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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 12/1/23 has been considered by the examiner.
Election/Restrictions
Applicant's election with traverse of claims 1-9 in the reply filed on 5/21/26 is acknowledged. The traversal is on the ground(s) that the International Searching Authority (ISA) did not reject these claims for lack of unity of invention, even though the ISA was considering the same prior art. This is not found persuasive because the examiner’s statement had identified that the only shared common technical feature among the three Groups was to a composition input for receiving a composition of component materials for one or more constituent parts of the item and a set of associated properties for the composition from a composition determination system, wherein at least some of the associated properties are specified properties for the constituent part or parts of the item. The examiner then demonstrated that such a feature is taught in the prior art, such as Sauza et at. (WO 2020072109 A1). As such, this shared technical feature is not special, but common, and therefore there exists lack of unity of invention a posteriori. The examiner maintains the position of lack of unity of invention as the specifics of such a position were not specifically argued by applicant.
The requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 18-20 are rejected under 35 U.S.C. 112(a), because the specification, while being enabling for optimising the composition property model until it is adapted to predict some properties for some material compositions, does not reasonably provide enablement for optimising the composition property model until it is adapted to predict associated properties for an arbitrary material composition. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims.
There are many factors to be considered when determining whether there is sufficient evidence to support a determination that a disclosure does not satisfy the enablement requirement and whether any necessary experimentation is "undue." These factors include, but are not limited to:
(A) The breadth of the claims;
(B) The nature of the invention;
(C) The state of the prior art;
(D) The level of one of ordinary skill;
(E) The level of predictability in the art;
(F) The amount of direction provided by the inventor;
(G) The existence of working examples; and
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988)
The broadest reasonable interpretation of claim 18 encompasses optimising the composition property model until it is adapted to predict associated properties (any property) for an arbitrary material composition (any material). The specification discloses sufficient information for one of ordinary skill in the art to optimize the composition property model until it is adapted to predict known properties for material compositions which are either in the database or combinations of materials in the database which, when combined, yield properties which can be predictability determined. However, the specification does not provide direction on how to optimize the composition property model until it is adapted to predict any property for any arbitrary material composition, such as including materials which are not in the database, or properties which are not reliably predictable. Considering Wands factor (A), the claims are broad enough to encompass all properties of all known materials (arbitrary), and all properties of any combination of all known materials, which is incredibility broad. At the time of filing, the state of the art was such that universities and research institutions are continually working on improving predictions of limited property sets of binary and/or ternary alloys. See, for example, Zhu et al., Experimental determination of the Ni-Cr-Ru phase diagram and thermodynamic reassessments of the Cr-Ru and Ni-Cr-Ru systems, Intermetallics, 64 (2015) pp. 86-95. Perry's Chemical Engineers' Handbook has been used by engineers and chemists for over 85 years to look up various properties of various materials. The current edition, Green et al., Perry's Chemical Engineers' Handbook, 9th Edition, McGraw Hill, 2018, has 2272 pages. A single model that was truly able to predict any property for any arbitrary material composition would be inventive full stop. Thus, the disclosed guidance provided in the specification does not bear a reasonable correlation to the full scope of the claim. Taking these factors into account, undue experimentation would be required by one of ordinary skill in the art to practice the full scope of claims 18-20.
Claim Rejections - 35 USC § 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 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.
Language from the reference(s) is shown in quotations. Limitations from the claims are shown in quotations within parentheses. Examiner explanations are shown in italics.
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 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.
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 1-5, 7-9, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sauza et at. (WO 2020072109 A1), previously cited in view of Pluke et al. (WO 2019180466 A1), from IDS.
