Detailed Office Action
Notice of Pre-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
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/16/2026 has been entered.
Response to Amendments
The amendment filed on 12/19/2025 has been entered. Claims 1 – 2 and 4 – 16 remain pending. Claims 12 – 13 remain withdrawn. Claim 1 has been amended and finds support in at least [0026, 0028].
Claim Rejections – U.S.C. §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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 2, 5 – 11 and 14 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490, cited in the IDS of 09/09/21) in view of Wasmer (“When AE meets AI”, NPL, 2017) and Mehr (US2018/0341248, cited with OA of 06/03/25) and in further view of Burlatsky (US2018/0229303, cited with OA of 10/28/25)
Regarding claims 1 and 10, Reinarz teaches producing a component via layer-wise manufacturing including disposing a powder layer and melting traces of it [Abstract], meeting the claim 10 of powder bed additive manufacturing. Reinarz teaches using a microphone as a sensor to detect noises when the coating slide (i.e. recoater) slides across a component and comes into contact with it (that is, when the recoater collides with the component) [Abstract, 0036], meeting the claimed limitation of step a). Reinarz teaches that the measured value from the sensor is checked against a stored limit value, in particular a plurality of stored limit values to initiate different actions [0047], meeting the claimed limitation of step b), step c), and step d) of classifying the incident, defining a first measure to counteract the malfunction, and defining a second measure to counteract.
Reinarz states that process monitoring reacts to incidents based on the detected measurement values which represent deviations [0028, 0029]. Reinarz further states that the measurement values are grouped and ordered by size in order to carry out different interventions [0053]. This reasonably suggests that the detected measurements values are classified based on their value and that the classifications are arranged/ordered, which meets the broadest reasonable interpretation of classifying based on “severity”. Additionally, the presence of a “highest” severity and “lowest” severity would be reasonably implied based on the measurement values being grouped and ordered by size. That is, deviations are represented by measurement values wherein a highest severity and lowest severity would naturally result from classifying the grouped measurement values.
Moreover, Reinarz states that stored limit values result in varied responses [0047]. For instance, a particular stored limit value would result in no direct effect/control on the process but would send a signal or alert [0048]. This is reasonably construed to suggest/imply the claimed limitation of “lowest severity” in which no build failure or hardware damage occurs.
Additionally, Reinarz expressly recognizes situations in which the recoater blade becomes jammed or is damaged as a result of a collision [0013, 0014]. Reinarz further states that in the greatest of all limit values, an emergency stop is generated [0059], including when a collision has taken place [0052]. Reinarz states that this can include excluding the component from the further production [0056]. This is reasonably construed to suggest/imply the claimed limitation of “highest severity” in which a build failure has occurred or hardware damage has occurred and that the component being built is rejected.
"[I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom." In re Preda, 401 F.2d 825, 826, 159 USPQ 342, 344 (CCPA 1968) (MPEP 2144.01). "A person of ordinary skill in the art is also a person of ordinary creativity, not an automaton." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 421, 82 USPQ2d 1385, 1397 (2007) (MPEP 2141.03 I). In this case, Reinarz reasonably suggests that the measured values should be grouped by stored limit values which represent different incidents and which can range from minor to major incidents. Moreover, a potential minor incident can result in the process continuing as before with an alert/message being sent out but no change/alteration in the process, which would reasonably suggest that no or negligible damage has occurred and that build failure has not occurred. Alternatively, a severe incident such as a collision with potential damage to recoater can result in emergency stoppage and the component being removed/not further produced, which would reasonably suggest that hardware damage or build failure has occurred.
An ordinarily skilled artisan possessing ordinary creativity could reasonably infer that the former situation would constitute a “lowest severity” incident and that the latter situation would constitute a “highest severity” incident based on the teachings of Reinarz. Therefore, Reinarz reasonably suggests/implies the limitations of the lowest severity and highest severity as claimed.
