DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to amendment filed on 5/13/2025, in which claims 1, 7, and 9 was amended, claim 6 was canceled, claims 10 – 12 was added, and claims 1 – 5 and 7 – 12 was presented for further examination.
3. Claims 1 – 5 and 7 – 12 are now pending in the application.
Response to Arguments
4. Applicant's arguments filed5/13/2025 have been fully considered but they are not persuasive. (see Remarks below).
Remarks
5.1 As per amended claim 1, applicant’s argues in substance in pages 7 – 8 that Choi et al (KR 2020-0052393 A), Hammond et al (EP 3408739 A1), and Cella et al (US 2021/0248514 A1) does not disclose wherein materials are predicted by the Al engine using a random walk process on a multi-dimensional element/property map.
Examiner respectfully disagrees.
In response to applicant’s argument, Examiner respectfully responds that the combine teaching of Choi et al (KR 2020-0052393 A), Hammond et al (EP 3408739 A1), and Cella et al (US 2021/0248514 A1) specifically disclose wherein materials are predicted by the Al engine using a random walk process on a multi-dimensional element/property map (see para.[01443]).
Choi discloses artificial physical property prediction system. The artificial intelligence system received material information and applies an artificial intelligence model algorithm to analyze and compare various composite of related information (see abstract).
Cella discloses different types of artificial intelligence circuits to classify a set of information, the artificial intelligence circuits includes a random walk system and a random forest system (see para.[01443]).
Choi discloses artificial intelligence for physical property prediction, Cella discloses different types of artificial intelligence to analyze information. As explained in the detailed rejection. It is very obvious to one of ordinary skill in the art to applied artificial intelligence based on random walk algorithm of the system of Cella into physical property prediction of Choi to analyze information and predict the physical property.
In addition, Examiner understand Cella does not apply the artificial intelligence based on random walk to predict physical property but specifically discloses various types of artificial intelligence algorithms that can be used to analyze information. Since Choi does not limit his physical property prediction to a specific artificial intelligence algorithm, it can be inferred form the disclosure of Choi that its system can accommodate different algorithm of artificial intelligence.
5.2 Thus, the rejection is maintained.
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.
6. Claims 1 – 5 and 7 - 11 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (KR 2020-0052393 A), in view of Hammond et al (EP 3408739 A1), and further in view of Cella et al (US 2021/0248514 A1).
As per claim 1, Choi et al (KR 2020-0052393 A) discloses,
An Al materials assistant, comprising: a materials database, the materials database comprising compositional, manufacturing process, and physical/mechanical properties of a plurality of materials (pg.2 lines 21 – 22; “artificial intelligence-based physical property prediction system according to an embodiment of the present invention includes a first DB unit 10 storing material-related information for a composite resin, and storing the first DB unit 10 at the user's request”).
and at least one user output interface in operative communication with the searching model for providing materials predicted by the Al engine or material properties predicted by the Al engine to users (pg.4 lines 11 – 12; “transmits it to the output unit 300 for output. …. the prediction property information is limited prediction property information for each composition information” and pg.4 lines 18 – 20; “the physical property comparison and determination unit 500 compares and determines predicted physical property information by the physical property model calculated by the physical property calculation unit 200, generates optimal composition
information according to the determination result, and outputs the output unit”).
Choi does not disclose an optimization engine, the optimization engine further comprising: an Artificial Intelligence (AI) engine in operative communication with the materials database, the Al engine being trained on data in the materials database and their associated compositional, manufacturing process and physical/mechanical properties, and a searching model in operative communication with the Al engine, at least one user input interface in operative communication with the searching model for inputting queries regarding potential materials or desired material properties.
