DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 04/02/2026 has been entered. Claims 1, 5-6 and 8-12 have been amended. Claims 7 and 13-20 remain canceled. Accordingly, claims 1-6 and 8-12 remain pending and are the claims addressed and examined below.
Response to Arguments
Applicant's arguments filed 04/02/2026 have been fully considered but they are not persuasive. Applicant submits the following argument:
“None of the cited references, in combination nor individually, disclose, teach, suggest, motivate
or otherwise describe a controller configured to receive a tuple of values including a first value
associated with a material property parameter of at least one of the plurality of materials of the
flowable mixture, a second value associated with an environmental parameter of the flowable
mixture, and a third value associated with a control parameter used by the controller to produce
the flowable mixture, to generate the flowable mixture by inputting the tuple of values into an
algorithm, the flowable mixture including the third value used as at least one of a plurality of
control parameters configured to control operation of the mixing plant and a 3D printing system
configured to receive the flowable mixture, and to generate the flowable mixture from the
mixing plant, the mixing plant being operated according to the plurality of control parameters,
and to provide the flowable mixture to the 3D printing system to form a structure in accordance
with a machine-learning algorithm configured to evaluate the flowable mixture to generate data
used to identify the flowable mixture as a trained mixture, wherein one or more environmental
parameters as a subset of the control parameters include one or more of ambient temperature,
humidity, and wind speed, wherein the controller is configured to facilitate deposition of the
trained mixture as a function of at least the one or more environmental parameters, whereby
the machine-learning algorithm is trained in real-time to provide the data used to identify the
flowable mixture as a trained mixture of the cementitious material for deposition as a portion of
a 3D structure printed using a construction printer having a moveable gantry coupled to a
dispensing system.” See pages 2-3.
The Examiner respectfully disagrees. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Moreover, as walked through in the rejections set forth below, Le Roux, modified by Daczko, disclose the claimed controller configured to receive a tuple of values including a first value associated with a material property parameter of at least one of the plurality of materials of the flowable mixture, a second value associated with an environmental parameter of the flowable mixture, and a third value associated with a control parameter used by the controller to produce the flowable mixture, to generate the flowable mixture by inputting the tuple of values into an algorithm, the flowable mixture including the third value used as at least one of a plurality of control parameters configured to control operation of the mixing plant and a 3D printing system configured to receive the flowable mixture, and to generate the flowable mixture from the mixing plant, the mixing plant being operated according to the plurality of control parameters, and to provide the flowable mixture to the 3D printing system to form a structure in accordance with a machine-learning algorithm configured to evaluate the flowable mixture to generate data used to identify the flowable mixture as a trained mixture, wherein one or more environmental parameters as a subset of the control parameters include one or more of ambient temperature, humidity, and wind speed, wherein the controller is configured to facilitate deposition of the trained mixture as a function of at least the one or more environmental parameters, whereby the machine-learning algorithm is trained in real-time to provide the data used to identify the flowable mixture as a trained mixture of the cementitious material for deposition as a portion of a 3D structure printed using a construction printer having a moveable gantry coupled to a dispensing system.
For at least the reasons set forth above, Applicant’s argument is not found persuasive and Le Roux and Daczko remain pertinent and applicable to the amended claims, as set forth in the rejections below.
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 1-6 and 8-12 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 claim 1: it is not clear if the recitation “second value associated with an environmental parameter” in line 8 and “wherein one or more environmental parameters” in line 18 are referring to the same or different environmental parameter(s). Claims 2-6 and 8-12 are rejected due to their dependency on claim 1.
Regarding claim 6: the recitation “the second value of an environmental parameter at the mixer” in line 2 is indefinite as it is not clear if this environmental parameter is the same or different from the “second value associated with an environmental parameter of the flowable mixture” in line 8 of claim 1.
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.
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.
Claims 1-6 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Le Roux (US 2020/0282593; of record) in view of Daczko et al. (US 2022/0129797; of record).
