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 .
This office action is a response to an amendment filed 03/11/2026.
Claims 1-20 are pending.
Claims 1-3, 5, 9-11, 13, and 17-19 are amended.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 9, and 17, the claims respectively recite the limitations “sending one or more instructions to use one or more initial machine learning models to analyze the available characteristic data associated with the received request; sending one or more instructions to construct a 4-dimensional (4D) object output by the initial machine learning models;” (Or a variation thereof. Emphasis added by the Examiner). It’s not clear if “the initial machine learning models” is the same as the “one or more initial machine learning models,” some different initial learning models, or in the case that the one or more learning models is only one model, what the additional learning models of “the initial machine learning models” are used for. There is insufficient antecedent basis for this limitation in the claim. For the purpose of examination, the limitation is being broadly interpreted to be any learning model.
Response to Arguments
Applicant’s arguments filed 03/11/2026 have been fully considered but they are moot in view of new grounds of rejection. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Rejections based on newly cited references(s) and interpretations of the previously cited prior art follow.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 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 1-4 and 7-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chinese Patent Publication No. CN111796518A to Zhou et al., (hereinafter Zhou. English translation of CN111796518A is included and cited in this office action), in view of US Patent Publication No. 2018/0081347 to Matei et al., (hereinafter Matei), and in further view of US Patent Publication No. 2021/0107230 to Rakshit et al., (hereinafter Rakshit)
Regarding claim 1, Zhou teaches a computer-implemented method, comprising:
sending one or more instructions to apply an initial influencing factor to smart materials of the 4D object (A smart material of an actuating object can be controlled by applying an influencing stimuli factor, such as a magnetic field, where the smart material can be a deformable shape memory alloy of an actuator, and thus a 4D object consistent with the instant application specification, see P4-p5, p8, Zhou), wherein the 4D object is configured to deliver one or more microparticles from a start location to a target location along a delivery path in response to the initial influencing factor being applied to the smart materials (A smart material is controlled by applying an a magnetic field influencing factor that drives the deformable material at the micro level to cause displacement, meaning the particles of the material are deformed (delivered) from a starting point to a target displacement position along a path range of the displacement, and as a result of the influencing factor, see P4-p5, p8, Zhou), wherein the smart materials are configured to physically deform in response to the initial influencing factor being applied thereto (Smart material that deforms in response to an applied factor, such as magnetic field, and static materials such as a magnetic field inducer, see p4-5, p8, Zhou);
sending one or more instructions to monitor movement of the 4D object along the delivery path in response to applying the initial influencing factor to the smart materials (In control of a smart material actuation, an error of the desired control, such as displacement, is determined, which has the implication that the displacement movement path is monitored on applied influencing factor, see P46, p4-5, p8, Zhou);
and in response to determining the 4D object has deviated from the delivery path (An error is an indication of the deviation of the desired control, such as the deviation from the desired displacement, see p46, p4-5, p8, Zhou), sending one or more instructions to use one or more secondary machine learning models to dynamically weight the initial influencing factor applied to the smart materials (A learning model is used in control using influence factor, and its parameters, including weights, are updated so as minimize the loss function, meaning to improve determined error/deviation from desired, and where said learning model can be interpreted as a termed secondary learning model, see p21-23, p10, p43-49, p63-67, p73-80, p98-101, p134-139, p4-5, p8, Zhou).
Although Zhou suggests instructions.
Matei from the same or similar field of controlled micro-objects, more explicitly teaches instructions (A controller commands application of a generating forces for movement of micro-objects, thus there are instructions to apply influencing factors, see p37, p14, Matei).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by Zhou and incorporating instructions, as taught by Matei.
One of ordinary skill in the art would have been motivated to do this modification in order to actually direct a component to perform a desired action as required in a control system for implementation of control (see p37, p14, Matei).
