DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The amendment filed on 09/03/2025 has been received and fully considered.
3. Claims 1-9, 16-17 are presented for examination.
Response to Arguments
4. Applicant's arguments filed 09/03/2025 have been fully considered but they are not persuasive. The rejection under 35 USC 101 has been withdrawn. Regarding applicant’s assertions that: “the rejection should be withdrawn because Ferrar is not analogous art to the claimed invention. Under the standard set forth in In re Bigio, 381 F.3d 1320 (Fed. Cir. 2004). While both Ferrar and the present application relate to powder degradation in additive manufacturing, they approach the problem using fundamentally different methodologies:” And that: “A person of ordinary skill in the art developing a predictive algorithm for powder degradation would not reasonably consult Ferrar’s empirical study. Therefore, it is respectfully submitted that Ferrar is non- analogous art and should not be considered in the 35 U.S.C. § 103 analysis.”, the Examiner respectfully disagrees and asserts that both Zeng et al. and Ferrar are clearly from the same field of endeavor “manufacturing method” via an additive process (see Zeng et al. para [0001] three-dimensional (3-D) printing is a term commonly used to describe processes used to make 3-D objects. In 3-D printing, an additive process may be used to successively layer material to create a 3-D object), and Ferrar para 0002] The present invention relates to a method of manufacture using an additive manufacturing process to build a container, contrary to applicant’s assertions. The Examiner further notes that in response to applicant's argument that Ferrar is non-analogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the Examiner provided a clearly mapping of the references to the claims addressing each limitation. Furthermore, the Examiner pointed to very specific portion within the cited references for motivation to combined the references and that the combination of the cited references clearly render obvious the claimed limitation. Therefore, prima facie of obviousness has clearly been established by the Examiner, contrary to applicant’s assertions.
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 1-9 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng et al. (WO 2016/171649 Al), in view Ferrar (USPG_PUB No. 2020/0376751).
6.1 In considering claim 1, Zeng et al. teaches a computer-implemented method for managing manufacturing power quality, the method comprising:
receiving object model data representing a three-dimensional (3D) object to be manufactured (see abstract, para [0009] Creating a voxel representation of a three-dimensional (3- D) object can include obtaining a shape specification of a 3-D object and a number of objectives for the 3-D object. As used herein, a 3-D object is an object that can be represented along an x-axis, a y-axis, and a z-axis. [0075] As shown at 472 a number of inputs are obtained. The inputs can include a shape specification, a material specification (not shown), more or less inputs than those shown herein can be used to create a voxel representation of the 3-D object at 473, para 0081); voxelizing the 3D object model data to generate a voxel-based representation of the 3D object (see title, abstract, fig.3 (334), “voxelization” para [0009], Creating a voxel representation of a 3-D object can include creating a voxel representation of the 3-D object by assigning a material type from a number of material types to each voxel of the voxel representation that defines the 3-D object. Creating a voxel representation of a 3-D object can include evaluating the voxel representation to determine whether the number of objectives are met. [0031], [0081] As shown at 473, a voxel representation of the 3-D object can be created as described in Figure 3. Creating a voxel representation of the 3-D object can further include classifying the voxels in the voxel representation as exterior voxels, surface voxels, and/or interior voxels, or shell voxels, among other classifications of voxels.); generating, using a variational autoencoder model executed by a processor, a latent space representation of the voxel-based representation (see para [0046] An example of a grid of voxels is provided in Figure 5. As used herein, a voxel represents a value on a 3-D space. The voxel can have a value of solid, VOID (e.g., empty), or a differently defined description of the 3-D space such as a material type. The grid of voxels that is created from the merging of the shape specification 330 and the material specification 332 can be an example of a voxel representation (e.g., model) of the 3-D object. [0093], the voxel representation of the 3-D object can be pre-expanded or/or pre-shrunk. For example, different portion of the voxel representation can be pre-expanded and/or pre-shrunk so that the final fabricated product will have the correct shape.); and outputting a control signal to an additive manufacturing system to adjust a build parameter (see fig.3, para [0011], [0022] In a number of examples, the voxel representation of the 3- D object can be sliced into slice data. Slice data is data derived from the model of the 3-D object that can be provided to and used by the 3-D printer to print the 3-D object. [0063], The tree data structure can be sliced 340 to provide slice data to the 3-D printer. [0070] Providing 346 the slice data to the printer can include progressively streaming the slice data to a 3-D printer to enable the printing of the 3-D object.).
