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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of the Claims
Claims 1,2, 10, 20, 24, 28, and 31 are amended,
claims 34 are added.
Claims 1-34 are now pending
The rejection under 35 USC 112 is withdrawn.
The rejection under 35 USC 101 is maintained.
In such an example, if a particular meal and/or day does not satisfy the constraints, menu verification system 1312 can provide feedback related to the failure to satisfy one or more of the constraints to menu generation system 1310, which can use the feedback to adjust the recipe selections to satisfy one or more of the constraints.
Response to Applicant Remarks
Applicant’s well-articulated remarks have been considered but are unpersuasive for the reasons below.
Regarding the rejection under 35 USC 101, Applicant argues that the claimed invention improves the technical process of producing curable materials and the functioning of machine learning systems. (Applicant’s 11/12/25 remarks, p.15). The examiner respectfully disagrees.
Although the invention utilizes machine learning, the examiner finds no improvement to the technology of machine learning per se. That is, the claimed technique of receiving information, and requesting a trained machine learning model to produce a result based on input, does not appear to be significantly more than utilizing machine learning as a tool. Although the purpose of the claimed invention is to determine formulations of curable materials, the examiner respectfully suggests that there is no “concrete” improvement to the technology of curable materials, beyond the framework that a taught machine learning model could potentially determine a curable recipe. Accordingly, the examiner does not agree that this disclosure arises to an improvement to that technical field.
Applicant also argues that the Ex Parte Desjardins decision supports eligibility of the claimed invention. (Applicant’s 11/12/25 remarks, p.15, “In Desjardins, the Appeals Review Panel-authored by Under Secretary John A. Squires- clarified that machine-learning-based process optimization constitutes eligible subject matter when the claims:
1. Recite a specific technological implementation, not a generic mathematical
abstraction;
2. Employ feedback from a physical system to retrain or adapt the model; and
3. Produce a concrete improvement in a technical field, such as materials engineering
or manufacturing.
The ARP emphasized that eligibility is satisfied when "the claimed invention uses a trained model as a control mechanism that effects a transformation in a physical article or manufacturing condition," thereby integrating the abstract concept into a practical application.
The present claims mirror those upheld in Desjardins:
The ML model determines recipes that alter material composition, not financial or
business data.
The claimed reteaching step uses empirical measurements of produced materials to
refine the model-precisely the physical feedback loop the ARP deemed critical.
The output directly governs a manufacturing process, satisfying Alice Step 2A
Prong 2 as a "practical application" under MPEP § 2106.04(d).”) The examiner respectfully disagrees.
The examiner notes that the invention at issue in Desjardins was directed to a technical improvement to a machine learning model. The examiner is unaware of any test articulated by the ARP of a “physical” article or feedback from a “physical system” relating to Desjardins. Applicant’s may possibly be in error of the case law being relied upon.
Applicant also argues that under “Step 2A Prong 1), the invention is not directed to a judicial exception, because the ML is confined to specific input and output tied to physical materials and it addresses a technological problem of formulating curables. (Applicant’s 11/12/25 remarks, p.16). The examiner respectfully disagrees.
Although Applicant’s invention is associated with curables, the examiner respectfully points out that the input/output of the invention is purely data that is abstract in nature. Although a ML is recited, the claimed technique is not dissimilar from simply asking an experienced engineer to draw upon experience and information regarding available materials to formulate a recipe. The output of the invention is data (e.g. a curable recipe) which is determined on a generic computer. There are no additional elements that may make the process statutory. (Compare Diamond v Diehr, 450 US 175 1981, The invention in this case relates to a method for determining how rubber should be heated in order to be best "cured." The invention utilizes a computer to calculate and control the heating times for the rubber. However, the invention (as defined by the claims) included not only the computer program, but also included steps relating to heating rubber, and removing the rubber from the heat.)
Applicant also argues that under “Step 2B ), the invention is significantly more than an abstract idea. (Applicant’s 11/12/25 remarks, p.16, “Even if the Office views the claims as reciting an abstract idea, the claims include inventive concepts that transform the idea into patent-eligible subject matter: 1. Specific ML training architecture-"taught on basis of comparing output with
ground-truth data comprising recipes for end-products having desired features"-is a
non-conventional technique tailored to the field of curable compositions.
2. Physical feedback (reteaching)-The system "receives determined feature
information of the product produced. and reteaches the model," creating a
continuous adaptive control loop not found in prior art.
3. Integration with manufacturing interfaces-User- and data-communication
interfaces link suppliers, raw-material data, and manufacturing sites to automatically
generate actionable production recipes.”). The examiner respectfully disagrees.
