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 Amendment
Applicant has submitted the following:
Claims 1-15 are pending examination; and
Claims 1-14 are newly amended.
Response to Arguments
Applicant's arguments filed 10/21/2025 have been fully considered but they are not fully persuasive.
Applicant has amended claim 9 to overcome the rejection under 35 USC 112.
Examiner finds the amendment has addressed the rejection, and therefore the rejection under 35 USC 112 is withdrawn.
Applicant argues that independent claim 1 has been amended to overcome the rejection under 35 USC 101. Specifically, Applicant argues that the amended limitations including “scanning a surface of the manufactured object to determine the measured surface comprising the surface deviation relative to the corresponding surface of the model” and “based on the measurement, causing adjustment of a control parameter or a calibration parameter of an additive manufacturing apparatus by which the manufactured object was manufactured; and causing manufacture of a subsequent object according to the adjusted control parameter and/or the adjusted calibration parameter of the additive manufacturing apparatus” particularizes the claimed invention, thereby integrating the judicial exception into a practical application. Applicant further argues the same analysis applies to independent claim 11.
Examiner finds the argument persuasive. The recited adjustment of the additive manufacturing apparatus for the manufacture of a subsequent object by scanning and based on the measurement taken integrates the abstract idea into a practical application.
Therefore, the rejection of claims 1-15 under 35 USC 101 is withdrawn.
Applicant argues that the prior art does not teach the newly amended independent claims 1 and 11. Specifically, Applicant argues that previously cited Chen does not teach a “manufactured object”, but a “object that is being fabricated”. Further, Applicant argues that none of the prior art teaches the “manufacture of a subsequent object according to the adjusted control or the adjusted calibration parameter”.
Examiner respectfully disagrees. Regarded the newly amended limitation that the object is a “manufactured object”, Chen teaches the object as a manufactured object ([0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”). Under broadest reasonable interpretation, a partially fabricated object (i.e. “object being manufactured”) is a manufactured object. Even if the manufacturing process is incomplete, the object is manufactured.
Regarding the newly amended limitation of independent claim 1, pertaining to “causing manufacture of a subsequent object”, newly cited Buller et al. (US 20200004225 A1) teaches an analogous method of taking measurements of a manufactured object (Abstract, “detection system” of [0111]), wherein subsequent objects are manufactured according to adjusted values (Fig. 7, step 712 and step 717). Similar analysis applies to the corresponding newly amended limitation of independent claim 11.
Therefore, claims 1-15 are rejected under 35 USC 103 (see detailed action below).
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.
Claim(s) 1-6 and 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20200215761 A1, previously cited) in view of Buller et al. (US 20200004225 A1).
Regarding claim 1, Chen teaches A method of making a measurement (Abstract; Fig. 1) relating to a manufactured object (object 121; ([0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”) manufactured (Fig. 1, printer 100) according to a model (model 190), the manufactured object having a measured surface ([0029] lines 4-15, “the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”) comprising a surface deviation relative to a corresponding surface of the model ([0038] lines 3-13, “In FIG. 3, the expected surface height is represented as a line 418. The expected surface height is useful for comparison to the actual printed surface height. For example, when fabricating the object 121, discrepancies between the model 190 and the actual material deposited often occur. Even if the jets are controlled to deposit a planned thickness, the achieved thickness of the layer may deviate from the plan due to variable or unpredicted characteristics of the jetting and/or due to changes after jetting such as during curing or flowing on the surface before curing.”), the method comprising:
scanning a surface of the manufactured object ([0029] “A sensor 160 (sometimes referred to as a scanner) is positioned above the object under fabrication 121 and is used to determine physical characteristics of the partially fabricated object. For example, the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”) to determine the measured surface comprising the surface deviation relative to the corresponding surface of the model ([0038] lines 4-6, “The expected surface height is useful for comparison to the actual printed surface height.” and lines 17-20, “The expected height 418 derived from the model 190 can act as a constraint on the depth reconstruction procedure 300 in that it can be used to resolve ambiguities in the 3D scanning process.”);
obtaining measured surface data representing at least a portion of the measured surface comprising the said surface deviation (Fig. 2, steps 301 and 302);
