DETAILED ACTION
This non-final rejection is responsive to the claims filed 10 June 2024. Claims 1-20 are pending. Claims 1 and 15 are independent claims.
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 .
Claim Interpretation – 35 U.S.C. § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim limitations of claims 1-14 have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “a first communication interface”, “a second communication interface”, “a parameter determination unit”, “a third communication interface”, “a fourth communication interface”, “a fifth communication interface”, “a probabilistic analysis unit”, “a machine learning unit”, “a reliability unit”, “a logging unit”, “a notification unit”, “an operator input evaluation unit”, “a performance evaluation unit”, “a simulation unit”, “a sixth communication interface”, “an output unit”, “an input unit”, coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1-14 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1-14 recite generic placeholders “a first communication interface”, “a second communication interface”, “a parameter determination unit”, “a third communication interface”, “a fourth communication interface”, “a fifth communication interface”, “a probabilistic analysis unit”, “a machine learning unit”, “a reliability unit”, “a logging unit”, “a notification unit”, “an operator input evaluation unit”, “a performance evaluation unit”, “a simulation unit”, “a sixth communication interface”, “an output unit”, and “an input unit”, coupled with functional language without reciting sufficient structure to achieve the function. These limitations invoke 35 U.S.C. §112(f). However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Dependent claims inherit the deficiencies of the independent claims.
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 1-3 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Givot (US 2021/0286329 A1) hereinafter known as Givot.
Regarding independent claim 1, Givot teaches:
An interactive proposal system for determining a set (P) of operational parameters for at least one machine tool, the interactive proposal system comprising: (Givot: Fig. 1 and ¶[0056] and ¶[0058]; Givot teaches a machine learning tool that uses feedback data from sensors, generates simulations, and continuously optimizes data.)
a first communication interface for receiving a job description (J) describing a job to be performed by the at least one machine tool, (Givot: Fig. 1 and ¶[0064]; Givot teaches selecting specific variables (wheel speed, roll speed, traverse speed, etc… The foregoing is interpreted as a job description.)
a second communication interface for receiving at least one historic job description (HJ) together with a corresponding set of historic operational parameters (HP), a corresponding historic operator input (HI), a corresponding parameter determination history (HD), and a corresponding historic result assessment (HA), (Givot: ¶[0060]; Givot teaches historic job descriptions (HJ) by teaching a master data file containing 98 grinding test…over 29 different test occasions to generate a predictive model. Further, ¶[0066] teaches historical operational parameters (HP) by teaching extracting information from historic grinding data to create models describing variable contributions to performance. Further, ¶[0068] teaches historic operator input (HI), a parameter determination history (HD), and historic result assessment (HA), by teaching the user reporting back the actual result; storing the selected settings; and the new data is generated that may then be used to improve the underlying model and the prediction.)
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An embodiment of Givot does not explicitly teach but another embodiment teaches:
a parameter determination unit being communicatively connected to the first communication interface and to the second communication interface, and being configured for determining a set (P) of operational parameters for the performance of the job according to the received job description (J), based on the received job description (J), the received at least one historic job description (HJ), the received set of historic operational parameters (HP), the received historic operator input (HI), the received parameter determination history (HD), and the received historic result assessment (HA), (Givot: Figs. 1 and 5 and ¶[0064] and ¶[0068]; Givot teaches that the machine learning tool makes predictions and present the three best next trials based on the results and probabilities of improved results using the parameter values
a third communication interface for providing the determined set (P) of operational parameters to an operator of the machine tool for review, rating and/or correction, the third communication interface being communicatively connected to the parameter determination unit, (Givot: ¶[0064]; Givot teaches presenting the three best next trials based on the results and probabilities. ¶[0067] further teaches presenting to the user the highest 10-20, to select the setting to use. Lastly, Fig. 6 and ¶[0078] teaches showing the values for the predicted parameters for the machine setting to the user.)
a fourth communication interface being communicatively connected to the parameter determination unit, wherein the fourth communication interface is configured for receiving an approval, rating and/or a correction of the determined set (P) of operational parameters, and (Givot: ¶[0064], ¶[0068], and ¶[0078]; Givot teaches selecting the test to re-run; the user using values to manually update the machine settings; and storing the user’s reported result.)
a fifth communication interface for providing the determined set (P) of operational parameters to an operation system of the machine tool, the fifth communication interface being communicatively connected to the parameter determination unit. (Givot: ¶[0078]; Givot teaches providing values to the ancillary controller 20 or main controller 16 to automatically control the operation of the servo motor 14.)