The examiner does not agree that the specificity of the claimed training data alters the architecture of the machine learning model. Machine learning models may be trained for all manner of niche tasks, and the examiner does not agree that the nature of the training data per se alters the general methodology or architecture of the machine learning model. That the machine learning model is taught using feedback is also not considered to be innovative. Just as a human may be learn from failures, a machine learning model may be receive feedback about its own performance. Regarding Applicant’s point 3, the examiner finds integration beyond some high level teaching. (e.g.the determined recipe may be communicated in some manner; a desired product may be communicated to a supplier; etc).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant also argues that the cited art does not teach claim 10’s “send target feature information related to sidestream based raw materials suitable for production of curable products to improve usability of at least one sidestream based raw material in production of a curable product or product component via the at least one of the user interface or communication interface, wherein the target feature information related to the sidestream based raw materials comprises a modification in at least one feature of the at least one sidestream based raw material”) (Applicant’s 11/12/25 remarks, p.20) . The examiner respectfully disagrees.
Although Chang does not disclose the use of sidestream, Chang does disclose informing a recipe recommendation system of available ingredient amounts. (Chang, par 0026, “[0026] The voice recognition module 202 is configured to analyze audio input received via the microphone 126 of the mobile computing device 102. As discussed in more detail below (see block 502 of FIG. 5), the user of the mobile computing device 102 may dictate a type of ingredient available to the user and the corresponding quantity of that ingredient. For example, in some embodiments, the user may go through a pantry, refrigerator, or other food storage area and tell the mobile computing device 102 all of the available ingredients and the amount remaining in each corresponding package.”) The examiner interprets this to be directed to a feature modification related to improving usability of an ingredient. To the extent that Applicant’s background discloses that industrial sidestream is an ingredient for curable production, the examiner submits that the combination of references addresses the limitation.
The amendment to Claim 20 is addressed by the newly cited art.
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.
Claim 2 is 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.
Claim 2 recites “and/or virgin raw materials”. It is unclear based on the phrasing whether virgin raw materials are a required element or an alternative.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding independent claims 1, 10, 20, 26-31 the claimed invention recites an abstract idea without significantly more. The claims recite the abstract idea of optimizing a recipe which is a mental process. Other than reciting a processor, memory, communication interface, training or reteaching machine learning nothing in the claims precludes the steps from being performed mentally. But for the processor, memory, communication interface the limitations on:
(receive material information, receive recipe request, determine recipe, send recipe) claims 1,26,29
(receive material information, send target information, send order request) claims 10,27, 30
(receive recipe request, determine recipe, send recipe, receive feature information, reteach based on feature information) claims 20,28,31
is a process that under its broadest reasonable interpretation could be performed by mentally but for the recitation of generic computer elements. If claim limitations, under the broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Thus, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. The computers are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computer environment is not a practical application of the abstract idea and does not take the claim out of the mental process grouping.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, with respect to integration of the abstract idea into a practical application, the additional element processor, memory, communication interface, ML amounts to no more than mere instructions to apply the exception using a generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Collecting, analyzing and displaying information, and receiving and transmitting over a network are conventional in the computing arts. (MPEP 2106.05h; See also MPEP 2106.05, Alice v. CLS, “. Nearly every computer will include a ‘communications controller’ and ‘data storage unit’ capable of performing the basic calculation, storage, and transmission functions required by the method claims.”). The claims are not patent eligible.
Regarding the dependent claims, these claims are directed to limitations which serve to limit the recipe optimization steps. The subject matter of claims 2 (recipe includes received materials), 3 (send target information), 4 (receiving material, location or feature information), 5/13/24 (determine recipe with ML), 6/11 (send order to supplier), 7/16 (order includes location information), 8/12 (information about amount, location or feature of materials), 9 (target or determined feature information), 14 (determine target feature information related to sidestream), 15 (determine additive), 17 (orders include additive), 18 (order to sidestream includes location), 19/23 (target information), 21 (feature information determined after use), 22 (measure feature with integrated sensor), 25 (provide determined feature information to ML), 32 (ML is a neural network), 33 (sidestream composition) , 34 (to produce curable product based on recipe) appear to add additional steps to the abstract idea, implemented by generic computers. The recitation of “ML” in claims 5/13/24 indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element of ML limits the recipe determination this type of limitation merely confines the use of the abstract idea to a particular technological environment (artificial intelligence) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). These limitations only recite the outcome of determining a recipe using some type of trained model and do not include any details about how the determining is accomplished. See MPEP 2106.05(f).
These claims neither introduce a new abstract idea nor additional limitations which are significantly more than an abstract idea. They provide descriptive details that offer helpful context, but have no impact on statutory subject matter eligibility.
Therefore the limitations on the invention, when viewed individually and in ordered combination are directed to in-eligible subject matter.
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 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.
