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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Examiner notes
Examiner cites particular columns, paragraphs, figures, and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 101
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.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Claim 1
A training method executed by a learning device to train a search model for searching a lay-up pattern of a laminate formed by laminating fiber sheets that are unidirectional materials in which alignment directions of fibers are one direction, the search model being a learning model that includes a constraint condition related to lay-ups of the fiber sheets and in which a policy function and a value function are used, the method comprising:
acquiring an initial lay-up pattern that is the lay-up pattern in an initial state;
and training the search model such that the lay-up pattern satisfies the constraint condition, using the initial layup pattern as an input.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The acquiring an initial layup pattern is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section (iv) Obtaining information about. . . (See MPEP §2106.05(g)(3)(iv)).
The training a search model limitation is directed towards is directed towards the abstract concept of mathematical relationships. Training a search model is just mathematical modeling.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 2.
The training method according to claim 1, wherein the search model is a learning model in which Monte Carlo tree search and deep reinforcement learning are combined.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)).This limitation is recited at a high level of generality; it does not bring this out of the realm of insignificant extra solution activity.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 3.
The training method according to claim 1, wherein the training the search model includes acquiring the lay-up pattern that satisfies the constraint condition as the initial lay-up pattern,
exchanging layers of the fiber sheets that are a part of the lay-up pattern which satisfies the constraint condition and generating the lay-up pattern that does not satisfy the constraint condition,
and selecting the lay-up pattern that does not satisfy the constraint condition as an input of the initial lay-up pattern.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The wherein the training the search model is directed towards is directed towards the abstract concept of mathematical relationships. Training a search model is just mathematical modeling.
The exchanging layers of the fiber sheets limitation is directed towards an insignificant extra-solution activity of selecting particular data (See 2106.05(g)(3)) in particular section iii. Selecting information, based on types of information and availability of information . . .
The Selecting the layup pattern is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement, or opinion (see MPEP § 2106.04(a)(2)(III). The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. This limitation could be performed in the human mind or with the aid of pen and paper.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 4.
The training method according to claim 1, wherein the training the search model includes, when searching for the lay-up pattern through the search model, exchanging layers of the fiber sheets adjacent to each other.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The wherein the training the search model is directed towards is directed towards the abstract concept of mathematical relationships. Training a search model is just mathematical modeling.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 5.
The training method according to claim 4, wherein the training the search model includes training in which reward in the value function increases as the number of exchanges between the layers of the fiber sheets decreases while searching for the lay-up pattern through the search model.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The wherein the training the search model is directed towards is directed towards the abstract concept of mathematical relationships. Training a search model is just mathematical modeling.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 6.
The training method according to claim 1, wherein the constraint condition includes at least any one of a condition related to continuity of the fiber sheets, in which adjacent fiber sheets have a same alignment direction, in a laminating direction and a condition related to a difference in an alignment angle formed by the alignment directions of adjacent fiber sheets in the laminating direction.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)). This limitation is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 7.
The training method according to claim 6, wherein the condition related to the continuity of the fiber sheets is a condition in which consecution of the fiber sheets is three or fewer layers, and the condition related to the difference in the alignment angle is a condition in which a difference in alignment angle between adjacent fiber sheets is 45° or smaller.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)). This limitation is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 8.
The training method according to claim 1, wherein the number of lay-ups in the lay-up pattern used in the search model is the number of lay-ups smaller than the number of lay-ups of the laminate.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)). This limitation is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 9.
A design method executed by a design device to design a design pattern that is a lay-up pattern of the laminate which satisfies the constraint condition by using the search model trained through the training method according to claim 1, the method comprising:
deriving a candidate pattern that is the lay-up pattern which is a candidate for the laminate through a predetermined algorithm; and deriving the design pattern that is the lay-up pattern which satisfies the constraint condition in response to an input of the candidate pattern of the laminate into the search model.
Step 1 – The claim is directed towards a device(machine), one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)). Deriving a candidate is just a mathematical calculation.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 10.
