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 .
2. This action is responsive to the following communication: Original claims filed 12/29/2022. This action is made non-final.
3. Claims 1-10 are pending in the case. Claims 1, 9 and 10 are independent claims.
Claim Rejections - 35 USC § 101
4. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a machine learning device claim. Claim 10 is a method claim. Therefore, claims 1-10 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1, 10:
2A Prong 1:
an action output unit configured to select a machining condition as an action from among a plurality of machining conditions and output the action to the laser machine (mental process – a user can select a condition);
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a state acquisition unit configured to acquire, as state information, image data generated through imaging of a machining state of a workpiece machined according to the action (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
a reward computing unit configured to compute a reward based at least on the laser scan wait time and a machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f));
and a learning unit configured to perform the machine learning of the machining conditions based on the state information acquired by the state acquisition unit and the reward computed by the reward computing unit (adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g));
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a state acquisition unit configured to acquire, as state information, image data generated through imaging of a machining state of a workpiece machined according to the action (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
a reward computing unit configured to compute a reward based at least on the laser scan wait time and a machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f));
and a learning unit configured to perform the machine learning of the machining conditions based on the state information acquired by the state acquisition unit and the reward computed by the reward computing unit (can be categorized as well understood, routine and conventional activity of “transmitting or receiving data over a network” and therefore does not provide significantly more. MPEP 2106.05(d)(ii)
With respect to claim 2:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the machining state includes one or more mid-machining machining states between a start of the machining and an end of the machining, and the machining condition includes machining conditions corresponding to the mid-machining machining states respectively. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the machining state includes one or more mid-machining machining states between a start of the machining and an end of the machining, and the machining condition includes machining conditions corresponding to the mid-machining machining states respectively. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 3:
2A Prong 1:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a state reward computing unit configured to compute a state reward for the action according to the machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)); and
an action reward computing unit configured to compute an action reward for the action based on at least the laser scan wait time included in the action, wherein the reward computing unit computes the reward for the action based on the state reward and the action reward. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a state reward computing unit configured to compute a state reward for the action according to the machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)); and
an action reward computing unit configured to compute an action reward for the action based on at least the laser scan wait time included in the action, wherein the reward computing unit computes the reward for the action based on the state reward and the action reward. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 4:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the state reward computing unit computes the machining accuracy of the machining state based on reconstructed image data outputted by inputting the state information acquired by the state acquisition unit into an autoencoder trained based only on image data generated through imaging of machining states of workpieces each having a high machining accuracy. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the state reward computing unit computes the machining accuracy of the machining state based on reconstructed image data outputted by inputting the state information acquired by the state acquisition unit into an autoencoder trained based only on image data generated through imaging of machining states of workpieces each having a high machining accuracy. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 5:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the action output unit outputs an action to the laser machine based on a policy for selecting one machining condition as an action from among a plurality of machining conditions, and the learning unit evaluates and improves the policy based on a plurality of pieces of the state information acquired by the state acquisition unit and a plurality of action rewards computed by the reward computing unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the action output unit outputs an action to the laser machine based on a policy for selecting one machining condition as an action from among a plurality of machining conditions, and the learning unit evaluates and improves the policy based on a plurality of pieces of the state information acquired by the state acquisition unit and a plurality of action rewards computed by the reward computing unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 6:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
further comprising an optimized action output unit configured to output the machining conditions to the laser machine based on a result of the learning by the learning unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
further comprising an optimized action output unit configured to output the machining conditions to the laser machine based on a result of the learning by the learning unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 7:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
compromising a plurality of the machine learning devices, wherein the machine learning of the machining conditions is distributed and performed among the plurality of machine learning devices via a network. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
compromising a plurality of the machine learning devices, wherein the machine learning of the machining conditions is distributed and performed among the plurality of machine learning devices via a network. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 8:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the learning unit performs reinforcement learning by an actor-critic method. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
wherein the learning unit performs reinforcement learning by an actor-critic method. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)).
With respect to claim 9:
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a control unit configured to control the laser machine based on the machining conditions (mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a control unit configured to control the laser machine based on the machining conditions (mere instructions to apply the exception using a generic computer component);
Claim Rejections - 35 USC § 112
5. 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.
6. With regard to claim 1, claim limitations “an action output unit configured to”, “a state acquisition unit configured to”, “a reward computing unit configured to” and “a learning unit configured to” and further claim 9 “a control unit configured to” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder (e.g. “device”) coupled with functional language (e.g. “configured to”, etc.) without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 2-9 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 § 102
7. 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.
8. Claims 1-10 are rejected under 35 U.S.C. 102(a)(1) as being rejected by anticipated by Masinelli (Adaptive Laser Welding Control: A Reinforcement Learning Approach, May 2020).