Zhou does not explicitly teach in response to receiving a microparticle delivery request, obtaining available characteristic data that corresponds to the received request; sending one or more instructions to use one or more initial machine learning models to analyze the available characteristic data associated with the received request; sending one or more instructions to construct a 4-dimensional (4D) object output by the initial machine learning models;
However, Rakshit, from the same or similar field of stimulus deformable smart materials teaches in response to receiving a microparticle delivery request, obtaining available characteristic data that corresponds to the received request (Characteristic data, such as characteristic change data (e.g. from video), is obtained as a result of a print fabrication of the 4D objects that has the implication of a request for delivery of microparticles, see p17, p16, p2, p38, Rakshit);
sending one or more instructions to use one or more initial machine learning models to analyze the available characteristic data associated with the received request (Learning models used to analyze characteristic data, such as component video, by a print manager associated with a requested print, and where said model used in printing can be interpreted as being termed as an initial learning model, see p38-p40, p17, p16, p2, Rakshit);
sending one or more instructions to construct a 4-dimensional (4D) object output by the initial machine learning models (A 4D object can be actually constructed based on output by learning model of printing parameters for an object based on characteristic data of a desired object, see p16-17, p38-p40, p2, 18-19, 30-32, 34-35, Rakshit);
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating characteristic data and learning models used in construction of a 4D object , as taught by Rakshit.
One of ordinary skill in the art would have been motivated to do this modification in order to better learn and provide adequate fabrication parameters that will provide a formed 4D object with desired characteristics by analyzing and learning from desired objects having the desired characteristics of an object incorporating deformable smart materials (see p34-35, 38-40, 16-17, p38-p40, p2, 18-19, 30-32, 34-35, Rakshit).
Regarding claim 2, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Zhou further teaches determining an amount of a control generated by a 4D object in response to an initial influencing factor being applied to a smart materials (In control of a smart material actuation, an error is determined. The error is of the desired control in view of an actual applied control, such as in relation to displacement control. This has the implication that a control amount generated is determined, see P46, p4-5, p8, Zhou); comparing an amount of a control generated by a 4D object, to a movement of the 4D object along a delivery path in response to applying an initial influencing factor to the smart materials (In control of a smart material actuation, an error is determined. The error constitutes a comparison of the desired control in view of an actual applied control, such as the desired control to the actual movement, see P46, p4-5, p8, Zhou); and generating a weight value configured to adjust movement of a 4D object back along a delivery path in response to applying the weight value to an initial influencing factor (A learning model is used in control using influence factor, and its parameters, including weights, are updated so as minimize the loss function, meaning to improve the system by adjusting control, such as movement, back towards desired, see p21-23, p10, p43-49, p63-67, p73-80, p98-101, p134-139, p4-5, p8, Zhou).
Matei further explicitly teaches a force as generated control (An amount of force is generated for movement control, see Abs., p31, 14, Matei)
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating consideration of force, as taught by Matei.
One of ordinary skill in the art would have been motivated to do this modification in order to provide an amount of a property capable of inducing movement in a desired object so as to accomplish a desired controlled movement (see Abs. p31, p37, p14, Matei).
Regarding claim 3, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Rakshit further teaches wherein a 4D object includes a smart materials and static materials (A 4D object can include smart materials and static materials, see p53, Rakshit).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating smart and static materials in a 4D object, as taught by Rakshit.
One of ordinary skill in the art would have been motivated to do this modification in order to better form objects with desired design characteristics, such as deformable portions where articulation may be desired and static sections where a component can move between states that are not the cause of movement (see p53, 26, Rakshit).
Regarding claim 4, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Zhou further teaches wherein smart materials are configured to generate a force capable of physically moving a 4D object, as a result of being physically deformed (Smart material that deforms to physically displace an actuator, thus a force is generated, see p4-5, p8, Zhou).
Regarding claim 7, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Zhou further teaches wherein an influencing factor is selected from the group consisting of: light, heat, magnetic fields, sound, and electricity (Smart materials can include materials (e.g. piezoelectric ceramic, shape memory alloys, etc.) that can be influences by factors such as magnetic fields, electricity, etc. see p4-5, p8, Zhou ).
Regarding claim 8, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Zhou further teaches 4D object and a delivery path (A smart material of an actuating object is controlled by applying an a magnetic field that drives the deformable material at the micro level to cause displacement, meaning the particles of the material are deformed (delivered) from a starting point to a target displacement position along a path range of the displacement, and as a result of the influencing factor, see P4-p5, p8, Zhou).
