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 .
Status of the Claims
This Office Action is in response to Application dated 6/26/2025, claims 1-20 are currently pending and being examined in this reply.
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.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without “significantly more.” Claims 1-20 are directed to certain methods of organizing human activity which is considered an abstract idea. Further, the claim(s) as a whole, when examined on a limitation-by-limitation basis and in ordered combination do not include an inventive concept.
Step 1 – Statutory Categories
In regard to claims 1-20 as indicated in the preamble of the claims, the examiner finds the claims are directed to a process, machine, or article of manufacture.
Step 2A – Prong One - Abstract Idea Analysis
Representative independent claim 1 recites the following abstract concepts, in italics below, which are found to include an “abstract idea”:
A system comprising: a processor; and a computer-readable medium storing instructions operative by the processor to: detect, using a machine learning (ML) model, that each image of a plurality of images depicts a dominant product and a non-dominant product, wherein the dominant product is a product with a highest detection rate across the plurality of images; cluster each of the plurality of images into one of: a dominant product group, wherein images clustered into the dominant product group depict more occurrences of the dominant product than the non-dominant product; and a non-dominant product group, wherein images clustered into the non- dominant product group depict more occurrences of the non-dominant product than the dominant product; select a first sample image from the dominant product group; select a second sample image from the non-dominant product group; and output at least one of the first sample image and the second sample image to be used to retrain the ML model for detecting the dominant product and the non-dominant product from future images processed by the ML model.
The claim features in italics above as drafted, under its broadest reasonable interpretation are certain methods of organizing human activity (fundamental economic practices and managing personal behavior or relationships or interactions between people) performed by generic computer components. That is, other than reciting “processor, memory, and ML model”, nothing in the claim element precludes the step from practically being a method of organized human activity. For example, but for the “processor, memory, and ML model”, the above italicized limitations in the context of this claim encompasses certain methods of organizing human activity. If the claim limitations, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people and fundamental economic practices, but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two - Abstract Idea Analysis
This judicial exception is not integrated into a practical application. In particular, the claim only recites 3 additional elements – “processor, memory, and ML model”. They are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)), data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)), and linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B - Significantly More Analysis
The claims do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processor, memory, and ML model” amounts to no more than mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use. Mere instructions to apply the exception using a generic computer component, insignificant extra-solution activity, and linking the use of the judicial exception to a particular technological environment or field of use, cannot provide an inventive concept. Further, the background and specification does not provide any indication that the “processor, memory, and ML model” is anything other than a generic, off-the-shelf computer components. For these reasons, there is no inventive concept.
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.
Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pinel United States Patent No. 11,600,085 B2 to Pinel (“Pinel”), in view of “LVIS: A Dataset for Large Vocabulary Instance Segmentation,” Proc. IEEE/CVF CVPR 2019, pp. 5356–5364 (“Gupta”), and further in view of United States Patent No. 7,792,353 B2 to Forman (“Forman”).
In regards to claims 1, 8, and 15, Pinel discloses the following limitations:
A system comprising: a processor; and a computer-readable medium storing instructions operative by the processor to: detect, using a machine learning (ML) model, that each image of a plurality of images depicts a dominant product and a non-dominant product, (Pinel discloses an identification component that, using a trained machine learning model, identifies a subset of products depicted within an image stream, and image capturing multiple recognized product identifiers. see at least Pinel Fig. 2, col. 3 ll. 60–67 and col. 4 ll. 38–col. 5 ll. 9).
output at least one of the first sample image and the second sample image to be used to retrain the ML model for detecting the dominant product and the non-dominant product from future images processed by the ML model. (Pinel discloses that the trained machine learning model is retrained using the selected sample images instead of the full plurality of images, so as to recognize the products in future processing see at least Pinel col. 3 ll. 1–10 and col. 9 l. 60–col. 10 l. 20).
