DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/25 has been entered.
Response to Arguments
Applicant's arguments filed 12/29/25 regarding the prior art have been fully considered but they are not persuasive.
Applicant argues that Butkewitsch does not teach a digital twin of a manufacturing machine. However, Butkewitsch discloses that the “digital twin 138 may include AM process parameter(s) associated with the exemplary AM process to be employed to manufacture the AM part and/or code instructions that are configured to direct an exemplary AM machine to build the AM part” (e.g., [0063]). Butkewitsch also discloses that the “additive manufacturing system may be configured, during the activity of item 112 to incorporate the AM machine setting data into the digital twin 138”.
Applicant also argues that Butkewitsch does not teach a simulation during the additive manufacturing process. However, Butkewitsch discloses to “re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 110, and 112 of Fig. 1 until quality metrics identified in item 116 meet the specification” (e.g., [0071]). Item 110 is the part build simulation activity. Thus, the simulation activity of Butkewitsch is in-situ, i.e., during the build process.
Regarding the rejections under 112(a), the rejections are being maintained and/or modified in light of the amendments for the reason noted in the rejection below.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“an additive manufacturing management platform configured to manage process workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network” in claim 1.
“a digital twin modeling system to encode a set of digital twins representing at least one distributed manufacturing network machine of the additive manufacturing management platform” in claim 1
“an artificial intelligence system executable by a data processing system in communication with the additive management platform…trained to generate process parameters for the process workflows based on data collected from the distributed manufacturing network entities” in claim 1
“a control system configured to adjust the process parameters during the additive manufacturing process performed by the distributed manufacturing network entities” in claim 1
“an adaptive intelligence system in communication with a plurality of sensors and configured to receive current sensor data from the plurality of sensors for use in encoding the set of digital twins” in claim 8
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
There does not appear to be support in the specification for the new features recited in claims 1 and 17, in particular for the limitations:
“detecting, based on the sensor data, at least one change of the process parameters of the at least one distributed manufacturing network machine” (emphasis added);
“processing the sensor data and the at least one change of the process parameters into simulation data” (emphasis added);
“performing a simulation of the printing stage to determine an outcome of a future state of the at least one distributed manufacturing network machine resulting from the simulation data including the sensor data and the at least one change of the process parameters” (emphasis added); and
“based on the outcome of the simulation, applying the at least one change to the process parameters of the at least one distributed manufacturing network machine during the print stage” (emphasis added).
The paragraphs of the publication cited by Applicant do not discloses detecting “at least one change of the process parameters” based on the sensor data or performing a simulation based on the “at least one change of the process parameters”. Paragraph [1446] does disclose adjusting “one or more process parameters” of an additive manufacturing unit based on artificial intelligence or modelling, but there is no disclosure that the process parameters that are adjusting are “at least one change of the process parameters” that were detected based on sensor data and input to a simulation as recited in claims 1 and 17.
Claims 2-16 and 18-20 depend from claims 1 and 17 and incorporate the same deficiencies.
Claims 1 and 17 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.
In claims 1 and 17, the limitation “determining at least one change of the process parameters” was replaced with “detecting…at least one change of the process parameters”. The term “detecting” implies that the change of the process parameters is measured or sensed, whereas the prior term “determining” implies that the change of the process parameters is calculated. Clarification is required.
Claims 2-16 and 18-20 depend from claims 1 and 17 and incorporate the same deficiencies.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Butkewitsch et al. WO 2019/055538 A1 (“Butkewitsch”).
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.
