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. 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, 3-6, 8, 10-13, 15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al. (Natarajan) (US 2024/0428937) in view of Ramesh et al. (Ramesh) (US 2022/0188646) . Regarding claim s 1 and 8 , Natarajan discloses a computing device (FIG. 1) configured to perform data pre-selection for an object detection system ([0349], object detection is performe d) , the computing device including a processing device configured to execute instructions stored in memory ([0005], a program stored in a memory is executed) to: receive a first dataset ([0036], inputs 102 and 106 provide image data and learned prompt values, [0354], the input data is in vector format) , wherein the first dataset includes unlabeled data corresponding to one or more images ([0068], unsupervised learning is performed on image data (i.e. the data is unlabeled)) ; provide the first dataset and a plurality of learnable prompt vectors to a pre-training model (112) (FIG. 1, [0036], [0068], [0069], [0284], [0354], image data and learned prompt vectors are input to trainer 112 for training the model generated machine learned model 108; [0068], the trainer applies unsupervised learning techniques to the input data ; [0359], pre training is applied in an unsupervised manner ) , wherein the learnable prompt vectors include text inputs ( [0053], [0292], the learnable prompt comprises text data) ; generate, using the pre-training model, an unsupervised learning prompt based on the first dataset and the plurality of learnable prompt vectors ([0068], an unsupervised learning model is applied to input learned prompt data to generate an unsupervised learned prompt; [0359], the pre-training pipeline is uns u pervised) , wherein the unsupervised learning prompt corresponds to a multi-modal feature of the one or more images of the first dataset ([0039], [0064], multi modal input data is processed by [0359] an unsupervised pretraining pipeline) ; extract features from either of the first dataset and a second dataset based on the unsupervised learning prompt ([0342], image recognition (i.e. feature extraction) is performed based on [0068] [0359] an unsupervised pipeline ; FIG. 1, [0036], machine learned model 108 operates on the initial input data (i.e. the first dataset) or data which is modified by model trainer 112 (i.e. a second dataset) ) ; select and label the extracted features ([0342], image classification (i.e. labeling) is performed based on the image features) ; and generate and output a labeled dataset based on the labeled instances ([0342], the machine-learned model(s) can process the image data to generate an image classification output). Natarajan is silent about select and label a subset of instances of the extracted features ; and generate and output a labeled dataset based on the labeled subset of instances . Ramesh from the same or similar field of endeavor discloses select ( ing ) and label ( ing ) a subset of instances of the extracted features ([0031], a subset of features ar e extracted and clustered for generating a label 46) ; and generate and output a labeled dataset based on the labeled subset of instances ([0031], a subset of features are extracted and clustered for generating a label 46). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramesh into the teachings of Natarajan for increasing the reliability of the machine learning prediction (Ramesh: ABSTRACT, [0252]). Regarding claims 3-6 , 10-13 , and 17 - 19 , Ramesh further discloses wherein the extracted features include clusters of unlabeled image data ([0026], unsupervised learning is applied to extract features and cluster the input data) ; wherein the selected subset of instances includes one or more of the clusters of unlabeled data ( [0026], unsupervised learning is applied to extract features and cluster the input data , [0031], a subset of features are extracted and clustered for generating a label 46) ; wherein the processing device is configured to execute instructions to select a representative image for labeling from each of the clusters of unlabeled image data ([0028], [0029], [0030], k-means clustering is performed for generating a cluster identifier based on a center (i.e. centroid or med oi d) of the cluster) ; and wherein the processing device is configured to execute instructions to select the representative image based on a medoid of a corresponding one of the clusters of unlabeled image data ([0028], [0029], [0030], k-means clustering is performed for generating a cluster identifier based on a center (i.e. centroid or med oi d) of the cluster) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramesh into the teachings of Natarajan for increasing the reliability of the machine learning prediction (Ramesh: ABSTRACT, [0252]). Regarding claim 15 , the limitations of claim 15 are rejected in the analysis of claim 8 (See claim 8 above). Natarajan further discloses an object detection system ([0349], object detection is performed) . Ramesh further discloses a computer-controlled machine (FIG. 1) , comprising: at least one sensor configured to generate an input image ([0041], on board camera) ; a control system configured to perform data pre-selection ([0031], a subset of features are extracted and clustered for generating a label 46) , the control system configured to receive a first dataset ([0038], a first set of data is input). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dai into the teachings of Natarajan in view of Ramesh for increasing the reliability of the machine learning prediction. Claim (s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al. (Natarajan) (US 2024/0428937) in view of Ramesh et al. (Ramesh) (US 2022/0188646) , and further in view of Dai et al. (Dai) (US 2024/0160858). Regarding claim s 2 , 9 , and 16 , Natarajan in view of Ramesh discloses t he computing device of claim 8 (see claim 8 above) and a pre training model (see claim 8 above). Natarajan in view of Ramesh is silent about wherein the pre-training model is a Bootstrapping Language-Image Pre-training (BLIP-2) model. Dai from the same or similar field of endeavor discloses wherein the pre-training model is a Bootstrapping Language-Image Pre-training (BLIP-2) model ([0091], a BLIP 2 model is applied for pre training image data). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dai into the teachings of Natarajan in view of Ramesh for increasing the reliability of the machine learning prediction . Claim (s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Natarajan et al. (Natarajan) (US 2024/0428937) in view of Ramesh et al. (Ramesh) (US 2022/0188646), in view of Fayyaz et al. (Fayyaz) (US 2024/0 354317 ) , and further in view of Husain (US 2021/0072033) . Regarding claim s 7, 14 , and 20 , Natarajan in view of Ramesh discloses t he computing device of claim 8 (see claim 8 above). Natarajan in view of Ramesh is silent about wherein the processing device is configured to execute instructions to calculate instance-level contrastive loss and cluster-level contrastive loss. Fayyaz from the same or similar field of endeavor discloses wherein the processing device is configured to execute instructions to calculate instance-level contrastive loss ([0080], [0087], instance level loss between image pairs is determined) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fayyaz into the teachings of Natarajan in view of Ramesh for increasing the reliability of the machine learning prediction. Natarajan in view of Ramesh is silent about calculate( ing ) cluster-level contrastive loss. Husain from the same or similar field of endeavor discloses calculate( ing ) cluster-level contrastive loss ([0150], a loss function based on cluster distance is calculated) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Husain into the teachings of Natarajan in view of Ramesh in view of Fayyaz for increasing the reliability of the machine learning prediction. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JEFFERY A WILLIAMS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-7579 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F 8:00-5:00 . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Sath Perungavoor can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 571-272-7455 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JEFFERY A WILLIAMS/ Primary Examiner, Art Unit 2488