However, Hammond et al (EP 3408739 A1) in an analogous art discloses,
an optimization engine, the optimization engine further comprising: an Artificial Intelligence (AI) engine in operative communication with the materials database (pg.3 para.[0017]; “Al database stores and indexes trained Al objects and its class of Al objects have searchable criteria. The Al database cooperates with the search engine to utilize search criteria supplied from a user to retrieve one or more Al data objects”).
the Al engine being trained on data in the materials database and their associated compositional, manufacturing process and physical/mechanical properties (pg.1 abstract; “Al database is coupled to an Al engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained Al data objects from the Al database into a new trained Al model”).
and a searching model in operative communication with the Al engine (pg.4 para.[131]; “search engine utilizes the user supplied criteria to query for relevant trained Al objects” and pg.4 para.132]; “Al database cooperate with the Al engine to supply one or more Al objects”)
at least one user input interface in operative communication with the searching model for inputting queries regarding potential materials or desired material properties weight
. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence (AI) database configure to cooperate with search engine and AI engine of the system of Hammond into graphical user interface for defining a proposed model of the system of choi for enabling user supplied search criteria from the user interfaces to find relevant trained Al objects stored in the Al data.
Choi and Hammond does not specifically disclose wherein materials are predicted by the Al engine using a random walk process on a multi-dimensional element/property map.
However, Cella et al (US 2021/0248514 A1) in an analogous art discloses,
wherein materials are predicted by the Al engine using a random walk process on a multi-dimensional element/property map (para.[01443]; “artificial intelligence circuit 8310 may include at least one system such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed forward neural network, a feedback neural network a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence that incudes random work of the system of Cella into graphical user interface for defining a proposed model of the system of choi for enabling adaptive intelligence in the manufacturing system of Choi.
As per claim 2, the rejection of claim 1 is incorporated, and further Hammond et al (EP 3408739 A1) discloses,
wherein the Al engine comprises a Machine Learning algorithm (pg.12 para.[62]; “Al engine can contain a vast array of machine learning algorithms”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence (AI) database configure to cooperate with search engine and AI engine of the system of Hammond into graphical user interface for defining a proposed model of the system of choi for enabling user supplied search criteria from the user interfaces to find relevant trained Al objects stored in the Al data.
As per claim 3, the rejection of claim 2 is incorporated, and further Choi et al (KR 2020-0052393 A) discloses,
wherein the Machine Learning algorithm using a multivariable regressions selected from the group consisting of: linear regression, polynomial regression, logistic regression, quantile regression, Lasso regression, ridge regression, elastic net regression, principal components regression, partial least squares regression, ordinal regression, Poisson regression, negative binomial regression, quasi Poisson regression, Cox regression, Tobit regression, support vector regression, random forest regression, decision tree regression, k-nearest neighbors (KNN) regression, and Gaussian process regression (pg.6 lines 20 – 22; “artificial intelligence-based material model generation algorithm of the model generator 100 includes multiple linear regression (MLR), support vector machine (SVM), and nearest neighbor classification (k-NN) , k-Nearest Neighbor, Deep Learning, Generic
Algorithm (GA), Boosted Trees, Generative Adversarial Network (GAN), Artificial Neural Network (ANN) )”).
As per claim 4, the rejection of claim 2 is incorporated, and further Choi et al (KR 2020-0052393 A) discloses,
wherein the Machine Learning algorithm using Gaussian process multivariable regression (pg.6 lines 20 – 22; “artificial intelligence-based material model generation algorithm of the model generator 100 includes multiple linear regression (MLR), support vector machine (SVM), and nearest neighbor classification (k-NN) , k-Nearest Neighbor, Deep Learning, Generic Algorithm (GA), Boosted Trees, Generative Adversarial Network (GAN), Artificial Neural Network (ANN) )”).
As per claim 5, the rejection of claim 2 is incorporated, and further Choi et al (KR 2020-0052393 A) discloses,
wherein the Al engine comprises a Machine Learning algorithm using deep learning and/or neural network (pg.2 lines 8 – 9; “artificial intelligence algorithms (machine learning, deep learning, etc.) based on the material-composition-physical-use database”).
As per claim 7, the rejection of claim 1 is incorporated, Cella et al (US 2021/0248514 A1) further discloses,
wherein the random walk process employs a plurality of walkers (para.[01443]; “artificial intelligence circuit 8310 may include at least one system such as a machine learning system, a model-based system, a rule-based system, a deep learning system, a hybrid system, a neural network, a convolutional neural network, a feed forward neural network, a feedback neural network a self-organizing map, a fuzzy logic system, a random walk system, a random forest system, a probabilistic system, a Bayesian system”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence that incudes random work of the system of Cella into graphical user interface for defining a proposed model of the system of choi for enabling adaptive intelligence in the manufacturing system of Choi.