determining a measurement surface (energy array/image 313) corresponding to the measured surface of the manufactured object ([0050] lines 1-11, “the smoothed data set 311 is provided as input to a sixth step 312 that processes the smoothed data set 311 to generate an energy array/image 313. As introduced above, the OCT data provides a response as a function of depth, but does not directly yield a surface height, or even a value that represents where the surface may be. To help identify the surface height, the energy array/image 313 is determined. The energy array/image 313 can be represented as a function that has a low value (as represented by a black or dark gray color) where the surface is present and high value where there is no surface.”) by performing a statistical analysis of at least a portion of the measured surface data (Fig. 2, steps 309-313; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”) in respect of an interpretation direction ([0029] lines 11-15, “the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”), the direction of the gradient is the interpretation direction, such that a portion of the measured surface data is offset from the measurement surface in the interpretation direction (Fig. 5, [0046] lines 7-20, “If the printer were to print perfectly, all the data in the aligned data set 309 would be aligned along one horizontal line. For example, making the data flat removes the variable delta (i.e., the amount of printed material) from the problem. At a point (x,y), a variable z is slightly oscillating above and below a level (i.e., the horizontal line). Put another way, shifting the data by the expected depth (after printing each layer) effectively aligns all surfaces (after printing each layer) to be along one horizontal line. Due to the inaccuracies in printing this is typically not the case but there should be a continuous line (e.g., from left to right in the figure)—this is because there is continuity in the surface as it is being printed”). The measured surface data not being aligned with the expected depth (e.g. horizontal line of Fig. 5), is offset from the measurement surface in the interpretation direction;
making a measurement relating to the manufactured object depending on the measurement surface (successive scan data 301; [0030] lines 1-11, “in the context of a digital feedback loop for additive manufacturing, the additive manufacturing system builds the object by printing layers. The sensor 160 captures the 3D scan information after the system 100 prints one or more layers. For example, the sensor 160 scans the partial object (or empty build platform), then the printer prints a layer (or layers) of material. Then, the sensor 160 scans the (partially built) object again. The new depth sensed by the sensor 160 should be at a distance that is approximately the old depth minus the thickness of layer”; [0033] “The controller 110 includes a sensor data processor 111 that implements a depth reconstruction procedure (described in greater detail below). The sensor data processor 111 receives the model 190 as well as scan data from the sensor 160 as input. As described below, the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”). The successive scan data, taken after the depth reconstruction is determined and the fabrication plan is modified, is based on the measurement surface.
based on the measurement, causing adjustment of a control parameter or a calibration parameter ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”) of an additive manufacturing apparatus by which the manufactured object was manufactured (Fig. 1). The modification of the fabrication plan based on the depth reconstruction is the adjustment of a control parameter of an additive manufacturing apparatus.
Chen does not teach the method comprising:
causing manufacture of a subsequent object according to the adjusted control parameter and/or the adjusted calibration parameter of the additive manufacturing apparatus.
Buller teaches an analogous method of making a measurement (Abstract; Fig. 1; detection system of [0111]), comprising:
causing manufacture of a subsequent object according to the adjusted control parameter and/or the adjusted calibration parameter (Fig. 7; [0137] lines 75-81, “ monitoring is done before, during and/or after printing. The monitoring may use historical measurements (e.g., as an analytical tool and/or to set a threshold value). Monitoring of one or more aspects of formation can optionally be used to (e.g., directly) modify the forming instructions (e.g., FIG. 7, 713) and/or adjust the one or more simulations (e.g., FIG. 7, 715).”; lines 119-131, “The roughness can be measured with a surface profilometer. In some cases, the analysis provides data concerning geometry of the object(s). In some cases, the analysis provides data concerning one or more material properties (e.g., porosity, surface roughness, grain structure, internal strain and/or chemical composition) of the object(s). In some embodiments, the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”; [0137] lines 104-111, “The target signal may be a value, a set of values, or a function (e.g., a time dependent function). The one or more 3D objects may optionally be analyzed (e.g., FIG. 7, 716). In some embodiments, a target (e.g., thermal) signal is obtained from historical data of 3D objects (or portions thereof) that have been analyzed. In some embodiments, the object(s) or portion(s) thereof is analyzed using an inspection tool”) of the additive manufacturing apparatus (Fig. 1, 3D printer 100). The adjustment of simulation in the formation of subsequent objects and modification of the forming instructions thereof, wherein the monitoring and adjustment may occur during or after printing, and wherein the adjustment includes a target based on historical data of 3D objects, is the causing the manufacture of a subsequent object according to the adjustment of a control parameter of the additive manufacturing apparatus.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chen to include the manufacture of a subsequent object of Buller because it would yield predictable and advantageous results. The manufacture of subsequent objects according to an adjusted control parameter based on previous measurements would predictably enable the additive manufacturing apparatus to be controlled to alter its manufacturing process after the surface deviate is determined. Further, it would yield advantageous results of, due to the adjustment of the control parameter, manufacturing a subsequent object without the surface deviation of the preceding manufactured object.