The design method according to claim 9, wherein in a case where the number of lay-ups of the design pattern of the laminate is the number of lay-ups larger than the number of lay-ups in the lay-up pattern used in the search model, the design method further comprising after the deriving the candidate pattern of the laminate,
extracting the lay-up pattern that does not satisfy the constraint condition from the derived candidate pattern of the laminate as an extraction pattern is executed,
and the deriving the design pattern that satisfies the constraint condition uses an input of the extraction pattern into the search model.
Step 1 – The claim is directed towards a device(machine), one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
Extracting the lay up pattern is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section whether the limitation amounts to necessary data gathering and outputting
The deriving the design pattern limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)). Deriving a design pattern is just a mathematical calculation.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 11.
The design method according to claim 9, wherein in a case where the number of lay-ups of the laminate is the number of lay-ups larger than the number of lay-ups in the lay-up pattern used in the search model, the deriving the design pattern that satisfies the constraint condition includes
setting a plurality of the lay-up patterns to be arranged to cover the candidate pattern of the laminate, and setting some of the plurality of arranged lay-up patterns to overlap each other.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
Setting the plurality of layout patterns is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement, or opinion (see MPEP § 2106.04(a)(2)(III). The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. This limitation could be performed in the human mind or with the aid of pen and paper.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 12.
The design method according to claim 9, wherein the deriving the design pattern that satisfies the constraint condition includes exchanging layers of the fiber sheets adjacent to each other to search for the lay-up pattern through the search model.
Step 1 – The claim is directed towards a device(machine), one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
This limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)). Deriving the design pattern is just a mathematical calculation.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 13.
A manufacturing method of a laminate, executed by a lay-up device, comprising:
laminating the fiber sheets based on the design pattern designed through the design method according to claim 9;
and integrating the laminated fiber sheets and forming the laminate.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The laminating the fiber sheets based on a design is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)). This limitation is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
The integrating the laminate limitations is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)). This limitation is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 14.
A design device of a design pattern that designs the design pattern which is a lay-up pattern of the laminate which satisfies the constraint condition using the search model trained through the training method of a learning device according to claim 1, the design device comprising:
a control unit configured to derive a candidate pattern that is the lay-up pattern which is a candidate for the laminate through a predetermined algorithm,
and derive the design pattern that is the lay-up pattern which satisfies the constraint condition in response to an input of the candidate pattern of the laminate into the search model.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations:
The deriving a candidate and design pattern limitations are directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)). Deriving is just a mathematical calculation.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
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.
Claims 1, 2, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al., US 2017/0228473 A1 (Kang) in view of McAleer et al., Solving the Rubik’s Cube Without Human Knowledge. (McAleer)
Claim 1.
Kang teaches A training method executed by a learning device to train a search model for searching a lay-up pattern of a laminate formed by laminating fiber sheets that are unidirectional materials in which alignment directions of fibers are one direction, (Kang 0005) “One embodiment is a method that includes designing a multi-layer composite part by subdividing the part into blocks that each comprise a contiguous stack of layers within the part, identifying rules that constrain how layers that have different fiber orientations are stacked within the part, and generating a guide for a block that prescribes a fiber orientation for each layer of the block and that complies with the rules.” {Examiners note: Kangs searches and selects ordered ply sequences, assigns a fiber orientation to each layer (a ply having one prescribed fiber orientation corresponds to the claimed fiber sheet where all fibers are one direction) The ordered collection of Kangs oriented plies is the claimed layup pattern.}
the search model being a learning model that includes a constraint condition related to lay-ups of the fiber sheets (Kang 0005) “part, identifying rules that constrain how layers that have different fiber orientations are stacked within the part,”
Kang does not explicitly teach, but McAleer teaches and in which a policy function and a value function are used, the method comprising: (McAleer Pg. 4 Section 4.1) “which takes an input state s and outputs a value and policy pair” {Examiners note: Policy output corresponds to the claimed policy function, while the value output corresponds to the claimed value function.}
acquiring an initial lay-up pattern that is the lay-up pattern in an initial state; (McAleer Pg. 2 Section 1) “the inputs to the neural network are created by starting from the goal state and randomly taking actions” equivalent to acquiring initial state data.
