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 .
Response to Remarks/Arguments
Applicant’s arguments regarding claims 1, 11, and 20 have been fully considered and are found to be persuasive. The Applicant’s arguments are directed towards newly added claim language which changed the scope of the claim and necessitated new grounds of rejection as set forth in the 35 USC 102 section below. Therefore, the Applicant’s arguments are now moot in view of new grounds of rejection.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 -20 are rejected under 35 U.S.C. 102(a) (2) as being anticipated by Paddock et al. (US 20220347930 A1.).
As per claim 1, Paddock et al. teach A system for error reduction in three-dimensional (3D) printing (abstract, para 12, additive manufacturing system), the system comprising:
a processor (para 68, application specific special processor, AASPs); and
a machine-readable medium in operable communication with the processor and having instructions thereon that (para 68, para 137, one or more central processing units, processor devices, 2121a, 2121b, 2121c, etc.) when executed, perform the following steps:
receiving printer data indicative of a 3D object to be printed by a 3D printer (para 61, a 3D file 102, part specifications 104, and additive manufacturing system (AMS) information 106, are input into an automatic settings engine 108. The AMS information 106 provides information about the additive manufacturing system 120. The combination of this input teaches receiving printer data);
identifying, before printing the 3D object, at least one spatial irregularity in the printer data (Para 61, Fig. 1, the AMS Additive manufacturing system 106 can provide information about the additive manufacturing system 120, also see Fig. 1, shows all the steps except sensor 122, all steps happen before actual printing or AMS. Also, para 88 describes tools users can use to predict the location, probability and root cause of failure before the 3D printing process. Para 88, “Unique Geometry”, “long sweeping lines” teaches spatial irregularity.);
determining, based on the at least one spatial irregularity and previously-collected printing information, at least one predicted defect-prone region associated with high-speed printing (para 59, machine learning platform for additive manufacturing systems being used to improve quality over time using the history data based on data analysis, also see paras 85-88, and 105 describe machine learning model train corrective measures in the printing process based on its learning/history. It also identifies “unique geometry”, “long sweeping lines”, Paras 67 mentions the information from the sensor 122 is fed to AI engine 124 to identify potential failure region. Also see para 116 describes defect prone region is predicted by the AI, “identify potential failure to regions and to improve process parameter to avoid failure”. Para 150, Fig. 24 explains region such as detecting overmelt regions); and
locally adjusting at least one printing kinematic parameter in the printer data for the at least one predicted defect-prone region to generate compensated printer data for execution of the printing of the 3D object by the 3D printer (para 105, “correcting for regional distortions”, the controller uses lesson learned from AI and the combined information presented from processor, sensor reading and use the combined information to reduce the likelihood of failure.).
As per claim 2, Paddock et al. teach
The system according to claim 1, wherein the at least one kinematic parameter comprises at least one of acceleration (para 98, axis acceleration), jerk, feed rate, an extruder parameter (para 67, volumetric extrusion, para 98, extrusion rate), printing speed of the 3D printer (para 98, motion speed), extrusion rate of the 3D printer (para 98, extrusion rate), and linewidth of the 3D printer ( para 98, extrusion line diameter, para 88, “long sweeping lines”).
As per claim 3, Paddock et al. teach
The system according to claim 1, wherein the printer data comprises G-code data generated before printing the 3D object, and wherein the compensated printer data comprises the G-code data with localized kinematic-parameter adjustments (Fig. 1, G-code Editor Engine 116, paras 64 -65, describe the G-code data is sent slice-by slice basis or line -by -line basis and continuously adjusted during the fabrication process and updated in the G-code connector engine 118. The most upgraded data then provided to AMS 120. Therefore, the G-code data is being adjusted before the 3D printing. Also, para 67 describes that the G-code editor engine 118 enables the AMS 120 to make adjustment of the printing process such as adjust temperature, control print head, adjust flow rate etc.).
As per claim 4, Paddock et al. teach
The system according to claim 1, wherein identifying the at least one spatial irregularity comprises a gap (para 106, gaps and voids identified by the machine learning using the dataset), non-uniform path spacing ( para 116, identifying features and issues in 3D space), locally sparse path distribution( para 14, path of print head) , spacing variation ( para 98, axis vibration), and non-uniformity between adjacent portions of a planned 3D printing path (also see para 88, "unique geometry",, "long sweeping lines" teaches spatial irregularity).
