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 .
DETAILED ACTION
1. Claims 1-18 are presented for examination.
Claim Objections
2. Claims 1, 4, 7, 10, 13, and 16 are objected to because of the following informalities:
As per Claim 1, 7, and 13, they recite the limitation “receiving a physical object usage data set including information about physical operations and/or ambient conditions in which the physical object has been used or may be used in the future” which contains language that suggests or makes optional but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation.
As per Claims 4, 10, and 16, they recite the limitation “withing” which would be better as “within”. Appropriate correction is required.
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.
3. Claims 1-18 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.
As per Claims 1, 7, and 13, they recite the limitation “determining an area where a material defect zone on or in the physical object where a material defect is likely to occur in the physical object based on the running of the computer simulation” which is indefinite because the nested “where … where” construction leaves unclear whether the recited “an area” and the “material defect zone” denote the same region, and the clause omits a verb specifying what is determined about the “material defect zone,” so that the metes and bounds of the step cannot be ascertained. Further the limitation “is likely to occur” is vague and indefinite since " likely to occur" does not set range.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claims 1-3, 5, 7-9, 11, 13-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US 8,109,150 B2) in view of Li (US 10,744,728 B2).
As per Claim 1, 7, and 13, Sato teaches a computer-implemented method / computer program product / computer system (FIG. 1-2; Col. 10 lines 61-67 “the CPU 1 realizes the crack-propagation prediction method described below by reading out and executing a crack-propagation prediction program stored in the hard disk 3”), comprising:
(Claim 7 and 13) a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations (FIG. 1-2; Col. 10 lines 61-67 “the CPU 1 realizes the crack-propagation prediction method described below by reading out and executing a crack-propagation prediction program stored in the hard disk 3”);
modeling an existing physical object as a computer based model including data representing physical dimensions of the physical object and material properties reflecting the materials of which the physical object is made (Col. 14 lines 56-67 “data of the material property factors, boundary condition factors, and shape factors for the first combination is input or read out, thermal/stress analysis is carried out, and the temperature distribution and stress distribution of the target site of the part are determined”: the part is represented by its material property factors and shape factors, i.e., the “computer based model” including material properties and physical dimensions as claimed);
receiving a physical object usage data set including information about physical operations and/or ambient conditions in which the physical object has been used or may be used in the future (Col. 15 lines 1-9 “the acquired temperature distribution and stress distribution, and the operating conditions of the gas turbine are input … the relationship between the number of starts and stops after crack initiation and the crack length is determined”: the input operating conditions and number of starts and stops are the “physical object usage data set” as claimed);
running a computer simulation on the computer based model of the physical object where the simulation includes computer simulation of the physical operations and/or ambient conditions included in the physical object usage data set (Col. 14 lines 56-67 “thermal/stress analysis is carried out, and the temperature distribution and stress distribution of the target site of the part are determined”: the thermal/stress simulation runs on the modeled part under the operating and ambient conditions in the usage data set); and
determining an area where a material defect zone on or in the physical object where a material defect is likely to occur in the physical object based on the running of the computer simulation (Col. 14 lines 56-67, Col. 15 lines 1-9 “the number of starts and stops at which a crack forms is determined by, for example, the above-mentioned Manson-Coffin Equation”; “the relationship between the number of starts and stops after crack initiation and the crack length is determined”: the simulation determines where and when a fatigue crack (the material defect) initiates and propagates in the part, i.e., the “material defect zone” as claimed).
In particular, Sato teaches an automated, computer-executed simulation that models a physical part from its material properties and its operating and ambient conditions, and predicts, over the part’s service life, the location and growth of a fatigue crack (a material defect) in the part.
However, Sato fails to teach explicitly applying self-healing material to the material defect zone of the physical object.
Li teaches applying self-healing material to the material defect zone of the physical object (Col. 4 lines 8-10 “The present invention provides application of a self-healing material in 3D printing, a self-healing material being taken as a 3D printing material”; Col. 6 lines 35-36 “(3) printing the 3D printing material obtained in step (2), so as to obtain a product”; Col. 10 lines 17-27 “After an area which has micro-cracks or is damaged by the outside is thermally treated, the imine bond may be dynamically exchanged with the adjacent amino group or aldehyde group, so as to form a new imine bond and a new amino group and aldehyde group, thereby healing the material”). In particular, Li teaches using a self-healing material as the 3D-printing material of an object so that the object detects its own micro-cracks and spontaneously heals local or overall damage.
Sato and Li are analogous art because they are both related to predicting and remediating material defects in physical objects.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Li into Sato’s invention for the purpose of predicting where a fatigue crack will occur in a physical part to provide a 3D printing material in which a crack is intelligently detected and spontaneously healed, preventing potential damage caused by generated micro-cracks (Li: Col. 3 lines 35-67).
As per Claim 2, 8, and 14, Sato fails to teach explicitly wherein the self-healing material is applied by a 3D printer.
Li teaches wherein the self-healing material is applied by a 3D printer (Col. 4 lines 8-10 “The present invention provides application of a self-healing material in 3D printing, a self-healing material being taken as a 3D printing material”; Col. 6 lines 35-36 “(3) printing the 3D printing material obtained in step (2), so as to obtain a product”).
As per Claim 3, 9, and 15, Sato fails to teach explicitly wherein the self-healing material is applied to exterior surfaces of the physical object.