Regarding claim 2, Chen in view of Buller teaches The method of claim 1, comprising determining the interpretation direction depending on a surface fit to reference surface data representing at least a portion of a reference surface of the manufactured object (Chen: [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”; [0058]” Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space.”). The interpretation direction (i.e. direction of gradient) depends on a surface fit to reference surface data representing at least a portion of a reference surface of the object (output estimated depth data, based on spatio-temporal or spatial neighborhood data of the object).
Regarding claim 3, Chen in view of Buller teaches The method of claim 2, wherein the reference surface of the manufactured object comprises a surface of the manufactured object other than the measured surface (Chen: [0058]- [0059] “Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space. Referring to FIG. 9, the optimal depth estimate mapped into the input space 321 is shown along with the successive scan data 301 and the expected height data 418 for the (x, y) position.”). The mapped optimal depth estimate, as shown in Fig. 9, is a surface other than the measured surface.
Regarding claim 4, Chen in view of Buller teaches The method of claim 1, wherein determining the measurement surface based on the statistical analysis comprises identifying a data point corresponding to a kth percentile data point of the portion of the measured surface data ordered in respect of the interpretation direction and determining the measurement surface depending on the identified data point (Chen: [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”; Buller: [0174] lines 1-3, “At times, an energy beam is directed onto a specified area of at least a portion of the target surface for a specified time period.”). The median filter, among other statistical approaches for determining the measurement surface, identifies a kth percentile data point, wherein the median is the 50th percentile.
Regarding claim 5, Chen in view of Buller teaches The method of claim 1, wherein determining the measurement surface by performing the statistical analysis comprises: orienting, or aligning and orienting (Chen: [0049] lines 3-7, “In some embodiments, a simple horizontal filter is applied to the array/image when the data is aligned. In other embodiments, the filter needs to follow (e.g., be oriented with) the depth changes when the data is not aligned.”), an interpretation surface with respect to the measured surface data (Chen: [0059] “Referring to FIG. 9, the optimal depth estimate mapped into the input space 321 is shown along with the successive scan data 301 and the expected height data 418 for the (x, y) position.”); and
providing the interpretation surface at a position along the interpretation direction depending on the statistical analysis (Chen: Fig. 9; [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”).
Regarding claim 6, Chen in view of Buller teaches The method of claim 1, wherein determining the measurement surface by performing the statistical analysis comprises determining the measurement surface based on a statistical analysis of a restricted portion of the measured surface data corresponding to a predefined portion of the measured surface (Chen: [0041] lines 4-8, “If the system does not print a given location in between the scans this means that the scan data should be the same for the two scans. For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data.”; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”). The median filter with limited extent is the statistical analysis of a restricted portion of the measured surface data, wherein each limited extent is a predefined portion. Further, the averaging of data where the system did not print between scans is performing a statistical analysis of a portion corresponding to a predefined portion of the measured surface.
Regarding claim 8, Chen in view of Buller teaches The method of claim 1, wherein the measurement surface has an orientation depending on the interpretation direction (Chen: [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”; [0058]” Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space.”). The surface determined (estimated depth data) has the orientation dependent on the interpretation direction (the direction given by the gradient).
Regarding claim 9, Chen in view of Buller teaches The method of claim 1, further comprising causing adjustment of a pre- compensation factor to be applied to the model (Chen: [0032]-[0033] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160. The controller 110 includes a sensor data processor 111 that implements a depth reconstruction procedure (described in greater detail below). The sensor data processor 111 receives the model 190 as well as scan data from the sensor 160 as input. As described below, the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”; Buller: [0137] lines 29-36, “The structural correction may comprise any pre-print correction to the model of the requested 3D object that may result in reduced deformation of the formed 3D object and adherence to the requested dimensionality constraints of the 3D object that is formed. The structural correction may comprise a geometric correction to the geometric model of the requested 3D object.”). The pre-print correction to the model, such that is used for the fabrication plan, is the pre-compensation factor applied to the model.
