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 .
Claims 1-19 are pending in the application.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c), which papers have been placed of record in the file.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of the first paragraph of 35 U.S.C. 112. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/520869, fails to provide adequate support or enablement in the manner provided by the first paragraph of 35 U.S.C. 112 for one or more claims of this application. Any claims pertaining to “update the trained AI model” would not be considered the benefit of a prior-filed application under 35 U.S.C. 119(e) [Provisional Application # 63/520869]
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/17/2024 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of Group 1 – claims 1-19 in the reply filed on 07/10/2026 is acknowledged.
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.
Claim 1-19 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Regarding claim 1:
Step 1: claim 1 is directed to a system, which is a machine and therefore falls within one of the four statutory categories of invention under 35 U.S.C. § 101.
Step 2A, Prong One: claim 1 recites a judicial exception because it recites mental processes and mathematical concepts.
Specifically, the claim recites limitations including:
receiving a model of a part and manufacturability-associated data;
performing a similarity search for shape and manufacturability-associated metrics;
identifying a set of candidate models;
determining or outputting a predictive value representing a likelihood that a manufacturer can manufacture the received part; and
presenting the candidate models and/or predictive value in a graphical user interface or report.
These limitations describe collecting information, comparing the received information with stored information, evaluating similarities, predicting an outcome, and presenting the resulting information. Such activities can practically be performed in the human mind or with the aid of pen and paper by a manufacturing engineer reviewing historical part information, identifying similar parts, estimating which manufacturers are capable of producing the part, and communicating the results.
Accordingly, the claim recites mental processes, including observations, evaluations, judgments, and opinions, which are identified as abstract ideas under MPEP § 2106.04(a)(2).
Furthermore, the limitation reciting:
"performing a similarity search for shape and manufacturability-associated metrics"
also recites a mathematical concept, because similarity searching inherently involves mathematical comparisons between data representations, such as determining similarity metrics, distances, or relationships between feature representations generated by the trained AI model. Likewise, generating a predictive value representing a likelihood of manufacturability constitutes mathematical analysis of data to produce a probability or prediction.
The recitation of a "trained AI model" does not remove the claim from the abstract idea because the claim merely invokes the AI model as a tool for performing the abstract analysis. The claim does not recite any specific improvement to artificial intelligence technology, machine learning architecture, model training technique, similarity-search algorithm, or computer functionality, but instead functionally recites that the model is trained using part shape and manufacturability-associated data and outputs candidate models and/or predictive values.
Accordingly, claim 1 recites abstract ideas in the form of mental processes and mathematical concepts.
Step 2A, Prong Two: The claim as a whole does not integrate the judicial exception into a practical application. The additional elements beyond the abstract idea include:
a processor;
a memory storing instructions;
a trained AI model;
a database;
a graphical user interface; and
a report.
These elements are recited at a high level of generality and merely perform their ordinary and conventional functions of storing data, executing instructions, processing information, retrieving data, and displaying results. The claimed processor merely executes the abstract idea.
The memory merely stores instructions.
The database merely stores historical part information.
The graphical user interface and report merely present the results of the analysis, which constitutes insignificant extra-solution activity, amounting to mere data output (see MPEP 2106.05(g)).
Although the claim recites a "trained AI model," the AI model is merely used as a tool to perform the abstract evaluation of similarity and manufacturability. The claim does not improve the functioning of the AI model itself, improve computer operation, improve database searching technology, improve CAD processing, improve additive manufacturing equipment, improve CNC machine operation, or otherwise improve any technological field. Rather, the claim merely uses generic computing technology to automate the abstract process of comparing parts and predicting manufacturing capability. Accordingly, the judicial exception is not integrated into a practical application.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception. The processor, memory, database, graphical user interface, and report are generic computer components performing their ordinary functions.
The recited AI model is likewise described only in functional terms and merely performs the conventional computer function of processing input data to generate predictions. The claim does not recite any unconventional machine-learning architecture, training methodology, embedding technique, similarity-search mechanism, or other technological improvement.
Considering the claim as an ordered combination, the additional elements merely automate the abstract idea using generic computer technology. Automating a mental process by employing generic computer components does not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Therefore, the claim does not amount to significantly more than the abstract idea itself.
Accordingly, claim 1 is directed to the abstract ideas of mental processes and mathematical concepts, does not integrate those judicial exceptions into a practical application, and does not include additional elements that amount to significantly more than the judicial exception. Therefore, claim 1 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Claims 2–12 depend from claim 1 and therefore incorporate the abstract idea recited in claim 1 of receiving part and manufacturability data, analyzing the data using a trained AI model to perform a similarity search and generate a manufacturability prediction, and presenting the results. The additional limitations of claims 2–12 do not alter the character of the claim as a whole.