Reinarz teaches that a data processing system [0022] is used but does not explicitly teach using non-parametric statistics, that artificial intelligence or an artificial neural network is used in monitoring, classifying, or defining, or that the artificial intelligence or neural network is trained during the manufacturing process.
Wasmer discusses the use of artificial intelligence for acoustic emission monitoring [Title]. Wasmer discloses that acoustic emission (AE) is an effective tool for monitoring and controlling processes and that artificial intelligence is a beneficial tool for processing the collected AE data because the data is complex [Introduction]. Wasmer teaches that AE signals from friction can be affected by numerous parameters and that artificial intelligence allows for bypassing many of the constraints that make monitoring AE data time-consuming, costly, and difficult [Introduction], meeting the claimed limitation of using artificial intelligence to classify data/incident. Wasmer discloses using Random Forest (RF) as a classification technique with a non-parametric framework [page 4]. Wasmer states that RF is beneficial for friction monitoring where there is a presence of outliers and noise with high variations [page 4]. Wasmer further states that RF is adaptable to a variety of different conditions with minimum effort.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have taken the method as disclosed by Reinarz and used an artificial intelligence in the data processing including an RF non-parametric framework, as taught by Weimer. Reinarz is directed to additive manufacturing monitoring systems for detecting abnormalities in the additive manufacturing process and Wasmer teaches that artificial intelligence is beneficial for monitoring and control of various processes, including additive manufacturing. As such, an ordinarily skilled artisan would have considered the teachings of Wasmer to be pertinent to invention of Reinarz. Moreover, Wasmer states that artificial intelligence can be used for additive manufacturing with Random Forest beneficial for monitoring AE data from friction and Reinarz explicitly acknowledges the acoustic data as being from friction/“scratching” [0031]. As such, an ordinarily skilled artisan would have had a reasonable expectation of success in applying the teachings of Wasmer to the invention of Reinarz.
Lastly, Wasmer teaches many benefits of using artificial intelligence for the processing data including being able to handle complex data processing and reducing time and costs and also states that RF is beneficial for handle outliers/noise from friction. Therefore, an ordinarily skilled artisan would have been motivated to apply the teachings of Wasmer to Reinarz.
Reinarz in view of Wasmer does not explicitly teach that the artificial intelligence or neural network is trained during the manufacturing process.
Mehr teaches a method for real-time adaptive control of an additive manufacturing process [Title]. Mehr teaches that the additive manufacturing process can be variety of processes [0043 – 0050]. The process including providing one or more sensors [0026], including acoustic data [0007, 0112], and providing a machine leaning algorithm to analyze and classify defects of the objects based on the sensor data [0026]. Furthermore, Mehr teaches that in-process data can be used to updated the training data [0031 – 0032], meeting the claimed limitation of being trained during the manufacturing process. Mehr teaches that the implementation of this real-time adaptive control results in improved process yields, throughput, and quality of the parts [0035].
It would have been obvious to one of ordinary skill in the art before the effective filing date to have taken the method as disclosed by Reinarz in view of Wasmer and used a data processing system that can update training data with in-process data, as taught by Mehr to achieve predictable results. Reinarz and Mehr are directed to additive manufacturing monitoring systems for detecting abnormalities in the additive manufacturing process (same field of endeavor). Furthermore, Mehr appreciates that a machine learning can be used to take in, analyze, and classify acoustic sensor information to detect defects/abnormalities in the process. As such, a person of ordinary skill in the art would have had a reasonable expectation of success in combining the teachings of Reinarz and Mehr. Additionally, an ordinarily skilled artisan would be motivated to apply a data processing system that updates training data with in-process data to the process of Reinarz because, as disclosed by Mehr, the real-time adaptive process results in improved process yields, throughput, and quality of the parts.