Regarding claim 10, Chen in view of Buller teaches The method of claim 1, wherein the portion of the measured surface data offset from the measurement surface in the interpretation direction (Chen: [0046] lines 7-20, “If the printer were to print perfectly, all the data in the aligned data set 309 would be aligned along one horizontal line. For example, making the data flat removes the variable delta (i.e., the amount of printed material) from the problem. At a point (x,y), a variable z is slightly oscillating above and below a level (i.e., the horizontal line). Put another way, shifting the data by the expected depth (after printing each layer) effectively aligns all surfaces (after printing each layer) to be along one horizontal line. Due to the inaccuracies in printing this is typically not the case but there should be a continuous line (e.g., from left to right in the figure)—this is because there is continuity in the surface as it is being printed”) comprises noise of the measured surface data (Chen: [0041] lines 6-9, “For these instances, the data reduction process might average the scan data or choose a value from one of the scans to reduce the data. Averaging data typically reduces the measurement noise.”), outlier data representing outlier portions of the measured surface data, or both noise and outlier data. The average of measurement data, including measurement noise of the measurement surface, reduces but does not eliminate the noise from the data, therefore the measured surface data is offset.
Regarding claim 11, Chen teaches An apparatus (Fig. 1) comprising processing circuitry (controller 110; [0071] lines 15-19, “Some or all of the processing may be performed on a special purpose computer, or using special-purpose hardware, such as coprocessors or field-programmable gate arrays (FPGAs) or dedicated, application-specific integrated circuits (ASICs).”) to:
obtain measured surface data ([0029] lines 4-15, “the sensor 160 measures one or more of the surface geometry (e.g., a depth map characterizing the thickness/depth of the partially fabricated object) and subsurface characteristics (e.g., in the near surface comprising, for example, 10s or 100s of deposited layers). The characteristics that may be sensed can include one or more of a material density, material identification, and a curing state. Very generally, the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”) representing at least a portion of a measured surface (Fig. 2, steps 301 and 302) of a manufactured object (object 121; [0034] “Referring to FIG. 2, the depth reconstruction module 111 processes the model 190 of the part being fabricated and the successive scan data 301 in a depth reconstruction procedure 300 to determine, for a particular (x, y) coordinate on a 3D object being manufactured, an optimal depth estimate for each scan performed during the additive manufacturing process.”), the portion of the measured surface of the manufactured object comprising a surface deformation ([0038] lines 3-13, “In FIG. 3, the expected surface height is represented as a line 418. The expected surface height is useful for comparison to the actual printed surface height. For example, when fabricating the object 121, discrepancies between the model 190 and the actual material deposited often occur. Even if the jets are controlled to deposit a planned thickness, the achieved thickness of the layer may deviate from the plan due to variable or unpredicted characteristics of the jetting and/or due to changes after jetting such as during curing or flowing on the surface before curing.”). The discrepancies and deviations from the expected height are the surface deformation;
determine a statistical metric based on a statistical analysis of at least a portion of the measured surface data (Fig. 2, steps 309-313; [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”) in respect of an interpretation direction ([0029] lines 11-15, “the measurements from the sensor 160 are associated with a three-dimensional (i.e., x, y, z) coordinate system where the x and y axes are treated as spatial axes in the plane of the build surface and the z axis is a height axis (i.e., growing as the object is fabricated).”; [0051] lines 2-6, “the energy image is constructed as a function of a gradient in the vertical direction combined with a weighting function, where gradients that are closer to the top of the image are weighted more than the later gradients.”), the direction of the gradient is the interpretation direction;
determine a virtual measurement surface (energy array/image 313) depending on the statistical metric (median and mean filters of [0048], statistical approaches of [0062]);
make a measurement depending on the virtual measurement surface (successive scan data 301; [0030] lines 1-11, “in the context of a digital feedback loop for additive manufacturing, the additive manufacturing system builds the object by printing layers. The sensor 160 captures the 3D scan information after the system 100 prints one or more layers. For example, the sensor 160 scans the partial object (or empty build platform), then the printer prints a layer (or layers) of material. Then, the sensor 160 scans the (partially built) object again. The new depth sensed by the sensor 160 should be at a distance that is approximately the old depth minus the thickness of layer”; [0033] “The controller 110 includes a sensor data processor 111 that implements a depth reconstruction procedure (described in greater detail below). The sensor data processor 111 receives the model 190 as well as scan data from the sensor 160 as input. As described below, the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”). The successive scan data, taken after the depth reconstruction is determined and the fabrication plan is modified, is based on the virtual measurement surface.
