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 following is in response to the amendment filed on November 4, 2025.
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.
Claims 34-53 are rejected under 35 U.S.C. 103 as being unpatentable over Daczko et al (EP 3924906) in view of “Evaluation of Concrete Strength By Monitoring Concrete Temperature Using Sensor” by Magesh et al.
With respect to claim 34:
Daczko teaches:
A method of generating concrete mixtures comprising:
Obtaining one or more desired parameters for a desired concrete mixture; (Page 4 discloses receiving requirements for a concrete mix)
Inputting the one or more desired parameters into one or more machine learning models trained using historical data of previously-sampled concrete mixtures; (Paragraph 4, inputting the requirement s into a trained model)
Applying the one or more machine learning models to the one or more desired parameters to generate a formulation corresponding to the desired concrete mixture; (Page 4, discloses outputting the concrete mixture (amounts and ratios))
Generating an actual concrete mixture based at least in part on the formulation corresponding to the desired concrete mixture; (Page 4, discloses the system controlling mixing machinery to produce the concrete mixture)
Obtaining, via one or more sensors, sensor data corresponding to the actual concrete mixture, wherein” (Page 7, discloses receiving sensor data of the actual concrete mixture)
Applying the one or more machine learning models to the sensor data to generate an updated formulation corresponding to the desired concrete mixture; and (G) adjusting the actual concrete mixture based at least in part on the updated formulation corresponding to the desired concrete mixture. (Page 7, discloses sensors may be deployed to generate data that can then be fed into the optimization logic to adjust the concrete mixture for the next batch)
Daczko does not appear to explicitly disclose:
A first subset of the sensor data comprises one or more fresh property measurements measured within 24 hours of production of the actual concrete mixture; and
A second subset of the sensor data comprises a first hardened property measurement measured at least one day after production of the actual concrete mixture and before 28 days after production of the actual concrete mixture and a second hardened property measurement measured at least one day after production of the actual concrete mixture and before 28 days after production of the actual concrete mixture, wherein the first hardened property measurement and the second hardened property measurement are measured at different times.
Magesh teaches:
A first subset of the sensor data comprises one or more fresh property measurements measured within 24 hours of production of the actual concrete mixture; and (Page 3, discloses using sensors to measure temperature including the initial temperature)
A second subset of the sensor data comprises a first hardened property measurement measured at least one day after production of the actual concrete mixture and before 28 days after production of the actual concrete mixture and a second hardened property measurement measured at least one day after production of the actual concrete mixture and before 28 days after production of the actual concrete mixture, wherein the first hardened property measurement and the second hardened property measurement are measured at different times. (Page 3, discloses plurality of sensor data (temperatures), each data point being recorded after 24 hours (1st hardened measure) and at 48 hours (2nd hardened measure))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Daczko and the teachings of Magesh, both in the same field of invention. This would allow to use the sensor information to determine strength and this would allow for an optimization of the mixture using the learning model.
With respect to claim 35:
Magesh teaches:
Wherein at least part of second subset of the sensor data is obtained by performing one or more dry concrete material property tests including at least one of a resistivity test and a compression test, on the actual concrete mixture or wherein at least part of the first subset of the sensor data is obtained by performing one or more fresh concrete material property tests including a test to determine at least one of a lump, a unit weight, a unit density, an air content, a moisture content, a temperature (Page 3 discloses collecting concrete temperatures), or a humidity, of the actual concrete mixture.
With respect to claim 36:
Daczko teaches:
Wherein the adjustment to the actual concrete mixture is automatically performed by a concrete production system. “The optimization logic 122 may also or alternatively be applied at the back end (after the construction composition 118 is produced) to modify the construction mixture 114 or construction admixture 116 in real time between successive batches of construction compositions… Similarly, the contractor 138 may manually input information about the job site 130 conditions or the delivered construction composition 118 (e.g., "too short a setting time," "too viscous," etc.). This information may also be provided via a contractor server 134 to the optimization logic 122, so that the construction mixture may be altered to account for the contractor's feedback” (par. 46), which recites the modification of an actual concrete formulation in order to better achieve a set of desired properties, based on the results of hard and fresh concrete tests.
With respect to claim 37:
Magesh teaches:
The one or more sensors are configured to obtain data in real time. (Page 3, data collected)
With respect to claim 38:
Daczko teaches:
Wherein adjusting the actual concrete mixture is performed in real time in response to the generated formulation, wherein the adjustment to the actual concrete mixture is configured to change one or more parameters of the actual concrete mixture to the one or more desired parameters (“The sensor data 136 may be fed into the optimization logic 122, which may (for example) adjust materials to be used in the next batch of construction admixture 116 to be used” (par. 46), which recites the modification of an actual concrete mixture using a generated formulation for concrete admixture based on the actual concrete properties.)