and training the search model such that the lay-up pattern satisfies the constraint condition, using the initial layup pattern as an input. (McAleer Pg. 2 Section 1) “an algorithm inspired by policy iteration [3, 34] for training a joint value and policy network. ADI trains the value function through an iterative supervised learning process. In each iteration, the inputs to the neural network are created by starting from the goal state and randomly taking actions. The targets seek to estimate the optimal value function by performing a breadth-first search from each input state and using the current network to estimate the value of each of the leaves in the tree.” {Examiners note: Generates training samples from states, evaluates possible actions from said states, and then sets policy target based on the action having the best estimated value.}
are both analogous to the claimed invention. Kang is from the same field of endeavor of
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kang and McAleer before him or her, to modify the ply search model of Kang with the reinforcement learning of McAleer to increase manufacturing efficiency as stated in 0003 of Kang.
Claim 2.
Modified Kang with McAleer teaches The training method according to claim 1, wherein the search model is a learning model in which Monte Carlo tree search and deep reinforcement learning are combined. (McAleer Pg. 4 Section 4-4.1) “We develop a novel algorithm called Autodidactic Iteration which is used to train a joint value and policy network. ADI is an iterative supervised learning procedure which trains a deep neural network f(s) with parameters which takes an input state s and outputs a value and policy pair.” (Pg. 3 Section 2) “We solve the Rubik’s Cube using pure reinforcement learning without human knowledge “ (Pg. 5 Section 4.2) “We employ an asynchronous Monte Carlo Tree Search augmented with our trained neural network f to solve the cube from a given starting state s0”
Claim 6.
Modified Kang teaches The training method according to claim 1, wherein the constraint condition includes at least any one of a condition related to continuity of the fiber sheets, in which adjacent fiber sheets have a same alignment direction, in a laminating direction and a condition related to a difference in an alignment angle formed by the alignment directions of adjacent fiber sheets in the laminating direction. From the above list of alternatives the Examiner is selecting “in which adjacent fiber sheets have a same alignment direction, in a laminating direction.” (Kang 0036) “the rules dictate that no more than four adjacent layers may have the same fiber orientation.” (table 1) “Do not stack more than a threshold number of adjacent layers having the same fiber orientation.” (table 2) “The same fiber orientation will not be used on more than four consecutive ply sequences.” {Examiners note: Kangs layers correspond to the claimed fiber sheets. The adjacent layers having the same fiber orientation corresponds to adjacent sheets have the same alignment direction.}
Claim 8.
Modified Kang teaches The training method according to claim 1, wherein the number of lay-ups in the lay-up pattern used in the search model is the number of lay-ups smaller than the number of lay-ups of the laminate. (Kang 0038) “sub laminate is a set of consecutive ply sequences for the block, and may be assigned on a panel-by-panel basis. Compatible sub laminates are subsets of the layers in the guide for the block.” (0065) “The optimization model used by controller 112 involves three steps as shown in FIG. 12. The first step involves generating sub laminates in step 1202. Hence, ply sequences that are compatible with each block should be determined. Once a library of compatible sub laminates has been generated, guides may be generated for the blocks in step 1204. Using a guide for the block, ply sequences may be optimized in step 1206. (0063) “Sublaminates may be chosen based upon a guide for a corresponding block (i.e., a template dictating a fiber orientation for each layer of the composite part). Thus, if a guide for a block includes eight ply sequences that each have a predefined fiber orientation, a sublaminate includes up to eight ply sequences for an ordered subset of those fiber orientations. In embodiments where a guide is used, not all ply sequences from the guide need to be present in the panel sub laminate.” For example, sublaminate "Sub1 could be written as [ 45/90/-45/ 45/90/-45/0], while sub laminate "Sub2" could be written as [45/90/0/-45/0]. "Subl" is compatible with "Sub2," because "Subl" can be generated by omitting layer 4 from the guide block laminate" {Examiners note: Under BRI, Kangs sequence of laminates constitutes a layup pattern. The sub laminates are generated, evaluated, and selected within an optimization model. Kang expressly provides an embodiment in which the guide contains 8 ply sequences, sublaminate sub1 contains 7 and sub laminate sub2 contains 5 sequences.}
Claims 3-5, and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al., US 2017/0228473 A1 (Kang) in view of McAleer et al., Solving the Rubik’s Cube Without Human Knowledge. (McAleer) in further view of Wang et al., Research on design rules for composite laminate (Wang).