As per claim 5, Paddock et al. teach
The system according to claim 1, wherein the instructions when executed further perform the step of providing to the 3D printer the compensated printer data (para 59, a machine learning platform provides the service for a user to use the data analysis and 3D visualization of process data and process correction. Also see Fig. 1. The G code connector engine 118 providing data to AMS 120 after going through the simulation and visualization process. Therefore, this step is providing compensated data to 3D printers or AMS 120.).
As per claim 6, Paddock et al. teach
The system according to claim 1 (abstract, para 12, additive manufacturing system), wherein the printer data comprises a planned G-code path of the 3D printer (Fig. 1, all the steps except the sensor 112 representing a path to G-code data receive and sending to AMS, also see para 65 for sending G-code to AMS 120),
wherein the identifying of the at least one spatial irregularity in the printer data comprises detecting a location of the spatial irregularity in the planned G-code path before execution of the planned G-code path (Para 61, Fig. 1, the AMS Additive manufacturing system 106 can provide information about the additive manufacturing system 120, also see Fig. 1, shows all the steps except sensor 122, all steps happen before actual printing or AMS. Also, para 88 describes tools users can use to predict the location, probability and root cause of failure, defect, or anomaly before the 3D printing process. “users are provided with the novel ability to take corrective action before a print is started”. Para 88, “Unique Geometry”, “long sweeping lines” teaches spatial irregularity.), and
wherein the locally adjusting of the at least one printing kinematic parameterat least one predicted defect-prone region without primarily redesigning an overall planned 3D printing path ( para 59, machine learning platform for additive manufacturing systems being used to improve quality over time using the history data based on data analysis, also see paras 85-88 describe machine learning model train corrective measures in the printing process based on its learning/history. It also identifies “unique geometry”, “long sweeping lines”. para 88 describes tools users can use to predict the location, probability and root cause of failure before the 3D printing process Paras 67, 116 describe defect prone region is predicted by the AI, “identify potential failure to regions and to improve process parameter to avoid failure”. Para 150, Fig. 24 explains region such as detecting overmelt regions. Also see para 105, “correcting for regional distortions”, the controller uses lesson learned from AI and the combined information presented from processor, sensor reading and use the combined information to reduce the likelihood of failure).
As per claim 7, Paddock et al. teach
The system according to claim 6, wherein the instructions when executed further perform the step of providing the 3D printer the compensated printer data (para 59, a machine learning platform provides the service for a user to use the data analysis and 3D visualization of process data and process correction. Also see Fig. 1. The G code connector engine 118 providing data to AMS 120 after going through the simulation and visualization process. Therefore, this step is providing compensated data to 3D printers/AMS.).
As per claim 8, Paddock et al. teach
The system according to claim 7, wherein the instructions when executed further perform the step of evaluating a quality and time of printing that was performed by the 3D printer based on the compensated printer data (para 71, timestamp for processing parameters, para 86, the onboard sensors collect information on output result includes a time of flight while measuring material flow rate during the printing process. Also see para 91 for reducing time for part verification or reducing machine downtime to improve higher print success such as “more effective amortization of machine with higher print success rates”, also see para 96, Fig. 1 for overall data acquisition and preprocessing for part property detection, para 59, machine learning platform for additive manufacturing systems being used to improve quality over time using the history data based on data analysis).
As per claim 9, Paddock et al. teach
The system according to claim 6, wherein the at least one predicted defect-prone region comprises at least one of a high-speed-printing-induced under-extrusion-prone region and a high-speed-printing-induced over- extrusion-prone-region (para 149, under extrusion, Fig. 23B, the arrows 2313 point to areas where voids or under extrusion exist. Also see para 148, Fig. 23A, the arrows 2301 point to areas where excess material was deposited, the excess material deposition teaches over extrusion.).
