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 1-9 is/are pending. Claims 10-20 are withdrawn per 03/23/2026.
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.
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) 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kolekar - WO 2023/215720 in view of Truong - EP 3591586 (IDS).
As to claim 1:
Kolekar discloses:
An apparatus comprising;
at least one processor; and at least one memory including computer program code; and at least one interface for communication with at least another apparatus, the at least one processor, (Fig 2, Fig. 12, network entities/functions implemented as computer devices/nodes with intrinsic processors/memory storing instruction) with the at least one memory and computer program code, being configured to cause the apparatus to:
transmit a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of machine learning models in said conveyed information which represents said data; (Page 4, lines 6-19, 25-28 sending by a consumer entity a storage request, wherein the request includes conveyed information such as data to be stored, an ML model (“Nadrf MLModelManagement StorageRequest containing the ML model or ML model address to be stored”), and several indicators of degree of involvement of the ML models, namely data specifications, analytics specifications, service operation, and other analytics information)
and receive a data storage acknowledge response (Page 4, lines 15-17, receiving an acknowledgement response message regarding the storage status)
Except Koledar does not explicitly a specific type of the ML model, namely a generative model whose involvement is indicated by the indicator.
Truong, in a related field of management and storage of ML models, in ¶0186, 0162, wherein the ML models include a generative model that may generate partially or fully synthetic data set, and per ¶0162, A model request may also become submitted indicating one or more of the type of model (e.g., neural network), the data schema, the type of training dataset (loan application data), the model task (prediction), and identifier of a dataset used to generate the data. Also ¶0060, “computing resources 101 can be configured to receive at least some training parameters from model optimizer 107 (e.g., batch size, number of training batches..)”
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that, for the system of Koleda, the type of ML models are not restrictive and also can include GANs that can provide new and original data for the operators. Furthermore, the request can convey dataset indicator that indicates the scope of dataset involved in the data generation by the GAN, allowing system transparency to ensure network nodes to properly expect and prepare for GAN operations.
As to claim 2:
Kolekar in view of Truong discloses all limitations in claim 1, wherein said indicator is indicative of that said conveyed information includes a full data set, and said conveyed information includes said full data set. (Truong in ¶0162, 0060 where indicator for dataset to indicate which dataset to be involved, which allow the system/operator to flexibly dictate the scope of data used to be used to generate data, which include either all of the datasets or simply as necessary. Thus one of ordinary skill in the art would contemplate a possibility of full pools of dataset to be stored herewith).
Examiner’s notes: this claim’s patentable weight is questionable at best because it is merely directed to the choice amount of data to be stored in the request, where the overall process of storing and issuing acknowledgement are not dependent on whether the data being full or partial. This is merely a design choice of how much data to be transferred and stored with no impact on the claimed system and process.
As to claim 3:
Kolekar in view of Truong discloses all limitations in claim 1, wherein said indicator is indicative of that said conveyed information includes a partial data set and at least one generative model configured to create a full data set based on said partial data set, and said conveyed information includes said partial data set and said at least one generative model. (Truong in, ¶0186, generates full dataset from inputs. ¶0162, 0060 where indicator for dataset to indicate which dataset to be involved, which allow the system/operator to flexibly dictate the scope of data used to be used to generate data, which include either all of the datasets or simply as necessary. Thus one of ordinary skill in the art would contemplate a possibility of full pools of dataset to be stored herewith).
Examiner’s notes: similar to claim 2, this claim’s patentable weight is questionable at best because it is merely directed to the choice amount of data to be stored in the request, where the overall process of storing and issuing acknowledgement are not dependent on whether the data being full or partial. This is merely a design choice of how much data to be transferred and stored with no impact on the claimed system and process.
As to claim 4:
Kolekar in view of Truong discloses all limitations in claim 1, wherein said indicator is indicative of that said conveyed information includes at least one generative model configured to create a full data set without reference to a part of said data set, and said conveyed information includes said at least one generative model.
(Kolekar, Page 4, lines 6-19, 25-28 sending by a consumer entity a storage request, wherein the request includes conveyed information such as data to be stored, an ML model (“Nadrf MLModelManagement StorageRequest containing the ML model or ML model address to be stored”). Also Truong, ¶0186, the GAN generate a partially or fully synthetic dataset. For example, a dataset generator may normalize the input dataset, and generate a synthetic training dataset while preserving the data structure. The data synthesis model may be a machine learning model (e.g., a GAN or recurrent neural network (RNN) model). At step 1806, process 1800 may train the data synthesis model based on a similarity metric value using the normalized input dataset and the synthetic dataset.)