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 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 of this title, 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 1,2,4,5,8,9,20-25,28,31,32,34 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Daczko US20220129797A1
Regarding Claim 1,
at least one processor; and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the system to:
receive information related to at least one of available sidestream based or virgin raw materials suitable for production of …products via at least one of a first user interface or data communication interface;
receive a request for delivering a recipe for a … product or product component via at least one of a second user interface or communication interface, the request comprising target feature information of the … product or product component;
determine a recipe for producing the requested … product or product component on basis of the target feature information of the … product or product component and the information related to at least one of available sidestream based or virgin raw materials; and
(Chang, abstract, “A method, device, and system for generating a list of recipe recommendations includes determining the type and quantity of ingredients available to a user of a mobile computing device or smart storage. The available ingredients may be determined using text input or voice input from the user. A camera may also be used to capture images of the available ingredients for analysis. The list of recipes may be generated as a function of the type and quantity of available ingredient(s), meal preferences of the user, and the context of the meal. Recipe complements and/or supplements may be suggested in response to the user selecting a recipe from the list of recipe recommendations. Further, a meal planner may be used to track the shelf life of the ingredient(s), plan a meal schedule, and generate a shopping list”)
Chang does not explicitly disclose
curable
by a taught machine learning model
send the determined recipe for producing the requested … product or product component via at least one of the second user interface or communication interface.
Aghdasi is directed to a machine learning system used to optimize formulations of concrete to reach user goals. (Aghdasi, para 0023, “It is noted that ML/optimization system(s) 110 can utilize a data-driven approach.
ML/optimization system(s) 110 can manage proprietary data and a fully-automated robotic
laboratory. ML/optimization system(s) 110 can collect large amounts of data (e.g. big data) for
training, development, and testing its machine learning models. Finally, ML/optimization
system(s) 110 can use its machine learning models backed by big data, generated through its
fully--automated lab, to invent and discover new cements, concretes, and other construction
materials to either license to other producers or to mass produce itself.”; para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”; para 0028, “In step 208, process 200 can optimize and adjust a concrete mix in real-time to be
able to deliver concrete products that meet the requirements of the project and the goals and
priorities of the customer as closely as possible. These could include but not limited to
minimizing costs, minimizing cement consurnptions, minirnizing carbon footprint, rnaxirnizing, or
achieving a certain over-night or early-age strength, maxirnizing durability, and the like using
local raw materials available to producers. For example, if the goal is to reduce the amount of
cement without compromising the final properties, ML/optimization system(s) 110 can enable
concrete producers to use 10-20% less cement in their concrete, therefore, reducing costs as
well as carbon footprint.” ). The examiner interprets concrete to be a curable material. It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Chang does not explicitly disclose
taught on basis of comparing output produced by the machine learning model for a particular teaching input with ground-truth data comprising recipes for end-products having desired features
Daczko is directed to a system for formulating concrete. (Daczko, abstract).
Daczko discloses that it is known for the system to learn from feedback regarding the concrete formulation to adapt future concrete mixtures. (Daczko, para 0070, “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. 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.” ; para 0100, “The optimization logic 122 may also include retraining logic 536. In contrast to the training logic 534, which operates on historical training data 532, the retraining logic may be configured to adapt the model or algorithm 540 on-the-fly based on newly-received information (e.g., information from the sensors 516 or contractor server 134 that may not be reflected in the training data 532). The retraining logic 536 may be configured to adapt the model or algorithm 540 more slowly or conservatively than the initial training process, under the assumption that a properly-trained model should not change rapidly in view of limited data. The speed of adaptation may be adjustable so that a user may modify the extent to which new data is accounted for. The speed of adaptation may also be changed automatically in certain circumstances. For instance, if feedback is received from the contractor server 134 indicating that a batch of concrete that has been delivered is unacceptable or fails a certain performance parameter, then rapid adaptation is likely required and the model or algorithm 540 should be adjusted immediately.”)
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang and Aghdasi with the feedback of Daczko with the motivation of improve machine learning performance. Id.
Regarding Claim 2, Chang, Aghdasi and Daczko disclose the system of claim 1.
wherein at least a portion of the raw materials contained in the determined recipe includes at least one of sidestream based or virgin raw materials in accordance with the information received through the at least one of the first user interface or communication interface,
See prior art rejection of claim 1.
and wherein the recipe comprises amounts of the available sidestream and/or virigin raw materials or their ratios to produce the curable product or product component.
(Chang, par 0026, “[0026] The voice recognition module 202 is configured to analyze audio input received via the microphone 126 of the mobile computing device 102. As discussed in more detail below (see block 502 of FIG. 5), the user of the mobile computing device 102 may dictate a type of ingredient available to the user and the corresponding quantity of that ingredient. For example, in some embodiments, the user may go through a pantry, refrigerator, or other food storage area and tell the mobile computing device 102 all of the available ingredients and the amount remaining in each corresponding package.”) The examiner interprets food ingredients to be virgin raw materials.
Regarding Claim 4, Chang, Aghdasi and Daczko disclose the system of claim 1.
wherein the at least one of the second user interface or communication interfaces further adapted to receive at least one of information on raw materials available from a manufacturer of the curable product or product component, location information of a manufacturing site of the curable product or product component, or determined feature information of the product or product component produced on basis of the sent recipe.
