System and Method for Optimizing Structural Properties of Concrete Mix
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 .
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the method must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 6 and 8 are objected to because of the following informalities: The word “model” should be “module”. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to 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 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 –
(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.
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hartman (20200062659; "Hartman").
Regarding claim 1, Hartman discloses, in table 1, a method for mixing and for placing a batch of concrete mix in forms (¶ 0084, “sixteen industrial-scale batches of concrete were prepared”), the method comprising: assigning a unique serial number to the batch of concrete (see table 1, column 1), that serial number (see previous comment) being stored in a database (the examine construes Hartman’s table to be a database consisting of the single table); admitting a measured quantity of each of the concrete ingredients into a mixing vessel (¶ 0012, examiner notes Hartman combines water and dry ingredients including cement mix, aggregate and sand within a mixing machine), the ingredients comprising: a cement quantity of cementitious material (¶ 0012, see table 1, column 2); a water quantity of water (¶ 0012, see table 1, column 5); a sand quantity of sand (¶ 0084, Hartman’s batches all contain 1350 lbs. of sand); and an aggregate quantity of aggregate (¶ 0012, see table 1, column 5); and storing each of the cement quantity, the water quantity, the sand quantity and the aggregate quantity as stored attributes in association with the serial number (see table 1); curing the batch of concrete mix (see table 1, examiner notes cylinder samples of Hartman’s batches were taken and tested according to ASTM-39 at various periods); testing the cured batch of concrete mix to derive at least one performance criterion (¶ 0091, compression strength was determined at various periods for each batch sample); and storing the performance criterion in association with the serial number (see table 1).
Claim Rejections - 35 USC § 103
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 2-9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hartman (20200062659; "Hartman") as applied to claim 1 above, and further in view of Bauchy (US 20230416164; "Bauchy").
Regarding claim 2, Hartman discloses, in table 1, fails to disclose the batch includes a plurality of batches (see table 1).
Hartman fails to disclose selecting a machine learning algorithm to train a neural network model.
Bauchy teaches, in figures 1-14, the batch includes a plurality of batches ((1002) ¶ 0088, examiner notes Bauchy’s data points are particular concrete mixtures) and further comprising: selecting a machine-learning algorithm (¶ 0088, Bauchy’s process may “cluster the data set 1002 using Euclidean distances and a clustering algorithm, such as, but not limited to, a K-means clustering algorithm”, ¶ 0089, to “select a subset of the clusters 1004 and create a training data set 1006”) to optimize the at least one performance criterion (¶ 0089, Bauchy’s prediction system generates new mixtures based on performance targets and the performance of existing clusters used to train the model); invoking the machine-learning algorithm (see previous comment) to train a neural network model (1008) with the at least one performance criterion to generate a group of performance criterion data (¶ 0093, Bauchy trained model receives input parameters including target performance parameters); analyzing the neural network model produced by training for an accuracy (3’, 1218); and improving the accuracy by iteratively repeating the training of the neural network model by performing the method of claim 1 until a defined constraint is met (see figures 10 and 12, examiner notes Bauchy changes model hyperparameters based on validation and retrains the model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Bauchy’s scheme of optimizing concrete mixtures based on target performance parameters into Hartman’s scheme of improving concrete performance through batch testing since it is well known to combine prior art elements according to known methods to yield predictable results. Doing so provides a reliable way of utilizing existing data to train and optimize concrete batch models.
Regarding claim 3, Hartman and Bauchy disclose, in Bauchy’s figures 1-14, the machine-learning algorithm is selected from a plurality of machine learning algorithms (¶ 0088, Bauchy’s process may “cluster the data set 1002 using Euclidean distances and a clustering algorithm, such as, but not limited to, a K-means clustering algorithm”, ¶ 0089, to “select a subset of the clusters 1004 and create a training data set 1006”).
Hartman and Bauchy fail to explicitly disclose selecting the algorithm from a database.
The Examiner takes official notice that databases are well-known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a well-known database to provide a selection of potential machine learning algorithm code from which Harman and Bauchy may base their selection. Doing so would provide a convenient source of code from which to compile the selected algorithm.
