DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending and examined herein.
Claims 3, 5-9, 12, 14-16, and 19 are rejected under 35 U.S.C. 112(b).
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
Information Disclosure Statement
The attached information disclosure statement(s) (IDS) filed on 09/29/2022 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Examiner’s Note
Independent claim 1 recites “A computer program product for facilitating processing within a computing environment, the computer program product comprising: one or more computer-readable storage media having program instructions embodied therewith, the program instructions being readable by a processing circuit to cause the processing circuit to perform a method comprising:”, and each of claims 2-9 depend on claim 1. According to the original speciation of the applicant, the utilization of computer-readable storage media is limited to a non-transitory computer-readable storage medium [i.e. [0020] "A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored."].
Claim Rejections - 35 USC § 112
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 3, 5-9, 12, 14-16, and 19 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “optimal” in claims 3, 12, and 19 is a relative term which renders the claims indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what would meet the requirements of an “optimal part for the product”, and thus, the claims are rendered indefinite. For purposes of examination, this will be interpreted as “a part for the product”.
Claims 5 and 14 recites the limitation "wherein training the machine learning model includes using at least one of supervised learning or unsupervised learning to build a model of part performance in different environmental conditions." It is unclear whether “a model of part performance in different environmental conditions” refers to the machine learning model previously referred to in the claim or if another model is built. Thus, the claim is rendered in definite. For purposes of examination, “a model of part performance in different environmental conditions” will be treated as referring to “the machine learning model” previously referred to in claim 5.
Dependent claims 6-9 and 15-16 fail to resolve the issue and are rejected with the same rationale.
The term “optimal” in claims 9 and 16 is a relative term which renders the claims indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what part would meet the requirements of an “optimal part for the product”, and thus, the claims are rendered indefinite. Though the claim includes finding a Euclidean distance, the claim does not clearly connect the Euclidean distance to the optimality of the part. For purposes of examination, this will be interpreted as “a part for the product”.
The term “most closely” in claims 8 and 16 is a relative term which renders the claim indefinite. The term “most closely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what environmental condition would meet the requirements of an “environmental condition most closely matches the environment of the different environments of the self-organized map.”, and thus, the claims are rendered indefinite. For purposes of examination, this limitation will be interpreted as “environmental condition that matches the environment of the different environments of the self-organized map.”
Dependent claim 9 fails to resolve the issue and is rejected with the same rationale.
Claim 9 recites the limitation “wherein the determining comprises determining whether the part is an optimal part for the product based on the environmental condition”. It is unclear to which determining step that “the determining” in this limitation refers to. There is a determining step in claim 8, "wherein using the machine learning model comprises determining that the environmental condition most closely matches the environment of the different environments of the self-organized map." There is also a determining step in claim 1, "using the machine learning model and the established score for the part in determining whether to use the part in the product based on the environmental condition." As it is unclear as to which step “the determining” refers to, the claim is rendered indefinite. For purposes of examination, this will be treated as a separate determination step.
Claim 9 further recites "the determining including determining a Euclidian distance of the part from the part failure cluster within the environment of the self-organized map." It is again unclear to which determining step that “the determining” in this limitation refers to. It could refer to the determining step in claim 1, claim 8, or claim 9, “wherein the determining comprises determining whether the part is an optimal part for the product based on the environmental condition”. As it is unclear as to which step “the determining” refers to, the claim is rendered indefinite. For purposes of examination, this will be treated as a separate determination step.
The term “optimal” in claim 9 is a relative term which renders the claims indefinite. The term “optimal” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what part would meet the requirements of an “optimal part for the product”, and thus, the claims are rendered indefinite. Though the claim includes finding a Euclidean distance, the claim does not clearly connect the Euclidean distance to the optimality of the part. For purposes of examination, this will be interpreted as “a part for the product”.
Claim 16 recites the limitation “wherein the determining comprises determining whether the part is an optimal part for the product based on the environmental condition”. It is unclear to which determining step that “the determining” in this limitation refers to. There is a determining step in claim 10, "wherein using the machine learning model comprises determining that the environmental condition most closely matches the environment of the different environments of the self-organized map." There is also a previous determining step in claim 16, " that the environmental condition most closely matches the environment of the different environments of the self-organized map”. As it is unclear as to which step “the determining” refers to, the claim is rendered indefinite. For purposes of examination, this will be treated as a separate determination step.