Regarding claim 1, Masinelli discloses a machine learning device for performing machine learning of machining conditions including at least laser scan wait time for controlling machining of a workpiece in a laser machine (wait time of 10s to permit the agent to update parameters and to allow the stage to move into a new unprocessed position [Fig 3]), the machine learning device comprising:
an action output unit (smart agent [Page 103807 Col 2. Paragraph 1]) configured to select a machining condition as an action from among a plurality of machining conditions and output the action to the laser machine (“The final building block is constituted by the smart agent whose purpose is to interacts with the environment — in this case, the laser process — by making actions, i.e., modulation of the laser power. Practically, the agents communicate to the output board that, in turn, delivers the control signal to the laser source” [Page 103807 Col 2. Paragraph 1] “based on the representation of the current sensory input provided by the encoder, the agent chooses an action” [Page 103807 Col 2. Paragraph 1]);
a state acquisition unit (Light Microscope [Page 103810 Col 2 paragraph 6][Fig 4]) configured to acquire, as state information, image data generated through imaging of a machining state of a workpiece machined according to the action (Light microscope images top views of the different episodes/welds (i.e. welds performed by the laser source is the action) [Page 103810 Col 2 paragraph 6][Fig 4]);
a reward computing unit configured to compute a reward based at least on the laser scan wait time and a machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (The average reward is computed over an episode. The average reward is computed based on the welding quality (i.e. machining accuracy) and based on a wait time of 10s to permit the agent to update parameters and to allow the stage to move into a new unprocessed position [Fig 3][ Page 103810 Col 2 paragraph 6 – Page 103810 Col 1 Paragraph 5][Table 1]); and
a learning unit configured to perform the machine learning of the machining conditions based on the state information acquired by the state acquisition unit and the reward computed by the reward computing unit (Performing reinforcement learning in light of the optical images of the weld and the computed reward [Page 103810 Col 2 – 103811 Col 1]).
Regarding claim 2, Masinelli discloses wherein the machining state includes one or more mid-machining machining states between a start of the machining and an end of the machining, and the machining condition includes machining conditions corresponding to the mid-machining machining states respectively (wait time of 10s to permit the agent to update parameters and to allow the stage to move into a new unprocessed position [Fig 3]).
Regarding claim 3, Masinelli discloses further comprising:
a state reward computing unit configured to compute a state reward for the action according to the machining accuracy of the machining state computed based on the state information acquired by the state acquisition unit (Light Microscope [Page 103810 Col 2 paragraph 6][Fig 4]) configured to acquire, as state information, image data generated through imaging of a machining state of a workpiece machined according to the action (Light microscope images top views of the different episodes/welds (i.e. welds performed by the laser source is the action) [Page 103810 Col 2 paragraph 6][Fig 4]); and
an action reward computing unit configured to compute an action reward for the action based on at least the laser scan wait time included in the action, wherein the reward computing unit computes the reward for the action based on the state reward and the action reward (The average reward is computed over an episode. The average reward is computed based on the welding quality (i.e. machining accuracy) and based on a wait time of 10s to permit the agent to update parameters and to allow the stage to move into a new unprocessed position [Fig 3][ Page 103810 Col 2 paragraph 6 – Page 103810 Col 1 Paragraph 5][Table 1]).
Regarding claim 4, Masinelli discloses wherein the state reward computing unit computes the machining accuracy of the machining state based on reconstructed image data outputted by inputting the state information acquired by the state acquisition unit into an autoencoder trained based only on image data generated through imaging of machining states of workpieces each having a high machining accuracy (To guide the smart agent, the feedback network and the encoder were trained to recognize not just the reference quality, but also several other counter-examples. For this reason, we collected the acoustic and optical signals from 15 weld experiments at various laser power, namely 20, 40, 60, 80, and 120 W. The signals were then grouped in 5 categories according to the corresponding weld quality in terms of penetration depth, which were identified via optical inspection of both the surfaces and the cross-sections of the workpieces, and further partitioned in samples of 20 ms. This time span was chosen by taking into consideration the requirement of very high classification accuracy and computation time within the range of 15 ms, see Conclusion, paragraphs 6-7).
Regarding claim 5, Masinelli discloses wherein the action output unit outputs an action to the laser machine based on a policy for selecting one machining condition as an action from among a plurality of machining conditions (see Policy Gradient paragraph and equations); and
the learning unit evaluates and improves the policy based on a plurality of pieces of the state information acquired by the state acquisition unit and a plurality of action rewards computed by the reward computing unit (see TABLE 1: Rewards assigned for every category detected by the classifier).
Regarding claim 6, Masinelli discloses further comprising an optimized action output unit configured to output the machining conditions to the laser machine based on a result of the learning by the learning unit (The principle of operation is the following: based on the representation of the current sensory input provided by the encoder, the agent chooses an action, which leads to a change in the sensory input, and receives a reward from the feedback network. From this experience made up by the past sensory input, the executed action, the current input, and the received reward the agent tries to optimize the outcomes of its actions over time, see C. Smart Agent, paragraph 2).
Regarding claim 7, Masinelli discloses comprising a plurality of the machine learning devices, wherein the machine learning of the machining conditions is distributed and performed among the plurality of machine learning devices via a network (see FIGURE 2. Structure of the complete control unit made up of three main building blocks: an encoder that processed the data from the sensory input to retain only the quality critic events, a smart agent interacting with the welding process, and a feedback network based on a convolutional neural network for quality monitoring).
Regarding claim 8, Masinelli discloses wherein the learning unit performs reinforcement learning by an actor-critic method (see FIGURE 2. Structure of the complete control unit made up of three main building blocks: an encoder that processed the data from the sensory input to retain only the quality critic events, a smart agent interacting with the welding process, and a feedback network based on a convolutional neural network for quality monitoring).
Regarding claim 9, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Regarding claim 10, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID E CHOI whose telephone number is (571)270-3780. The examiner can normally be reached on M-F: 7-2, 7-10 (PST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bechtold, Michelle T. can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID E CHOI/Primary Examiner, Art Unit 2148