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 .
Claim Rejections - 35 USC § 102
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, 5, 7, 8, 12, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Styger US 2025/0013793 A1.
Styger teaches:
1. A computer-implemented method, comprising:
analyzing collected data relating to physical attributes of a structure [“digital model
“] and load distribution patterns; [para. 0035 “digital model” and para. 0040, “The iterative algorithm may be designed for considering one or more of: at least one unchangeable area such as due to predefined interfaces to other components; acting forces; kinematics; predefined fixation points; manufacturing method; material; accessibility of tools during production and/or assembly; stackability; or nesting of individual components, e.g. for efficient logistics.” (emphasis added.)]
simulating loading scenarios on the structure based on one or more structural analysis tools; [target constraint 118 can be acting forces and para. 0131, “Further, the method 110 may comprise a step 142 of validating the prototyped complex part 140 by comparing at least one property of the prototyped complex part 140 with at least one property of a simulated complex part and/or rating the prototyped complex part 140 by using at least one property of the prototyped complex part 140 in comparison to the at least one target constraint 118.”]
identifying weak points and failure points based on simulation; [para. 0048, “The analyzing may comprise using at least one Finite-Element-Method (FEM) simulation, also called FEM analysis. The FEM simulation may be configured for determining, e.g. and visualizing, a behavior of the candidate complex part under one or more of at least one force, deformation, stress, a condition such as defined by a temperature, or loads. The FEM simulation may be configured for identifying failures such as weak points.” (Emphasis added.)]
computing modifications to the structure based on the weak points and failure points; [para. 0042; iteratively modifies digital model to strengthen fail points]
generating a digital model of the modifications to the structure; [para. 0042, “The iterative algorithm may comprise a topology optimization. For example, the topology optimization may comprise iteratively determining a geometry of the complex part.“] and
three-dimensional (3D) printing the modifications to the structure by adding material to the weak points and failure points on the structure utilizing a layer-by-layer deposition process. [para. 0055, “The manufacturing method further comprises manufacturing the complex part of the in-vitro diagnostic instrument based on the designed complex part, i.e. on the design solution, by using at least one manufacturing process selected from the group consisting of: milling, such as computer numerical control milling; casting; molding; additive manufacturing, such as Powder Bed Fusion, Binder Jetting, Material Jetting, 3D printing; cutting, such as laser cutting and/or water jet cutting.”]
Styger teaches:
5. The computer-implemented method of claim 1, wherein computing modifications to the structure further comprises: using genetic algorithms to generate and test various cross-sectional modification proposals. [para. 0050, “Additionally or alternatively, the iterative algorithm, e.g. the determining of the candidate complex part and the comparing of the candidate complex part, may be performed using at least one genetic algorithm. The iterative algorithm using a genetic algorithm may comprise generating a population of solutions in each iteration. In each iteration randomly solutions from the previous population may be selected. These selected solutions may be used as parents to produce children for the next population generation. The optimal solution may be reached after successive iterations.”]
Styger teaches:
7. The computer-implemented method of claim 1, further comprising: performing a quality assurance check after adding material to verify that printed modifications align with design specifications from a computational analysis. [para. 0073, “Furthermore, the proposed methods and devices may, compared to known methods and devices, allow for simplifying production, assembly, warehousing, documentation, service and quality assurance.”]
Regarding claims 8, 12, 19-20, these apparatus or system claims recite the functions for executing the method claims above and the storage of the instructions for executing the steps above and are rejected on the same grounds and rationale as corresponding claims above.
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.
Claims 2-3, 9-10, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Styger US 2025/0013793 A1 in view of Zhou et al. US 2022/0143911 A1.
Styger does not teach the following limitations, however, Zhou teaches:
2. The computer-implemented method of claim 1, wherein adding the material to the weak points and failure points further comprises:
deploying robotic units to collaboratively add the material to the weak points and failure points; [para. 0057, “To simplify the problem, an exemplary embodiment will be disclosed below for operation using two robots with applicability to larger scaling deploying many robots.”] and
Styger teaches:
redistributing any increased load resulting from added materials. [iterative algorithm can redistribute loads, para. 0048, “The FEM simulation may be configured for determining, e.g. and visualizing, a behavior of the candidate complex part under one or more of at least one force, deformation, stress, a condition such as defined by a temperature, or loads.”]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Zhou with those of Styger. A person having ordinary skill in the art would have been motivated to combine the teachings because Styger suggest the use of swarm intelligence (para. 0073). Zhou teaches deploying a plurality of robots for 3d printing because the differing robots can have different capabilities and printheads, allowing faster printing because printheads do not need to be changed. (See para. 0005).
Styger teaches:
3. The computer-implemented method of claim 2, further comprising: recording an outcome of the modifications, wherein recordings are fed back for continuous learning. [para. 0037, design model the result of iterative algorithm]
Regarding claims 9-10 and 16-17 these apparatus and CRM claims are rejected on the same grounds and rationale as corresponding method claims above.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Styger US 2025/0013793 A1 in view of Sundstrom et al. US 2021/0132593 A1.
Styger does not teach the following limitations, however, Sundstrom teaches:
4. The computer-implemented method of claim 1,
wherein the one or more structural analysis tools includes a machine learning technique, [para. 0006, “The operations further include predicting, by a machine learning model, a final quality metric for the component based on the updated instruction set.”]
wherein the machine learning technique includes a Long Short-Term Memory (LSTM) network to simulate various load patterns and predict potential weak points. [para. 0043, “In some embodiments, prediction module 210 may implement a long short-term memory (LSTM) model to output the final quality metric.”]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Sundstrom with those of Styger. A person having ordinary skill in the art would have been motivated to combine the teachings because Sundstrom teaches that use of LSTM model is useful to solve the “vanishing gradient problem”. (See para. 0043).
Regarding claims 11 and 18, these claims recite the apparatus and CRM of the method claims above and are rejected on the same grounds and rationale as corresponding claims above.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Styger US 2025/0013793 A1 in view of Schmidt et al. US 2016/0305271 A1.
Styger does not teach the following limitations, however, Schmidt teaches:
6. The computer-implemented method of claim 1, wherein analyzing collected data further comprises: collecting data from embedded Internet of Things (IoT) sensors in the structure. [para. 0003, “One exemplary embodiment of this disclosure relates to an article having a multi-layer wall structure having an embedded sensor. Further, the multi-layer wall structure and the sensor are bonded together.” And para. 0044, “The sensor 76 is selected from a non-exhaustive list of optical fibers, pressure transducers, temperature sensors (thermocouples), strain gauges, position sensors, etc., including combinations thereof. Sensor 76 could be made elsewhere and integrated during manufacturing or manufactured directly into the article during processing using an additive technique.”]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Schmidt with those of Styger. A person having ordinary skill in the art would have been motivated to combine the teachings because Schmidt teaches additive manufacture with embedded sensors in order to “monitor the health conditions of the associated component.” (para. 0002).
Regarding claim 13, this system claim recites the apparatus for exectuting the method claim above and is rejected on the same grounds and rationale as claim 6 above.
Conclusion
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/GARY COLLINS/Primary Examiner, Art Unit 2115
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