Li teaches wherein the self-healing material is applied to exterior surfaces of the physical object (Col. 6 lines 35-36 “(3) printing the 3D printing material obtained in step (2), so as to obtain a product”: the self-healing material constitutes the printed product, including its exterior surfaces).
As per Claim 5, 11, and 17, Sato teaches wherein the physical object relates to one of the following areas of commercial enterprise: aircraft, land vehicles, water vehicles, space vehicles, mining equipment, mass production of consumer goods, agricultural equipment, computer hardware, furniture, architectural structures and/or physical infrastructure (Col. 15 lines 1-3 “the operating conditions of the gas turbine are input”: a gas turbine is an aircraft / power structure within the recited commercial-enterprise areas).
5. Claims 4, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US 8,109,150 B2) in view of Li (US 10,744,728 B2), further in view of Melzer-Jokisch (US 8,996,156 B2).
Sato as modified by Li teaches most all the instant invention as applied to claims 1-3, 5, 7-9, 11, 13-15, and 17 above.
As per Claim 4, 10, and 16, Sato as modified by Li fails to teach explicitly wherein the application of self-healing material includes removal of material from the physical object so that the self-healing material will be applied to an interior zone withing the physical object.
Melzer-Jokisch teaches wherein the application of self-healing material includes removal of material from the physical object so that the self-healing material will be applied to an interior zone withing the physical object (Melzer-Jokisch, Col. 4 lines 29-30 “Block 104 involves the removing of material from the identified damaged portion of the blade by machining”; Col. 3 lines 44-47 “the blade 1 is hollow, having a cavity 7 (visible in the cross-sectional view of the blade shown in FIG. 2A) that extends from the tip 8 of the blade 1 to the base 9 of the root portion 4”: damaged material is machined out and replacement material is deposited to reach the part’s interior cavity, i.e., the “interior zone” as claimed).
In particular, Melzer-Jokisch teaches an automated repair that strips and inspects a component to identify a damaged portion, machines out the damaged material, and deposits replacement material to restore the component, including its hollow interior cavity.
Sato, Li, and Melzer-Jokisch are analogous art because they are all related to predicting and remediating material defects in physical objects.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Melzer-Jokisch into Sato as modified by Li’s invention for the purpose of predicting where a fatigue crack will occur in a physical part to provide a 3D printing material in which a crack is intelligently detected and spontaneously healed, preventing potential damage caused by generated micro-cracks (Li: Col. 3 lines 35-67) and to provide removing of material from the identified damaged portion accurately by machining so that replacement material reaches the interior cavity of the part (Melzer-Jokisch: Col. 1 lines 45-47, Col. 4 lines 1-8).
6. Claims 6, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sato (US 8,109,150 B2) in view of Li (US 10,744,728 B2), further in view of Sundararajan (US 11,934,755 B2).
Sato as modified by Li teaches most all the instant invention as applied to claims 1-3, 5, 7-9, 11, 13-15, and 17 above.
As per Claim 6, 12, and 18, Sato as modified by Li fails to teach explicitly receiving historical data training sets respectively including information regarding performance of the computer simulation in the past against real world observations of historical physical objects; and performing unsupervised machine learning with respect to software controlling the computer simulation based on the historical data training sets.
Sundararajan teaches receiving historical data training sets respectively including information regarding performance of the computer simulation in the past against real world observations of historical physical objects (Sundararajan, Col. 5 lines 18-33 “Moreover, historical data describing the performance of the physical asset, as well as other similar physical assets, with similar performance outputs, uses, and working environments can be incorporated into the knowledge corpus and may be used to influence the conclusions drawn by a knowledge corpus when generating simulation results and recommendations”; Col. 23 lines 62-67, Col. 24 lines 1-5 “the knowledge corpus 137 may be trained using historical data and/or collected datasets 135”); and
performing unsupervised machine learning with respect to software controlling the computer simulation based on the historical data training sets (Sundararajan, Col. 23 lines 62-67, Col. 24 lines 1-22 “Embodiments of the knowledge corpus 137 may arrive at such recommendations, suggestions, and/or conclusions using supervised learning, unsupervised learning, and/or semi-supervised learning techniques”; “Unsupervised learning techniques on the other hand may be used when there may be a lack of historical data describing the physical asset 117, digital twin models 134 of the physical asset”). In particular, Sundararajan teaches a digital-twin simulation whose knowledge corpus ingests historical performance data of a physical asset and similar assets and refines its simulation conclusions using machine learning, including unsupervised learning techniques.
Sato, Li, and Sundararajan are analogous art because they are all related to simulating physical objects to predict their behavior.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Sundararajan into Sato as modified by Li’s invention for the purpose of predicting where a fatigue crack will occur in a physical part to provide a 3D printing material in which a crack is intelligently detected and spontaneously healed, preventing potential damage caused by generated micro-cracks (Li: Col. 3 lines 35-67) and to provide a knowledge corpus trained using historical data describing the physical asset and one or more similar physical assets, employing unsupervised learning techniques accurately (Sundararajan: Col. lines 50-56, Col. 5 lines 18-33).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Zhang (US 11,704,942 B2) teaches predicting wear of a machine undercarriage using a machine learning model trained on historical sensor and inspection data.
Davami (“Additively Manufactured Self-Healing Structures with Embedded Healing Agent Reservoirs”) teaches additively manufactured structures with embedded reservoirs that release a self-healing agent to bond crack faces at locations of damage.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/ Primary Examiner, Art Unit 2188
Prosecution Insights