Specifically:
Claim 2 merely specifies that the AI model is trained using additional material-property data (e.g., density, tensile strength, and melting point). This merely identifies additional data used in the abstract analysis and does not improve computer technology or AI technology.
Claim 3 limits the training data to particular categories of parts (e.g., bearings, bushings, gears, shafts, screws, etc.). Restricting the field of use or identifying particular subject matter for the analysis does not integrate the abstract idea into a practical application.
Claim 4 recites that the AI model includes first and second encoding networks connected by a fully connected linear layer. This merely specifies a generic machine-learning architecture used to perform the abstract analysis. The claim does not recite any technological improvement to neural-network operation, model training, or computer functionality, but instead merely identifies conventional AI components performing their ordinary functions.
Claim 5 adds that the AI model is trained using additional part properties and searches property values contained within the manufacturability data. This merely expands the information analyzed by the abstract idea.
Claims 6–9 recite updating, retraining, or federated learning of the AI model using local data or updated model configurations. These limitations merely describe acquiring additional data and updating the analytical model using generic machine-learning techniques. The claims do not recite any unconventional training methodology or improvement to distributed computing or federated-learning technology.
Claims 10 and 11 merely recite implementing the system in cloud infrastructure or on a remote server. Implementing an abstract idea on generic networked computer infrastructure constitutes no more than using a generic computer environment as a tool to perform the abstract idea.
Claim 12 recites receiving a database of parts over a web interface and retraining the AI model using the received database. Receiving data over a network and retraining an AI model with additional data are conventional data gathering and model updating activities that merely support the underlying abstract analysis.
Under Step 2A, Prong Two, the additional limitations of claims 2–12 merely add conventional data types, generic machine-learning architectures, routine model maintenance, conventional distributed-learning techniques, or generic computing environments. None of these limitations improve the functioning of a computer, improve the operation of the AI model itself, improve database technology, or otherwise effect an improvement in another technology or technical field. Instead, they merely use generic computing technology to automate the abstract process of analyzing part information and predicting manufacturability.
Under Step 2B, the additional elements—including material-property data, particular categories of parts, encoding networks, retraining, federated learning, cloud infrastructure, remote servers, web interfaces, and model updates—are individually and in combination merely conventional computer and machine-learning components performing their ordinary functions. Viewed as an ordered combination, the claims simply automate the abstract idea using generic computing technology and therefore do not recite an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Accordingly, claims 2–12 are directed to abstract ideas and do not recite significantly more than the judicial exception. Therefore, claims 2–12 are not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding claims 13-19, they art substantially similar to claims 1-12 that merely directed to the method to implement the system of claims 1-12, and do not correct the issues set forth above. The claims are likewise not eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xiaoliang Yan et al. “Process-aware part retrieval for cyber manufacturing using unsupervised deep learning”, CIRP Annals – Manufacturing Technology on 14 April 2023 (“Yan”)1.
Regarding claim 1, Yan discloses a system comprising:
a processor; and
a memory having instructions stored thereon, wherein execution of the instructions by the processor causes the processor to execute a part retrieval search by executing a set of processes to:
The models were constructed and trained using PyTorch, a python-based deep learning library, on a high-performance computing node (PACE Phoenix Cluster with 1 NVIDIA Tesla V100 16GB GPU). [page 398 – 3. Deep unsupervised part retrieval (DUPR) model]
receive a model of a part and manufacturability-associated data for the part [See fig. 1 and 6];
As shown in Fig. 6, the query part input to the models is converted to a latent vector from which pairwise similarities between the latent vector of the query part and all latent vectors in the manufacturable parts database are computed. The vectorial cosine distance is used as the similarity metric to retrieve the closest matching parts. [pages 399-400 – 4. Part retrieval experiment]
This paper proposes a manufacturing process-aware part retrieval method using deep unsupervised learning that considers both part shape and material properties. [See Abstract]
performing a similarity search for shape and manufacturability-associated metrics via a trained AI model of the received model against a database of parts embedded within the trained AI model to identify a set of candidate models [See fig. 1 and 4],
A possible solution is to compare the similarity between the query part and previously manufactured parts in an existing database. By retrieving the most similar parts, candidate manufacturers for the query part can be identified…Shape descriptors are used to convert a 3D shape into vectorial representations, from which pairwise similarity of 3D shapes can be assessed.
Such advances have useful applications in 3D data-rich domains such as design and medical scanning [7]. In the context of manufacturing, however, pure shape similarity assessment of 3D CAD models is insufficient for identifying candidate manufacturers, whose production capabilities depend on other manufacturing capability information such as material properties and achievable part quality.