(Aghdasi, para 0019, “The current model is run with the training dataset
and produces a result, which is then compared with the target, for each input vector in the
training dataset. Based on the result of the comparison and the specific learning algorithm
being used, the parameters of the model are adjusted.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Regarding Claim 5, Chang, Aghdasi and Daczko disclose the system of claim 1.
Chang does not explicitly disclose
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the system to:
teach the machine learning model on basis of the determined feature information of the product or product component produced on basis of the sent recipe.
See prior art rejection of claim 1 regarding Daczko.
Regarding Claim 8, Chang, Aghdasi and Daczko disclose the system of claim 1.
wherein the information related to_at least one of available sidestream based or virgin raw materials suitable for production of curable products comprise at least information on at least one of amount, location or at least one feature of at least one of available sidestream based or virgin raw materials suitable for production of curable products.
See prior art rejection of claim 1 regarding Chang.
Regarding Claim 9, Chang, Aghdasi and Daczko disclose the system of claim 1.
wherein at least one of the target feature information or the determined feature information of the curable product or product component includes at least one of the following: compressive strength, flexural tensile strength, splitting tensile strength, density, structural weight, operating conditions, CO2 emissions, natural resources consumption or price.
See prior art rejection of claim 1 regarding Aghdasi.
Regarding Claim 20,
at least one processor; and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the device to:
determine, …, a recipe for producing the requested …. Product or product component on basis of the target feature information of the … product or product component and the information related to available sidestream based and/or virgin raw materials
receive a request to deliver a recipe to be used in production of a curable product or product component via at least one of a user interface or communication interface,
the request comprising target feature information of the curable product or product component;
See prior art rejection of claim 1 regarding Chang
Chang does not explicitly disclose
curable
send the recipe for producing the requested curable product or product component via the at least one of the user interface or communication interface;
Aghdasi disclose that a concrete ML model may send real time adjustments to improve concrete production. (Aghdasi, para 0022, “[0022] In case of cement companies, concrete ML/optimization system(s) 110 uses
customer data (e.g. 3rd party database{s) 104 obtained via 3rd party server{s) 112, etc.)
augmented by example proprietary datasets {e.g. raw materials database 102, machine learning
database(s) 106, etc.) as well as its ML/Al tools, to provide real-time feedback to cement
production plants with the purpose of optimizing cement production, efficiency, and quality
control, order to avoid undesirable cement properties before and after being used in the final
concrete product.”) The examiner interprets this to be sending a recipe.
receive determined feature information of the product or product component produced on basis of the sent recipe via the at least one of the user interface or communication interface.
(Aghdasi, para 0019, “The current model is run with the training dataset
and produces a result, which is then compared with the target, for each input vector in the
training dataset. Based on the result of the comparison and the specific learning algorithm
being used, the parameters of the model are adjusted.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Chang does not explicitly disclose
Taught on basis of comparing output produced by the machine learning model for a particular teaching input with ground truth data comprising recipes for end products having desired features
and reteach the machine learning model on basis of the determined feature information of the product or product component produced on basis of the sent recipe.
See prior art rejection of claim 1 regarding Daczko
Regarding Claim 21, Chang, Aghdasi and Daczko disclose the device of claim 20.
wherein at least one of the target feature information or the determined feature information of the curable product or product component includes feature information determined during manufacturing process of the product or product component, feature information determined during use of at least one of the product or product component, or feature information determined after use of the product or product component.
See prior art rejection of claim 1 regarding Aghdasi
Regarding Claim 23, Chang, Aghdasi and Daczko disclose the device of claim 20.
wherein at least one of the target feature information of the curable product or product component or the determined feature information includes at least one of the following: compressive strength, flexural tensile strength, splitting tensile strength, density, structural weight, operating conditions, CO2 emissions, natural resources consumption or price.
See prior art rejection of claim 1 regarding Aghdasi
Regarding Claim 24, Chang, Aghdasi and Daczko disclose the device of claim 20.
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the device to: provide the target feature information of the requested curable product or component to a machine learning model adapted to produce the recipe for producing the requested curable product or product component on basis of the target feature information of the curable product or product component and information related to at least one of available sidestream based or virgin raw materials; and
receive the recipe for producing the requested curable product or product component from the machine learning model.
See prior art rejection of claim 1 regarding Aghdasi.
Regarding Claim 25, Chang, Aghdasi and Daczko disclose the device of claim 24.
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the device to: provide determined feature information of the product or product component produced on basis of the sent recipe to the machine learning model for teaching the machine learning model.