Regarding claim 1, Sauza teaches that “the present inventors observed that it may be desirable to have different physical properties at different regions of an additively manufactured part” (which reads upon “a method of additive manufacture of a constituent part of an item, comprising providing a specification of characteristics for the constituent part”, as recited in the instant claim; paragraph [0025]). Sauza teaches that “the system and method according to the present disclosure utilizes at least two different powders having different compositions” (which reads upon “a composition comprising a plurality of component materials”, as recited in the instant claim; paragraph [0031]). Sauza teaches that “the composition, physical properties, and size of each powder may be chosen based on desired physical properties of the powder for the additive manufacturing process and/or the physical properties of a part or part region formed from the powder” (which reads upon “a composition comprising a plurality of component materials predicted to comply with the specification of characteristics”, as recited in the instant claim; paragraph [0036]). Sauza teaches that “the parts described herein may be manufactured via any appropriate additive manufacturing technique described in ASTM F2792-12a, and that in one embodiment, an additive manufacturing process includes depositing successive layers of powder and then selectively melting and/or sintering the powder to create, layer-by-layer, a part” (which reads upon “manufacturing the constituent part of the item according to the composition identified by the composition determination system using an additive manufacturing system”, as recited in the instant claim; paragraph [0064]). Sauza teaches that “the first powder may be configured with a high strength and a low ductility, and the second powder may be configured with a low strength and high ductility comparative to the first powder, and that the first powder and second powder may be disposed in different pre-selected regions of the part to create part regions that are stronger than other region and part regions that are more ductile than other regions” (paragraph [0036]). Sauza teaches that “the part 400 can comprise various physical properties in different regions of the part 400, and the composition of individual layers and regions of individual layers can be varied by suitable selection of individual powders or powder blends to produce gradients of properties as one moves from location to location in the part 400” (paragraph [0058]).
Sauza is silent regarding using a composition determination system, wherein the composition determination system is a system trained using a database of known materials to identify a composition.
Pluke is similarly concerned with the field of manufacturing and particularly, but not exclusively, to a technique of dynamically controlling an additive manufacturing apparatus to optimise preparation of materials for printing with the correct constitution (page 1). Pluke teaches that “the performance of an additive manufacturing system is largely determined by the available printing hardware and materials for deposition, but advancements can also be made to the control and driving of such hardware, in terms of the selection of process parameters and materials to be used, and optimal definition of a print path based on a product design file” (page 1). Pluke teaches that “such developments enable printing performance to be improved, when measured in terms of parameters such as material costs, speed, structural integrity and resolution of produced objects, which extends the range of applications in which additive manufacturing can be used reliably, in many cases replacing more conventional manufacturing techniques” (page 1). Pluke teaches that “as additive manufacturing becomes more and more prevalent, as described above, and as hardware choices, desired material compositions and functional properties become more variable, it becomes more and more difficult to determine exactly which combination of process parameters and material selections should be made in order to produce a particular product with particular features, such as mechanical strength or flexibility, with a given machine” (page 2). Pluke teaches “a method of optimising material selection for use in dynamic control of a manufacturing apparatus” (which reads upon “using a composition determination system”, as recited in the instant claim; page 3). Pluke teaches a “means for receiving a specification of a product to be manufactured, and user preferences, and means for determining one or more optimal material configurations to be used to manufacture the specified product, which satisfies one or more conditions set out in the user preferences, wherein the means for determining the one or more optimum material configurations uses a machine learning algorithm to process historical data from manufacturing processes in the prediction of the configuration of a product manufactured according to the one or more optimal material configurations for the additive manufacturing system” (which reads upon “using a composition determination system, wherein the composition determination system is a system trained using a database of known materials to identify a composition comprising a plurality of component materials predicted to comply with the specification of characteristics”, as recited in the instant claim; page 4). Pluke teaches that “information stored in said one or more databases 26 may include customer preferences, material specifications and machine specifications, and action plans and their success, to be described in more detail below” (which reads upon “a database of known materials”, as recited in the instant claim; page 16). Pluke teaches that “the model is initialised to output a linear proportional property predictions, and then trained using historical data from testing of material mixtures of varying ratios, and that the training data can be generated by manually creating different mixtures and testing them or by automatically generating the mixtures via the additive manufacturing apparatus described above” (which reads upon “wherein the composition determination system is a system trained using a database of known materials”, as recited in the instant claim; page 24). Pluke teaches that “products can be thus manufactured using controllable material constitution, whether a highly specialised material for a particular voxel, or a particular allocation of material constitutions across a plurality of voxels within a geometry in order to achieve particular properties in the printed product” (pages 6-7). Pluke teaches that “it is possible to customise or optimise the design of a particular product such that parameters such as cost-saving and sustainability can be maximised and such that functional properties can be achieved with optimum material selections” (pages 8-9).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Sauza to optimize preparation of materials for printing with the correct constitution, (which reads on using a composition determination system, wherein the composition determination system is a system trained using a database of known materials to identify a composition as stated above) as taught by Pluke in order to manufacture products using controllable material constitution, whether a highly specialized material for a particular voxel, or a particular allocation of material constitutions across a plurality of voxels within a geometry in order to achieve particular properties in the printed product while enabling printing performance to be improved, when measured in terms of parameters such as material costs, speed, structural integrity and resolution of produced objects, thus extending the range of applications in which additive manufacturing can be used reliably, in many cases replacing more conventional manufacturing techniques.