While the term powder degradation is not shown within Zeng et al., he provides for evaluating the 3-D object describing the voxel representation which include evaluating a performance of the 3-D object including performance associated with the 3-D object engineering properties of the 3-D object and/or a performance of fabrication properties of the 3-D object. The performance of the 3-D can meet the 3-D object objectives and/or the 3-D criteria. The engineering properties can include volumetric properties such as stiffness or surface properties such as texture and/or color among other volumetric properties (para 0096), which amounts to how the model degrades over time.
Nonetheless, Ferrar teaches a method of manufacturing that includes predicting, using the variational autoencoder model, a powder degradation metric based on the latent space representation and one or more manufacturing attributes (see title, abstract, para [0015] The AM machine may be caused to build a plurality of containers during a single build, the plurality of containers comprising one or more type of container, each type of container comprising a different type of structure. The unfused powder from each container may then be analyzed separately to determine the extent of degradation caused by the build process. This enables the effect on powder in building different types of structure to be measured. The extent of degradation of powder from at least some of the containers may then be used to predict powder degradation. See further para 0031-34 and [0072] The information obtained may be used to predict how a powder will be degraded when used to build a given article.);
Zeng et al. and Ferrar are analogous art because they are from the same field of endeavor and that the model analyzes by Ferrar is similar to that of Zeng et al. Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng to provide data to the manufacturing system to adjust a build parameter based on the predicted powder degradation metric because Ferrar teaches a more accurate prediction system (para 0067).
6.2 As per claim 2, the combined teaching of Zeng et al. and Ferrar teach the step of flattening a voxel to produce an image (see Zeng et al para [0066], The slice plane can be independent of a voxel size and/or voxel resolution associated with the voxel representation (e.g., the grid of voxels). Independence between the voxel resolution and the slice plane such as a 2D slide can be created due to the data types stored in association with each of the voxels in the grid of voxels); and inputting the image to the variational autoencoder model to determine the latent space representation (para 0047, The multiple data types associated with the voxel can be used to decode (e.g., reconstruct) a number of features from a particular voxel and/or a group of voxels. [0066], For example, the data types can provide the ability to create a slice plane that can be used to provide shape features, via decoding “decode module 112” from within the voxels without dividing a voxel into multiple voxels. The shape features can be reconstructed using surface triangles that are calculated using the data types (e.g., edge data, surface data, volumetric data, and/or nodal data). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.3 With regards to claim 3, the combined teaching of Zeng et al. and Ferrar teach that wherein the latent space representation comprises disentangled latent representation vectors (see Zeng et al. para [0038], A material specification can be a three-dimensional mathematical function that describes a distribution of a material attribute (e.g., planar shapes) with continuous variation of a material quantity (e.g., thermal conductivity, a concentric shape, wavelets, etc.). A distribution can also describe desired variations (discrete or continuous) in a 3-D space that enable the use of arbitrary material distribution patterns including both that of continuous (e.g., a concentric pattern) and that of discrete (e.g., a binary planar pattern). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.4 Regarding claim 4, the combined teaching of Zeng et al. and Ferrar teach the step of concatenating an attribute to the latent space representation (see Zeng et al. para [0026], merge engine 224 can include hardware and/or a combination of hardware and programming, to merge the specifications of the 3-D object to create a voxel representation of the 3-D object. See further para [0031-0032], [0113] linkage of data). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.5 As per claim 5, the combined teaching of Zeng et al. and Ferrar teach that wherein predicting the predicted powder degradation metric is used to predict an amount of degradation of a manufacturing powder (see Zeng et al. para Ferrar para [0015] The AM machine may be caused to build a plurality of containers during a single build, the plurality of containers comprising one or more type of container, each type of container comprising a different type of structure. The unfused powder from each container may then be analyzed separately to determine the extent of degradation caused by the build process. This enables the effect on powder in building different types of structure to be measured. The extent of degradation of powder from at least some of the containers may then be used to predict powder degradation. Further [0032]-[0034]). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.6 Regarding 6, the combined teaching of Zeng et al. and Ferrar teach that wherein the variational autoencoder model is used without a decoder of the variational autoencoder model to determine the latent space representation (see Zeng et al. para [0021], a number of features associated with the 3-D object can be encoded within each of the grid of voxels. The sub-voxels features are encoded by storing data (e.g., data types and/or material types) within a voxel. Para [0047] creating the grid of voxels, each of the voxels of the grid of voxels is defined, to include encoding shape features at a voxel. In a number of examples, the grid of voxels can be encoded with shape features after the grid of voxels are defined. Encoding a shape feature can include storing multiple data types associated with a shape of the 3-D object other than a solid and/or empty value).