(Aghdasi, para 0019, “The current model is run with the training dataset
and produces a result, which is then compared with the target, for each input vector in the
training dataset. Based on the result of the comparison and the specific learning algorithm
being used, the parameters of the model are adjusted.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Regarding Claim 28,
receiving by a device a request to deliver a recipe to be used in production of a … product or product component via at least one of the-a_user interface or communication interface,
determine, … , a receipe for producing the requested… produce or product component on basis of the target feature information of the curable product or product component and the information related to available sidestream based and/or virgin raw materials;
the request comprising target feature information of the … product or product component;
See prior art rejection of claim 1 regarding Chang
Chang does not explicitly disclose
curable
by a taught machine learning model…
sending by the device a recipe for producing the requested curable product or product component via the at least one of the user interface or the communication interface; and
(Aghdasi, para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”;
receiving the determined feature information of the product or product component produced based on the recipe sent by the device via the at least one of the user interface or the communication interface.
(Aghdasi, para 0019, “The current model is run with the training dataset
and produces a result, which is then compared with the target, for each input vector in the
training dataset. Based on the result of the comparison and the specific learning algorithm
being used, the parameters of the model are adjusted.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Chang does not explicitly disclose
Taught on basis of comparing output produced by the machine learning model for a particular teaching input with ground truth data comprising recipes for end products having desired features
See prior art rejection of claim 1 regarding Daczko.
Regarding Claim 31
instructions to receive by a device a request to deliver a recipe to be used in production of a … product or product component via at least one of a user interface or communication interface,
instructions to determine, …a recipe for producing the requested curable product or product component on basis of the target feature information of the curable product or product component and the information related to available sidestream based and/or virgin raw materials;the request comprising target feature information of the … product or product component;
See prior art rejection of claim 1 regarding Chang.
Chang does not explicitly disclose
curable
by a taught machine learning model,
instructions to send by the device a recipe for producing the requested curable product or product component via the at least one of the user interface or the communication interface; and
(Aghdasi, para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”;
instructions to receive the determined feature information of the product or product component produced based on the recipe sent by the device via the at least one of the user interface or the communication interface.
(Aghdasi, para 0019, “The current model is run with the training dataset
and produces a result, which is then compared with the target, for each input vector in the
training dataset. Based on the result of the comparison and the specific learning algorithm
being used, the parameters of the model are adjusted.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Chang does not explicitly disclose
Taught on basis of comparing output produced by the machine learning model for a particular teaching input with ground truth data comprising recipes for end products having desired features
See prior art rejection of claim 1 regarding Daczko.
Regarding Claim 32, Chang, Aghdasi and Dazcko disclose the system of claim 1.
Chang does not explicitly disclose
wherein the trained machine learning model comprises a neural network.
(Aghdashi, para 0039, “Examples of machine learning techniques that can be used herein include, inter alia: decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity, and metric learning, and/or sparse dictionary learning.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. (Aghdasi, para 0026)
Regarding Claim 34, Chang, Aghdasi and Dazcko disclose the system of claim 1.
wherein the system is to produce the requested curable product or product component based on the determined recipe.
See prior art rejection of claim 1 regarding Aghdasi.
Claims 26,29 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519
Regarding Claim 26,
receiving by a device information related to at least one of available sidestream based or virgin raw materials suitable for production of … products via at least one of a first user interface or communication interface;
receiving by the device a request to deliver a recipe of a … product or product component, the request comprising target feature information of the curable product or product component via at least one of a second user interface or communication interface;
determining by the device a recipe for producing the requested … product or product component based on target feature information of the requested curable product or product component and information related to the at least one of available sidestream based or virgin raw materials; and
See prior art rejection of claim 1 regarding Chang.
Chang does not explicitly disclose
curable
sending the recipe determined by the device for producing the requested curable product or product component via the at least one of the second user interface or the communication interface.
(Aghdasi, para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”;
Regarding Claim 29,
instructions to receive by a device information related to at least one of available sidestream based or virgin raw materials suitable for production of … products via at least one of a first user interface or communication interface;
instructions to receive by the device a request to deliver a recipe of a … product or product component, the request comprising target feature information of the curable product or product component via at least one of a second user interface or communication interface;
instructions to determine by the device a recipe for producing the requested … product or product component based on target feature information of the requested … product or product component and information related to at least one of available sidestream based or virgin raw materials; and
See prior art rejection of claim 1 regarding Chang.
Chang does not explicitly disclose
curable
wherein the instructions to determine the recipe are performed by a taught machine learning model
instructions to send the recipe determined by the device for producing the requested curable product or product component via the at least one of the second user interface or the communication interface.
(Aghdasi, para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”;) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Claims 3 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Daczko in view of AAPA
Regarding Claim 3, Chang, Aghdasi an Daczko disclose the system of claim 1.
Chang does not explitly disclose
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the system to:
determine target feature information related to … raw materials suitable for production of curable products to improve usability of at least one … raw material in production of the curable product or product component on basis of the target feature information of the curable product or product component,
wherein the at least one of the first user interface or communication interface is adapted to send the target feature information related to the … raw materials suitable for production of curable products.