Reinarz teaches that the collision measurement can be compared against stored limit values to initiate different limit value-dependent actions [0047]. Reinarz does state that when a limit value is exceeded (i.e. and incident is detected and classified) the intervention can include modifying production parameters but Reinarz in view of Wasmer and Mehr does explicitly teach that such an intervention/parameter is to delay the exposure of subsequent irradiation vector with an energy beam by a defined time.
Burlatsky is directed to control of powder fusion processes [Title] including a work bed [Abstract] in which an energy beam is operably controlled to vary power and scan rate based on the detection of a defect or non-defect condition [Abstract]. Burlatsky explicitly states that when controlling the power and scan rate (parts of the energy density) this control can extend to the time parameter between when the energy beam stops and when energy beam is started again, such that time is added [0030]. To this, Burlatsky discloses that heat accumulation in the part causes defects such as surface roughness (i.e. elevations that could collide with a recoater).
As such, it would have been obvious to one of ordinary skill in the art before the effective filing date to have taken the method of Reinarz as-modified and included the control and addition of time between irradiation applications, as disclosed by Burlatsky, as one of the production parameters modified when intervening due to a collision. Reinarz and Burlatsky are all directed to the detection, measurement, and/or control of defects in an additive manufacturing process, and Reinarz explicitly appreciates that production parameters can be controlled/modified as an intervention when a collision is detected. As such, an ordinarily skilled artisan would have a reasonable expectation of success in combining Reinarz and Burlatsky. Furthermore, an ordinarily skilled artisan would be motivated to modify/increase the time between irradiations in response to a defect because Burlatsky discloses that excessive heat accumulation is a cause of surface roughness defects which would be defects likely to collide with a recoater. Wherein the cooling and shrinking of the component would naturally result due to increasing time between the next irradiation step.
Regarding claim 2, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches that the collision measurement can be compared against stored limit values, wherein the stored limit values can be a plurality of stored limit value that can initiate different limit value-dependent actions [0047], meeting the claimed limitation that the incident (i.e. the collision of the recoater/coating blade) is classified amongst several nuances.
Regarding claim 5, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz states that when a limit value is exceeded (i.e. and incident is detected and classified) the laser energy can be increased or decreased as well as sped up or slowed down [0050], meeting the claimed limitation of “vary the energy put in the respective powder layer by an energy beam based on the classification of the incident”
Regarding claim 6, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches that an action when the limit value is exceeded is that the layer thickness is increased which is performed by coating several times [0051], wherein this would imply a back-and-forth motion of the recoater which would meet the broadest reasonable interpretation of “select a recoating direction of a recoater in the additive manufacturing process” in response to a particular limit value.
Regarding claim 7, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 6. Reinarz teaches that the additive manufacturing system contains a recoater that spreads powder across the build platform that eventually forms the next layer after melting/sintering [0002, 0013]. As such, the recoater/coating blade moves parallel to the plane of the upper build surface of the component. That is, the recoater/coating blade moves parallel to the previously manufactured layer when spreading powder for the next layer [Fig A]. Furthermore, Reinarz teaches that overhang structures can be produced in additive manufacturing processes [0012]. Therefore, it would have been obvious before the effective filing date for an ordinarily skilled artisan to have selected a component design that contained an overhang structure to be built in the method of Reinarz. As a result, an overhang structure manufactured in the previous layer would have the recoater/coating blade move parallel to it surface when spreading the next layer of powder on top, meeting the claimed limitation.
Regarding claim 8, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches the microphone can be placed inside the production chamber in a stationary manner [0036] and that a camera can be placed inside as well in order to monitor and determine the current state/situation of the production method/cycle [0054], wherein this would imply that monitoring is ongoing during the additive manufacturing process.
Regarding claims 9 and 15, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches that a camera can be included in the additive manufacturing system [0054]. Reinarz states that the camera can be used to determine the nature of the detected situation and the current state of production by chronologically correlating with the limit value being exceeded [0054, 0055], meeting the claimed limitation of claim 9. Wherein the camera produces photos and/or video [0055] and can be placed inside the production system, meeting the claimed limitation of “the camera record comprises an optical image of a built-in camera”.