With respect to claim 39:
Daczko teaches:
Wherein gthe one or more desired parameters include one or more of: desired wet slump, desired density, desired air content, desired fresh concrete temperature, desired setting time, desired compressive strength at different ages, desired electrical resistivity at different ages, desired drying shrinkage values, or desired cost (“receiving, at a producer server 106, a job specification (120) for a construction composition (118), the job specification (120) specifying one or more characteristics for the construction composition (202-250)” (claim 10), in unison with:“The method of claim 10, wherein the characteristics comprise one or more of slump, stickiness, bleed, slump loss, strength deviation, and volume change of the manufacturing blend” (claim 14), which recites that the desired parameters include slump; and“The job specification 120 may specify parameters relating to the fresh properties 202 of the product. Fresh properties refer to the properties of a fresh (i.e., unhardened) product. Examples of fresh properties include workability 204, workability retention 206, air content 208, stability 210, uniformity 212, viscosity 214, finishability 216, and setting time 218” (par. 49), which recites that the desired parameters include air content and setting time.)
With respect to claim 40:
Daczko teaches:
Obtaining a list of one or more available materials and one or more material properties of the one or more available materials (“The available raw materials may be represented in a components library 124, which may identify the raw materials and may include further information about the raw materials, such as the effect of the raw materials on performance parameters, any certifications that the components meet, the concentration of the raw materials, etc.” (par. 43).)
applying the one or more machine learning model to the list of the one or more available materials and the one or more material properties of the one or more available materials to generate the formulation corresponding to the desired concrete mixture based at least in part on the one or more available materials and the one or more material properties of the one or more available materials (“The optimization logic 122 may be capable of selecting different available raw materials from the raw material silos 110 and defining their amounts or relative proportions, percentages, or ratios” (par. 43), which recites that the formulation is generated with respect to the availability of raw materials; and“The optimization logic 122 may include an artificial intelligence, a machine learning algorithm (e.g., a neural network, a supervised learning process, an unsupervised learning process, a reinforcement learning process, etc.), a predictive model, etc. The optimization logic 122 may be trained using labeled training data, which may include historical or current data” (par. 44), which further contextualizes the use of the machine learning model in the raw material-availability-based mix formulation.)
With respect to claim 41:
Daczko teaches:
Generating the formulation corresponding to the desired concrete mixture comprises generating one or more formulations corresponding to the desired concrete mixture (“Next, a job specification 120 may be transmitted from the designer server to the producer server. In response to receiving the job specification 120, the producer server may initiate a mixture formulation process 604, which applies the model of algorithm to the received job specification 120 to generate one or more suitable mixtures that meet or best approximate the requirements of the job specification” (par. 93), followed by:“If multiple construction mixtures are generated, the system may output a comparison of the construction mixtures and allow one to be selected” (par. 94), which together teach the generation of multiple formulation iterations based on a desired mixture specification.)
With respect to claim 42:
Daczko teaches:
The one or more machine learning models comprise at least one of: a regression model, a decision tree, a random forest, a Monte Carlo algorithm, a Bayesian model, a K-nearest neighbor’s algorithm, or a neural network. (Page 4, discloses using a machine learning algorithm)
With respect to claim 43:
Daczko teaches:
The one or more desired parameters comprises at least one of a slump, density, air content, fresh concrete temperature, setting time, compressive strength, electrical resistivity, drying shrinkage values, flowability or cost. (Page 2, discloses a parameter for formulation to have a consistent setting time)
With respect to claim 52:
Daczko teaches:
The historical data set of previously sampled concrete mixtures comprises one or more of: historical weights if ingredients and raw materials, historical aggregate gradations, historical cement properties, historical admixture properties, historical absorption and moisture measurements, or historical environmental data. (Page 4, discloses using historical data such as ingredients to train the learning model)
Claims 44-51 and 53 are rejected accordingly to claims 34-43 and 53.
Response to Arguments
Claim Rejections - 35 USC § 102
Applicant’s arguments have been considered but are moot in view of the new ground(s) of rejection.
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 MARIELA D REYES whose telephone number is (571)270-1006. The examiner can normally be reached Monday-Friday, 7:30 am -5:00 pm.
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/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142