Aghdasi discloses that a ML model may receive user goals for concrete production. (Aghdasi, para 0028”In step 208, process 200 can optimize and adjust a concrete mix in real-time to be
able to deliver concrete products that meet the requirements of the project and the goals and
priorities of the customer as closely as possible. These could include but not limited to
minimizing costs, minimizing cement consurnptions, minirnizing carbon footprint, rnaxirnizing, or
achieving a certain over-night or early-age strength, maxirnizing durability, and the like using
local raw materials available to producers. For example, if the goal is to reduce the amount of
cement without compromising the final properties, ML/optimization system(s) 110 can enable
concrete producers to use 10-20% less cement in their concrete, therefore, reducing costs as
well as carbon footprint.”) Aghdasi discloses that the ML may consider many varieties of raw materials and admixtures in formulating optimal concrete mixes. (Aghdasi, para 0020, “More specifically, concrete ML/optimization system(s) 110 can use proprietary
datasets supplemented by partner data, as well as cutting edge Al tools. These can include,
inter alia: such as model-based reinforcement learning, Bayesian optimization, model-based
multi-armed bandits, convolutional neural networks, generative adversarial networks and other
cutting-edge machine learning algorithms to predict the properties of millions of combinations
of raw materials as well as organic and chemical admixtures and supplementary cementitious
materials used in cement and concrete production, and producing and testing material
structures and properties using robotic systems to quickly converge on optimal mixes or to
discover new cements or concrete materials. This enables drastic improvements in cost and
performance characteristics for each project.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Chang does not explicitly disclose
sidestream based
However, Applicant’s Admitted Prior Art discloses that sidestreams are a known component of concrete production. (Applicant’s specification, para 0002, ”[0002] Various sidestreams are generated in industrial processes, the valorisation of which makes sense not only from an economic but also from an environmental point of view. One potential application for industrial sidestream based raw materials is the production of curable compositions for replacing concrete-based products.” As Aghdasi already discloses a ML that considers different materials in optimizing concrete (see above), the examiner respectfully suggests that substituting another known type of concrete ingredient would have been obvious. The claim would have been obvious because the substitution of one known element for another would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claims 10-19,27,30 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Fowler US20020091593A1 in view of AAPA
Regarding Claim 10,
at least one processor; and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor,
cause the device to: receive information related to at least one of available sidestream based or virgin raw materials suitable for production of … products via at least one of a the-user interface or communication interface;
See prior art rejection of claim 1 regarding Chang
Wherein the target feature information related to … raw materials comprises a modification in at least one feature of the at least one sidestream based raw material;
Although Chang does not disclose the use of sidestream, Chang does disclose informing a recipe recommendation system of available ingredient amounts. (Chang, par 0026, “[0026] The voice recognition module 202 is configured to analyze audio input received via the microphone 126 of the mobile computing device 102. As discussed in more detail below (see block 502 of FIG. 5), the user of the mobile computing device 102 may dictate a type of ingredient available to the user and the corresponding quantity of that ingredient. For example, in some embodiments, the user may go through a pantry, refrigerator, or other food storage area and tell the mobile computing device 102 all of the available ingredients and the amount remaining in each corresponding package.”) The examiner interprets this to be directed to a feature modification related to improving usability of an ingredient. To the extent that Applicant’s background discloses that industrial sidestream is an ingredient for curable production, the examiner submits that the combination of AAPA below addresses the limitation.
Chang does not explicitly disclose
curable
send target feature information related to … raw materials suitable for production of curable products to improve usability of at least one … raw material in production of a curable product or product component via the at least one of the user interface or communication interface; and
Aghdasi is directed to a machine learning system used to optimize formulations of concrete to reach user goals. (Aghdasi, para 0023, “It is noted that ML/optimization system(s) 110 can utilize a data-driven approach.
ML/optimization system(s) 110 can manage proprietary data and a fully-automated robotic
laboratory. ML/optimization system(s) 110 can collect large amounts of data (e.g. big data) for
training, development, and testing its machine learning models. Finally, ML/optimization
system(s) 110 can use its machine learning models backed by big data, generated through its
fully--automated lab, to invent and discover new cements, concretes, and other construction
materials to either license to other producers or to mass produce itself.”; para 0026, “In step 204, process 200 can retrieve/select specified ML techniques from
specified machine learning database(s) 106. Process 200 can select other ML/Al tools as well.
These ML/Al tools can be dynamically selected to provide optimized real-time feedback to
cement production plants. These ML/Al tools can be dynamically selected to provide optimized
real-time feedback this time to concrete producers (e.g. concrete ready-mix companies or
concrete precast plants) based on real-time observations from the time concrete is mixed until it
is delivered to the site.”; para 0028, “In step 208, process 200 can optimize and adjust a concrete mix in real-time to be
able to deliver concrete products that meet the requirements of the project and the goals and
priorities of the customer as closely as possible. These could include but not limited to
minimizing costs, minimizing cement consurnptions, minirnizing carbon footprint, rnaxirnizing, or
achieving a certain over-night or early-age strength, maxirnizing durability, and the like using
local raw materials available to producers. For example, if the goal is to reduce the amount of
cement without compromising the final properties, ML/optimization system(s) 110 can enable
concrete producers to use 10-20% less cement in their concrete, therefore, reducing costs as
well as carbon footprint.” ). The examiner interprets concrete to be a curable material. It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
sidestream based
See prior art rejection of claim 3.
send an order request to at least one supplier of raw material via the at least one of the user interface or communication interface.