While Pinel discloses selecting sample images from grouped images and outputting the selected samples to retrain the trained machine learning model. (see at least Pinel col. 3 ll. 30–45 and col. 9 ll. 60-col. 10 ll. 20) Pinel does not appear to specifically disclose the following limitations:
wherein the dominant product is a product with a highest detection rate across the plurality of images; cluster each of the plurality of images into one of: a dominant product group, wherein images clustered into the dominant product group depict more occurrences of the dominant product than the non-dominant product; and a non-dominant product group, wherein images clustered into the non- dominant product group depict more occurrences of the non-dominant product than the dominant product; select a first sample image from the dominant product group; select a second sample image from the non-dominant product group
Examiner provides Gupta to teach the following limitations:
wherein the dominant product is a product with a highest detection rate across the plurality of images; cluster each of the plurality of images into one of: a dominant product group, wherein images clustered into the dominant product group depict more occurrences of the dominant product than the non-dominant product; and a non-dominant product group, wherein images clustered into the non- dominant product group depict more occurrences of the non-dominant product than the dominant product; select a first sample image from the dominant product group; (Gupta teaches categories by their frequency of occurrence across the image set, a most-frequent (“dominant”) category versus less frequent (“non-dominant”) categories, and organizes image sampling on that basis. see at least Gupta p. 5359, § 2.2 “Reduced Workload,” teaching undersampling the most frequent categories, and p. 5360, Stage 2, “to prevent frequent categories from dominating the dataset and to reduce the overall workload, we subsample the frequent categories”).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system and method as taught by Pinel the teachings of Gupta in order to balance the retraining set between frequently and infrequently observed products, and since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Examiner provides Forman to teach the following limitations:
select a first sample image from the dominant product group; select a second sample image from the non-dominant product group (Forman teaches that samples are divided into clusters, each cluster is evaluated for whether it is proportionally represented, and selection is biased toward samples in under-represented clusters, which are then submitted to retrain the classifier. see at least Forman col. 9 ll. 1–20).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system and method as taught by Pinel the teachings of Forman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
In regards to claims 3, 10, and 17, The combination discloses the following limitations:
wherein the computer-readable medium further stores instructions operative by the processor to: cluster each of the plurality of images into one of the dominant product group and one of a plurality of non-dominant product groups, wherein each of the plurality of non-dominant product groups is associated with a distinct non-dominant product. (Gupta teaches a long tail of many distinct categories each with its own occurrence count, such that images are organized by each distinct category. see at least Gupta p. 5359, § 2.2 and p. 5361, § 4.1; and Pinel teaches determining plural distinct products/product identifiers within the image stream. See at least Pinel col. 6 ll. 30–55).
In regards to claims 4, 11, and 18, the Combination discloses the following limitations:
wherein the first sample image and the second sample image are selected for retraining the ML model in order to reduce computing resources associated with retraining the ML model. (Pinel discloses that the model is retrained using the selected sample images instead of the plurality of images, which can be voluminous and resource-intensive to process. see at least Pinel col. 3 ll. 30–45; and Forman teaches biasing selection toward representative samples so that fewer but more informative samples are used for retraining. See at least col. 9 ll. 1–17).
In regards to claims 5, 12, and 19, the Combination discloses the following limitations:
wherein the computer-readable medium further stores instructions operative by the processor to: determine, using the ML model, that each image of a plurality of second images depicts products that the ML model is unable to associate with recognized product identifiers; (Pinel discloses first images containing items not detected by the trained model as associated with a recognized product identifier. see at least Pinel col. 4 ll. 43–56 and col. 5 ll. 55–67).
identify visual features for each of the plurality of second images; group each of the plurality of second images into one of a plurality of datasets based on the identified visual features; and (Pinel discloses allocating the first (unrecognized) images into one of a plurality of datasets, and grouping images based on visual/textual similarity of depicted items. see at least Pinel col. 6 ll. 1–30).
select a third sample image from one of the plurality of datasets for use in retraining the ML model to associate the depicted products of the plurality of second images with recognized product identifiers. (Pinel discloses selecting a sample from at least one of the plurality of datasets/groups and outputting it to retrain the model so it can subsequently recognize the previously unrecognized products. see at least Pinel col. 3 ll. 30–45 and col. 9 l. 60–col. 10 l. 20).