Claim 3.
Modified Kang with McAleer teaches The training method according to claim 1, wherein the training the search model includes acquiring the lay-up pattern that satisfies the constraint condition as the initial lay-up pattern, (Kang 0037) “generates a guide (i.e., an arrangement of fiber orientations for a block) that complies with the rules.”
and generating the lay-up pattern that does not satisfy the constraint condition, (McAleer Pg. 2 Section 1) “the inputs to the neural network are created by starting from the goal state and randomly taking actions.” {Examiners note: By beginning at the solved state and scrambling it a few times, the resulting scramble are different from the valid goal, and teaches the network how to return to the goal.}
and selecting the lay-up pattern that does not satisfy the constraint condition as an input of the initial lay-up pattern. (McAleer Pg. 4 Section 4.1) “We then train f on these training samples and targets.” {Examiners note: McAleer generates scrambled states from goal state, and places the generated states into a training sample set. Under BRI, a generated state as a member of the input training set constitutes selecting that state as an input.}
Kang, and McAleer does not explicitly teach, but Wang teaches exchanging layers of the fiber sheets that are a part of the lay-up pattern which satisfies the constraint condition (Wang Pg. 317 Section 3.1.2) “The swap approach uses the exchange of genes between two adjacent loci. . . only the stacking order is changed between adjacent layers” {Examiners note: Genes represent the orientations of laminate plies at corresponding positions.}
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kang, McAleer, and Wang before him or her, to modify the ply search model of Kang with the reinforcement learning of McAleer and the genetic algorithm of Wang to maximize the strength of the composite for a given thickness as suggested in Wang (316 Section 1).
Claim 4.
Modified Kang with McAleer and Wang teaches The training method according to claim 1, wherein the training the search model includes, when searching for the lay-up pattern through the search model, exchanging layers of the fiber sheets adjacent to each other. (Wang Pg. 317 Section 3.1.2) “The swap approach uses the exchange of genes between two adjacent loci. . . only the stacking order is changed between adjacent layers” {Examiners note: Genes represent the orientations of laminate plies at corresponding positions.}
Claim 5.
Modified Kang with McAleer and Wang teaches The training method according to claim 4, wherein the training the search model includes training in which reward in the value function increases as the number of exchanges between the layers of the fiber sheets decreases while searching for the lay-up pattern through the search model. (McAleer Pg. 3 Section 3) “After selecting an action, the agent observes a new state st+1 = A(st, at) and receives a scalar reward, R(st+1), which is 1 if st+1 is the goal state and -1 otherwise.” (Pg. 5 Section 4.2) “weights. A full breath-first search is then applied on T to find the shortest predicted path from the starting state to solution.” (Pg. 5 Section 4.2) “We employ an asynchronous Monte Carlo Tree Search augmented with our trained neural network f to solve the cube from a given starting state s0”{Examiners note: +1 to the action that reaches the goal, and -1 to every action that does not reach a goal. Therefore a path requiring fewer actions accumulates fewer negatives. The system prefer solutions with fewer actions.}
Claim 9.