Matei further teaches further comprising: in response to determining that an object has not deviated from a desired, sending one or more instructions to maintain an initial influencing factor applied to materials (Micro-objects are controlled for movement to a desired location, and while there is a deviation from the objects not reaching the desired location, a signal to maintain movement through an applied influence is maintained, as can be seen by the loop in fig. 12 that is formed while a desired location is not reached, see Fig. 12, abs., p31, 14, 57, 60, Mitei); and in response to determining that the object has reached a target location, sending one or more instructions to remove the initial influencing factor from being applied to the materials (Micro-objects are controlled for movement to a desired location, and when there is a desired location has been reached, the control ends, as seen in fig. 12. This has the implication that the field inducing force is removed, see Fig. 12, abs., p31, 14, 57, 60, Mitei).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating control while needed, as taught by Matei.
One of ordinary skill in the art would have been motivated to do this modification in order to properly output a needed control variable while the desired target of the control has not been attained, and to stop application of the control when the target has been reaches so as to not overshoot the desired target, and as the purpose of control a loop (see Fig. 12, abs., p31, 14, 57, 60, Matei).
Claim 9 is rejected on the same grounds as claim 1 (regarding claim 9, it is noted that the instant specification, in paragraph 29, states that storage medium is not to be construed as transitory signals per se).
Claim 10 is rejected on the same grounds as claim 2.
Claim 11 is rejected on the same grounds as claim 3.
Claim 12 is rejected on the same grounds as claim 4.
Claim 15 is rejected on the same grounds as claim 7.
Claim 16 is rejected on the same grounds as claim 8.
Claim 17 is rejected on the same grounds as claim 1.
Claim 18 is rejected on the same grounds as claim 2.
Claim 19 is rejected on the same grounds as claim 3.
Claim 20 is rejected on the same grounds as claim 8.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou, in view of Matei, in further view of Rakshit, and in further view US Patent Publication 2025/0383244 to Kitano et al., (hereinafter Kitano)
Regarding claim 5, the combination of Zhou, Matei, and Rakshit teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Zhou does not explicitly teach wherein one or more machine learning models are trained using a repository of characteristic data corresponding to different influencing factors and how they impact a physical deformation of different smart materials.
However, Kitano from the same or similar field of flexible materials, more explicitly teaches wherein one or more machine learning models are trained using a repository of characteristic data corresponding to different influencing factors and how they impact a physical deformation of different smart materials (A learning model for a flexibly deformable material is trained, using learning data that includes characteristics of stimuli for the material, and is stored, see p11, 69, p5, p135, Abs., Kitano).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating a specified training set and repository, as taught by Kitano.
One of ordinary skill in the art would have been motivated to do this modification in order to better properly train a model with desired data that had been collected, and is indicative of a characteristic of interest for such as stimuli that affects a material (see p135, 69, Abs., p11, 69, p5, p135, Kitano).
Regarding claim 6, the combination of Zhou, Matei, Rakshit, and Kitano teaches all the limitations of the base claim as outlined above, and are analyzed as previously discussed with regard to that claim.
Kitano further teaches wherein a repository includes characteristic data corresponding to different ambient environments and how they impact a physical deformation of a respective smart materials in the repository (A storage of characteristics of stimuli impacting material, where the data is stored , see 133, p11, 69, p5, p135, Abs., Kitano).
It would have been obvious to a person of ordinary skill in the art before the filing date of the claimed invention to modify the micro level influence based control as described by the combination that includes Zhou and incorporating a specified training set and repository, as taught by Kitano.
One of ordinary skill in the art would have been motivated to do this modification in order to better properly train a model with desired data that had been collected, and is indicative of a characteristic of interest for such as stimuli that affects a material (see p135, 69, Abs., p11, 69, p5, p135, Kitano).
Claim 13 is rejected on the same grounds as claim 5.
Claim 14 is rejected on the same grounds as claim 6.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ceriani, US. Patent Publication No. 2022/0274178 teaches 3D printed parts and a shape-memory element that can be trained to learn different physical forms at different temperatures.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILIO J SAAVEDRA whose telephone number is (571)270-5617. The examiner can normally be reached M-F: 9:30am-5:30pm (EST).
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert E Fennema can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILIO J SAAVEDRA/Primary Patent Examiner, Art Unit 2117