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 .
This communication is in response to the Amendment filed 5/20/2026.
Response to Arguments
Claims 1 – 14 and 16 – 21 are pending in this Office Action. After a further search and a thorough examination of the present application, claims 1 – 14 and 16 – 21 are rejected.
Applicant's arguments filed with respect to claims 1 – 14 and 16 – 21 have been fully considered but they are moot in view of new rejection.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 14 and 16 – 21 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Kakuda et al. (US 20240394603 A1) (‘Kakuda’ herein after) further in view of David Neale Carley (US 20220067181 A1) (‘Carley’ herein after).
With respect to claim 1, 17, 20,
Kakuda discloses a method comprising: receiving a representation of a parameter (figure 1 #111 input data, Kakuda) of a first clustering model, wherein the first clustering model was generated in a first environment and training data (figure 1 #121, parameter figure 1 #111 input data, the training data, paragraphs 10, 23 teach a clustering model configured to generate a trained clustering model when a training dataset is input, and classify the training dataset into N clusters, Kakuda); based on the parameter of the first clustering model, generating a second clustering model in a second environment (figure 1, #131 trained clustering model is equivalent to the second clustering model, paragraphs 10, 23, Kakuda); providing, to the second clustering model, a prediction request (figure 1, #design condition x and paragraph 15 – 17, 37 – 39 teaches acquiring data used for the predicting; identifying using the trained clustering model generated by the program of generating a model for predicting a material characteristic, determining, when the data used for the predicting is input, Kakuda) and generating, by using the second clustering model (figure 1, #131 trained clustering model is equivalent to the second clustering model, paragraphs 10, 23, Kakuda), a prediction result based on the prediction request (figure 1, #characteristic value y and paragraph 69 teaches a response variable (characteristic value) corresponding to the explanatory variable (design condition) used as the input is obtained as output data to output characteristic value, paragraph 103 – 105 the prediction device outputs the predicted characteristic value, as prediction data for the input data (design condition x) of the prediction target, Kakuda).
Kakuda teaches prediction model generating device trains the clustering model and the prediction model using a training dataset stored in a material data storage part and generates a trained clustering model and a trained prediction model but does not teach this to be explicitly claimed as in first and second programming languages and the parameter being in a format readable by both languages.
However, Carley teaches using data for training in accordance with first and second programming languages in paragraphs 33 and 34 teaching that where a training device may be dedicated to a specific programming language (e.g., PYTHON, C++, JAVA, R., JAVASCRIPT, or the like), allowing a feature set that most closely matches the programming paradigms of the language. Optionally, a training device may be a multiple programming language device. In some embodiments, training devices may use training data to train one or more implementations of machine learning various algorithms. Furthermore, the parameter would be readable by both languages because it is a condition set for the clusters.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Carley with the teachings of Kakuda it incorporates the advantage of using the clustering training models for different data. Furthermore the combination also brings a trained machine learning model from the training device, evaluating the trained machine learning to determine whether the trained machine learning model satisfies an evaluation criterion, and encrypting the trained machine learning model if the trained machine learning model satisfies the evaluation criterion and then using the encrypted machine learning model to make predictions using live data.
With respect to claim 2, 18,
Kakuda as modified discloses the method of claim 1, wherein the second clustering model is operative to make predictions using the parameter (figure 1(b) #131 trained clustering model is the second clustering model used to make predictions, paragraphs 14 – 17, Kakuda).
With respect to claim 3, 19,
Kakuda as modified discloses the method of claim 1, wherein each of the first clustering model and the representation of the parameter is determined using the training data (figure 1 #111 and #121, paragraphs 23 – 24 teaches a clustering model configured to generate a trained clustering model when a training dataset is input, and classify the training dataset into N clusters and a weight defining part configured to calculate a distance between centroids of the clusters of the classified clusters, and a weight between the clusters, using both the calculated distance between the centroids of the clusters and a parameter representing a feature of the training dataset, Kakuda).
With respect to claim 4,
Kakuda as modified discloses the method of claim 1, wherein each of the first clustering model and the representation of the parameter was created in a training environment by applying a training algorithm to the training data using the first programming language(figure 1 #111, #121 paragraphs 23 – 25, Kakuda).
With respect to claim 5,
Kakuda as modified discloses the method of claim 4, wherein generating the second clustering model does not involve applying the training algorithm to the training data (figure 1 (b) #131 the second clustering model is already a trained clustering model and does not involve applying the training algorithm, Kakuda).
With respect to claim 6,
Kakuda as modified discloses the method of claim 1, wherein the second clustering model executes in a prediction environment (figure 1 (b) the second clustering model #131 is executed in a prediction device environment, Kakuda).