Regarding claim 4, Hartman and Bauchy disclose, in Bauchy’s figures 1-14, the invoking trains the neural network model with the machine-learning algorithm of a first portion of the performance criterion data (¶ 0089, examiner notes Bauchy uses 80% of the training data or a subset of the clusters to train the algorithm); and wherein the analyzing analyzes the accuracy based on a second portion of the performance criterion data (¶ 0089, Bauchy uses 20% of the training data or the remaining clusters not used to train the algorithm to validate the accuracy of the predictions).
Regarding claim 5, Hartman and Bauchy fail to explicitly disclose the machine-learning algorithm is invoked in a stateless manner to train the neural network model.
The Examiner takes official notice that training a neural network model in a stateless manner using a machine learning algorithm are well-known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a well-known technique of training Harman and Bauchy model in a stateless manner. Doing so increases robustness and scalability of the design.
Regarding claim 6, applicant’s admission of prior art discloses cellular plug-and-play modules are well known technology.
Hartman and Bauchy further disclose, in Bauchy’s figure 14, a plurality of machine-learning algorithms are stored in a plug-in modules ((1404) ¶ 0137, “flash drive”) wherein each machine-learning algorithm can be dynamically edited, added or deleted (¶ 0144, “elements or functions may be deleted, executed out of order from that shown, or discussed, including substantially concurrently or in reverse order, depending on the functionality involved”).
Regarding claim 7, the selected machine-learning algorithm comprises a combination of the plurality of machine-learning algorithms (¶ 0088, Bauchy’s process may “cluster the data set 1002 using Euclidean distances and a clustering algorithm, such as, but not limited to, a K-means clustering algorithm”, ¶ 0089, to “select a subset of the clusters 1004 and create a training data set 1006”).
Hartman and Bauchy fail to explicitly disclose selecting the algorithm from a database.
The Examiner takes official notice that databases are well-known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a well-known database to provide a selection of potential machine learning algorithm code from which Harman and Bauchy may base their selection. Doing so would provide a convenient source of code from which to compile the selected algorithm.
Regarding claim 8, Hartman and Bauchy disclose, in Hartman’s figure 14, a combination of the plurality of machine-learning algorithms (¶ 0088, Bauchy’s process may “cluster the data set 1002 using Euclidean distances and a clustering algorithm, such as, but not limited to, a K-means clustering algorithm”, ¶ 0089, to “select a subset of the clusters 1004 and create a training data set 1006”) in the plug-in modules ((1404) ¶ 0137, “flash drive”).
Regarding claim 9, Hartman and Bauchy disclose, in Hartman’s table 1, attributes selected from an additional attributes group consisting of: a slump of the batch at the time of transfer of the batch into a truck vessel on a mixing truck; a slump of the batch at the time of emptying the batch from the truck vessel into a concrete pumper (see Hartman’s table 1, column 7).
Regarding claim 11, Hartman and Bauchy disclose, in table 1, the performance criterion is selected from a performance criterion group, the performance criterion group consisting of: air content (see Hartman’s table 1, column 8), compressive strength (see Hartman’s table 1, column 10 and 11, ¶ 0083, examiner notes Hartman’s compression test are performed as recommended by the American Concrete Institute).
Hartman and Bauchy fail to explicitly disclose air content as defined by ASTM C231; air content of fresh concrete by volumetric method as defined by ASTM C173; compressive strength as defined by ACI 318; compressive strength of concrete by Schmidt hammer as defined by ASTM C805; and cast in place cylinder compressive strength as defined by ASTM C873.
The Examiner takes official notice that ASTM C231, ASTM C173, ACI318, ASTM C805 and ASTMC873 are well-known standards in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to base Harman and Bauchy’s performance criterion on well-known industry standards. Doing so elevates results to industry acceptable standards.
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 10, examiner notes a search has not revealed art teaching or suggesting the method of Hartman and Bauchy’s, as combined in claims 1 and 9, including comparing the weight of the batch in the mixing vessel to an ingredient weight based upon the cement quantity, the water quantity, the sand quantity, and the aggregate quantity; and where the weight of the batch in the mixing vessel exceeds the ingredient weight, generating an alert to indicate the presence of additional ingredients. The examiner concludes prior existence of the combination is improbable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY P GRAVES whose telephone number is (469)295-9072. The examiner can normally be reached M-F 8 a.m. - 5 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Macchiarolo can be reached at 571-272-2375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY P GRAVES/Primary Examiner, Art Unit 2855