As to claim 1: Le Roux discloses the claimed system (i.e., construction system 10) (Le Roux at [0042], [0101], FIG. 1, FIGs. 25-27), comprising:
a portable mixing plant (i.e., construction system 10 includes material delivery system 400 which mixes extrudable building material at the construction site; material system 400 is disposed on trailer 402 and is attached to a vehicle via hitch 404 and towed to a construction site) including a plurality of containers configured to receive and separately store a plurality of materials including at least a liquid, an aggregate, and a binding material (i.e., material delivery system 400 includes one or more water storage tanks 410, and a dry ingredient hopper 412 which includes storage capacity for powders, gravel and other dry ingredients of a cement mixture) (Le Roux at [0101], [0102], [0103], [0105], FIGs. 28-29), and
a mixer configured to receive and to mix the plurality of materials from the plurality of containers and to provide a flowable mixture including cementitious material (i.e., material delivery system 400 includes mixing unit 414, and the mixing unit 414 receives water from tanks 410 via line 411 and dry ingredients from hopper 412 via line 413 in order to mix the liquid and dry ingredients during operation and deliver the cement mixture for subsequent use within printing assembly 150) (Le Roux at [0103], [0104], [0105], [0107], FIGs. 28-30).
Le Roux discloses the material delivery system 400 including a controller 450, and during mixing operations controller 450 actuates pump 422 and auger 432 via drivers 423 and 430, respectively, to deliver water from tank 410 and dry ingredients from hopper 412 into volume 415 of mixing unit 414; where the controller 450 and mixing unit 414 form a rheometer for ensuring that the extrudable building material delivered to printing assembly 150 includes a consistent mixture for subsequent use within printing assembly 150 movably disposed on gantry 50 (Le Roux at [0100], [0108], [0112]-[0118], FIGs. 25-26, FIGs. 28-30).
Though, Le Roux fails to explicitly disclose the claimed controller configured to receive a tuple of values including a first value associated with a material property parameter of at least one of the plurality of materials of the flowable mixture, a second value associated with an environmental parameter of the flowable mixture, and a third value associated with a control parameter used by the controller to produce the flowable mixture, to generate the flowable mixture by inputting the tuple of values into an algorithm, the flowable mixture including the third value used as at least one of a plurality of control parameters configured to control operation of the mixing plant and a 3D printing system configured to receive the flowable mixture, and to generate the flowable mixture from the mixing plant, the mixing plant being operated according to the plurality of control parameters, and to provide the flowable mixture to the 3D printing system to form a structure in accordance with a machine-learning algorithm configured to evaluate the flowable mixture to generate data used to identify the flowable mixture as a trained mixture, wherein one or more environmental parameters as a subset of the control parameters include one or more of ambient temperature, humidity, and wind speed, wherein the controller is configured to facilitate deposition of the trained mixture as a function of at least the one or more environmental parameters, whereby the machine-learning algorithm is trained in real-time to provide the data used to identify the flowable mixture as a trained mixture of the cementitious material for deposition as a portion of a 3D structure printed using a construction printer having a moveable gantry coupled to a dispensing system.
However, in the same field of endeavor – mixing cementitious materials, Daczko teaches a system and method for formulating or evaluating a construction composition (Daczko at Title). Daczko further teaches a computing architecture 700 that may comprise a plurality of common computing elements, including controllers, that may execute optimization logic 122 for optimizing construction mixture 114, construction admixture 116 and/or the final construction composition 118 based on the inputs to the optimization logic 122 (i.e., a controller configured to receive a tuple of values to generate the flowable mixture by inputting the tuple of values into an algorithm) (Daczko at [0067], [0140], FIG. 4).
Daczko further teaches the inputs to the optimization logic 122 including job specification 120, the job specification 120 specifying parameters relating to the fresh properties 202 such as viscosity 214 (i.e., including a first value associated with a material property parameter of at least one of the plurality of materials of the flowable mixture) (Daczko at [0032], [0073], FIG. 4); real-time sensor data 414 including thermometers for measuring ambient temperature (i.e., a second value associated with an environmental parameter of the flowable mixture; wherein one or more environmental parameters as a subset of the control parameters include one or more of ambient temperature, humidity, and wind speed; ) (Daczko at [0032], [0070], [0084] FIG. 4); and variables/mapping 412 including variables such as the amount of aggregate, water, or other materials used (i.e., a third value associated with a control parameter used by the controller to produce the flowable mixture; the flowable mixture including the third value used as at least one of a plurality of control parameters configured to control operation of the mixing plant and a 3D printing system configured to receive the flowable mixture, and to generate the flowable mixture from the mixing plant, the mixing plant being operated according to the plurality of control parameters) (Daczko at [0032], [0079], FIG. 4).