Givot is in the same field of endeavor as the present invention, as it is directed to a machine learning tool that uses feedback data from sensors and continuously optimizes data. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine a system that receives various historical parameters with further using such parameters to generate settings to present to the user. As such, it would have been obvious to one of ordinary skill in the art to combine these teachings because the combination would allow to make prediction, as suggested Givot: ¶[0064].
Regarding claim 2, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
wherein the parameter determination unit comprises a probabilistic analysis unit being configured for probabilistically analyzing at least one of the received job description (J), the received at least one historic job description (HJ), the received set of historic operational parameters (HP), the received at least one historic operator input (HI), the received parameter determination history (HD), and the received historic result assessment (HA), and being configured for deriving therefrom a set (P) of operational parameters for the performance of the job according to the received job description (J). (Givot: ¶[0062]-¶[0066] and ¶[0076]; Givot teaches using statistical tools by machine learning tool 24 to extract information from historic grinding data…to create models describing variable contributions to performance with good predictive ability.)
Regarding claim 3, Givot further teaches the interactive proposal system of claim 2.
Givot further teaches:
wherein the probabilistic analysis unit comprises a machine learning unit for determining the set (P) of operational parameters. (Givot: ¶[0011] and ¶[0058]; Givot teaches a machine learning tool that learns the optimal operating conditions of the grinding wheel and that the model is provided to a predictive algorithm to identify parameter values and the predictive algorithm output the parameter values.)
Regarding claim 5, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
wherein the parameter determination unit comprises a reliability unit being configured for attributing a confidence interval and/or an occurrence probability to at least one element of the set (P) of determined operational parameters. (Givot: ¶[0064] and ¶[0074]; Givot teaches outputting parameter values for the predetermined number of best next trials and probabilities of improved results using the parameter values.)
Regarding claim 6, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
wherein the parameter determination unit comprises a logging unit being configured for documenting the determination of the set (P) of operational parameters. (Givot: ¶[0059]; Givot teaches all completed test and operations is stored in the cloud data storage to maintain a continuous record of the continuous optimization performed by the machine learning tool.)
Regarding claim 7, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
further comprising; a notification unit being communicatively connected to the first communication interface, to the second communication interface, and to the third communication interface, and being configured for providing at least one notification (N) concerning the performance of the job according to the received job description (J), based on the received job description (J), the received at least one historic job description (HJ), the received set of historic operational parameters (HP), the received historic operator input (HI), the received parameter determination history (HD), and the received historic result assessment (HA). (Givot: Fig. 2 and ¶[0062] and ¶[0103]; Givot teaches evaluating the degree of stretch in the use data in relation to the user database using a distance to model plot 30. ¶[0093] further teaches showing if the process is behaving normally or not.)
Regarding claim 8, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
wherein the parameter determination unit comprises an operator input evaluation unit being configured for evaluating an operator input (I). (Givot: ¶[0062]; Givot teaches validating correctness of the user supplied data or settings for the maximum or best possible performance and evaluate the degree of stretch in the user data in relation to the user database.)
Regarding claim 9, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
comprising a performance evaluation unit being communicatively connected to the parameter determination unit and being configured for receiving a performance evaluation provided by the operator. (Givot: ¶[0068]; Givot teaches a step where the user reports back the actual result.)
Regarding claim 10, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
wherein the parameter determination unit comprises a simulation unit being configured for simulating the performance of the job according to the received job description (J) using the set (P) of determined operational parameters. (Givot: ¶[0088]; Givot teaches simulations of different operating conditions may be generated to show the predicted results for specified input machine parameters without actually having to run the industrial process.)
Regarding claim 11, Givot further teaches the interactive proposal system of claim 1.