See prior art rejection of claim 6 regarding Fowler
Regarding Claim 11, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 10.
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the device to: send an order request to at least one supplier of virgin raw material.
See prior art rejection of claim 6 regarding Fowler
Regarding Claims 12,13,14,
See prior art rejection of claims 8,13,9
Regarding Claim 15, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 10.
wherein the at least one memory and the program code are further configured to, with the at least one processor, cause the device to: determine at least one additive for producing the curable product or product component based on the determined recipe.
(Aghdasi, para 0028, “[0028] In step 208, process 200 can optimize and adjust a concrete mix in real-time to be
able to deliver concrete products that meet the requirements of the project and the goals and
priorities of the customer as closely as possible.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang, Fowler and AAPA with the ML model of Aghdasi with the motivation of satisfying user formulation requirements. Id.
Regarding Claim 16, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 10.
wherein the order request to at least one supplier of virgin raw material includes location information of the manufacturing site of the curable product or product component.
See prior art rejection of claim 7.
Regarding Claim 17, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 15.
wherein the order request to at least one supplier of virgin raw material includes information on the determined at least one additive.
See prior art rejection of claim 15.
Regarding Claim 18, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 10.
wherein the order request to at least one supplier of … raw material includes location information of a manufacturing site of the curable product or product component.
See prior art rejection of claim 7.
sidestream based
See prior art rejection of claim 3
Regarding Claim 19, Chang, Aghdasi, AAPA and Fowler disclose the device of claim 10.
wherein the target feature
information of the curable product or product component includes at least one of the following:
compressive strength, flexural tensile strength, tensile strength, density, structural weight,
operating conditions, CO2 emissions, natural resources consumption or price.
See prior art rejection of claim 1 regarding Aghdasi.
Regarding Claim 27,
receiving by a device information related to at least one of available … or virgin raw materials suitable for production of … products via at least one of the-a user interface or the communication interface;
See prior art rejection of claim 1 regarding Chang
Chang does not explicitly disclose
curable
sending by the device target feature information related to …raw materials suitable for production of … products to improve usability of at least one … raw material in production of the curable product or product component via the at least one of the user interface or the communication interface; and
See prior art rejection of claim 10 regarding Aghdasi
sidestream based
See prior art rejection of claim 3
sending by the device an order request to at least one supplier of … raw material via the at least one of the user interface or the communication interface.
See prior art rejection of claim 7 regarding Fowler
Regarding Claim 30,
instructions to receive by a device information related to at least one of available … or virgin raw materials suitable for production of … products via at least one of a user interface or the communication interface;
see prior art rejection of claim 1 regarding Chang
curable
instructions to send by the device target feature information related to … raw materials suitable for production of curable products to improve usability of at least one … raw material in production of the curable product or product component via the at least one of the user interface or the communication interface; and
See prior art rejection of claim 10 regarding Aghdasi
sidestream based
see prior art rejection of claim 3
instructions to send by the device an order request to at least one supplier of … raw material via the at least one of the user interface or the communication interface.
See prior art rejection of claim 7 regarding Fowler
Claims 6,7 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Daczko in view of Fowler US20020091593A1 in view of AAPA
Regarding Claim 6, Chang, Aghdasi and Daczko disclose the system of claim 1.
Chang does not explicitly disclose
wherein the at least one of the first user interface or communication interface is further adapted to send an order request to at least one supplier of … raw material on basis of the determined recipe.
The examiner notes that Chang discloses formulating a shopping list of needed materials, but does not explicitly disclose sending an order. (See e.g., Chang, claim 9). Fowler is directed to an inventory management system. (Fowler, abstract). Fowler discloses that the system may automatically generate and transmit purchase orders to replenish inventory. (Fowler, para 0052, “Then inventory of on-hand stock, tabulated by use of the scanner provides the input to determine the replenishment quantities of stock. These quantities are utilized to create and transmit purchase orders. Therefore the problems of perpetual inventory and ordering are solved by the operable interaction between the hand-held computer scanner and a point of sale operably configured to produce data that is derived from a combination of each.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang and Aghdasi with the ordering system of Fowler with the motivation of replenishing inventory. Id.
sidestream based
See prior art rejection of claim 3.
Regarding Claim 7, Chang, Aghdasi, Daczko, Fowler and AAPA discloses the system of claim 6.