Moreover, Daczko teaches optimization logic 122 including a machine learning algorithm such that the optimization logic 122 may be trained using historical or current data and may learn how various components can be mixed together to achieve target performance parameters (i.e., to provide the flowable mixture to the 3D printing system to form a structure in accordance with a machine-learning algorithm configured to evaluate the flowable mixture to generate data used to identify the flowable mixture as a trained mixture; wherein the controller is configured to facilitate deposition of the trained mixture as a function of at least the one or more environmental parameters) (Daczko at [0068], [0070], [0095]-[0096])
Additionally, Daczko teaches a non-transitory computer-readable storage medium storing logic for a machine learning algorithm configured to select a combination of construction mixtures and admixtures; a hardware interface configured to receive training data, the training data comprising a predefined construction composition and associated performance characteristics for the predefined construction composition; and a hardware processor circuit configured to: train the machine learning algorithm based on the training data; receive, via the interface, one or more performance requirements for a new construction composition, and use the machine learning algorithm to select the new construction composition based on the received performance requirements, wherein the new construction composition is output using the interface (i.e., whereby the machine-learning algorithm is trained in real-time to provide the data used to identify the flowable mixture as a trained mixture of the cementitious material for deposition as a portion of a 3D structure printed using a construction printer having a moveable gantry coupled to a dispensing system) (Daczko at [0037], [0051]).
It is noted the limitations requiring controlling the operation of a 3D printing system is interpreted as an intended use of the claimed apparatus and as such is given little patentable weight.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify or combine the Le Roux invention of an apparatus for a portable mixing plant for cementitious material, as discussed above, with the Daczko teaching of optimizing the cementitious material ingredients based on a material property parameters, environmental parameters and control parameters in accordance with a machine learning algorithm configured to derive the mixture as a trained mixture. One would be motivated to combine them by a desire to gain the benefit of an optimized material mix ratio formula and to eliminate construction compositions that are unlikely to meet job requirements, as taught by Daczko at [0053] and [0085].
As to claims 2-3: the limitations of claim 1 from which claims 2-3 depend are disclosed by the combination Le Roux and Daczko as discussed above. Daczko discloses the control parameters include at least a mix/material ratio, as discussed above.
As to claims 4-5: the limitations of claim 1 from which claims 4-5 depend are disclosed by the combination Le Roux and Daczko as discussed above. Daczko discloses the plurality of materials and the material property parameter is inclusive of admixtures (Daczko at [0082]). Determining the mix ratio control parameter necessarily includes material dosage.
As to claim 6: the limitations of claim 1 from which claim 6 depends are disclosed by the combination Le Roux and Daczko as discussed above. Daczko further disclose the sensors may include environmental measurements that may be fed into the controller for optimization of the construction material (Daczko at [0070]).
As to claim 8: the limitations of claim 1 from which claim 8 depends are disclosed by the combination Le Roux and Daczko as discussed above. Daczko further discloses the controller may receive input from the sensors and/or performance report (506) from the contractor, the data providing real-time feedback that allows the producer to update the construction composition (mixture) formulation between batches (Daczko at [0121]; Fig. 6A). The controller may perform a quality control (a value associated with a quality parameter of a dispensed mix), reformulation, or retraining process that updates the mixture formulation and may update the algorithm (Daczko at [0121]), thus training the algorithm using the value. It is noted the limitation requiring the value is from a site of the 3D printing system is interpreted as an intended use of the claimed apparatus and as such is given little patentable weight. However, Daczko discloses the data may come from the contractor at the job site (Daczko at [0121]). It is further noted Le Roux discloses the job site may be a 3D printing system, as discussed above.