Regarding claims 11 and 16, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 10. Mehr teaches that process control parameters can be adapted during manufacturing to reduce defects and improve quality [0107]. Mehr teaches that laser power, laser power distribution, laser beam size, shape, focal length, and/or wavelength are all process parameters that can be considered [0053 – 0055, 0057 – 0059], meeting claimed limitation of adapting an irradiation parameter (claim 11) and varying an irradiation strategy (claim 16).
Regarding claim 14, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz states using the microphone to detect noises when the coating slide (i.e. recoater) slides across a component and comes into contact with it, meeting the claimed limitation of “wherein the further structure comprises as an already established part of the component to be additively manufactured”.
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490, cited in the IDS of 09/09/21) in view of Wasmer (“When AE meets AI”, NPL, 2017), Mehr (US2018/0341248, cited with OA of 06/03/25), and Burlatsky (US2018/0229303, cited with OA of 10/28/25), as applied to claim 1, in further view of Mizutani (US2016/0144429, cited with OA of 12/10/24).
Regarding claim 4, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches that various action in response to one or more limit values being exceed (i.e. measures to counteract the incident/malfunction) [0047]. However, Reinarz does not expressly teach that one such measure is to change a speed of the recoater.
Mizutani teaches an additive manufacturing process/apparatus that includes depositing a powder material layer via a recoater [Abstract]. Mizutani teaches that the process is monitored, specifically, the recoater head and its movement are monitor to detect clashes with an obstacle [Abstract]. Mizutani teaches that when a collision is detected the recoater driving mechanism is placed in a non-control state such that the recoater bounds off of the obstacle and runs idly in the opposite direction and loses speed (meeting the broadest reasonable interpretation of “change a speed of a recoater in the additive manufacturing process based on the classification of the incident”) [0046, 0047]. Mizutani teaches that this action gives the advantage of responding quicker to collisions/clashes which reduces/prevents damage to the recoater [0047]
It would have been obvious to one of ordinary skill in the art before the effective filing date to have applied the response action of placing the recoater arm in a non-control state to the method of Reinarz, as taught by Mizutani. This modification would reduce/prevent damage to the recoater when a collision was detected, as taught by Mizutani. Furthermore, given that Reinarz and Mizutani are in the same field of endeavor of powder bed based additive manufacturing with detection/monitoring and response to collision between the recoater and objects, a person of ordinary skill in the art would have a reasonable expectation of success in applying the teachings of Mizutani to the method of Reinarz.
Regarding claim 6, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 1. Reinarz teaches that various action in response to one or more limit values being exceed (i.e. measures to counteract the incident/malfunction) [0047]. However, Reinarz does not expressly teach that one such measure is selecting a direction of the recoater.
Mizutani teaches an additive manufacturing process/apparatus that includes depositing a powder material layer via a recoater [Abstract]. Mizutani teaches that the process is monitored, specifically, the recoater head and its movement are monitor to detect clashes with an obstacle [Abstract]. Mizutani teaches that when a collision is detected the recoater driving mechanism is placed in a non-control state such that the recoater bounds off of the obstacle and runs idly in the opposite direction and loses speed [0046, 0047]. Mizutani teaches that this action gives the advantage of responding quicker to collisions/clashes which reduces/prevents damage to the recoater [0047]. Following this, the obstacle is removed by a rotary cutting tool including the entire sintered layer and the recoater is placed back into a control state after a period of elapsed time to continue operation [0048 – 0050]. Wherein this would imply that the direction of recoater is changed to backwards to be placed back at the start, which would meet the broadest reasonable interpretation of “select a recoating direction of a recoater in the additive manufacturing process”.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have applied the response action of placing the recoater arm in a non-control state followed by resetting the placement of the recoater after removing the obstacle/defective layer, to the method of Reinarz, as taught by Mizutani. This modification would reduce/prevent damage to the recoater when a collision was detected and can also allow resumption of the build process without cancellation or restarting the whole component [Mizutani, 0050]. Furthermore, given that Reinarz and Mizutani are in the same field of endeavor of powder bed based additive manufacturing with detection/monitoring and response to collision between the recoater and objects, a person of ordinary skill in the art would have a reasonable expectation of success in applying the teachings of Mizutani to the method of Reinarz.