Chang does not explicitly disclose
wherein the order request includes location information of the manufacturing site of the curable product or product component.
Fowler is directed to an inventory management system. (Fowler, abstract; Disclosed is a method and apparatus to electronically tabulate items marked with a bar code. The system may optionally operate to manage, control and tabulate inventory, produce vendor-correct purchase orders, manage multiple locations, multiple buildings, multiple clients and multiple stores to facilitate efficiency for a wholesaler or retailer or alternatively for a distributor, broker or sales agent. The system may alternatively be used to manage inventory for a manufacturing environment or for tracking evidence or items in a chain of custody environment useful for governmental and scientific functions.”) Fowler discloses that the system may automatically generate and transmit purchase orders to replenish inventory. (Fowler, para 0052). As Fowler discloses management of multiple finite locations the examiner respectfully suggests that it would be common sense to include in a purchase order the location where the goods are needed. The claim would have been obvious because a person of ordinary skill has good reason to pursue the known options within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense. It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang and Aghdasi with the ordering system of Fowler with the motivation of replenishing inventory. Id.
Claims 22 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Dazcko in view of
“SmartRock™
The leading wireless concrete sensor for measuring temperature and strength.”, 2019,
https://web.archive.org/web/20190706040349/https://www.giatecscientific.com/products/concrete-sensors/smartrock-maturity-meter/
Regarding Claim 22, Chang, Aghdasi and Daczko disclose the device of claim 20.
Chang does not explicitly disclose
wherein the feature information determined during use of the product or product component includes data measured by at least one sensor integrated in the product or product component or data derived based on data measured by at least one sensor integrated in the product or product component.
The examiner notes that Aghdasi discloses testing produced structures to determine optimal concrete mixtures. (Aghdasi, para 0020, “These can include,
inter alia: such as model-based reinforcement learning, Bayesian optimization, model-based
multi-armed bandits, convolutional neural networks, generative adversarial networks and other
cutting-edge machine learning algorithms to predict the properties of millions of combinations
of raw materials as well as organic and chemical admixtures and supplementary cementitious
materials used in cement and concrete production, and producing and testing material
structures and properties using robotic systems to quickly converge on optimal mixes or to
discover new cements or concrete materials. This enables drastic improvements in cost and
performance characteristics for each project.”) Smartrock is a embedded sensor for measuring concrete strength and parameters. (Smartrock, p.3, “2 Install your sensor
Attach the Smart Rock™ maturity meter to the rebar by twisting the metal wires together to activate. 3 Pour your concrete After installing the concrete sensor in the formwork, pour concrete as usual. 4 Get real-time results Open the SmartRock™ Concrete app to view immediate readings of concrete strength and temperature.”) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang, Aghdasi and Daczko with the sensor of Smartrock with the motivation of collecting concrete data. Id.
Claims 33 are rejected under 35 U.S.C. 103 as being unpatentable over Chang US20140095479A1 in view of Aghdasi US20220253734A1 As evidenced by provisional application 63114519 in view of Daczko in view Isteri, “Production and properties of ferrite-rich CSAB cement from metallurgical industry residues”,
4/2020, https://www.sciencedirect.com/science/article/pii/S0048969719362047
Regarding Claim 33, Chang, Aghdasi and Daczko disclose the system of claim 1.
Chang does not explicitly disclose
wherein the sidestream based raw material comprises at least one of coal-fired power plant ash, steel industry slag, green liquor sludge, waste incineration ash or slag, tailings or side stones from mining industry, or neutralizing waste; and/or wherein the virgin raw material comprises at least one of clay, silt, desert sand, acidic or alkaline soil, or Latossolo-type soil.
(Isteri, abstract, “Blast furnace slag from the steel industry is commercially utilized as a cement replacement material without major processing requirements; however, there are many unutilized steel production slags which differ considerably from the blast furnace slag in chemical and physical properties. In this study, calcium sulfoaluminate belite (CSAB) cement clinkers were produced using generally unutilized metallurgical industry residues: AOD (Argon Oxygen Decarburisation) slag from stainless steel production, Fe slag from zinc production, and fayalitic slag from nickel production. CSAB clinker with a target composition of ye'elimite-belite-ferrite was produced by firing raw materials at 1300 °C. The phase composition of the produced clinkers was identified using quantitative XRD analyses, and the chemical composition of the clinker phases produced was established using FESEM-EDS and mechanical properties were tested through compressive strength test. It is demonstrated that these metallurgical residues can be used successfully as alternative raw materials for the production of CSAB cement that can be used for special applications. In addition, it is shown that the available quantities of these side-streams are enough for significant replacement of virgin raw materials used in cement production. “) It would have been obvious to one of ordinary skill in the art before the filing date of the invention to combine Chang, Aghdasi and Daczko with the sidestream of Isteri with the motivation of replacing raw materials in cement. Id.
Conclusion
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/ALLEN C CHEIN/Primary Examiner, Art Unit 3627