Butkewitsch discloses:
1. An information technology system for a distributed manufacturing network, the information technology system comprising:
an additive manufacturing management platform configured to manage process workflows for a set of distributed manufacturing network entities platform (e.g., Fig. 1 #106, [0058], e.g., Fig. 1 #110, [0062], Fig. 1 #114, [0067]-[0069], Fig. 1 #108, [0059]-[0061]) associated with the distributed manufacturing network (e.g., [0045]: “distributed network environment”), wherein the additive manufacturing management includes a digital twin modeling system to encode a set of digital twins representing at least one distributed manufacturing network machine of the additive manufacturing management platform (e.g., Fig. 1 #138, [0063]-[0064], “updated digital twin”, the digital twin is continually updated modified and thus constitutes a set of digital twins);
an artificial intelligence system executable by a data processing system in communication with the additive manufacturing management platform, wherein the artificial intelligence system is trained to generate process parameters for the process workflows based on data collected from the distributed manufacturing network entities (e.g., [0074], [0110], [0116]-[0122]); and
a control system configured to adjust the process parameters during the additive manufacturing process performed by the distributed manufacturing network entities (e.g., Fig. 1 #126, [0070]-[0071], [0073]) by,
initiating a print stage of the additive manufacturing process, wherein operation of the at least one distributed manufacturing network machine during the print stage is based on the process parameters of the distributed manufacturing network machine (e.g., Fig. 1 #114, [0067]-[0069]),
receiving, during the print stage, sensor data associated with the at least one distributed manufacturing network machine during the print stage (e.g., Fig. 1 #116, [0067]-[0069]),
detecting, based on the sensor data, at least one change of the process parameters of the at least one distributed manufacturing network machine (e.g., Fig. 1 #124, [0067]-[0074]),
processing the sensor data (e.g., Fig. 1 #116, [0067]-[0069]) and the at least one change of the process parameters into simulation data (e.g., Fig. 1 #124, [0067]-[0074]),
outputting the simulation data to the digital twin modeling system representing the at least one distributed manufacturing network machine (e.g., Fig. 1 #138),
performing a simulation of the print stage to determine an outcome of a future state of the at least one distributed manufacturing network machine resulting from the simulation data (e.g., Fig. 1 #124,110) including the sensor data (e.g., Fig. 1 #116) and the at least one change of the process parameters (e.g., Fig. 1 #124, [0071]: “re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 110, and 112 of Fig. 1 until quality metrics identified in item 116 meet the specification”, [0067]-[0074]), and
based on the outcome of the simulation, applying the at least one change to the process parameters of the at least one distributed manufacturing network machine during the print stage (e.g., Fig. 1 #126, [0067]-[0074]).
2. The system of claim 1, wherein the set of distributed manufacturing network entities includes:
a first additive manufacturing unit configured to perform a first additive manufacturing process (e.g., Fig. 2 #210: “AM machine 1”); and
a second additive manufacturing unit configured to perform a second additive manufacturing process (e.g., Fig. 2 #210: “AM machine 2”),
wherein the first additive manufacturing process is different than the second additive manufacturing process (e.g., [0076]).
3. The system of claim 1, wherein training data for the artificial intelligence system includes at least one of, (i) outcomes; (ii) data collected; and (iii) prior/historical process parameters (e.g., [0063], [0066], [0074]).
4. The system of claim 1, wherein the additive manufacturing process is a hybrid task requiring at least two different types of additive manufacturing units (e.g., Fig. 2, [0076]).
5. The system of claim 1, wherein the additive manufacturing management platform is cloud-based (e.g., [0045]-[0053]).
6. The system of claim 1, wherein the artificial intelligence system is distributed across more than one distributed manufacturing network entity (e.g., [0045]-[0053]).
7. The system of claim 1, wherein the digital twin modeling system is further configured to encode a set of digital twins representing a product of the additive manufacturing management platform (e.g., Fig. 1 #138, [0063]-[0064], “updated digital twin”, the digital twin is continually updated modified and thus constitutes a set of digital twins), and the set of digital twins enable the additive manufacturing management platform to manufacture a physical replica (e.g., Fig. 1 #120) of the product (e.g., Fig. 1 #138).
8. The system of claim 1, wherein the artificial intelligence system includes an adaptive intelligence system in communication with a plurality of sensors and configured to receive current sensor data from the plurality of sensors for use in encoding the set of digital twins (e.g., [0111]).
9. The system of claim 1, wherein the artificial intelligence system is distributed across more than one distributed manufacturing network entities from the set of distributed manufacturing network entities (e.g., [0045]-[0053], [0075], Fig. 2).
10. The system of claim 7, wherein the set of digital twins representing the product includes a simulated future condition state of the product (e.g., [0062]-[0064]).
11. The system of claim 1, wherein the at least one change of the process parameters of the at least one distributed manufacturing network machine (e.g., Fig. 1 #126) is based on a change detected in the data collected from the distributed manufacturing network entities during the print stage (e.g., Fig. 1 #116).