These questions are answered by developing a deep unsupervised learning-based part retrieval (DUPR) model shown schematically in Fig. 1, where both 3D part shape and material properties are embedded in the latent vector representation. Here, we assume that the query part function and the required process can be inferred from shape and material property information, and therefore the process and function labels are assigned to both the query and existing parts
only for performance evaluation. [page. 397 – 1. Introduction]
In this paper we proposed a deep unsupervised learning-based part retrieval (DUPR) model, which considers both shape and material properties of query parts as inputs to retrieve the closest matching parts from a previously manufactured parts database. [page 400 – 5. Conclusions]
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wherein the trained AI model was trained on both part shape and manufacturability-associated data of parts in the database and is configured to output a set of candidate models See fig. 3];
causing the set of candidate models See fig. 6].
Regarding claim 2, Yan discloses trained AI model was trained on material properties that include at least one material property, such as a density parameter, a tensile strength parameter, and a melting point parameter [See Table 1 on page 398].
Regarding claim 3, Yan discloses he trained AI model was trained on shapes that include at least one of a bearing, a bushing, a gear, a shaft collar, a gear rack, a screw, a shaft, and a key produced by one or more manufacturing processes [See Table 1 on page 398].
Regarding claim 4, Yan discloses the trained AI model was trained via a first encoding network associated with the part shape and a second encoding network associated with the material properties, wherein the first and second encoding networks are connected by a fully connected linear layer [See fig. 1].
Regarding claim 5, Yan discloses the trained AI model was additionally trained on a part property, wherein the similarity search of the shape and manufacturability-associated data includes a search of a property value defined in the manufacturability-associated data [The architecture of the DUPR model is based on the 3D autoencoder model [8], and is shown in Fig. 3. In addition to the shape encoder-decoder pair, the DUPR model also has a property encoder-decoder pair that is used to encode material property information shown in Table 1. The objective function of the DUPR model consists of the shape reconstruction loss and the property prediction loss, as follows – See page 398 and introduction].
Regarding claims 13-15, they are directed to the method of steps to implement the system as set forth in claims 1-5. Therefore, they are rejected on the same basis as set forth hereinabove.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claim(s) 6-8, 10-11, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan as applied to claim 1 or 16 above, and further in view of Xu et al. US Pub. No. 2024/0020380 (“Xu”).
Regarding claim 6, Choi does not teach update the trained AI model by re-training the trained AI model with local data.
Xu teaches another machine learning models in numerous application fields. Specifically, Xu teaches update the trained AI model by re-training the trained AI model with local data.
[0004] Therefore, collaborative learning may enable data of a plurality of organizations (or devices, or individuals) to participate in a collaborative learning process together on the basis of ensuring data privacy, so as to obtain a global model capable of adapting to various data. In particular, collaborative learning involves a plurality of collaborators, each collaborator trains a local model with locally owned data. These local models may be aggregated by a central server, or may be aggregated in a peer to peer (P2P) form, etc. An aggregated model is referred to as a global model. Then, the global model is distributed to all collaborators, and the collaborators continue to retrain a collaboration model on the basis of the global model by using respective local data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Yan with update the trained AI model by re-training the trained AI model with local data as taught by Xu. The motivation for doing so would have been, as suggested by Xu, to help solve the problem of data islands and also alleviates pressure of computing resources and network transmission compared with a traditional centralized learning method.
Regarding claim 7, Xu teaches update the trained AI model with local data as a local AI model with updated configurations [See steps of fig. 1 – the system performs operations to obtain “trusted local models and trusted local model parameters of the trusted local models”. These parameters represent the updated configurations of the trained AI model after it has incorporated the local data. The apparatus includes a model parameter cluster module (201) and a similarity calculating unit (202) in fig. 5 designed to handle these specific configuration once they are derived from the local terminals],
wherein the updated configurations are transmitted to another system for global learning for a federated AI model that includes the trained AI model and trained AI models of other systems [See abstract - A clustering-based adaptive robust collaborative learning method includes: local models uploaded by a plurality of collaborative terminals and at least part of training data of the plurality of the collaborative terminals for training the local models are obtained, a trusted global model is trained based on the at least part of the training data…].
Regarding claim 8, Xu teaches receive (i) an updated AI model [7 - obtaining local models uploaded by a plurality of collaborative terminals and at least part of training data of the plurality of the collaborative terminals for training the local models] or (ii) AI model configurations to update the trained AI model.
Regarding claim 10, Xu teaches the system is implemented in cloud infrastructure [par. 29-33].
Regarding claim 11, Xu teaches the system is implemented as a remote server [par. 29-33].
Regarding claim 16, Xu teaches updating the trained AI model by re-training the trained AI model with (i) local data or (ii) data received from an external database [par. 0004].
Regarding claim 17-18, See discussion in claim 7-8.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan as applied to claim 1 or 16 above, and further in view of Bhose et al. US Pub. No. 2023/0048920 (“Bhose”).
Regarding claim 9, Choi does not teach receive (i) an updated AI model or (ii) AI model configurations to update the trained AI model as a local AI model with updated configurations, wherein the updated configurations are transmitted to another system for global learning for a federated AI model that includes the trained AI model and trained AI models executed at other systems.
