 and Claim 17 which are incorporated herein by reference. With respect to Claim 18, Ohya et al disclose as follows:
The computer-implemented method of claim 17, the robotized intervention further comprising any one of the followings:
retrieving a board from the flow comprising an anomaly from the production line; (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
picking one or more boards causing the anomaly from the production line and placing the picked board in order to revert to a normal flow of the production line; and (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
directing one or more boards causing the anomaly to a stacker. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 170(robot), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, using a robot to remove a board with a defect from the production line to prevent a board with a defect from being processed) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art. Moving the board with a defect to a stacker would be a choice and decision made by one with skill in the art over other placement locations.
With respect to Claim 20, which depends from independent Claim 16, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 16 which are incorporated herein by reference. With respect to Claim 20, Ohya et al disclose as follows:
The computer-implemented method of claim 16 further comprising:
automatically stopping the production line when the flow is classified as comprising an anomaly; and (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
resuming the production line flow when the board identified one or more of the boards causing the anomaly has been removed. (See Pars. 0041-0045, 0048; Figs. 1A, 2B, 3, 4A,4B; Ref. Numerals 131(AI engine), 200(board), "production line"(production line)
A person with skill in the art would be motivated to incorporate the teachings of Ohya et al because they are a known work in the same field of endeavor (ie, automatically stopping the production line and then removing a board with a defect from the production line to prevent a board with a defect from being processed) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Leordeanu et al in view of Ohya et al, as applied to the claims set forth hereinabove, and in further view of Voyer et al (US Published Application No 2017/00148071).
With respect to Claim 5, which depends from independent Claim 1, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 1 which are incorporated herein by reference. With respect to Claim 5, Voyer et al disclose as follows:
The computer-implemented method of claim 1, the automatic classification further comprising
mapping in three dimensions (3D) the identified board, (See Pars. 0032-0034)
identifying a contour of the mapped board and (See Pars. 0032-0034)
determining the attributes of the mapped board based on the contour. (See Pars. 0032-0034)
It 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, to modify Leordeanu et al and Ohya et al with the teachings of Voyer et al to have the automatic classifier employ mapping in 3D so as to clearly identify all the defects that may be identified on the board. A person with skill in the art would be motivated to incorporate the teachings of Voyer et al because they are a known work in the same field of endeavor (ie, identifying defects on a board by 3D mapping) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
With respect to Claim 19, which depends from independent Claim 16, Leordeanu et al and Ohya et al together teach all of the limitations of Claim 16 which are incorporated herein by reference. With respect to Claim 19, Voyer et al disclose as follows:
The computer-implemented method of claim 16, the automatic classification using one or more of the followings criteria:
speed of the production line (See Pars. 0031-0033) and
linearity of the production line. (See Pars. 0031-0033)
It 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, to modify Leordeanu et al and Ohya et al with the teachings of Voyer et al to have the automatic classifier use speed of the production line or linearity of the production line to ensure to identifying all the defects that may be identified on the board. A person with skill in the art would be motivated to incorporate the teachings of Voyer et al because they are a known work in the same field of endeavor (ie, identifying defects on a board by using speed or linearity of the production line) which would prompt its use in the same field of application based on improvements to a system that are predictable and would be recognized by one of ordinary skill in the art.
Allowable Subject Matter
Claims 10-15 are allowed. Independent Claim 10 contains subject matter in a limitation relating to automatically classifying a grade of a board by assessing whether the determined grade is either over under a predetermined threshold as being either a bad board or a good board, respectively. This subject matter was neither found nor taught or fairly suggested in the prior art of record.
Claims 3 and 21 are rejected 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. In the alternative, either one of these claims may be incorporated as further limitations into the respective rejected independent base claim from which they depend. These allowable claims would make the rejected independent base claim allowable because the rejected base claim would then contain subject matter that was neither disclosed nor taught or fairly suggested in the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure because the prior art references contain subject matter that relates to one or more of Applicant’s claim limitations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS RANDAZZO whose telephone number is (313)446-4903. The examiner can normally be reached between 9:00am and 4:00pm ET Monday through Thursday and between 9:00am and 11:00am ET on Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Jacob Scott, can be reached on 571-270-3415. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS RANDAZZO/Primary Examiner, Art Unit 3655 February 25, 2026