As per claim 8, the rejection of claim 1 is incorporated, and further Choi et al (KR 2020-0052393 A) discloses,
wherein predicted material properties include one or both groups consisting of confidence levels and error bars (pg.7 lines 39 – 40; “the artificial intelligence-based physical property model algorithm generated based on the information stored in the first DB unit”).
As per claim 9, the rejection of claim 1 is incorporated, Hammond et al (EP 3408739 A1) further discloses
further comprising an analysis engine, the analysis engine comprising: an analysis Al engine in operative communication with the materials database (pg.1 abstract; “Al database is coupled to an Al engine
to allow any of reuse, reconfigure ability, and recomposition of the one or more trained Al data objects from the Al database into a new trained Al model”).
the analysis Al engine being trained on data in the materials database and their associated compositional, manufacturing process and physical/mechanical properties and an analysis model in operative communication with the analysis Al engine (pg.3 para.[20]; “Al database 341 stores and indexes trained Al objects and the class of Al objects” and pg.3 para.[24]; “learner module 328 carries out an actual execution of the underlying Al learning algorithms during a training session”).
Choi et al (KR 2020-0052393 A) further discloses,
at least one user input interface in operative communication with the analysis model for inputting a plurality of material properties and a user-determined weight for each property (pg.9 lines 20 – 22; “request input of a weight setting. Weight setting means that when composition + physical properties are combined, input can be requested to suggest physical properties (or composition) by extracting a similar mixture according to the weight (for example, filler during composition or tensile strength among physical properties)”).
and at least one user output interface in operative communication with the analysis model for providing performance indexes for a plurality materials to users based on materials properties in the materials database and the user-determined weights for the material properties (pg.3 lines 35 – 38; “the output unit 300, the model generation unit 100, and the property calculation unit 200 perform communication using a wireless or wired network to input user requests in real time or receive judgment information accordingly. The model generating unit 100 is preferably applied to the artificial intelligence-based physical property model algorithm generated based”). on the information stored in the first DB unit 10 by the user input information”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence (AI) database configure to cooperate with search engine and AI engine of the system of Hammond into graphical user interface for defining a proposed model of the system of choi for enabling user supplied search criteria from the user interfaces to find relevant Al objects stored in the Al data.
As per claim 10, the rejection of claim 1 is incorporated and further Choi et al (KR 2020-0052393 A) discloses,
wherein the multi-dimensional element/property map comprises a first axis corresponding to a proportion of a first element, a second axis corresponding to a proportion of a second element, and a third axis orthogonal to the first axis and to the second axis and corresponding to a property of a composition having the relative proportions of the first and second elements at that location on the map (pg.11 lines 5 – 6; “material information corresponding to the limited composition information to the model generation unit 100 to which the artificial intelligence-based physical property model generation algorithm generated from the first DB”).
As per claim 11, the rejection of claim 1 is incorporated and further Choi et al (KR 2020-0052393 A) discloses,
wherein the multi-dimensional element/property map comprises a lateral dimension for each element/temper in the materials database and a vertical dimension for each property in the materials database (pg.11 lines 5 – 6; “material information corresponding to the limited composition information to the model generation unit 100 to which the artificial intelligence-based physical property model generation algorithm generated from the first DB”).
7. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Choi et al (KR 2020-0052393 A), in view of Hammond et al (EP 3408739 A1).
As per claim 12, Choi et al (KR 2020-0052393 A) discloses,
An Al materials assistant, comprising: a materials database, the materials database comprising compositional, manufacturing process, and physical/mechanical properties of a plurality of materials (pg.2 lines 21 – 22; “artificial intelligence-based physical property prediction system according to an embodiment of the present invention includes a first DB unit 10 storing material-related information for a composite resin, and storing the first DB unit 10 at the user's request”).
