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 .
Response to Arguments
Applicant's arguments filed 08/18/2025 have been fully considered but they are not persuasive. The Applicant amends 30, cancels claim(s) 1-16, 25-26, and add new claim 32. Upon rereading and reviewing application the secondary reference DE102019000457 by Takigawa does disclose an input vector in paragraph [0017].
The addition of new claim 32 under consideration is rejected in further view of, Sugiyama (JP-2012076088).
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.
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.
Claim(s) 17-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spoerl (DE10113518), in view of, Takigawa (DE102019000457).
Spoerl teaches:
In regards to claim 17, Spoerl teaches a method for monitoring the condition of a laser machining head, said method comprising the steps of: (1, 13, 16, 21, 2 fig. 1, ‘laser processing head’, ‘detectors’)
acquiring measurement data by means of at least one sensor unit arranged on or in said laser machining head; (2, 3, 13, 16, 21, 2 fig. 1, ‘evaluation circuit’, ‘machine control’, ‘detectors’)
It would have been obvious before the effective filing date of the invention for Spoerl to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
Spoerl does not teach:
determining an input vector based on the acquired measurement data; and
determining an output vector by applying a model trained by machine learning to the input vector;
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head;
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models.
Takigawa teaches:
determining an input vector based on the acquired measurement data; and (Takigawa: para [0017], recites: “The neurons N .sub.11 to N .sub.13 give each one z .sub.11 to z .sub.13 out. In 8th are z .sub.11 to z .sub.13 in total by a feature vector z .sub.1 designated, and z .sub.1 may be considered as a vector obtained by taking a feature amount of an input vector.”)
determining an output vector by applying a model trained by machine learning to the input vector; (para [0017])
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head; (13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’)
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models. (para [0002, 0019, 0051, 0054-0056, 0058, 0066], ‘machine learning device at least one physical model or more.’; ‘failure occurrence conditions are identified, and checked.’)
It would have been obvious before the effective filing date of the invention for Takigaw to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
In regards to claim 18, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Spoerl teaches wherein the condition data include: a type of soiling of an element, a degree of soiling of an element, a degree of wear of an element, a degree of aging of an element, a remaining service life of an element, a changed focal length of an element, a deviation of a current focus position of the laser beam from a target focus position, and/or an indication of the functionality of the laser machining head. (Spoerl: abstract; para(s) [0031-0040]; 1 fig. 1, ‘laser processing head’)
In regards to claim 19, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigawa teaches wherein the input vector is further determined based on at least one process parameter. (Takigawa: para [0017], recites: “The neurons N .sub.11 to N .sub.13 give each one z .sub.11 to z .sub.13 out. In 8th are z .sub.11 to z .sub.13 in total by a feature vector z .sub.1 designated, and z .sub.1 may be considered as a vector obtained by taking a feature amount of an input vector.”)
In regard to claim 20, Spoerl & Takigaw teach a method according to claim 19, (see claim rejection 19) Spoerl teaches wherein the at least one process parameter comprises one of: a laser power, a focus position of the laser machining head, a gas pressure, a feed rate of the laser machining head, an imaging ratio of the laser machining head, a focal length of the laser machining head, or a distance of the laser machining head from a workpiece. (Spoerl: abstract; para(s) [0031-0040]; 1 fig. 1, ‘laser processing head’)
In regards to claim 21, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Spoerl teaches wherein the at least one element comprises at least one of: an optical element, a protective glass, a beam splitter, a mirror, a lens, a lens group, a focusing lens, focusing optics, collimating optics, a collimating lens, a mechanical element, an actuator, a motor, a nozzle, a nozzle electrode, a ceramic part, a cutting gas duct, a cooling element, an electrical and/or electronic element, a board, a control board, a communication board, a power component , and a motor control. (Spoerl: 1, 7, 8, 9, 13, 16, 21, 24 fig. 1, ‘laser processing head’, ‘lens arrangement’, ‘focal point’, ‘detectors’, ‘protective glass’, ‘radiation detectors’,’temperature detector’; para [0046], ‘cutting process’)
In regards to claim 22, Spoerl & Takigaw method according to claim 17, (see claim rejection 17) wherein the measurement data include values of at least one of the following measurement variables:
humidity, humidity in an interior space of said laser machining head, humidity in an area surrounding said laser machining head, temperature, a temperature of an area surrounding said laser machining head, a temperature of a housing of said laser machining head, a temperature in an interior space of said laser machining head, a temperature of an element of said laser machining head, a coolant temperature, a thermal radiation intensity, a radiation intensity, a scattered light intensity, an intensity of a radiation reflected and/or scattered by an element of said laser machining head, an intensity of scattered light in an interior space of said laser machining head, an intensity of scattered light from an optical element, electric currents from or to an element, electrical voltages at an element, communication signals of an element, a gas pressure in an interior space of said laser machining head, a gas pressure between two optical elements and/or on an optical element, an acceleration of an element of said laser machining head and/or said laser machining head, and a vibration of an element of said laser machining head and/or said laser machining head. (Takigaw: para(s) [0054], para(s) [0049-0058, 0101-0106])
In regards to claim 23, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigaw teaches wherein the model is based on at least one of the following algorithms: random forest, support vector machine, a neural network, a recurrent neural network, a convolutional neural network and a deep convolutional neural network. (Takigaw: para(s) [0019, 0023, 0042, 0079, 0102, 0103-0104, 0107-0108]; claim(s) 3, 5)
In regards to claim 24, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigaw teaches wherein the model is configured for transfer learning and/or is adaptable for reinforcement learning. (Takigaw: para(s) [0020-005]; fig(s) 4, 7-8)
In regards to claim 25, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigaw teaches wherein the method for condition monitoring is carried out during a test cycle. (Takigaw: claim 1, ‘state variable….time-series data’; claim 8, ‘drive condition’, ‘event history’; para(s) [0054-0113])
In regards to claim 26, Spoerl & Takigaw teach a method according to claim 25, (see claim rejection 25) Spoerl teaches wherein, during the test cycle, a focus position is set such that at least one optical element of said laser machining head is illuminated maximally or minimally. (Spoerl: 1, 7-9, 12, fig. 1, ‘laser processing head’, ‘lens arrangement’, ‘protective glass’, ‘focal point’, intensity of scattered radiation)
In regards to claim 27, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigaw teaches wherein said method for condition monitoring is carried out during a laser machining process and/or wherein the output vector is determined repeatedly and/or continuously during a laser machining process. (Takigaw: 13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’; para(s) [0053-0054])
In regards to claim 28, Spoerl & Takigaw teach a method according to claim 17, Spoerl & Takigaw teach a further comprising:
outputting the output vector to an operator of said laser machining head or information about a condition of the at least one element and/or the laser machining head by means of a user interface; and/or outputting at least one recommendation for action to the operator of said laser machining head by means of a user interface. (Takigaw: 13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’; para(s) [0053-0054]; 11, 12, 13, 14, 16 fig(s) 1, ‘learning unit’, ‘determination result acquisition unit’, ‘state observation unit (state variables)’, ‘decision-making unit’, ‘error calculation unit’)
In regards to claim 29, Spoerl & Takigaw teach a method according to claim 17, (see claim rejection 17) Takigaw teaches wherein said method for condition monitoring is carried out during a laser machining process and further comprises at least one of the steps of: controlling the laser machining process by changing at least one process parameter based on the determined output vector; (Takigaw: 13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’) and stopping the laser machining process. (Takigaw: para [0068], ‘stop the machine learning’; 9 fig. 1, ‘control unit)
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.
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.
Claim(s) 30-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spoerl (DE10113518), in view of, Takigawa (DE102019000457).
Spoerl teaches:
In regards to claim 30, Spoerl teaches a laser machining system, comprising: (1, 13, 16, 21, 2 fig. 1, ‘laser processing head’, ‘detectors’)
a laser machining head having at least one sensor unit for acquiring measurement data which is arranged on or in a housing of said laser machining head; and ; (abstract; 2, 3, 13, 16, 21, 2 fig. 1, ‘evaluation circuit’, ‘machine control’, ‘detectors’)
It would have been obvious before the effective filing date of the invention for Spoerl to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
Spoerl does not teach:
a computing unit configured to determine an input vector based on the acquired measurement data and
to determine an output vector by applying a model trained by machine learning to the input vector;
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head, and
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models, and
Takigaw teaches wherein the model is configured for transfer learning and/or is adaptable for reinforcement learning.
