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 .
Status
Claims 1-17 are pending. Claim 15 is rejected under 35 U.S.C. 102(a)(1). Claims 1-14 and 16-17 are allowed.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The lengthy 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.
Claim Rejections - 35 USC § 102
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 15 is rejected under 35 U.S.C. 102(a)(1) as anticipated by Balic (US 2005/0251284 A1).
Regarding claim 15, Balic discloses a machine tool comprising: (Abstract “A computer numerical control unit”)
a numerical machine controller (Abstract “The key module of the computer numerical control unit is a neural network (NN) device that learns to generate the numerical control programs through an neural network teaching module”); and
a machining unit (Abstract “computer numerical control machining centers for milling, drilling and similar operations”),
wherein the machine tool is configured to be used in a specific usage environment ([0045] “The output of the neural network milling module 27 is the numerical control program 28 for the processed part, which includes the geometric data about the mode of cutting tool path (linear G01 or circular G02/G03 interpolation), the coordinates of the cutting tool path (e.g. milling cutter), the technological data (revolution speed, feed-rate, depth of cutting) and auxiliary data (coordinates of reference, zero and starting points, direction of rotation of the main spindle M02/M03, change of cutting tools M06, etc.).” The machine environment includes different cutting tools, etc.),
wherein the numerical machine controller is configured to receive computerized numerical control data sets and to convert them into control routines which are configured to be used to control the machining unit to machine a workpiece ([0003] “The numerical control positions program is send through a comparison unit and an amplifier unit to a step motor of the numerical control machine.” The instructions are converted and sent to the step motor.),
wherein the machine tool further comprises:
a computer-readable storage medium configured to store the control data sets and component data sets on which the control data sets are based ([0044] “In the programming mode, the computer numerical control unit 1 gets the data package of the CAD part model 5 from the conventional, commercially available CAD/CAM system 29 intended for programming the computer numerical control machines. The model is then transmitted to the neural network device 7, which identifies and classifies the individual geometric and technological features 25 of the CAD part model.” The CAD part model is retrieved from a computer readable medium. [0003] The control data is read/transmitted by a computer.);
a processor which is configured to generate the control routines from the control data sets ([0045] “The output of the neural network milling module 27 is the numerical control program 28 for the processed part,“), wherein a computerized numerical control data set is configured to be modified into a changed control data set by a trained machine learning algorithm loaded by the processor (Under the broadest reasonable interpretation, only one of the listed alternatives must be taught.), from which changed control data set the control routines are generated ([0047] “The position meter 20 perceives the movement and sends a regulated value 22 into the position-measuring module 16, which transmits the data to comparison unit 15, where the difference between the actual and the programmed position is calculated.” [0049] “In the learning mode, the learned numerical control programming system based on the principle of a neural network is fed to the neural network device 7 through the teaching module 4, which conducts the teaching of the neural network device 7.”), and
wherein the processor is also configured to compile an additional training data set from the changed control data set and the associated component data set assigned to the usage environment ([0049] “In the learning mode, the learned numerical control programming system based on the principle of a neural network is fed to the neural network device 7 through the teaching module 4, which conducts the teaching of the neural network device 7.” Abstract “Upon completion of learning process the neural network device can generate automatically, without any intervention of the operator, merely on the basis of the CAD 2D, 2,5D or 3D part models, taken from a conventional CAD/CAM system, various new numerical control programs for different parts, which have not been in the machining process before” The learning is based on a CAD part model and associated path.); and
a training data output configured to output the additional training data set to a usage-environment-specific training database ([0044] “In the programming mode, the computer numerical control unit 1 gets the data package of the CAD part model 5 from the conventional, commercially available CAD/CAM system 29 intended for programming the computer numerical control machines. The model is then transmitted to the neural network device 7, which identifies and classifies the individual geometric and technological features 25 of the CAD part model.”).
Allowable Subject Matter
Claims 1-14 and 16-17 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, the prior art as exemplified by Balic, Dittrich, and Lovell fails to teach, alone or in obvious combination, “compiling a first additional training data set from the component data set and the created computerized numerical control data set, and outputting the first additional training data set to a usage-environment-specific training database;
updating the machine learning algorithm by setting usage-environment-specific values for the parameters, wherein the usage-environment-specific values were determined by training the machine learning training algorithm using the usage-environment-specific training database;”, in combination with the other limitations of the claims.
Claims 2-14 and 16-17 depend on claim 1 and are allowable for the same reason.
Prior Art of Record
The Prior art of reference Balic (US 2005/0251284 A1) discloses a computer-implemented method, which is carried out by one or more computers, for creating computerized numerical control data sets for controlling machine tools in a usage environment ([0033] “The learning process and the automatic intelligent generation of the numerical control programs 28 take place in a neural network (NN), built-in in a special neural network device 7, which receives the learning instructions from the neural network teaching module 4.”), the control data sets being read in from associated machine tools for machining starting materials ([0033], Fig. 1 The control data is sent to the CNC device.), the method comprising:
receiving a first component data set representing a digital design model of a first component (Fig. 14b The CAD part is sent to the Neural Network (NN) unit. );
creating a first computerized numerical control data set for the first component data set using control program generation software ([0045] “The output of the neural network milling module 27 is the numerical control program 28 for the processed part,“), wherein the control program generation software comprises an assessment routine which uses a trained machine learning algorithm with settable parameters ([0045] “The output of the neural network milling module 27 is the numerical control program 28 for the processed part, which includes the geometric data about the mode of cutting tool path (linear G01 or circular G02/G03 interpolation), the coordinates of the cutting tool path (e.g. milling cutter), the technological data (revolution speed, feed-rate, depth of cutting) and auxiliary data (coordinates of reference, zero and starting points, direction of rotation of the main spindle M02/M03, change of cutting tools M06, etc.).”), wherein starting values of the parameters were determined by training a machine learning training algorithm which corresponds to the trained machine learning algorithm ([0045] The trained neural network output includes various technological data and auxiliary data parameters.);
receiving a second component data set representing a digital design model of a second component ([0050] “The origin for the teaching process is the engineering drawing 35 of a prismatic part, suitable for processing on machining centers, designed for milling, drilling and similar operations.”);
creating a second computerized numerical control data set for the second component data set by using the control program generation software and running through the assessment routine ([0044] “In the programming mode, the computer numerical control unit 1 gets the data package of the CAD part model 5 from the conventional, commercially available CAD/CAM system 29 intended for programming the computer numerical control machines. The model is then transmitted to the neural network device 7, which identifies and classifies the individual geometric and technological features 25 of the CAD part model.”).
The Prior art of reference Dittrich et al. (“Self-optimizing tool path generation for 5-axis machining processes”, 2018) discloses “using machine learning methods, the resulting shape error is predicted and the tool path adapted automatically.”
The Prior art of reference Lovell et al. (CN 115769156 A) discloses “providing at least a portion of the three-dimensional model to a machine learning algorithm using reinforcement learning to generate a tool path capable of being controlled by a computer for manufacturing at least a portion of the manufacturing object”.
Conclusion
The examiner respectfully requests, in response to this Office action, support is shown for language added to any original claims on amendment and any new claims. Indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s).
When responding to this Office Action, the applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TROY A MAUST whose telephone number is (571)272-1931. The examiner can normally be reached on Monday-Friday from 8AM to 4PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen, can be reached at telephone number (571) 272-3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.A.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189