CTNF 18/238,978 CTNF 101330 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4,7,9-13,16, 18, and 19 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Rakshit (US 20210107230 A1.) . As per claim 1, Rakshit teaches A computer-implemented method (abstract, para 3) , comprising: receiving a request to deliver one or more microparticles from a start location to a target location along a delivery path ( para 51-52, receiving component video 200 that can be input for printing parameter, this teaches receiving printing request, para 53, “Fabricating the deformable component 104 includes sending the printing parameters 206 to the 4D printer 100 , where the 4D printer 100 reads the printing parameters 206 and prints the deformable component 104 according to the printing parameters 206 .” This sending printing parameter and receiving the parameters by the 4D printer to read and print teaches the delivery path. Also see in para 26, “ The base material 114 can be used in portions of the deformable component 104 that generally retain a static state during deformation. In other words, static portions of the deformable component 104 can move between states, but they are not the cause of the movement. Rather, smart materials 112 in articulating portions of the deformable component 104 can cause changes in orientation or position of base materials 114 in static portions of the deformable component 104.” The base material teaches microparticle; the base material moves with the smart material 112 from a first geometry, orientation, and/or state to a second geometry, orientation {see para 19}; See example of base material in para 26) ; obtaining available characteristic data corresponding to: (i) 4-dimensional (4D) objects capable of delivering microparticles (deformable component 104 including smart material 112 teaches 4D object {para 31}; para 38, “dimensions and geometries of the 4D printed object”) , (ii) the one or more microparticles (para 38, “material data for various portions of the 4D printed object”, that means it includes material data of base material/microparticles) , (iii) the delivery path (para 38, “a design file containing the dimensions and geometries of the 4D printed object”; geometry and dimension indicates along which materials to be printed. Also, para 38 indicated “video of a 4D printed object transforming from a first state to a second state”. Para 19 teaches “change from a first geometry, orientation, and/or state to a second geometry, orientation, and/or state as a result of a stimulus. Here, the differences between a first state and a second state can relate to one or more of (1) changes in dimensions (e.g., expand, contract, elongate, shrink, etc.), (2) changes in properties (e.g., changes in weight, conductivity, magnetism, color, transparency, etc.), (3) changes in orientation (e.g., folded, unfolded, etc.), and/or other changes”), and (iv) one or more ambient environments along the delivery path (para 28, “generating an appropriate environmental stimulus to cause deformation in the deformable component 104 . The deformation unit 108 can be configured to manage one or more properties”, para 29, “When the environmental stimulus is moisture, the deformation unit 108 can include a solution bath, a mister, or a different mechanism for providing moisture to the deformable component 104 .” The ambient environment is referring to the process of generating environmental stimulus and configuring to manage the properties such as moisture, temperature, etc.) ; and using one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location in response to an influencing factor being applied to the 4D object (para 31, the 4D printer includes print manager 116 that can be configured to generate printing parameters and stimulus parameters (to create moisture, for example) for fabricating the deformable component 104. The mechanism of generating stimulus by the print manager teaches influencing factor, para 38-39, machine learning 202 includes corpus 204, “The corpus 204 can include, for example, one or more of a video of a 4D printed object transforming from a first state to a second state, a design file containing the dimensions and geometries of the 4D printed object, material data for various portions of the 4D printed object, processing parameters (e.g., nozzle temperature, backpressure, feed rate, etc.), and/or other properties related to the 4D printed object.”) . As per claim 10, Rakshit teaches A computer program product (para 67) , comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a processor, executable by the processor, or readable and executable by the processor, to cause the processor to (para 67 ): receive a request to deliver one or more microparticles from a start location to a target location along a delivery path (please refer to the analysis of claim 1) ; obtain available characteristic data corresponding to: (i)4-dimensional (4D) objects capable of delivering microparticles (please refer to the analysis of claim 1) , (ii) the one or more microparticles (please refer to the analysis of claim 1) , (iii) the delivery path (please refer to the analysis of claim 1) , and (iv) one or more ambient environments along the delivery path (please refer to the analysis of claim 1) ; and use one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location in response to an influencing factor being applied to the 4D object (please refer to the analysis of claim 1) . As per claim 19, Rakshit teaches A system, comprising (para 67) : a processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to: receive a request to deliver one or more microparticles from a start location to a target location along a delivery path (Please refer to the analysis of claim 1) ; obtain available characteristic data corresponding to: (i) 4-dimensional (4D) objects capable of delivering microparticles (Please refer to the analysis of claim 1) , (ii) the one or more microparticles (Please refer to the analysis of claim 1) , (iii) the delivery path (Please refer to the analysis of claim 1) , and (iv) one or more