As per claim 10, Paddock et al. teach
The system according to claim 6, wherein at least one spatial irregularity comprises a gap (para 106, gaps and voids identified by the machine learning using the dataset) or non-uniform spacing between adjacent printing lines in the planned G-code path (para 116, identifying features and issues in 3D space, also see para 88, "unique geometry", "long sweeping lines” teaches spatial irregularity).
As per claim 11, Paddock et al. teach
A method for error reduction in three-dimensional (3D) printing (abstract, para 12), the method comprising:
receiving printer data indicative of a 3D object to be printed by a 3D printer (please refer to the analysis of claim 1 above);
identifying, before printing the 3D object, at least one spatial irregularity in the printer data (please refer to the analysis of claim 1 above);
determining, based on the at least one spatial irregularity and previously collected printing information, at least one predicted defect-prone region associated with high-speed printing (please refer to the analysis of claim 1 above); and
locally adjusting at least one printing kinematic parameter in the printer data for the at least one predicted defect-prone region to generate compensated printer data for execution of the printing of the 3D object by the 3D printer (please refer to the analysis of claim 1 above).
As per claim 20, Paddock et al. teach
A system for error reduction in three-dimensional (3D) printing, the system comprising:
a 3D printer (para 117, 3D printer);
a processor in operable communication with the 3D printer (para 68, application specific special processor, AASPs); and
a machine-readable medium in operable communication with the processor and having instructions thereon that, when executed, perform the following steps (para 68, para 137, one or more central processing units, processor devices, 2121a, 2121b, 2121c, etc.):
receiving printer data indicative of a 3D object to be printed by the 3D printer (please refer to the analysis of claim 1 above);
identifying, before printing the 3D object, at least one spatial irregularity in the printer data (please refer to the analysis of claim 1 above);
determining, based on the at least one spatial irregularity and previously-collected printing information, at least one predicted defect-prone region associated with high-speed printing (please refer to the analysis of claim 1 above);
locally adjusting at least one printing kinematic parameter in the printer data for the at least one predicted defect-prone region to generate compensated printer data for execution of the printing of the 3D object by the 3D printer (please refer to the analysis of claim 1 above); and
providing the 3D printer the compensated printer data (Please refer to the analysis of claim 5 above),
wherein the at least one printing kinematic parameter comprises at least one of acceleration, jerk, feed rate, an extruder parameter, printing speed of the 3D printer, extrusion rate of the 3D printer, and linewidth of the 3D printer (please refer to the analysis pf claim 2 above),
wherein the (Please refer to the analysis of claim 6 above),
wherein the identifying of the at least one spatial irregularity in the printer data comprises detecting a location of the spatial irregularity in the planned G-code path before execution of the planned G-code path (Please refer to the analysis of claim 6 above),
wherein the locally adjusting of the at least one printing kinematic parameter comprises modifying extruder kinematics in the planned G-code path near the at least one predicted defect-prone region without primarily redesigning an overall planned 3D printing path (Please refer to the analysis of claim 6 above),
wherein the at least one predicted defect-prone region comprises at least one of a high-speed- printing-induced under-extrusion-prone region and a high-speed-printing-induced over-extrusion- prone-region (Please refer to the analysis of claim 9 above), and
wherein the at least one spatial irregularity comprises a gap or non-uniform spacing between adjacent printing lines in the planned G-code path of the 3D object by the 3D printer (Please refer to the analysis of claim 4 above).
As per method claim 12, refer to the analysis of system claim 2, as they recite the same limitations.
As per method claim 13, refer to the analysis of system claim 3, as they recite the same limitations.
As per method claim 14, refer to the analysis of system claim 4, as they recite the same limitations.
As per method claim 15, refer to the analysis of system claim 5, as they recite the same limitations.
As per method claim 16, refer to the analysis of system claim 6, as they recite the same limitations.
As per method claim 17, refer to the analysis of system claim 7, as they recite the same limitations.
As per method claim 18, refer to the analysis of system claim 8, as they recite the same limitations.
As per method claim 19, refer to the analysis of system claim 9, as they recite the same limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to the form 892.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rokeya Alam whose telephone number is (571)272-0083. The examiner can normally be reached on 7:30am - 4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Scott Baderman can be reached at telephone number (571-272-3644). The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118
/SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118