Takigawa teaches:
a computing unit configured to determine an input vector based on the acquired measurement data and (Takigawa: para [0017], recites: “The neurons N .sub.11 to N .sub.13 give each one z .sub.11 to z .sub.13 out. In 8th are z .sub.11 to z .sub.13 in total by a feature vector z .sub.1 designated, and z .sub.1 may be considered as a vector obtained by taking a feature amount of an input vector.”)
to determine an output vector by applying a model trained by machine learning to the input vector; (para [0017])
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head, and (13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’)
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models, and
Takigaw teaches wherein the model is configured for transfer learning and/or is adaptable for reinforcement learning. (Takigaw: para(s) [0020-0025], fig(s) 4, 7-8)
It would have been obvious before the effective filing date of the invention for Takigaw to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
In regards to claim 31, Spoerl & Takigaw teach a laser machining system according to claim 30, wherein the sensor unit includes at least one of the following sensors: a humidity sensor, a temperature sensor, a photodetector, a pressure sensor, a scattered light sensor, an acceleration sensor, a current sensor, a voltage sensor, a distance sensor, a sound sensor, an acceleration sensor, and a vibration sensor. (Takigaw: para [0054])
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.
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.
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Spoerl (DE10113518), in view of, Takigawa (DE102019000457), in further view of, Sugiyama (JP-2012076088).
Spoerl teaches:
In regards to claim 32, Spoerl teaches a method for monitoring the condition of a laser machining head, said method comprising the steps of: (1, 13, 16, 21, 2 fig. 1, ‘laser processing head’, ‘detectors’)
acquiring measurement data by means of at least one sensor unit arranged on or in said laser machining head; (2, 3, 13, 16, 21, 2 fig. 1, ‘evaluation circuit’, ‘machine control’, ‘detectors’)
It would have been obvious before the effective filing date of the invention for Spoerl to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
Spoerl does not teach:
determining an input vector based on the acquired measurement data; and
determining an output vector by applying a model trained by machine learning to the input vector;
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head;
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models,
Takigawa teaches:
determining an input vector based on the acquired measurement data; and (Takigawa: para [0017], recites: “The neurons N .sub.11 to N .sub.13 give each one z .sub.11 to z .sub.13 out. In 8th are z .sub.11 to z .sub.13 in total by a feature vector z .sub.1 designated, and z .sub.1 may be considered as a vector obtained by taking a feature amount of an input vector.”)
determining an output vector by applying a model trained by machine learning to the input vector; (para [0017])
wherein the output vector contains estimated condition data of at least two elements of said laser machining head for determining the overall condition of said laser machining head; (13 fig. 1, ‘state observation unit (state variables)’; para [0017], ‘output data’)
wherein the model comprises a plurality of first models for determining condition data of one element of said laser machining head each and a second model for determining condition data of said laser machining head from the condition data of the elements obtained from the first models, (para [0002, 0019, 0051, 0054-0056, 0058, 0066], ‘machine learning device at least one physical model or more.’; ‘failure occurrence conditions are identified, and checked.’)
wherein the method for condition monitoring is carried out during a test cycle, and (Takigaw: claim 1, ‘state variable….time-series data’; claim 8, ‘drive condition’, ‘event history’; para(s) [0054-0113])
It would have been obvious before the effective filing date of the invention for Takigaw to provide a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
Takigawa does not teach:
wherein, during the test cycle, a focus position is set such that at least one optical element of said laser machining head is illuminated maximally or minimally.
Sugiyama teaches:
wherein, during the test cycle, a focus position is set such that at least one optical element of said laser machining head is illuminated maximally or minimally. (para(s) [0013-0014, 0022, 0027-0029, 0032-0034])
It would have been obvious before the effective filing date of the invention for Sugiyama to provide at least one optical element of a laser that is illuminated maximally or minimally for a method for observing the desired parameters of a laser processing head in order to detect the desired error readings sought for the processing of a workpiece by means of the laser beam.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references cited Heintz (WO2023004155), Boco (US 2022/0331911), Izumi (US 10,556,295) and Schwarz (DE102018129441) references further describe a sampling module with multiple flow paths as described by the claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN C BUTLER whose telephone number is (571)270-3973. The examiner can normally be reached 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephanie E Bloss can be reached on (571)272-3555. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.C.B/Examiner, Art Unit 2852
/STEPHANIE E BLOSS/Supervisory Primary Examiner, Art Unit 2852