Claims 11 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490, cited in the IDS of 09/09/21) in view of Wasmer (“When AE meets AI”, NPL, 2017), Mehr (US2018/0341248, cited with OA of 06/03/25), and Burlatsky (US2018/0229303, cited with OA of 10/28/25), as applied to claim 10, in further view of Gold (US2017/0144250, cited with OA of 12/10/24)
Regarding claims 11 and 16, Reinarz in view of Wasmer, Mehr, and Burlatsky teaches the invention as applied in claim 10. Reinarz nor Mehr teaches that a CAM or irradiation parameter is adapted specifically in response to a recoater collision.
Gold teaches a method of monitoring an additive manufacturing process including a recoating process and powder bed [Abstract]. Gold teaches that a vibration signature of the recoating process is measured and the process is controlled in response to this [Abstract]. Specifically, Gold teaches that these vibrations can indicate collision between the recoater and a defect or irregularity in the workpiece [0031, 0032]. In response, the process/recoater can be stopped and the apparatus can be used to correct the defect [0037]. Furthermore, the monitoring process can determine if the build process is creating the defects/irregularities and subsequently can be used to change process parameters including powder level and scan velocity in order to eliminate the source of the defects [0038 – 0039], meeting the claimed limitation of an irradiation parameter/strategy being adapted/varied of claims 11 and 16.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have applied the teachings of Gold to the method of Reinarz in view of Mehr such that the method contained real-time feedback to adjust the process parameters of the build process using the trained artificial intelligence order to eliminate the source of defects in the build process. Given Gold and Reinarz are directed to powder bed based additive manufacturing, a person of ordinary skill in the art would have a reasonable expectation of success in achieving predictable results. Furthermore, an ordinarily skilled artisan would be motivated to apply the teachings of Gold to the method of Reinarz in view of Mehr because adapting the process parameters in real-time would eliminate/reduce the occurrence of defects which would beneficial to the process implementation.
Response to Arguments
Applicant's amendments and arguments thereto have overcome the previous rejections. The examiner agrees that Reinarz alone or in view of the prior art relied upon does not teach or suggest using non-parametric statistics to classify. The rejections are withdrawn.
However, upon further consideration, a new rejection is made of:
Claims 1 – 2, 5 – 11 and 14 – 16 under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490) in view of Wasmer (“When AE meets AI”, NPL, 2017) and Mehr (US2018/0341248) and in further view of Burlatsky (US2018/0229303)
Claims 4 and 6 under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490) in view of Wasmer (“When AE meets AI”, NPL, 2017), Mehr (US2018/0341248), and Burlatsky (US2018/0229303), as applied to claim 1, in further view of Mizutani (US2016/0144429).
Claims 11 and 16 under 35 U.S.C. 103 as being unpatentable over Reinarz (US2015/0115490) in view of Wasmer (“When AE meets AI”, NPL, 2017), Mehr (US2018/0341248), and Burlatsky (US2018/0229303), as applied to claim 10, in further view of Gold (US2017/0144250)
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vlasea – Real-time process control of powder bed AM including monitoring and responding to defects and faults from recoating mechanism damage or recoater and super-elevation collisions
Scime – detection and classification of various anomalies during AM process including determining severity
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AUSTIN POLLOCK whose telephone number is (571)272-5602. The examiner can normally be reached M - F (8 - 5).
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/AUSTIN POLLOCK/Examiner, Art Unit 1738 /SALLY A MERKLING/SPE, Art Unit 1738