Claim 16 further recites "the determining including determining a Euclidian distance of the part from the part failure cluster within the environment of the self-organized map." It is again unclear to which determining step that “the determining” in this limitation refers to. It could refer to any of the determining steps in claim 1 or claim 16. As it is unclear as to which step “the determining” refers to, the claim is rendered indefinite. For purposes of examination, this will be treated as a separate determination step.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1-9 are directed to an article of manufacture, claims 10-16 are directed to a machine, and claims 17-20 are directed to a process. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following are abstract ideas:
evaluation of a part for use in a product based on an environmental condition; (Evaluation of a part based on environmental conditions can be practically performed in the human mind. This is a mental process.)
establishing, … , a score for the part by comparing the measurement data for the part to a specification for the part; and (Creating a score by comparing data can be practically performed in the human mind. This is a mental process.)
determining whether to use the part in the product based on the environmental condition (A determination is a mental process of evaluation. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A computer program product for facilitating processing within a computing environment, the computer program product comprising: (This limitation recites generic computer components and processes; this amounts to mere instructions to apply an exception.)
one or more computer-readable storage media having program instructions embodied therewith, the program instructions being readable by a processing circuit to cause the processing circuit to perform a method comprising: (This limitation recites generic computer components and processes; this amounts to mere instructions to apply an exception.)
training a machine learning model to facilitate (This limitation recites generic machine learning components and processes; this amounts to mere instructions to apply an exception.)
receiving, at the processing circuit, measurement data for the part; (Receiving data is a known process in computing; this amounts to mere instructions to apply an exception.)
by the processing circuit (This limitation recites generic computer components and processes; this amounts to mere instructions to apply an exception.)
using the machine learning model and the established score for the part in … (This limitation recites generic machine learning components and processes; this amounts to mere instructions to apply an exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. The following are abstract ideas:
searching one or more data resources for past performance of the part in different environmental conditions to generate a historical performance dataset for the part, (One could practically generate a dataset by searching data in the human mind. This is a mental process.) determining whether to use the part in the product based on the environmental condition. (A determination is a mental process of evaluation. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein using the machine learning model comprises … (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
and using by the machine learning model the historical performance dataset in (This recites generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Further, the following is an abstract idea:
wherein determining whether to use the part in the product comprises determining whether the part is an optimal part for the product in relation to the environmental condition. (A determination using data is a mental process of evaluation.)
Claim 3 does not recite any additional elements.
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, the following is an abstract idea:
wherein establishing the score for the part comprises comparing the measurement data for the part to a specification tolerance for the part, the established score being based on where the measurement data falls within the specification tolerance. (Establishing a score by comparing data based on where the data falls within a tolerance can be practically performed in the human mind. This is a mental process.)
Claim 4 does not recite any additional elements.
Regarding claim 5, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein training the machine learning model includes using at least one of supervised learning or unsupervised learning to build a model of part performance in different environmental conditions. (This describes generic machine learning components and processes. This amounts to mere instructions to apply an exception.)
Regarding claim 6, the rejection of claim 5 is incorporated herein. The following are abstract ideas:
identifying part performance clusters that relate to the different environments within the self-organized map. (Identifying clusters can be practically performed in the human mind, i.e. looking at map output and making clusters. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the model of part performance in different environmental conditions includes a self-organized map of different environments, and training the machine learning model comprises (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
Regarding claim 7, the rejection of claim 6 is incorporated herein. The following are abstract ideas:
further comprising using clustering and anomaly detection within an environment of the clustered environments of the self-organized map for part scores and part performances within the environment to establish a part failure cluster within that environment. (Identifying clusters can be practically performed in the human mind, i.e. looking at map output and making clusters. This is a mental process. Anomaly detection can also be practically performed in the human mind i.e. looking for points further away from the clusters on the map. This is a mental process.)
Claim 7 does not recite any additional elements.
Regarding claim 8, the rejection of claim 7 is incorporated herein. The following is an abstract idea:
determining that the environmental condition most closely matches the environment of the different environments of the self-organized map. (This determination can be practically performed in the human mind i.e. comparing data and finding closest match. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein using the machine learning model comprises (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
Regarding claim 9, the rejection of claim 8 is incorporated herein. The following are abstract ideas:
wherein the determining comprises determining whether the part is an optimal part for the product based on the environmental condition, (A determination using data is a mental process of evaluation.) the determining including determining a Euclidian distance of the part from the part failure cluster within the environment of the self-organized map. (A determination using data is a mental process of evaluation.)