In regards to claims 6, 13, and 20, the Combination discloses the following limitations:
wherein the computer-readable medium further stores instructions operative by the processor to, for each of the plurality of datasets: cluster each image in the dataset into one of a plurality of groups based on a degree of resemblance between the image and other images in the dataset; and select the third sample image from one of the plurality of groups. (The combination teaches sub-clustering each dataset by degree of resemblance and selecting the sample from one of the resulting groups. see at least Pinel col. 6 ll. 1–30, clustering images within a dataset based on a degree of resemblance of depicted items relative to other images in the dataset; and Forman col. 9 ll. 1–17, dividing samples into clusters by feature-set similarity and selecting representative samples from the clusters).
In regards to claims 7 and 14, the combination of Pinel and Forman discloses the following limitations:
wherein, for each of the plurality of datasets, the plurality of groups comprises: a homogenous group wherein, for each image, the degree of resemblance is greater than a high threshold; a heterogeneous group wherein, for each image, the degree of resemblance is between a low threshold and the high threshold; a low similarity group wherein, for each image, the degree of resemblance is below the low threshold; and an individual group wherein, for each image, there is no degree of resemblance. (see at least Forman col. 9 ll. 1–17, clustering samples by degree of feature-set similarity into groups of differing similarity levels and biasing selection accordingly; and Pinel col. 6 ll. 1–30, degree-of-resemblance clustering of images within a dataset). The combination does not specifically enumerate four bands (homogenous / heterogeneous / low similarity / individual); however, The Examiner notes, defining a set of similarity thresholds to partition a resemblance into a high-similarity band, an intermediate band, a low-similarity band, and a no-resemblance (individual) band does not modify the operation of the combination of Pinel, Forman, and Gupta. To have modified the combaintion to have included defining a set of similarity thresholds to partition a resemblance into a high-similarity band, an intermediate band, a low-similarity band, and a no-resemblance (individual) band would have been obvious to the skilled artisan because the inclusion of such resemblance thresholds would have been an obvious matter of design choice in light of the method already disclosed by the combination. Such modification would not have otherwise affected the combination and would have merely represented one of numerous steps that the skilled artisan would have found obvious for the purposes already disclosed by the combination. Additionally, applicant has not persuasively demonstrated the criticality of providing this arrangement versus the arrangement disclosed in the combination. See In re Japikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Pinel United States Patent No. 11,600,085 B2 to Pinel (“Pinel”), in view of “LVIS: A Dataset for Large Vocabulary Instance Segmentation,” Proc. IEEE/CVF CVPR 2019, pp. 5356–5364 (“Gupta”), and further in view of United States Patent No. 7,792,353 B2 to Forman (“Forman”), in further view of United States Patent Application Publication No. 2015/0169978 A1 to Pillai (“Pillai”)
In regards to claims 2, 9, and 16, the combination does not appear to specifically disclose the following limitations:
wherein the first sample image is selected by the processor based on the first sample image depicting at least a predetermined threshold of the dominant product.
The Examiner provides Pillai to teach the following limitations:
wherein the first sample image is selected by the processor based on the first sample image depicting at least a predetermined threshold of the dominant product. (Pillai teaches selecting a representative image based on a headshot/coverage score reflecting the portion of the image occupied by the entity, e.g., a rectangular area occupying a threshold fraction such as 0.75 of the image. see at least Pillai ¶¶ 0040–0042 and ¶¶ 0061–0066).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the system and method as taught by Pinel the teachings of Pillai since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M MUTSCHLER whose telephone number is (313)446-6603. The examiner can normally be reached 0600-1430.
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/JOSEPH M MUTSCHLER/Examiner, Art Unit 3627
/A. Hunter Wilder/Primary Examiner, Art Unit 3627
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