12. The system of claim 1, wherein the artificial intelligence system improves at least one of a set of machine learned models using the outcome of the print stage, wherein the training includes adjusting at least one of a weight, a rule, or a parameter (e.g., [0074], [0110], [0116]-[0122]).
13. The system of claim 3, wherein the set of training data includes deformation data about a product of the additive manufacturing process, and wherein the digital twin simulation system further models impact of process parameter changes on product deformation during processing (e.g., [0067]-[0068], [0090]-[0093], [0102], [0137]).
14. The system of claim 13, wherein the deformation data includes one of expansion or contraction (e.g., [0102]: “After or during production, an AM part/product may be deformed (e.g., by one or more of rolling, extruding, fording, stretching, compressing)”) and wherein at least one of a set of machine learned models includes at least one coefficient of material for a material used in the at least one distributed manufacturing network entities (e.g., [0137]: “material properties including…thermal expansion coefficient”).
15. The system of claim 13, wherein generating process parameters includes adjusting the process parameters to reduce product deformation (e.g., [0067]-[0068], [0090]-[0093], [0102]).
16. The system of claim 4, wherein the artificial intelligence system further determines a combination of additive manufacturing types to produce a product of the additive manufacturing process (e.g., Fig. 2 #210: “AM machine 1”, “AM machine 2”, [0076]).
17. A computer implemented method of distributed manufacturing, comprising:
creating a set of digital twins (e.g., Fig. 1 #138, [0063]-[0064], “updated digital twin”, the digital twin is continually updated modified and thus constitutes a set of digital twins), each digital twin representing at least one distributed manufacturing network machine of a set of distributed manufacturing network entities (e.g., [0045]: “distributed network environment”);
initiating a print stage of an additive manufacturing process, wherein operation of the at least one distributed manufacturing network machine during the print stage is based on process parameters of the distributed manufacturing network machine (e.g., Fig. 1 #114, [0067]-[0069]);
receiving, during the print stage, sensor data associated with the at least one distributed manufacturing network machine during the print stage (e.g., Fig. 1 #116, [0067]-[0069]);
detecting, based on the sensor data, at least one change of the process parameters of the at least one distributed manufacturing network machine (e.g., Fig. 1 #124, [0067]-[0074]);
processing the sensor data (e.g., Fig. 1 #116, [0067]-[0069]) and the at least one change of the process parameters into simulation data (e.g., Fig. 1 #124, [0067]-[0074]),
outputting the simulation data to the digital twin modeling system representing the at least one distributed manufacturing network machine (e.g., Fig. 1 #138),
performing a simulation of the print stage to determine an outcome of a future state of the at least one distributed manufacturing network machine resulting from the simulation data (e.g., Fig. 1 #124,110) including the sensor data (e.g., Fig. 1 #116) and the at least one change of the process parameters (e.g., Fig. 1 #124, [0071]: “re-run the iterative adjustments (items 122 and/or 124) to affect values of items 108, 110, and 112 of Fig. 1 until quality metrics identified in item 116 meet the specification”, [0067]-[0074]), and
based on the outcome of the simulation, applying the at least one change to the process parameters of the at least one distributed manufacturing network machine during the print stage (e.g., Fig. 1 #126, [0067]-[0074]).
18. The method of claim of claim 17, further comprising: manufacturing a physical replica of one of the set of digital twins on an additive manufacturing device (e.g., Fig. 1 #120), wherein the one of the set of digital twins represents a product (e.g., Fig. 1 #138).
19. The method of claim 17, wherein the set of distributed manufacturing network entities includes:
a first additive manufacturing unit configured to perform a first additive manufacturing process (e.g., Fig. 2 #210: “AM machine 1”); and
a second additive manufacturing unit configured to perform a second additive manufacturing process (e.g., Fig. 2 #210: “AM machine 1”),
wherein the first additive manufacturing process is different than the second additive manufacturing process (e.g., [0076]).
20. The method of claim 17, wherein the additive manufacturing process is a hybrid task requiring at least two different types of additive manufacturing units (e.g., Fig. 2, [0076]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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/RYAN A JARRETT/Primary Examiner, Art Unit 2116
09/01/26
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