Regarding claim 10, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A computer system for facilitating processing within a computing environment, the computer system comprising: (This recites generic computer parts and processes; this amounts to mere instructions to apply an exception.)
a memory; and (This recites a generic computer part; this amounts to mere instructions to apply an exception.)
at least one processor in communication with the memory, wherein the computer system is configured to perform a method, said method comprising: (This recites generic computer parts and processes; this amounts to mere instructions to apply an exception.)
The remainder of claim 10 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 11-13 recite substantially similar subject matter to claims 2-4 respectively and are rejected with the same rationale, mutatis mutandis.
Regarding claim 14, the rejection of claim 10 is incorporated herein. The following is an abstract idea:
identifying part performance clusters that relate to the different environments within the self-organized map. (Identifying clusters can be practically performed in the human mind, i.e. looking at map output and making clusters. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein training the machine learning model includes using at least one of supervised learning or unsupervised learning to build a model of part performance in different environmental conditions, and (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
wherein the model of part performance in different environmental conditions includes a self-organized map of different environments, and (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
training the machine learning model comprises (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
Regarding claim 15, the rejection of claim 14 is incorporated herein. The following is an abstract idea:
further comprising using clustering and anomaly detection within an environment of the clustered environments of the self-organized map for part scores and part performances within the environment to establish a part failure cluster within that environment. (Identifying clusters can be practically performed in the human mind, i.e. looking at map output and making clusters. This is a mental process. Anomaly detection can also be practically performed in the human mind i.e. looking for points further away from the clusters on the map. This is a mental process.)
Claim 15 does not recite any additional elements.
Regarding claim 16, the rejection of claim 15 is incorporated herein. The following are abstract ideas:
wherein using the machine learning model comprises determining that the environmental condition most closely matches the environment of the different environments of the self-organized map, and (This determination can be practically performed in the human mind i.e. comparing data and finding closest match. This is a mental process.)
wherein the determining comprises determining whether the part is an optimal part for the product based on the environmental condition, the determining including (A determination using data is a mental process of evaluation.) determining a Euclidian distance of the part from the part failure cluster within the environment of the self-organized map. (A determination using data is a mental process of evaluation.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein using the machine learning model comprises (This describes generic machine learning components and processes, which amounts to mere instructions to apply an exception.)
Regarding claim 17, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A computer-implemented method of facilitating processing within a computing environment, the computer-implemented method comprising: (This recites generic computer parts and processes; this amounts to mere instructions to apply an exception.)
The remainder of claim 17 recites substantially similar subject matter to claim 1 and is rejected with the same rationale, mutatis mutandis.
Claims 18-20 recite substantially similar subject matter to claims 2-4 respectively and are rejected with the same rationale, mutatis mutandis.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-9, 10-12,and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salasoo (US 2020/0242496 A1) and Khandazeh (“Quantifying Geometric Accuracy With Unsupervised Machine Learning”, March 2018.
Regarding claim 1, Salasoo teaches
A computer program product for facilitating processing within a computing environment, the computer program product comprising: ([0054] – [0056] state "FIG. 6 is a block diagram of apparatus 600 according to some embodiments. Apparatus 600 may comprise a general-purpose or special-purpose computing apparatus and may execute program code to perform any of the functions described herein. Apparatus 600 may comprise an implementation of one or more elements of system 100. Apparatus 600 may include additional elements which are not shown, according to some embodiments. [0055] states “Apparatus 600 includes processor 610 operatively coupled to communication device 620, data storage device/memory 630, one or more input devices (not shown), and one or more output devices 630.")
one or more computer-readable storage media having program instructions embodied therewith, the program instructions being readable by a processing circuit to cause the processing circuit to perform a method comprising: ([0055] states “Apparatus 600 includes processor 610 operatively coupled to communication device 620, data storage device/memory 630, one or more input devices (not shown), and one or more output devices 630." [0057] states "The storage device 640 stores a program and/or platform logic for controlling the processor 610. The processor 610 performs instructions of the programs and thereby operates in accordance with any of the embodiments described herein, including but not limited to the processes.")