an optimization engine, the optimization engine further comprising: an Artificial Intelligence (Al) engine in operative communication with the materials database (pg.6 lines 9 – 12; “artificial intelligence-based physical property model generation algorithm generated based on the information stored in the first DB part 10, and the physical property comparison and determination unit 400 compares and determines predicted physical property information by the physical property model, It is preferable to generate optimal
composition information according to the determination result and transmit it to the output unit”).
the Al engine being trained on data in the materials database and their associated compositional, manufacturing process and physical/mechanical properties (pg.6 lines 41 – 42; “a part of the material-composition-material-use database information may be used as a test set to verify the accuracy of the
generated prediction model”).
an analysis Al engine in operative communication with the materials database (pg.4 lines 11 – 12; “transmits it to the output unit 300 for output. …. the prediction property information is limited prediction property information for each composition information” and pg.4 lines 18 – 20; “the physical property comparison and determination unit 500 compares and determines predicted physical property information by the physical property model calculated by the physical property calculation unit 200, generates optimal composition
information according to the determination result, and outputs the output unit”)
the analysis Al engine being trained on data in the materials database and their associated compositional, manufacturing process and physical/mechanical properties (pg.6 lines 24 – 26; “artificial intelligence based physical property prediction system of the present invention can be applied by predicting the composition and physical properties by applying various artificial intelligence algorithms (machine learning, deep learning, etc.) based on the material composition-material-use database of the functional composite resin”).
at least one user output interface in operative communication with the searching model for providing materials predicted by the Al engine or material properties predicted by the Al engine to users (pg.4 lines 11 – 12; “transmits it to the output unit 300 for output. …. the prediction property information is limited prediction property information for each composition information” and pg.4 lines 18 – 20; “the physical property comparison and determination unit 500 compares and determines predicted physical property information by the physical property model calculated by the physical property calculation unit 200, generates optimal composition information according to the determination result, and outputs the output unit”);
at least one user input interface in operative communication with the analysis model for inputting a plurality of material properties and a user-determined weight for each property (pg.9 lines 20 – 22; “request input of a weight setting. Weight setting means that when composition + physical properties are combined, input can be requested to suggest physical properties (or composition) by extracting a similar mixture according to the weight (for example, filler during composition or tensile strength among physical properties)”).
where one property of the plurality of material properties comprises a material price (pg.11 lines 21 – 22; “DB unit 30 stores environmental regulation related information on the composite resin, and the fourth DB unit 40 preferably stores price related information on the composite resin”).
and at least one user output interface in operative communication with the analysis model for providing performance indexes for a plurality of materials to users based on materials properties in the materials database and the user-determined weights for the material properties (pg.3 lines 35 – 38; “the output unit 300, the model generation unit 100, and the property calculation unit 200 perform communication using a wireless or wired network to input user requests in real time or receive judgment information accordingly. The model generating unit 100 is preferably applied to the artificial intelligence-based physical property model algorithm generated based”). on the information stored in the first DB unit 10 by the user input information”).
Choi does not specifically disclose a searching model in operative communication with the Al engine and an analysis model in operative communication with the analysis Al engine; at least one user input interface in operative communication with the searching model for inputting queries regarding potential materials or desired material properties.
However, Hammond et al (EP 3408739 A1) in an analogous art discloses,
and a searching model in operative communication with the Al engine (pg.1 abstract; “Al database is coupled to an Al engine to allow any of reuse, reconfigure ability, and recomposition of the one or more trained Al data objects from the Al database into a new trained Al model” and pg.4 para.[131]; “search engine utilizes the user supplied criteria to query for relevant trained Al objects” and pg.4 para.132]; “Al database cooperate with the Al engine to supply one or more Al objects”).
and an analysis model in operative communication with the analysis Al engine; at least one user input interface in operative communication with the searching model for inputting queries regarding potential materials or desired material properties (pg.3 para.[0020]; “search engine's 343 use of the user supplied search criteria from the user interfaces to find relevant trained Al objects stored in the Al data”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate artificial intelligence (AI) database configure to cooperate with search engine and AI engine of the system of Hammond into graphical user interface for defining a proposed model of the system of choi for enabling user supplied search criteria from the user interfaces to find relevant trained Al objects stored in the Al data.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/AUGUSTINE K. OBISESAN/
Primary Examiner
Art Unit 2156
8/22/2025