based on the measurement, causing adjustment of a control parameter or a calibration parameter ([0032] “The controller 110 uses a model 190 of the object to be fabricated to control motion of the build platform 130 using a motion actuator 150 (e.g., providing three degrees of motion) and control the emission of material from the jets 120 according to non-contact feedback of the object characteristics determined via the sensor 160.”; [0033] lines 5-11, “the sensor data processor 111 processes the model 190 and a time history of scan data (referred to as ‘successive scan data’) from the sensor 160 to determine a high-quality depth reconstruction of the 3D object being fabricated. The depth reconstruction is provided to a planner 112, which modifies a fabrication plan for the 3D object based on the depth reconstruction.”) of the apparatus by which the manufactured object was manufactured (Fig. 1). The modification of the fabrication plan based on the depth reconstruction is the adjustment of a control parameter of an additive manufacturing apparatus.
Chen does not teach the apparatus comprising:
manufacture of a subsequent object according to the adjusted control parameter and/or the adjusted calibration parameter of the additive manufacturing apparatus.
Buller teaches an analogous apparatus for making a measurement (Abstract; Fig. 1; detection system of [0111]), comprising:
manufacture of a subsequent object according to the adjusted control parameter and/or the adjusted calibration parameter (Fig. 7; [0137] lines 75-81, “ monitoring is done before, during and/or after printing. The monitoring may use historical measurements (e.g., as an analytical tool and/or to set a threshold value). Monitoring of one or more aspects of formation can optionally be used to (e.g., directly) modify the forming instructions (e.g., FIG. 7, 713) and/or adjust the one or more simulations (e.g., FIG. 7, 715).”; lines 119-131, “The roughness can be measured with a surface profilometer. In some cases, the analysis provides data concerning geometry of the object(s). In some cases, the analysis provides data concerning one or more material properties (e.g., porosity, surface roughness, grain structure, internal strain and/or chemical composition) of the object(s). In some embodiments, the analysis data is compared to requested data. For example, a geometry of the printed object(s) may be compared with the geometry of the requested object(s). In some embodiments, the analysis data is used (e.g., FIG. 7, 717) to adjust the simulation (e.g., FIG. 7, 710). The adjusted simulation may be used, for example, in formation of subsequent object(s).”; [0137] lines 104-111, “The target signal may be a value, a set of values, or a function (e.g., a time dependent function). The one or more 3D objects may optionally be analyzed (e.g., FIG. 7, 716). In some embodiments, a target (e.g., thermal) signal is obtained from historical data of 3D objects (or portions thereof) that have been analyzed. In some embodiments, the object(s) or portion(s) thereof is analyzed using an inspection tool”) of the apparatus (Fig. 1, 3D printer 100). The adjustment of simulation in the formation of subsequent objects and modification of the forming instructions thereof, wherein the monitoring and adjustment may occur during or after printing, and wherein the adjustment includes a target based on historical data of 3D objects, is the causing the manufacture of a subsequent object according to the adjustment of a control parameter of the additive manufacturing apparatus.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Chen to include the manufacture of a subsequent object of Buller because it would yield predictable and advantageous results. The manufacture of subsequent objects according to an adjusted control parameter based on previous measurements would predictably enable the additive manufacturing apparatus to be controlled to alter its manufacturing process after the surface deviate is determined. Further, it would yield advantageous results of, due to the adjustment of the control parameter, manufacturing a subsequent object without the surface deviation of the preceding manufactured object.
Regarding claim 12, Chen in view of Buller teaches The apparatus of claim 11, wherein the processing circuitry further: determines the interpretation direction based on a virtual surface (Chen: expected surface height 418; [0061] “While the discussion above relates mostly to the use of temporal data to improve a depth reconstruction, it is notes that the techniques above can also use spatio-temporal data (e.g., rather than simply tracking a single (x, y) position over time, the algorithm can additionally track a spatial neighborhood of positions around (and beneath in the z direction) the (x, y) position to improve the depth reconstruction”). The expected surface height, wherein the spatial neighborhood is tracked, is the be virtual surface. based on reference surface data representing a reference surface of the object (Chen: [0038] “The model 190 is used by the depth reconstruction procedure 300 to derive an expected surface height for each layer of the partially fabricated object 121.”).
