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 .
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 1–10, 12, and 13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (abstract idea) and does not include additional elements that amount to significantly more than the judicial exception.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Step 1 – Statutory Category
Claims 1–10, 12, and 13 are directed to statutory categories (device/system, method, and non-transitory computer-readable medium). Therefore, the analysis proceeds to Alice Step 2A.
Step 2A, Prong One – Judicial Exception
Identified Abstract Idea
The claims are directed to collecting, analyzing, and using image-based retail shelf information to:
identify stockout regions,
select a representative image, and
generate planogram data.
These activities constitute data collection, analysis, and display of results, which are recognized abstract ideas (see Electric Power Group, SAP America).
Claim 1, as a whole, recites analyzing visual retail data and organizing it into a planogram, which is an abstract information-processing concept.
Claims 2–3: Selecting an image based on thresholds or least stockout is merely applying rules to data, a mental process implemented on a computer.
Claims 4–7: Identifying stockout product candidates and estimating products based on:
sales data,
weight,
shelf position,
constitutes evaluation and inference using business heuristics, which are abstract analytical activities.
Claims 8–10: Estimating stockout products based on extent of stockout region or product size is a form of data comparison and rule-based inference, which is abstract.
Claim 12 (Method Claim) Recites the same abstract steps of claim 1 in method form and is directed to the same judicial exception.
Claim 13 (Computer-Readable Medium): Merely stores instructions for performing the abstract method of claim 12 and is likewise directed to an abstract idea.
Conclusion – Step 2A, Prong One
Claims 1–10, 12, and 13 recite an abstract idea involving image-based data analysis and retail information organization.
Step 2A, Prong Two – Integration Into a Practical Application
The claims do not integrate the abstract idea into a practical application because:
The claims do not improve image-processing technology itself.
No specific computer vision technique, algorithm, or image-processing architecture is recited.
The claimed steps are result-oriented and describe desired outcomes rather than technical solutions.
Generic processors and memory are used as tools to perform the abstract idea.
Thus, the claims merely automate a manual retail analysis process using a computer.
Step 2B – Inventive Concept: The claims do not recite significantly more than the abstract idea because:
The hardware components (processor, memory, storage medium) are generic.
Image acquisition and analysis are described at a high level of generality.
Using product metadata (sales, size, weight) reflects conventional retail analytics.
The combination of elements is routine and conventional in retail inventory systems.
Accordingly, the claims lack an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter.
Thus, after considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims are not enough to transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea. Accordingly, claims 1–10, 12, and 13 are directed to non-statutory subject matter under 35 U.S.C. § 101.
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 1–10, 12, and 13 are rejected under 35 U.S.C. § 103(a) as being unpatentable over Chaubard (US 2020/0005225 A1) in view of Shaw et al. (US 2021/0042588 A1, hereinafter Shaw).
With respect to claims 1, 12 and 13 Chaubard discloses a computer system including one or more processors and memory configured to perform image-based shelf analysis (¶¶ [0031], [0042]; Fig. 1).
acquiring a first image including a product shelf for displaying products
(¶¶ [0044], [0047]; Fig. 2).
specifying a stockout region of the product shelf included in the first image
(¶¶ [0056]–[0059]; Fig. 4 discloses detecting empty regions / voids on a product shelf indicative of out-of-stock conditions by analyzing shelf images).
determining a second image from among a plurality of first images based on the stockout region (¶¶ [0062], [0065]; Fig. 6 teaches capturing and analyzing multiple images of the same shelf over time and selecting images for further processing based on detected stock conditions),
Selecting an image with fewer or no voids would have been an obvious design choice to one of ordinary skill in the art to improve accuracy of shelf analysis, and
Shaw teaches generating and evaluating planogram data by analyzing shelf images to determine product placement, compliance, and shelf layout (¶¶ [0021], [0034], [0040]; Figs. 3–5).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine Chaubard with Shaw because: Chaubard detects stockout regions from shelf images and Shaw provides planogram-based product identification and shelf layout analysis. Combining these teachings yields a predictable result of generating planogram data using images selected based on stockout conditions.
With respect to claim 2 Chaubard discloses the feature of second image includes the stockout region less than a threshold (¶¶ [0058], [0060] discloses identifying degrees of shelf emptiness and comparing detected voids to thresholds to determine stock status).
With respect to claim 3 Chaubard discloses the feature of second image includes the least stockout region (¶¶ [0062], [0065] discloses comparing multiple shelf images to identify images with fewer empty regions).
With respect to claim 4 Shaw discloses the feature of specifying stockout product candidates by comparing displayed products with marketed products (¶¶ [0036], [0041]; Fig. 4 discloses comparing detected shelf products with expected planogram / product databases to identify missing or mismatched products).
With respect to claim 5 Shaw discloses the feature of acquiring product information including sales number, size, weight, or price (¶¶ [0038], [0042] discloses using product metadata associated with detected products, including size and sales-related attributes, to analyze shelf status).
With respect to claim 6 using sales volume as a criterion for determining missing products is an obvious optimization of Chaubard’s stockout detection when combined with Shaw’s product data usage (¶¶ [0042], [0045]).
With respect to claim 7 Shaw discloses the feature of estimating stockout product based on weight or shelf position (¶¶ [0039], [0043]; Fig. 5 discloses associating physical attributes and shelf position with detected products to infer product identity).
With respect to claim 8 Chaubard discloses the feature of estimating stockout product based on extent of stockout region (¶¶ [0059], [0061] teaches quantifying the size of empty shelf regions and using region size in stockout analysis).
With respect to claim 9 Chaubard discloses the feature of estimating stockout product further based on extent of stockout region (¶ [0061] teaches further evaluation using region size metrics).
With respect to claim 10 Shaw discloses the feature of estimating stockout product based on size equal to or less than extent of stockout region (¶¶ [0040], [0044] discloses correlating product dimensions with shelf space to identify products suitable for a given region).
Conclusion
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/ROKIB MASUD/Primary Examiner, Art Unit 3627