As to claim 9: the limitations of claim 1 from which claim 9 depends are disclosed by the combination Le Roux and Daczko as discussed above. Le Roux further discloses the controller is configured to detect a torque imparted to mixer shaft (436) due to the viscosity (a value of a quality parameter) of the building materials (mixture) within the mixer, and if the torque load is above a first threshold the controller may determine that the viscosity of the mixture is too high and add water, and if the torque load is below a second threshold the controller may determine that the viscosity of the mixture is too low and add dry ingredients (Le Roux at [0112]). Because viscosity can be inferred from torque load, the torque load is a further value associated with a solid content of the dispensed mix. Thus, the controller and the mixer may form a rheometer for ensuring the mixture delivered to the 3D printing system is consistent to provide consistent performance during a construction operation (Le Roux at [0112]).
As to claim 10: the limitations of claim 1 from which claim 10 depends are disclosed by the combination Le Roux and Daczko as discussed above. Daczko discloses the controller is configured to generate a modified mixture by inputting the value of the material property parameter into the algorithm, the modified mixture including one or more modified values associated with one or more of the plurality of the control parameters, as discussed in detail above. It is noted the limitation requiring the 3D printing system printing a structure using the modified mixture is interpreted as an intended use of the claimed apparatus and as such is given little patentable weight. However, Daczko discloses batches of the construction composition (mixture) are made and shipped to a contractor for use (Daczko at [0112]). It is further noted Le Roux discloses the mixture may be for a 3D printing system, as discussed above.
As to claim 11: the limitations of claim 1 from which claim 11 depends are disclosed by the combination Le Roux and Daczko as discussed above. Daczko further discloses the controller may receive input from the sensors and/or performance report (506) from the contractor, the data providing real-time feedback that allows the producer to update the construction composition (mixture) formulation between batches (Daczko at [0121]; Fig. 6A). The controller may perform a quality control (a value associated with a quality parameter of a dispensed mix), reformulation, or retraining process that updates the mixture formulation and may update the algorithm (Daczko at [0121]), thus training the algorithm in real-time using the value.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Le Roux and Daczko as applied to claim 1 above, and further in view of Radjy (US 2016/0114498; of record).
As to claim 12: the limitations of claim 1 from which claim 12 depends are disclosed by the combination Le Roux and Daczko as discussed above. The combination Le Roux and Daczko does not disclose the controller is configured to determine if the first value associated with the material property parameter exceeds a parameter threshold value and, responsive to the determination, transmit a notification indicating the first value of the material property parameter exceeds a parameter threshold value.
In the same field of endeavor, mixing cementitious materials, Radjy discloses systems and methods of managing a closed-loop production management system used for production and delivery of a formulation-based cementitious material in the concrete industry (Radjy at [0062]). Product management system (controller, 10) comprises a database module (11), production modules (12-16), and an alert module (17) that can transmit alerts to customers (Radjy at [0068]-[0072]), operates at the production facility and has stored data as to the specifics of the individual components or raw ingredients on hand at the facility (Radjy at [0090]). If the characteristics of the raw ingredients (value of the material property parameter) fall outside acceptable tolerances (parameter threshold value), then the database transmits an alert (notification) by communication line (33) to alert module which transmits an alert to the customer (Radjy at [0094]; Fig. 3). Suggested practice for producers of the cementitious material includes the use of an alert system for alert reporting and notification of out-of-tolerance monitored variable (Radjy at [0160]). One of ordinary skill in the art before the effective filing date of the claimed invention would have reasonably drawn the inference that the notification generated by the alert module indicated the first value associated with the material property parameter that exceeds the parameter threshold value.
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify or combine the combination Le Roux and Daczko invention as discussed above, with the Radjy teaching of using an alert system to notify when a raw material is out-of-tolerance. One would be motivated to combine them by a desire to gain the benefit of preventing the production of potentially out-of-tolerance cementitious material.
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 BAILEIGH K. DARNELL whose telephone number is (469)295-9287. The examiner can normally be reached M-F, 9am-5pm, MST.
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, Galen H. Hauth can be reached at (571)270-5516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BAILEIGH KATE DARNELL/Examiner, Art Unit 1743 /GALEN H HAUTH/Supervisory Patent Examiner, Art Unit 1743
Prosecution Insights