training a machine learning model to facilitate evaluation of a part for use in a product based on an environmental condition; ([0034] states "In disclosed embodiments, an association is created between all input variables and some form of quantified notion of quality score, which may be discrete or continuous (e.g., low/medium/high or a real number). An initial version of model (e.g., a regression model) may be used to build a direct association from input variables to the output quality score. Such a model may use an equation expressed in terms of the input variables with coefficients, i.e., a regression model. The relationship between the input variables and the output may be highly non-linear and complex, as there are potentially a large number of inputs (e.g., the intensity of each pixel of a 256x256 pixel image) and potentially only one output, i.e., the quality score. Transformations of the input variables may be created, i.e., explicitly transforming the input variables into "feature space," or neural networks, decision trees, etc., may be used, i.e., machine learning. This provides a space where the problem of mapping is made easier. In other words, one may start with direct variables and construct latent variable spaces to simplify the problem. Machine learning, in particular, can be used to take high-dimension, multiple-variable space and map it to an output where the underlying relationship is known to be complex, non-linear, and non-trivial." [0041] states "The sensor data 130 and nominal build file 120 are input to a machine learning algorithm 310 which is trained to produce a quality score for a built part. The machine learning algorithm 310 is trained using a response surfaces/maps 250 which, as discussed above, is derived from physical testing of the part" Therefore, a model is trained for evaluation of a part. [0032] states "In disclosed embodiments, the response map (e.g., a "response surface") may be, for example, a direct illustration of experimental data on a 2D, 3D, or 3D with color coding plot, or a mathematical function derived via experiments where there are inputs given by: (i) a set of parameters, such as, laser power, focus offset or beam spot-size, scanning speed, hatch spacing, and layer thickness, and/or (ii) measured or derived process variables, such as melt-pool depth, melt-pool width, melt-pool temperature, and/or thermal gradient." The inputs are interpreted as the environmental conditions, as they include manufacturing conditions. Thus, as the machine learning model is trained to evaluate the part using the inputs, it evaluates the parts based on the environmental conditions.)
receiving, at the processing circuit, measurement data for the part; ([0043] states "To train the machine learning algorithm 310, cut ups of built parts may be performed to produce response surfaces/maps 250. In disclosed embodiments, images of the cut ups can be divided into smaller sub-regions, e.g., regions of 3x3 pixel space (kxk, in general, where k can be treated as a parameter), thereby turning the image into vectors, i.e., flattening the image." The images of cuts ups of built parts are interpreted as the measurement data.)
using the machine learning model and the established score for the part in determining whether to use the part in the product based on the environmental condition. ([0013] states "The output of the quality score generator may be binary, e.g., pass/fail, or may have multiple levels, e.g., high, medium, and low, in which case parts with a high quality score could be deemed premium parts, parts with a medium quality score could be deemed acceptable parts (i.e., parts for use in less critical applications), and parts with a low quality could be rejected. The output of the quality score generator may be a set of values indicative of particular types of post-processing required for the part, such as, for example, post-process A, post-process B, and reject." Therefore, the score and machine learning is used. One of ordinary skill in the art would realize that rejecting a part is determining whether to use the part in the product. [0041] states "As explained above, the quality score generator 140 receives sensor data 130 from the AMM, the nominal build file 120 for the part being produced, and reference data 250 derived from testing of built parts. The sensor data 130 and nominal build file 120 are input to a machine learning algorithm 310 which is trained to produce a quality score for a built part." [0029] states "The sensor data may include data from actuator sensors 210 associated with actuators 220 in theAMM 110, such as galvanometer position sensors. The sensor data may also include data from environmental sensors 230, such as, for example, atmospheric pressure, oxygen level, airflow, smoke, etc. The sensor data may also include data from sensors 240 monitoring characteristics of the melt pool, such as, for example, photodiode, pyrometer, acoustic emission, etc. Also, as explained in further detail below, the quality score generator 140 receives training data, e.g., response surfaces/maps 250 derived from experimental testing of built parts." As the machine learning model uses the environmental conditions as inputs, the determination is based on the environmental conditions.)