Regarding claim 2, modified Sauza teaches the method of claim 1 as stated above. Pluke teaches that “if the material is a new material, for example a material created by the Material Generator that has a mixture of materials that has not been manufactured before, then the Machine Database may not contain any information relating to how to operate a suitable production means, and that in such an instance it may be necessary to undertake a calibration phase as defined below” (pages 24-25). Pluke teaches that “the calibration phase provides data to build a model that determines predicted machine parameters or settings that should be utilised for the target material profile” (which reads upon “further materials properties are established by the composition determination system, and wherein these further materials properties are used for determining settings for the additive manufacturing system”, as recited in the instant claim; page 25). Pluke teaches that “the machine (the production means) is run and a sample of the material is printed, and that the sample material can be analysed in real time (for example by using spectroscopy to identify the quantity of each different material in a material combination) whilst the material is being printed, and/or by testing after printing of the sample has been completed” (page 25). Pluke teaches that “the analysis can be used to determine the optimum setting for the machine parameters (for example by Action Plans as defined below), for example via Scenario Builder as explained in embodiments below” (page 25). Pluke teaches that “if the material and the production means are known then there will already be information in the database and a model that can be utilised to determine how to operate the production means to produce the product” (page 25).
Regarding claim 3, modified Sauza teaches the method of claim 1 as stated above. Sauza teaches that “utilizing different powder compositions can create parts comprising complex metal matrix composite materials” (paragraph [0032]). Sauza teaches that “the composition, physical properties, and size of each powder may be chosen based on desired physical properties of the powder for the additive manufacturing process and/or the physical properties of a part or part region formed from the powder” (paragraph [0036]). Sauza teaches that “the first powder and second powder may be disposed in different pre-selected regions of the part to create part regions that are stronger than other region and part regions that are more ductile than other regions” (paragraph [0036]).
Regarding claim 4, modified Sauza teaches the method of claim 3 as stated above. Sauza teaches that “within each layer 112, 114 the composition of the powder can change” (paragraph [0037]). Sauza teaches that “region 112a of layer 112 may comprise a first powder composition that is different than a second powder composition in region 112b of layer 112” (paragraph [0037]). Sauza teaches that “the change in the powder composition from the first powder composition in region 112a to the second powder composition in region 112b can be gradual or steep” (paragraph [0037]). Sauza teaches that “the individual layers of the part 400 can comprise a gradient composition as one moves along a layer 402a-402n from a first side 400a to a second side 400b of the particular layer, and that the composition of individual layers also can differ from one layer to another and, therefore, a gradient composition also can be present as one moves from layer to layer from the third side 400c to the fourth side 400d of the part 400” (paragraph [0058]). Sauza teaches that “the part 400 can comprise various physical properties in different regions of the part 400, and the composition of individual layers and regions of individual layers can be varied by suitable selection of individual powders or powder blends to produce gradients of properties as one moves from location to location in the part 400” (paragraph [0058]).
Regarding claim 5, modified Sauza teaches the method of claim 3 as stated above. Sauza teaches that “the additively manufactured parts can be utilized in at least one of the aerospace field (e.g., aerospace component), automotive field (e.g., automotive component), transportation field (e.g., transportation component), or building and construction field (e.g., building component or construction component)” (paragraph [0075]).
Regarding claim 7, modified Sauza teaches the method of claim 1 as stated above. Sauza teaches “a dispenser communicating with the first reservoir and the second reservoir, the dispenser adapted to blend the first powder from the first reservoir with the second powder from the second reservoir in a preselected ratio to form blended powder” (paragraph [0079], clause 10; adapted to blend reads on mixer). Sauza teaches “to selectively sinter and/or melt powder in a selected region of an exposed layer of powder in the powder bed deposition region 102 to fuse the powder in the selected region of the layer together and to an immediately adjacent underlying layer” (paragraph [0042]).
Regarding claim 8, modified Sauza teaches the method of claim 1 as stated above. Pluke teaches that “if the material is a new material, for example a material created by the Material Generator that has a mixture of materials that has not been manufactured before, then the Machine Database may not contain any information relating to how to operate a suitable production means, and that in such an instance it may be necessary to undertake a calibration phase as defined below” (pages 24-25). Pluke teaches that “the calibration phase provides data to build a model that determines predicted machine parameters or settings that should be utilised for the target material profile” (page 25). Pluke teaches that “the machine (the production means) is run and a sample of the material is printed, and that the sample material can be analysed in real time (for example by using spectroscopy to identify the quantity of each different material in a material combination) whilst the material is being printed, and/or by testing after printing of the sample has been completed” (page 25). Pluke teaches that “the analysis can be used to determine the optimum setting for the machine parameters (for example by Action Plans as defined below), for example via Scenario Builder as explained in embodiments below” (page 25).