ambient environments along the delivery path (Please refer to the analysis of claim 1) ; and use one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location in response to an influencing factor being applied to the 4D object (Please refer to the analysis of claim 1) . As per claim 2, Rakshit teaches The computer-implemented method of claim 1, wherein the 4D object includes static materials and smart materials, (Fig. 1, Smart material 112 and base Material 114, para 19, Nozzle 106 can include smart material 112, para 26, the material deposited by the nozzle 106 can further include base material 114, para 31, deformable component 104 including smart material 112 teaches 4D object, also the deforming process includes smart material and base material, base material teaches static material) wherein the smart materials are configured to physically deform in response to the influencing factor being applied to the smart materials of the 4D object ( para 16,“4D printing can use one or more smart materials to create objects that undergo transformation when exposed to environmental stimulus, para 17,using 4D printer for fabricating 4D objects using one or more smart materials., the environmental stimulus such as moisture, temperature, electricity ,magnetism teach influencing factor; para 38-39 describe the fabrication and deformation process, also see para 94) . As per claim 3, Rakshit teaches The computer-implemented method of claim 2, further comprising: training the one or more machine learning models using a repository of characteristic data corresponding to different influencing factors and how they impact the physical deformation of different smart materials (para 38-39, in para 38 “machine learning model 202 having a corpus 204. The corpus 204 can include, for example, one or more of a video of a 4D printed object transforming from a first state to a second state, a design file containing the dimensions and geometries of the 4D printed object, material data for various portions of the 4D printed object, processing parameters (e.g., nozzle temperature, backpressure, feed rate, etc.), and/or other properties related to the 4D printed object.”) . As per claim 4, Rakshit teaches The computer-implemented method of claim 3, wherein the repository includes characteristic data corresponding to different ambient environments and how they impact the physical deformation of the respective smart materials in the repository (para 28, “generating an appropriate environmental stimulus to cause deformation in the deformable component 104 . The deformation unit 108 can be configured to manage one or more properties”, para 29, “When the environmental stimulus is moisture, the deformation unit 108 can include a solution bath, a mister, or a different mechanism for providing moisture to the deformable component 104 .” The ambient environment is referring to the process of generating environmental stimulus and configuring to manage the properties such as moisture, temperature, etc.) para 19, material weight, color conductivity, orientation, para 44, material volume, processing characteristics filament, diameter, melting point, viscosity function teach the descriptive data or characteristic data for the smart and base material. para 23, temperature, electrical current a magnetic field, light, or a solution that apply as stimuli teach the ambient environment, para 38-39 describe the fabrication and deformation process, also see para 94) . As per claim 7, Rakshit teaches The computer-implemented method of claim 1, wherein the characteristic data obtained that corresponds to the one or more microparticles, includes: a shape, a size, and a weight of the microparticles (para 23, an example of smart material 112 is a shape memory polymer can retain two or more shape , para 24, reversable s hap e, para 18, Fig. 1, nozzle 106 can deposit predetermined size of the material, para 19, smart material can change in weigh t, volume/size in para 44, base material and smart material both have a material weight ) . As per claim 9, Rakshit teaches The computer-implemented method of claim 1, further comprising: sending one or more instructions to construct the 4D object (Fig. 4, instruction 400, para 59, computer 400 receives instructions. “The computer 400 is incorporated into the 4D printer 100 and/or the print manager 116.”) . As per claims 11-13,16, and 18, please refer to the analysis of claims 2-4,7, and 9 . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries 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 no obviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 5,14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit (US 20210107230 A1.), and in view of Stahl (US 20240316870 A1.) . As per claim 5, Rakshit teaches The computer-implemented method of claim 2, wherein using the one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location includes (Rakshit, para 38-39, para 94): However, Rakshit does not teach determining a minimum amount of force capable of physically moving the one or more microparticles along the delivery path; and identifying a subset of sample 4D objects that are each configured to generate greater than the minimum amount of force. In the same field of endeavor Stahl teaches determining a minimum amount of force capable of physically moving the one or more microparticles along the delivery path (Stahl, para 3, “The forces that occur during this process depend, among other things, on the exact configuration of the pull-off movement (direction, speed, etc.). On the one hand, these forces must be kept as low as possible to avoid damage to the component; on the other hand, too careful detachment means unnecessarily slow printing. If the time at which the component is detached cannot be detected, then unnecessary travel is always required to ensure detachment, which makes the process even slower.”); Stahl teaches for 3D printing, the force has to be configured accurately to maintain the solidified layer mechanically separated from the bottom. This can prevent the damage and maintain the printing