Salasoo does not appear to explicitly teach
establishing, by the processing circuit, a score for the part by comparing the measurement data for the part to a specification for the part; and
However, Khanzadeh—directed to analogous art—teaches
establishing, by the processing circuit, a score for the part by comparing the measurement data for the part to a specification for the part; and (Page 3, section 3.1.2 states "A desktop 3D laser scanner (NextEngine) is used to scan the surface of FFF test parts and obtain point-by-point coordinate measurements of the geometry, referred to as a 3D point cloud." Page 3, section 3.1.2 further states "Geometric deviations of the fabricated parts can be calculated by comparing the point cloud data to the original computer-aided design (CAD) model." The geometric deviations are interpreted as the score for the part, the 3D point cloud is interpreted as the measurement data, and the original CAD model is interpreted as the specification for the part.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Salasoo and Khanzadeh because, as Khanzadeh states on page 11, "Thus far, GD&T characteristics have been primarily used to quantify the geometric accuracy of AM parts; this traditional approach was shown to be ineffective. GD&T characteristics need to be carefully customized to distinguish between parts made using different FFF AM process conditions. Our approach provides a data-driven framework to profile the types of geometric deviations, which are uniquely defined by the design and process conditions.”
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, Salasoo teaches
wherein using the machine learning model comprises searching one or more data resources for past performance of the part in different environmental conditions to generate a historical performance dataset for the part, and using by the machine learning model the historical performance dataset in determining whether to use the part in the product based on the environmental condition. ([0043] states "To train the machine learning algorithm 310, cut ups of built parts may be performed to produce response surfaces/maps 250. In disclosed embodiments, images of the cut ups can be divided into smaller sub-regions, e.g., regions of 3x3 pixel space (kxk, in general, where k can be treated as a parameter), thereby turning the image into vectors, i.e., flattening the image." [0047] states "A second type of response surfaces/maps 250 may include laser parameters such as those mentioned above in combination with sensor output data. For example, while the manufacturing process is being run, sensors such as photo diodes and cameras may be used to measure characteristics of the melt pool, e.g., size and temperature. The sensor data may, for example, show that laser parameters do not necessarily translate into stable melt pool characteristics. For example, the measured photodiode signal may not be constant, i.e., it may have variation and may not be a clean signal with respect to spatial locations of the part. Therefore, the characteristics of the sensor outputs, e.g., the photodiode output signal, may provide another way to predict the quality of a part." Therefore, data is gathered under different environmental conditions (characteristics of the melt pool). [0040] states "Iterative learning control (ILC) is used to learn from historical builds and correct in subsequent builds, which may be considered to be a "feed forward" control process." One of ordinary skill in the art would realize that, as the machine learning model is used, using the iterative learning control means inputting the historical builds, interpreted as the data resources for past performance of the part, into the model, meaning that the data resources are searched and a historical performance dataset is generated. "[0013] states "The output of the quality score generator may be binary, e.g., pass/fail, or may have multiple levels, e.g., high, medium, and low, in which case parts with a high quality score could be deemed premium parts, parts with a medium quality score could be deemed acceptable parts (i.e., parts for use in less critical applications), and parts with a low quality could be rejected. The output of the quality score generator may be a set of values indicative of particular types of post-processing required for the part, such as, for example, post-process A, post-process B, and reject." Therefore, the score and machine learning is used. One of ordinary skill in the art would realize that rejecting a part is determining whether to use the part in the product" As the environmental conditions are input in the model, the determination is based on the environmental condition.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Salasoo teaches
wherein determining whether to use the part in the product comprises determining whether the part is an optimal part for the product in relation to the environmental condition. (See 112(b) rejection for interpretation. [0013] states "The output of the quality score generator may be binary, e.g., pass/fail, or may have multiple levels, e.g., high, medium, and low, in which case parts with a high quality score could be deemed premium parts, parts with a medium quality score could be deemed acceptable parts (i.e., parts for use in less critical applications), and parts with a low quality could be rejected. The output of the quality score generator may be a set of values indicative of particular types of post-processing required for the part, such as, for example, post-process A, post-process B, and reject." Therefore, the quality score determines whether to use the part in the product. As it is accepted/rejected, a determination is made. [0039] states "In disclosed embodiments, given various inputs, e.g., sensor inputs and process parameters, a model can predict quality score which, in turn, can be used to determine whether the built part will be acceptable." As the input to the model is the current environmental conditions, the determination is based on the environmental condition.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Salasoo teaches
wherein training the machine learning model includes using at least one of supervised learning or … to build a model of part performance in different environmental conditions. ([0037] states "The sensor data 130, the quality score calculated by the quality score generator 140, and the output of the thermal model are input to an iterative learning control (ILC) 160. As described in further detail below, the ILC 160 uses machine learning algorithms to produce an updated build file 170 based on these inputs. The ILC 160 thus creates a mapping between the scan parameters of a build file and the resulting quality score of a part produced using the build file, which allows a build file to be optimized using an iterative machine learning process." As the data is labeled with a quality score, the method is supervised. As the model inputs include different environmental conditions and the output is part performance, the model is of part performance in different environmental conditions.)