Modified Kang, McAleer, and Wang teaches A design method executed by a design device to design a design pattern that is a lay-up pattern of the laminate which satisfies the constraint condition (Kang Abstract) “a controller configured to generate a design for the part.” (0005) “and comply with the rules.”
by using the search model trained through the training method according to claim 1, the method comprising: deriving a candidate pattern that is the lay-up pattern which is a candidate for the laminate through a predetermined algorithm; (Wang Pg. 316 Section 1) “an improved standard GA (SGA) using rule operators is suggested, which is particularly applicable for the stacking sequence optimization of composite laminates.” (Pg. 316 Section 2) “Nonconforming individuals will be transformed into other form of conforming configuration.”
and deriving the design pattern that is the lay-up pattern which satisfies the constraint condition in response to an input of the candidate pattern of the laminate into the search model. (Wang Pg. 316 Section 2) “Nonconforming individuals will be transformed into other form of conforming configuration.” {Examiners note: Wang transform a laminate stacking candidate that does not conform to design rules into a stacking configuration that does conform. Nonconforming individual corresponds to the candidate layup pattern, forming configuration corresponds to the resulting design pattern.}
Claim 10.
Modified Kang with McAleer, teaches The design method according to claim 9, wherein in a case where the number of lay-ups of the design pattern of the laminate is the number of lay-ups larger than the number of lay-ups in the lay-up pattern used in the search model, the design method further comprising after the deriving the candidate pattern of the laminate, (Kang 0035) “subdivides part 150 into blocks (e.g., blocks B1-B2). Each block comprises a contiguous subset/ stack of the layers of part 150 into block.” {Examiners note: Under BRI, the complete multi layered part corresponds to the claimed laminate design pattern, a block ordered subset of plies corresponds to the shorter lay up pattern processed during optimization, because each block contains only a subset of the complete laminate layers, the number of lay ups in the complete laminate is greater than the number in the block level pattern.}
extracting the lay-up pattern that does not satisfy the constraint condition from the derived candidate pattern of the laminate as an extraction pattern is executed, (Kang 0070) “In this example, the controller further determines which blocks will be considered inner blocks where rule violations are allowed.” {Examiners note: Identifies localized, noncompliant portion withing a larger laminate design. Under BRI, select the ply sequence of an identified violating block for localized processing is “extracting a lay up pattern that does not satisfy the constraint conditions.}
and the deriving the design pattern that satisfies the constraint condition uses an input of the extraction pattern into the search model. (McAleer Pg. 4 Section 4.1) “which takes an input state s and outputs a value and policy pair” {Examiners note: McAleer teaches beginning <CTS with a given starting state and using the trained policy/value neural network to guide the search from that starting state toward the goal.}
Claim 11.
Modified Kang teaches The design method according to claim 9, wherein in a case where the number of lay-ups of the laminate is the number of lay-ups larger than the number of lay-ups in the lay-up pattern used in the search model, (Kang 0035) “subdivides part 150 into blocks (e.g., blocks B1-B2). Each block comprises a contiguous subset/ stack of the layers of part 150 into block.” {Examiners note: Under BRI, the complete multi layered part corresponds to the claimed laminate design pattern, a block ordered subset of plies corresponds to the shorter lay up pattern processed during optimization, because each block contains only a subset of the complete laminate layers, the number of lay ups in the complete laminate is greater than the number in the block level pattern.}
the deriving the design pattern that satisfies the constraint condition includes setting a plurality of the lay-up patterns to be arranged to cover the candidate pattern of the laminate, (Kang 0040) “Controller 112 further selects a compatible sublaminate for each panel in each block, based on the compatible sublaminates for neighboring panels in step 512.” (0006) “The apparatus further includes a memory configured to store the design as a combination of selected sub laminates.” (0070) “The design may be subdivided by controller 112 into blocks based on input selecting a number of blocks, or based on input indicating a number of layers to include within each block.”
and setting some of the plurality of arranged lay-up patterns to overlap each other. (Kang 0032) “Each block comprises a set of contiguous layers within part 150.” (0070) “The design may be subdivided by controller 112 into blocks based on input selecting a number of blocks, or based on input indicating a number of layers to include within each block.” {Examiners note: One possible result of teaching that different laminates may be combined or stacked through laminate thickness, under BRI is that the laminates could overlap.}
Claim 12.