speed. identifying a subset of sample 4D objects that are each configured to generate greater than the minimum amount of force . (Stahl, para 9,"offers the advantage over the active control that no sensor and control components required for active control of the pull-off movement need to be provided with or installed in the 3D printing system to which the invention is applied. The optimization of the pull-off movement according to the invention includes further optimization modes in addition to “maximum speed at given maximum force”, such as minimum force at given maximum movement time.”). Stahl teaches a neural network that provides optimization of the pull-off movement and does not require to have active control or sensor involvement. Therefore, the maximum speed wild be aligned with maximum force and minimum force will be given at maximum movement time. It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify the teaching of Rakshit and to include the teaching of Stahl’s 3D printing with printing force control method into the system. This would have been obvious because both Rakshit and Stahl teach a printing method that require force. Stahl’s optimization of the pull-off movement will control the printing speed and prevent damage (Stahl, para3, para 9). As per claims 14 and 20, please refer to the analysis of claim 5 above . 07-21-aia AIA Claim s 6,8,15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rakshit1 (US 20210107230 A1.), and in view of Rakshit2 (US 20210276269 A1.) . As per claim 6, Rakshit1 teaches The computer-implemented method of claim 1, wherein the influencing factor is selected from the group consisting of: light, heat, magnetic fields, and electricity (para 28, “The deformation unit 108 can be configured to manage one or more properties of: temperature (including rate of change between temperatures and/or dwell times at preset temperatures), relative humidity, moisture content, light (including wavelength, frequency, and/or intensity), magnetism (e.g., a magnetic field ), electrical properties (e.g., current, voltage, resistance, etc.)”, para 29, heating element) . However, Rakshit1 does not teach The computer-implemented method of claim 1, wherein the influencing factor is selected from the group consisting of: sound. In the same field of endeavor Rakshit 2 teaches The computer-implemented method of claim 1, wherein the influencing factor is selected from the group consisting of: sound (Rakshit2, para 30, a (GUI) system that use command line interface to process client sound/voice commands, para 39, printing device include audio sensor) . It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify the teaching of Rakshit1 and to include the teaching of Rakshit2 of adding sound into the influential groups to the 4D object configuration. This would have been obvious because the combination of Rakshit1 and Rakshit2 (in both inventions of Rakshit) apply influential factors for 4D object configuration. By adding Rakshit2’s sound influence, or audio sensor, the user can provide instruction to the printing device. The sound influence will make the overall printing process more convenient. As per claim 15, please refer to the analysis of claim 6 above. As per claim 8, Rakshit1 teaches The computer-implemented method of claim 1 (abstract, para 3) , However, Rakshit1 does not teach wherein using the one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location includes evaluating whether the 4D object is configured to receive a container holding the one or more microparticles. In the same field of endeavor, Rakshit2 teaches wherein using the one or more machine learning models to analyze the available characteristic data and determine a 4D object that is configured to deliver the one or more microparticles to the target location includes evaluating whether the 4D object is configured to receive a container holding the one or more microparticles (Rakshit2, Fig. 2, Figs. 4 & 5 para 42 “case a first printing material facilitates self-movement path creation via dice 281”, As can be seen from Fig.4 dice 281 is a container) . It would have been obvious to a person ordinary skilled in art, before the effective filing date of the claimed invention, to modify the teaching of Rakshit1 and to include the teaching of Rakshit2’s teaching of a container to deliver the printing material into the printing system. This would have been obvious because the combination of Rakshit1 and Rakshit2 (in both inventions of Rakshit) use the method of delivering printing materials though a path. By adding Rakshit2’s configured container, it will deliver the printing material within a self-movement path more efficiently. As per claim 17, please refer to the analysis of claim 8 above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please refer to the form PTO-892 Notice of References Cited. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rokeya Alam whose telephone number is (571)-272-0083. The examiner can normally be reached on 7:30am - 4:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Scott Baderman can be reached at telephone number (571-272-3644). The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /ROKEYA SHAWALI ALAM/Examiner, Art Unit 2118 /SCOTT T BADERMAN/Supervisory Patent Examiner, Art Unit 2118 Application/Control Number: 18/238,978 Page 2 Art Unit: 2118 Application/Control Number: 18/238,978 Page 3 Art Unit: 2118 Application/Control Number: 18/238,978 Page 4 Art Unit: 2118 Application/Control Number: 18/238,978 Page 5 Art Unit: 2118 Application/Control Number: 18/238,978 Page 6 Art Unit: 2118 Application/Control Number: 18/238,978 Page 7 Art Unit: 2118 Application/Control Number: 18/238,978 Page 8 Art Unit: 2118 Application/Control Number: 18/238,978 Page 9 Art Unit: 2118 Application/Control Number: 18/238,978 Page 10 Art Unit: 2118 Application/Control Number: 18/238,978 Page 11 Art Unit: 2118 Application/Control Number: 18/238,978 Page 12 Art Unit: 2118 Application/Control Number: 18/238,978 Page 13 Art Unit: 2118 Application/Control Number: 18/238,978 Page 14 Art Unit: 2118