Regarding claim 9, modified Sauza teaches the method of claim 1 as stated above. Pluke teaches that “the apparatus may further comprise means for determining an optimum configuration of an additive manufacturing system for manufacturing the product according to the one or more optimal material configurations, which satisfies one or more conditions set out in the user preferences, wherein the means for determining the optimum configuration of the manufacturing apparatus uses a machine learning algorithm in the prediction of the configuration of a product manufactured according to the one or more optimal material configurations for a particular manufacturing apparatus” (page 4).
Regarding claim 18, modified Sauza teaches the method of claim 1 as stated above. Art is applied to an enabled portion of the scope of claim 18 in light of the 112(a) rejection above. Pluke teaches that “if the material is a new material, for example a material created by the Material Generator that has a mixture of materials that has not been manufactured before, then the Machine Database may not contain any information relating to how to operate a suitable production means, and that in such an instance it may be necessary to undertake a calibration phase as defined below” (pages 24-25). Pluke teaches that “the calibration phase provides data to build a model that determines predicted machine parameters or settings that should be utilised for the target material profile” (page 25). Pluke teaches that “the machine (the production means) is run and a sample of the material is printed, and that the sample material can be analysed in real time (for example by using spectroscopy to identify the quantity of each different material in a material combination) whilst the material is being printed, and/or by testing after printing of the sample has been completed” (page 25). Pluke teaches that “the apparatus may further comprise means for determining an optimum configuration of an additive manufacturing system for manufacturing the product according to the one or more optimal material configurations, which satisfies one or more conditions set out in the user preferences, wherein the means for determining the optimum configuration of the manufacturing apparatus uses a machine learning algorithm in the prediction of the configuration of a product manufactured according to the one or more optimal material configurations for a particular manufacturing apparatus” (page 4). Pluke teaches that “the apparatus may further comprise means for updating behaviour modelled by the machine learning algorithm based on monitoring of the output of the manufacturing process” (page 5). Pluke teaches that “the apparatus may further comprise means for generating test data by modelling the expected output from a manufacturing process performed using the additive manufacturing system using a plurality of different manufacturing process parameters and material configurations, and for training the behaviour modelled by the machine learning algorithm using the test data” (page 5). Pluke teaches that “wherein determining the one or more optimum material configurations uses a machine learning algorithm to process historical data from manufacturing processes in the prediction of the configuration of a product manufactured according to the one or more optimal material configurations for the additive manufacturing system” (page 4).
Regarding claim 20, modified Sauza teaches the method of claim 18 as stated above. Pluke teaches that “the machine (the production means) is run and a sample of the material is printed, and that the sample material can be analysed in real time (for example by using spectroscopy to identify the quantity of each different material in a material combination) whilst the material is being printed, and/or by testing after printing of the sample has been completed” (page 25). Pluke teaches that “the analysis can be used to determine the optimum setting for the machine parameters (for example by Action Plans as defined below), for example via Scenario Builder as explained in embodiments below” (page 25).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sauza et at. (WO 2020072109 A1), previously cited, and Pluke et al. (WO 2019180466 A1), from IDS, as applied to claim 5 above, and further in view of Schiffres et al. (US 20200049415 A1).
Regarding claim 6, modified Sauza teaches the method of claim 5 as stated above. Sauza teaches that “the additively manufactured parts can be utilized in at least one of the aerospace field (e.g., aerospace component), automotive field (e.g., automotive component), transportation field (e.g., transportation component), or building and construction field (e.g., building component or construction component)” (paragraph [0075]).
Sauza is silent regarding wherein the component is a heatsink.