Bhose teaches systems and methods for implementing federated learning engine for integration of vertical and horizontal AI. Specifically, Bhose teaches
receive (i) an updated AI model or (ii) AI model configurations to update the trained AI model as a local AI model with updated configurations [See further par. 78-80],
[0002] One aspect of the present relates to a method. The method includes receiving a global model from a central aggregator communicatingly connected with a plurality of user environments, the global model including a plurality of layers, training a mini model on top of the global model with data gathered within the user environment, uploading the at least a portion of the mini model to the central aggregator, receiving a plurality of mini models, and creating a fusion model based on the received plurality of mini models.
[0005] In some embodiments, the plurality of mini models are received from the central aggregator. In some embodiments, creating the fusion model based on the received plurality of mini models includes training the fusion model on top of the received mini models. In some embodiments, creating the fusion model based on the received plurality of mini models includes combining data representing layers and weights from each of the plurality of mini models.
[0052] Each of the customer environments 212 can include a fusion server 214. The fusion server 214 can receive the global model from the aggregator 202, can store the global model, can generate a mini model on top of the global model, can send the global model to the aggregator 202 for storage in the mini model store 208, can receive one or several mini models and/or updates from the aggregator 202, can train and/or build a fusion model based on these received one or several mini models and/or updates from the aggregator 202, and can use the mini model and/or the global model to generate one or several outputs and/or predictions.
wherein the updated configurations are transmitted to another system for global learning for a federated AI model that includes the trained AI model and trained AI models executed at other systems [See further fig. 4; par. 82-85].
[0007] One aspect of the present relates to a system including a memory and at least on processor. The at least one processor can receive a global model from a central aggregator communicatingly connected with a plurality of user environments, the global model including a plurality of layers, train a mini model on top of the global model with data gathered within the user environment, upload the at least a portion of the mini model to the central aggregator, receive a plurality of mini models, and create a fusion model based on the received plurality of mini models.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Yan with teach teachings of Bhose. The motivation for doing so would have been to improve global AI model, improve model accuracy by incorporating learning from multiple distributed system while preserving local data.
Regarding claim 19, see discussion in claim 9 above.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan as applied to claim 1 above.
Regarding claim 12, Yan teaches receive a database of parts over a web interface; and trains the AI model using the received database [To develop a deep learning-based part retrieval model, a dataset consisting of 3D parts with both function (e.g., bearings, bushings) and manufacturing process class labels (e.g., milling, injection moulding) must be created. In addition, other manufacturing capability information (e.g., material properties) must be associated with shape data. We curated and processed a subset of publicly available 3D parts data from the FabWave CAD repository – See page 398]. Yan does not expressly teach retraining a trained AI model. However, it would be obvious to one of ordinary skill in the art to modify the system of Yan so that, upon receiving an additional database of parts, the previous trained AI model is retrained using the received database. Persons of ordinary skill in the art recognized that machine learning models benefit from iterative or incremental training as new training data become available thereby improving prediction accuracy and generalization without discarding previously learning model parameters. Such modification merely applies a known technique (incremental retraining/fine-tuning of an existing model) to a known machine-learning system to obtain the predicable result of improving the model’s performance on an expanded dataset.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 2026/0089434 to Hansen et al. teach a method includes retraining the AI simulated data generation system on the real life data to improve performance of the AI simulated data generation system in generating simulated data sets. The method also includes where the local AI system is trained initially on a simulated data set and then subsequently retrained iteratively over time with real-world data, and the AI simulated data generation system that is located remotely to the local control system is also retrained with the real-world data to improve simulated data sets the AI simulated data generation system generates.
US Pub. No. 20220222208 to Sawer et al. teach an apparatus for geometric part searching is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. At least a processor is configured to generate a search index as a function of a plurality of part specification files. At least a processor is configured to receive an input part specification file. At least a processor is configured to generate a query for an input part as a function of an input part specification file and a search index. A query is configured to output a comparison of an input part specification file to a part estimation specification file. At least a processor is configured to identify a matching part estimation file from a plurality of query results.
US Pub. No. 20240370612 to Maillard et al. teach computer-implemented method of machine-learning for CAD model retrieval based on a mating score. The method includes obtaining a dataset of pairs of Boundary Representations (B-Reps) representing mechanical parts, each pair being labeled with mating compatibility data, the mating compatibility data representing an extent of mating compatibility between the mechanical parts represented by the pair. The method also includes training a neural network based on the dataset, the neural network being configured for taking as input a pair of B-reps representing mechanical parts, and outputting a mating score of a pair of single embeddings, each single embedding corresponding to a B-Rep of the pair, the mating score representing a score of mating compatibility between the mechanical parts represented by the pair.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115
1 Cited by IDS filed on 12/17/2024
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