Givot further teaches:
further comprising; a sixth communication interface being configured for receiving at least one job execution parameter (E) from the operation system of the machine tool, wherein the parameter determination unit comprises a monitoring unit being communicatively connected to the sixth communication interface, the monitoring unit being configured for comparing the job execution parameter (E) to the set (P) of determined operational parameters and/or to a simulation result being produced on the basis of the set (P) of determined operational parameters. (Givot: ¶[0058]; Givot teaches during operation, sensors 18 provide feedback regarding the operation of the grinding wheel 12 and the feedback sensor data is stored during operation in cloud storage 22 and provided to a machine learning tool. ¶[0064] further teaches the saved data points during the re-run are saved. ¶[0087] further teaches the saved actual results are tested against the model, i.e. “if the new data points improve model fit and/or predictive ability”. Lastly, ¶[0093] teaches plotting the results to show deviation from model predictions.)
Regarding claim 12, Givot further teaches A control system for a machine tool, comprising a proposal system according to claim 1,
Givot further teaches:
wherein a storage unit is communicatively connected to the second communication interface of the proposal system, the storage unit comprising at least one historic job description (HJ) together with a corresponding set of historic operational parameters (HP), a corresponding historic operator input (HI), a corresponding parameter determination history (HD), and a corresponding historic result assessment (HA), wherein an output unit is communicatively connected to the third communication interface of the proposal system, the output unit being configured for providing a determined set (P) of operational parameters to an operator of the machine tool, and wherein an input unit is communicatively connected to the fourth communication interface of the proposal system, the input unit being configured for receiving an approval, rating and/or a correction of the determined set (P) of operational parameters. (Givot: ¶[0058]; Givot teaches feedback sensor data is stored during operation in cloud storage 22. ¶[0059] further teaches storing application data from all competed test and operations in cloud data storage 22. ¶[0060] teaches the master data file. ¶[0068] teaches storing selected setting and reported results. Fig. 6 and ¶[0078] teaches displaying of predicted parameters and performance values. Lastly, ¶[0064], ¶[0067], and ¶[0078] teaches the app/web interface through which the user selects, edits, and reports back.)
Regarding claim 13, Givot further teaches A machine tool, especially grinding machine, comprising a control system according to claim 12,
Givot further teaches:
being coupled to an operation system of the machine tool for controlling the operation of the machine tool. (Givot: ¶[0058]; Givot teaches grinding wheel, servo, controllers, for a grinding application.)
Regarding claim 14, Givot further teaches The machine tool of claim 13,
Givot further teaches:
wherein the operation system of the machine tool comprises at least one process zone and at least one sensor unit being coupled to the process zone, wherein the sensor unit is connected to the proposal system of the control system, and wherein the proposal system, the process zone and the sensor unit form a closed feedback loop. (Givot: ¶[0058]; Givot teaches grinding wheel with servo and sensors 18 providing feedback regarding the operation of the grinding wheel. The feedback sensor data is provided to a machine learning tool 24, the tool learns the optimal operating conditions of the grinding wheel and provides the corresponding parameters to the ancillary controller or main controller to provide continuously optimizing control during the operation of the grinding wheel.)
Regarding claims 15-20, these claims recite a method that performs the function of the system of claims 1-14; therefore, the same rationale for rejection applies.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Givot in view of Unno (US 5,473,532 A) hereinafter known as Unno.
Regarding claim 4, Givot further teaches the interactive proposal system of claim 3.
Givot does not explicitly teach but Unno teaches:
wherein the machine learning unit comprises an artificial neural network for determining the set (P) of operational parameters. (Unno: Fig. 8 and 15 and col. 7, lines 39-47; Unno teaches using a neural network to determine optimum machining conditions on the baes of the attribute data.)
Givot and Unno are in the same field of endeavor as the present invention, as the references are directed to using machine learning to optimize machining operations. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine a system that receives various historical parameters and use such parameters to generate settings to present to the user as taught in Givot with further using a neural network as taught in Unno. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Givot to include teachings of Unno, because the combination would allow determining optimum machining conditions, as suggested by Unno: col. 7, lines 39-47.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang (US 2020/0171671 A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEX OLSHANNIKOV whose telephone number is (571)270-0667. The examiner can normally be reached M-F 9:30-6.
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/ALEKSEY OLSHANNIKOV/Primary Examiner, Art Unit 2118
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