With respect to claim 7,
Kakuda as modified discloses the method of claim 1, wherein generating the second clustering model comprises loading the parameter into the second clustering model (figure 1 #131 the second clustering model which is the trained clustering model is generated by using the input data parameters and the training data into the first clustering model and then generating the trained clustering model which is the second clustering model, Kakuda).
With respect to claim 8,
Kakuda as modified discloses the method of claim 1, wherein the parameter defines, for a cluster in the first clustering model and in the second clustering model, a centroid of the cluster in an n-dimensional space or a distance from a boundary of the cluster to the centroid in the n-dimensional space (figures 1, 3 and paragraphs 23 – 25 and 61 – 63 teach a clustering model configured to generate a trained clustering model when a training dataset is input, classify the training dataset into N clusters and a weight defining part configured to calculate a distance between centroids of the clusters of the classified clusters, weight between the clusters, using both the calculated distance between the centroids of clusters and a parameter representing a feature of the training data and a prediction model configured to generate respective trained prediction models using the clusters and weight, Kakuda).
With respect to claim 9,
Kakuda as modified discloses the method of claim 1, wherein the first programming language is different from the second programming language (paragraphs 33 and 34, Carley).
With respect to claim 10,
Kakuda as modified discloses the method of claim 1, wherein the first clustering model is based on k-means clustering, Gaussian mixture model clustering, density-based spatial clustering of applications with noise, or ordering points to identify a clustering structure (paragraphs 18, 60, 142, Kakuda).
With respect to claim 11,
Kakuda as modified discloses the method of claim 1, wherein the parameter is one of a plurality of parameters of the first clustering model, and wherein the first clustering model was generated based on determining the plurality of parameters by applying a training algorithm to the training data (figures 1, 3, paragraphs 142 – 143, Kakuda).
With respect to claim 12,
Kakuda as modified discloses the method of claim 1 further comprising: receiving second training data; updating the second clustering model based on the parameter and the second training data in the second programming language; providing, to the second clustering model as updated, a second prediction request and generating, by using the second clustering model as updated, a second prediction result based on the second prediction request (figure 1, paragraphs 10, 15 – 17, 23, Kakuda).
With respect to claim 13,
Kakuda as modified discloses the method of claim 12, wherein updating the second clustering model based on the parameter and the second training data comprises adjusting sizes of one or more clusters defined by the second clustering model or assignments of objects to the one or more clusters defined by the second clustering model (paragraphs 33 – 34, Carley).
With respect to claim 14,
Kakuda as modified discloses the method of claim 13, further comprising: receiving a second representation of a second parameter of the second clustering model as updated, wherein the second parameter is in the second programming language and updating the first clustering model based on the second parameter in the first programming language(figure 1, paragraphs 10, 15 – 17, 23, Kakuda).
With respect to claim 16,
Kakuda as modified discloses the method of claim 1, wherein the first programming language is interpreted and dynamically typed, and wherein the second programming language is compiled and statically typed (paragraphs 33 – 34, Carley).
With respect to claim 21,
Kakuda as modified discloses the method of claim 12, wherein updating the second clustering model based on the parameter and the second training data in the second programming language occurs in the second environment (paragraphs 33 and 34, Carley).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210109906 A1 teaches specifying the job identifier to a clustering analysis application causing the analysis configuration, the clustering models and the data regularize to load into the clustering analysis application and receiving a plurality of scores resulting from a cluster analysis performed by the clustering analysis application based on the job identifier.
US 20030212713 A1 teaches in-database clustering comprises a first data table and a second data table, each data table including a plurality of rows of data, means for building a clustering model using the first data table using a portion of the first data table, wherein the portion of the first data table is selected by partitioning, density summarization, or active sampling of the first data table, and means for applying the clustering model using the second data table to generate apply output data.
US 20180365249 A1 teaches data clustering in a model property vector space. Input data is received comprising a plurality of data instances in a data vector space. An output is generated comprising a plurality of data segments and one or more clustering rules. For each data cluster, a predictive model is constructed for each data segment of the plurality of data segments.
US 20170154280 A1 teaches incremental generation of models with dynamic clustering. A first set of data is received. A first set of clusters based on the first set of data is generated. A respective first set of models for the first set of clusters is created. A second set of data is received. A second set of clusters, based on the second set of data and based on a subset of the first set of data, is generated. A respective second set of models for the second set of clusters, based on a subset of the first set of models and based on the second set of data, is created.
US 20230004842 A1 teaches retrieving a set of nodes and execute various clustering algorithms in order to segment the nodes into different clusters. The systems and methods described herein also describe generating one or more prediction models, such as time-series models, for each cluster of nodes. When a node with unknown/limited data and attributes is identified, the methods and systems described herein first identify a cluster that is most similar the new node, identify a corresponding prediction model, and execute the identified prediction model to calculate future attribute of the new node.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is 571-272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 8/19/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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