Regarding claim 13, Chen in view of Buller teaches The apparatus of claim 12 wherein the reference surface of the manufactured object comprises a surface of the manufactured object other than the measured surface (Chen: [0058]- [0059] “Referring again to FIG. 2, the optimal depth estimate 317 is provided as input to an eighth step 318 that maps the optimal depth estimate 317 back to the input space by inverting the column shift step (i.e., step 308) and expands the compressed columns (e.g., those compressed in the data reduction process step 304). The output (sometimes referred to as ‘estimated depth data’) 321 of the eighth step 318 is the optimal depth estimate for the scan at each (x, y) position in the original input space. Referring to FIG. 9, the optimal depth estimate mapped into the input space 321 is shown along with the successive scan data 301 and the expected height data 418 for the (x, y) position.”). The mapped optimal depth estimate, as shown in Fig. 9, is a surface other than the measured surface.
Regarding claim 14, Chen in view of Buller teaches The apparatus of claim 11 wherein determining the statistical metric comprises determining a kth percentile data point of the portion of the measured surface data in respect of the interpretation direction (Chen: [0048] “Referring again to FIG. 2, the aligned data set 309 is provided as input to fifth step 310, which smooths the aligned data 309 to regularize the aligned data 309, generating a smoothed dataset 311. In practice, such a smoothing operation may be performed by a filtering the aligned data set 309 using, for example, a Gaussian filter, a median filter, or mean filter with limited extent (e.g.,3-8).”; [0062] lines 9-20, “As another alternative, a statistical approach may be used in which for each deposited layer, there is an expected achieved thickness and a variance (square of the standard deviation) of that thickness. For example, calibration data may be used to both determine the average and the variance. Similarly, the OCT data may yield an estimate of the height (e.g., based on the rate of change from low to high response), and the variance of that estimate may be assumed or determined from the data itself. Using such statistics, the estimate of the height after the t.sup.th layer may be tracked, for example, using a Kalman filter or other related statistical approach.”; Buller: [0174] lines 1-3, “At times, an energy beam is directed onto a specified area of at least a portion of the target surface for a specified time period.”). The median filter, among other statistical approaches for determining the measurement surface, identifies a kth percentile data point, wherein the median is the 50th percentile.
Regarding claim 15, Chen in view of Buller teaches A non-transitory machine-readable storage medium having executable instructions stored thereon which (Chen: [0071] lines 1-11, “The software may be stored in non-transitory form, such as being embodied in a volatile or non-volatile storage medium, or any other non-transitory medium, using a physical property of the medium (e.g., surface pits and lands, magnetic domains, or electrical charge) for a period of time (e.g., the time between refresh periods of a dynamic memory device such as a dynamic RAM). In preparation for loading the instructions, the software may be provided on a tangible, non-transitory medium, such as a CD-ROM or other computer-readable medium (e.g., readable by a general or special purpose computing system or device)”), when executed, cause processing circuitry (Chen: controller 110) to perform the method of claim 1.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Buller as applied to claim 1 above, and further in view of Spink et al. (US 20180250745 A1, previously cited).
Regarding claim 7, Chen in view of Buller teaches The method of claim 1.
Chen in view of Buller does not teach wherein the surface deviation of the measured surface comprises a sink.
Spink teaches an analogous method of measuring the surface deviation of an object made by additive manufacturing, wherein the surface deviation of the measured surface comprises a sink ([0226] lines 17-23, “The exterior surface can have regions of relative depression (e.g., FIG. 51B, 5122 or FIG. 51C, 5142) and regions of relative elevation (e.g., FIG. 51B, 5124 or FIG. 51C, 5144). The regions of relative elevation may be referred to as peaks. The regions of relative depression may be referred to as valleys. The peaks and valleys may be an alternating series of peaks and valleys.”; [0301] lines 1-10, “In some examples, at least a portion of the 3D object can be vertically displaced (e.g., sink) in the material bed. At least a portion of the 3D object can be surrounded by pre-transformed material within the material bed (e.g., submerged). At least a portion of the 3D object can rest in the pre-transformed material without substantial vertical movement (e.g., displacement). Lack of substantial vertical displacement can amount to a vertical movement (e.g., sinking) of at most about 40%, 20%, 10%, 5%, or 1% of the layer thickness.”). The valleys and vertically displaced sinks are the sink of the surface deviation.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Chen in view of Buller to include the sink of Spink because measuring a surface deviation comprising a sink would yield predictable results including detecting where the object has deviated from the model, such as a lack of deposited material in the manufacturing process, thereby advantageously enabling diagnosis of deviation and correction to be taken.
Conclusion
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.
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/B.B.G./Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863