As to claim 5:
Kolekar in view of Truong discloses all limitations in claim 3, wherein said conveyed information includes a generative model description specifying utilization of said at least one generative model. (Kolekar, Page 4, lines 6-19, 25-28 sending by a consumer entity a storage request, wherein the request includes conveyed information such as data to be stored, an ML model (“Nadrf MLModelManagement StorageRequest containing the ML model or ML model address to be stored”), and several indicators of degree of involvement of the ML models, namely data specifications, analytics specifications, service operation, and other analytics information))
As to claim 6:
Kolekar in view of Truong discloses all limitations in claim 1, wherein the apparatus is further caused to: create said data utilizing said at least one generative model. (See Truong, ¶0186, At step 1806, process 1800 may train the data synthesis model based on a similarity metric value using the normalized input dataset and the synthetic dataset)
As to claim 7:
Kolekar discloses:
An apparatus comprising;
at least one processor; and at least one memory including computer program code; and at least one interface for communication with at least another apparatus, the at least one processor, with the at least one memory and computer program code, (Fig 2, Fig. 12, network entities/functions having communication interfaces to couple to each other and implemented as computer devices/nodes with intrinsic processors/memory storing instruction) being configured to cause the apparatus to:
receive a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of machine learning models in said conveyed information which represents said data; (Page 4, lines 6-19, 25-28 receiving from a consumer entity a storage request, wherein the request includes conveyed information such as data to be stored, one or more ML models (“Nadrf MLModelManagement StorageRequest containing the ML model or ML model address to be stored”), and several indicators of degree of involvement of the ML models, namely data specifications, analytics specifications, service operation, and other analytics information)
store said conveyed information; and transmit a data storage acknowledge response. (Page 4, lines 15-17, transmitting an acknowledgement response message regarding the storage status)
As to claim 8:
Kolekar in view of Truong discloses all limitations in claim 7, wherein said indicator is indicative of that said conveyed information includes a full data set, and said conveyed information includes said full data set. (Truong in ¶0162, 0060 where indicator for dataset to indicate which dataset to be involved, which allow the system/operator to flexibly dictate the scope of data used to be used to generate data, which include either all of the datasets or simply as necessary. Thus one of ordinary skill in the art would contemplate a possibility of full pools of dataset to be stored herewith).
Examiner’s notes: this claim’s patentable weight is questionable at best because it is merely directed to the choice amount of data to be stored in the request, where the overall process of storing and issuing acknowledgement are not dependent on whether the data being full or partial. This is merely a design choice of how much data to be transferred and stored with no impact on the claimed system and process.
As to claim 9:
Kolekar in view of Truong discloses all limitations in claim 7, wherein said indicator is indicative of that said conveyed information includes a partial data set (Truong in, ¶0186, generates full dataset from inputs. ¶0162, 0060 where indicator for dataset to indicate which dataset to be involved, which allow the system/operator to flexibly dictate the scope of data used to be used to generate data, which include either all of the datasets or simply as necessary. Thus one of ordinary skill in the art would contemplate a possibility of full pools of dataset to be stored herewith), and at least one generative model configured to create a full data set based on said partial data set, and said conveyed information includes said partial data set and said at least one generative model. (Kolekar, Page 4, lines 6-19, 25-28 sending by a consumer entity a storage request, wherein the request includes conveyed information such as data to be stored, an ML model (“Nadrf MLModelManagement StorageRequest containing the ML model or ML model address to be stored”). Also Truong, ¶0186, the GAN generate a partially or fully synthetic dataset. For example, a dataset generator may normalize the input dataset, and generate a synthetic training dataset while preserving the data structure. The data synthesis model may be a machine learning model (e.g., a GAN or recurrent neural network (RNN) model). At step 1806, process 1800 may train the data synthesis model based on a similarity metric value using the normalized input dataset and the synthetic dataset.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rajan (US 2023/0135327) - A data generation system can include a computing device that is configured to receive a request to generate a training dataset for an attribute and identify a set of item identifiers from an item database based on an engagement indication. The computing device is further configured to, for each item identifier of the set of item identifiers, obtain a query list including queries resulting in an engagement between the corresponding item identifier and a user and, in response to a portion of queries of the query list including the attribute being above a threshold, assign the corresponding item identifier to the training dataset for the attribute. The computing device is also configured to store the training dataset for the attribute in a training dataset database.
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/QUAN M HUA/Primary Examiner, Art Unit 2645
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