Modified Kang, McAleer, and Wang teaches The design method according to claim 9, wherein the deriving the design pattern that satisfies the constraint condition includes exchanging layers of the fiber sheets adjacent to each other to search for the lay-up pattern through the search model. (Wang Pg. 316 Section 1) “an improved standard GA (SGA) using rule operators is suggested, which is particularly applicable for the stacking sequence optimization of composite laminates.” (Pg. 316 Section 2) “Nonconforming individuals will be transformed into other form of conforming configuration.” {Examiners note: Wang represents a laminate stacking sequence as chromosome in which genes correspond to ply orientations. The gene locus corresponds to a ply position, adjacent loci corresponded to adjacent positions through laminate thickness, exchanging genes at these loci exchanges the order of the adjacent plies }
Claim 13.
Modified Kang teaches A manufacturing method of a laminate, executed by a lay-up device, comprising: (Kang 0006) “The apparatus further includes a memory configured to store the design as a combination of selected sub laminates for use by an Automated Fiber Placement (AFP) machine constructing the part.”
laminating the fiber sheets based on the design pattern designed through the design method according to claim 9; (Kang 0031) “A layer may therefore designate each of the plies that are laid by AFP machine 140 onto part 150, before part 150 undergoes curing to consolidate the plies onto part 150.”
and integrating the laminated fiber sheets and forming the laminate. (Kang 0002) “and cured to consolidate into the composite part.”
Claim 14.
Modified Kang, McAleer, and Wang teaches A design device of a design pattern that designs the design pattern which is a lay-up pattern of the laminate which satisfies the constraint condition using the search model trained through the training method of a learning device according to claim 1, the design device comprising: (Kang 0006) “and a controller configured to generate a design for the part by subdividing the part into blocks that each comprise a contiguous stack of layers within the part. . . The controller is further configured to generate the design by identifying rules that constrain how layers that have different fiber orientations are stacked within the part.”
a control unit configured to derive a candidate pattern that is the lay-up pattern which is a candidate for the laminate through a predetermined algorithm, (Wang Pg. 316 Section 1) “an improved standard GA (SGA) using rule operators is suggested, which is particularly applicable for the stacking sequence optimization of composite laminates.”
and derive the design pattern that is the lay-up pattern which satisfies the constraint condition in response to an input of the candidate pattern of the laminate into the search model. (Wang Pg. 316 Section 2) “Nonconforming individuals will be transformed into other form of conforming configuration.” {Examiners note: Wang teaches transforming a laminate stacking sequence individual that violates the applicable design rules into a configuration conforming to those rules.}
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al., US 2017/0228473 A1 (Kang) in view of McAleer et al., Solving the Rubik’s Cube Without Human Knowledge. (McAleer) in further view of Wang et al., Research on design rules for composite laminate (Wang) in further view of May et al.,
Damage resistance of composite structures with unsymmetrical stacking sequence subjected to high velocity bird impact. (May)
Claim 7.
Kang, McAleer, and Wang do not explicitly teach, but May teaches The training method according to claim 6, wherein the condition related to the continuity of the fiber sheets is a condition in which consecution of the fiber sheets is three or fewer layers, (May Pg. 1 Section 1 #2) “no more than three plies of the same orientation must be stacked together.”
and the condition related to the difference in the alignment angle is a condition in which a difference in alignment angle between adjacent fiber sheets is 45° or smaller. (May Pg. 1 Section 1 #24) “ the mismatch angle between adjacent ply blocks was fixed to 45.”
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Kang, McAleer, and Wang before him or her, to modify the ply search model of Kang with the reinforcement learning of McAleer and the genetic algorithm of Wang and the alignment angles of May in order to eliminate extension-bending coupling, as suggested in May (Pg. 1 Section 1)
Conclusion
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/JOHN DAVID HAGLER/ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189
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