Salasoo does not appear to explicitly teach
unsupervised learning
However, Khanzadeh—directed to analogous art teaches
unsupervised learning (Page 1 states "Accordingly, the objective of this work is to use an unsupervised machine learning (ML) algorithm called self-organizing map (SOM) to overcome this open research challenge.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Salasoo and Khanzadeh for the reasons given above in regards to claim 1.
Regarding claim 6, the rejection of claim 5 is incorporated herein. Salasoo does not appear to explicitly teach
wherein the model of part performance in different environmental conditions includes a self-organized map of different environments, and training the machine learning model comprises identifying part performance clusters that relate to the different environments within the self-organized map.
However, Khanzadeh—directed to analogous art—teaches
wherein the model of part performance in different environmental conditions includes a self-organized map of different environments, and training the machine learning model comprises identifying part performance clusters that relate to the different environments within the self-organized map. (Page 1 states "Accordingly, the objective of this work is to use an unsupervised machine learning (ML) algorithm called self-organizing map (SOM) to overcome this open research challenge. The SOM is used for demarcating or categorizing the point cloud measurements into limited number (tens) of clusters such that measurement points within the same cluster have similar shape deviations in terms of their severity (magnitude) and direction" Page 1 further states "The central hypothesis is that each SOM-derived cluster represents a unique type of geometric deviation specific to the process-material combination associated with the part, and thus, the clusters are surrogate signatures of the part geometric accuracy." The process-material combination/process parameters are interpreted as the different environments. Page 6 states "The chosen unsupervised ML approach is the concept of SOM, which clusters the various geometric deviations into multiple classification types according to their directions and magnitudes. Parts within each SOM-identified cluster are similar in magnitude and direction of geometric deviations. Thus, each cluster represents a unique type of geometric deviation. This is useful to identify different types of geometric deviations associated with specific process conditions." Page 7 states "Self-organizing map results provide an intuitive representation of the types of deviations for each combination of process parameters. Figure 9(a) depicts different types of geometric deviations, based on SOM clustering, from parts fabricated using two sets of process conditions: circle denoting the process condition
(
t
e
=
225
°
C
,
I
f
=
100
%
)
and square for the process condition
(
t
e
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230
°
C
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I
f
=
100
%
)
)." Therefore, the clusters relate to the different process parameters (environments).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Salasoo and Khanzadeh for the reasons given above in regards to claim 1.
Regarding claim 7, the rejection of claim 6 is incorporated herein. Salasoo does not appear to explicitly teach
further comprising using clustering and anomaly detection within an environment of the clustered environments of the self-organized map for part scores and part performances within the environment to establish a part failure cluster within that environment.
However, Khanzadeh—directed to analogous art—teaches
further comprising using clustering and anomaly detection within an environment of the clustered environments of the self-organized map for part scores and part performances within the environment to establish a part failure cluster within that environment. (Page 7 states "The results of the SOM clustering for two combinations of process parameters are shown in Fig. 8. We apply a 5 5 SOM to the point cloud data to profile the types of geometric deviations for each fabricated part. Figure 8 illustrates that the fabricated part with process condition
(
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225
°
C
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I
f
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100
%
)
has more clusters than process condition
(
t
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230
°
C
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I
f
=
100
%
)
), this means that processing condition
(
t
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=
225
°
C
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f
=
100
%
)
results in more types of deviations in terms of direction and magnitude. The significance of this result is twofold: (1) SOM can provide an informatics indicator about the overall geometric accuracy of the part fabricated using each combination of process parameters and (2) the deviations of a part associated with specific combination of process parameters can be characterized by the critical types of deviations, which can significantl