6.7 As per claim 7, the combined teaching of Zeng et al. and Ferrar teach that wherein the variational autoencoder model is trained with a decoder (see Zeng et al. para [0023], [0029], features of the 3-D object that have a higher resolution than the resolution provided by the grid of voxels from a given voxel can be decoded such as to train the model. Features with a higher resolution than the resolution provided by any voxel from the grid of voxels can be decoded to print a 3-D object with a higher resolution than the resolution provided by the grid of voxels. [0054] the use of learning modules which allow for training the model. [0105] Examples of learning algorithm can include a genetic learning module, among other types of learning algorithms. Using a genetic learning module to assign material types, the assignment and/or the re-assignment of material types to voxels can be generations of assignments). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.8 With regards to claim 8, the combined teaching of Zeng et al. and Ferrar teach that wherein the variational autoencoder model is trained with a training dataset that is augmented by scaling, translating, or rotating training data (see Zeng et al. para [0029], features of the 3-D object that have a higher resolution than the resolution provided by the grid of voxels from a given voxel can be decoded such as to train the model. Features with a higher resolution than the resolution provided by any voxel from the grid of voxels can be decoded to print a 3-D object with a higher resolution than the resolution provided by the grid of voxels. [0054] the use of learning modules which allow for training the model. [0105] Examples of learning algorithm can include a genetic learning module, among other types of learning algorithms. Using a genetic learning module to assign material types, the assignment and/or the re-assignment of material types to voxels can be generations of assignments), [0062], printing resolution and a number of materials allowed by a 3-D printer can be obtained to print a 3-D object using the tree data structure. The printing resolution and the number of materials can be compared to a default resolution of the tree data structure and the number of materials used in the tree data structure. Based on the comparison, the tree data structure can be scaled 338 to meet the resolution of a particular 3-D printer. [0054]). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.9 Regarding claim 9, the combined teaching of Zeng et al. and Ferrar teach that wherein the variational autoencoder model is trained with a training dataset that is augmented by varying an object distance to a boundary or by varying a disappearance of an object (see Zeng et al. para [0029], features of the 3-D object that have a higher resolution than the resolution provided by the grid of voxels from a given voxel can be decoded such as to train the model. Features with a higher resolution than the resolution provided by any voxel from the grid of voxels can be decoded to print a 3-D object with a higher resolution than the resolution provided by the grid of voxels. [0054] the use of learning modules which allow for training the model. [0105] Examples of learning algorithm can include a genetic learning module, among other types of learning algorithms. [0062], printing resolution and a number of materials allowed by a 3-D printer can be obtained to print a 3-D object using the tree data structure. The printing resolution and the number of materials can be compared to a default resolution of the tree data structure and the number of materials used in the tree data structure. Based on the comparison, the tree data structure can be scaled 338 to meet the resolution of a particular 3-D printer. [0054]). Therefore, it would have been obvious to a person of skill in the art at the time of filing of the application to combine the method of Ferrar with that of Zeng because Ferrar teaches a more accurate prediction (para 0067).
6.10 As per claim 16, the combined teaching of Zeng et al. and Ferrar teach that wherein the one or more manufacturing attributes include one or more of temperature, agent exposure, and voxel location (see Zeng et al. para [0018], instance. An example of mechanical strength requirements includes stress requirements, tension requirements, compression requirements, and/or temperature requirements. A temperature requirement can define a low and/or high temperature that a 3-D object can bear without a shape change and/or without a shape change within a predefined variance. [0085] The surface voxels can also include voxels that are exposed to a shape boundary of the 3-D object. A surface voxel can be exposed to the shape boundary of the 3-D object when a surface of a voxel, an edge of a voxel, and/or a corner of a voxel is exposed to the shape boundary).
6.11 Regarding claim 17, the combined teaching of Zeng et al. and Ferrar teach that wherein the variational autoencoder model is a machine learning model (see Zeng et al. para [0029], [00105] The re-assignment of the material types can be based on a learning algorithm. An example of a learning algorithm can include a genetic learning module, among other types of learning algorithms. Using a genetic learning module to assign material types, the assignment and/or the re-assignment of material types to voxels can be generations of assignments.).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
7.1 Weeks et al. (WO2021123737A1) teaches a method of producing an article by additive manufacturing including the steps of predicting regions of stress in the article, identifying an optimal build orientation for the article and dispensing a first powder and/or a second powder to form the article.
8. Claims 1-9 and 16-17 are rejected and claims 10-15 remain withdrawn from consideration; THIS ACTION IS MADE FINAL. 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.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571) 272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON C PUENTE can be reached on 571-272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 September 28, 2025