Schiffres is similarly concerned with additive manufacturing (paragraph [0002]). Schiffres teaches that “laser or electron-beam additive manufacturing of metal structures on ceramic and glass substrates” (paragraph [0024]). Schiffres teaches that “as glasses, ceramics and metals are widely used in various industries, such as semiconductor, automotive, aerospace, defense, medical, environmental control, it is often beneficial and useful to build structures containing glass or ceramics and metals” (paragraph [0015]). Schiffres teaches that “one benefit of building on ceramic or glass is to enable direct manufacture of heat removal devices and/or electrical connections on opto-electronic substrates, integration of conventional composite materials into metal additive, heterogeneous integration of dissimilar materials, and for easy parting of the desired part from the support structure and/or the buildplate” (which reads upon “heatsink”, as recited in the instant claim; paragraph [0026]; heat removal devices reads on heatsink). Schiffres teaches “layers of a 3D printed heat removal device made on a metallized silicon substrate” (paragraph [0170]). Schiffres teaches that “it is another object to provide a method of forming a heatsink for an integrated circuit, comprising: depositing a metallic powder on an integrated circuit substrate; and locally heating the metallic power to a sufficient temperature to melt the metallic powder with focused energy, having limited duration at a particular region to avoid heat-induced functional damage to the integrated circuit, and cooling the melted metallic powder to form a solid layer, to form an adherent bond between the integrated circuit substrate and the solid layer” (which reads upon “wherein the component is a heatsink”, as recited in the instant claim; paragraph [0077]). Schiffres teaches that “the substrate may comprise an integrated circuit having a deposited metal layer, the solid layer is metallic, the fused interface layer comprises an intermetallic composition, and the regional pattern is configured as a heatsink for the integrated circuit” (paragraph [0103]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the aerospace component, automotive component, transportation component, building component or construction component, of Sauza to include an integrated circuit and a heatsink, as taught by Schiffres because integrated circuits are a key component in the automotive and aerospace industries, and heatsinks provide heat removal allowing the integrated circuit to operate in wider temperature ranges.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Sauza et at. (WO 2020072109 A1), previously cited, and Pluke et al. (WO 2019180466 A1), from IDS, as applied to claim 18 above, and further in view of Wang, X., Xiong, W. Uncertainty quantification and composition optimization for alloy additive manufacturing through a CALPHAD-based ICME framework. npj Comput Mater 6, 188 (2020). https://doi.org/10.1038/s41524-020-00454-9.
Regarding claim 19, modified Sauza teaches the method of claim 18 as stated above.
Sauza and Pluke are silent regarding adjusting the hyperparameters of the model.
Wang is similarly concerned with properties of additive manufacturing (AM) components (page 1). Wang teaches that “we analyzed the process–structure–property relationships for 450,000 compositions around the nominal composition of HSLA-115” (page 1; HSLA is high-strength low-alloy steel). Wang teaches that “properties that are critical for the performance, such as yield strength, impact transition temperature, and weldability, were evaluated to optimize the composition” (page 1). Wang teaches that “the influence of uncertainty in the chemical composition of feedstock is often overlooked” (page 1). Wang teaches that “high-strength low-alloy (HSLA)-115 (115 corresponds to minimum achievable tensile yield strength in ksi, which is equivalent to 793 MPa) steel was chosen to demonstrate the effectiveness of this design framework” (page 1). Wang teaches that “the composition and processing parameters were taken as inputs for the decision tree model, CALPHAD-based thermodynamic model, and Graville diagram, and that the outputs from these models, such as the dislocation density, matrix composition, and etc. were coupled with the physics-based strengthening, ITT, and weldability evaluation models to calculate the yield strength, ITT, and weldability that includes the freezing range and Graville diagram index for each composition” (page 3). Wang teaches that “finally, the calculated properties for each composition were used to find the optimized composition for AM that will give the highest chance of a successful build that meets all property requirements” (page 3). Wang teaches that “the model parameter uncertainty is originated from the fact that some parameters used in the model are not accurate enough” (page 7). Wang teaches that “for example, the Hall–Petch coefficient used in this work is determined from references, while it may not be precisely the same for the alloy composition studied in this work, and it may lead to a discrepancy between the model output and experiments” (page 7). Wang teaches that “such uncertainties can be minimized by performing experiments to measure the parameters or collecting more literature to gain a more robust understanding of the value of these parameters” (page 7). Wang teaches that “such uncertainties can be reduced by generating more and unbiased training data, optimizing the hyper-parameters, etc.” (which reads upon “wherein optimising the composition property model comprises adjusting the hyperparameters of the model”, as recited in the instant claim; page 7).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the model of Pluke by optimizing the hyper-parameters, as taught by Wang to minimize the model parameter uncertainty.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA JANSSEN whose telephone number is (571)272-5434. The examiner can normally be reached on Mon-Thurs 10-7 and alternating Fri 10-6.
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/REBECCA JANSSEN/Primary Examiner, Art Unit 1733
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