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Patent Application 18154259 - RETAIL RESOURCE MANAGEMENT ALLOCATION SYSTEM - Rejection

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Patent Application 18154259 - RETAIL RESOURCE MANAGEMENT ALLOCATION SYSTEM

Title: RETAIL RESOURCE MANAGEMENT ALLOCATION SYSTEM

Application Information

  • Invention Title: RETAIL RESOURCE MANAGEMENT ALLOCATION SYSTEM
  • Application Number: 18154259
  • Submission Date: 2025-05-15T00:00:00.000Z
  • Effective Filing Date: 2023-01-13T00:00:00.000Z
  • Filing Date: 2023-01-13T00:00:00.000Z
  • National Class: 705
  • National Sub-Class: 021000
  • Examiner Employee Number: 83145
  • Art Unit: 3627
  • Tech Center: 3600

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 2

Cited Patents

The following patents were cited in the rejection:

Office Action Text


    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 .

Status of Claims
Claims 1-20 filed January 13, 2023 are pending and are hereby examined.

Claim Rejections - 35 USC § 101
3.	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.
4.	Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 
5.	Step 1 Statutory Category: Claims 1-10 are directed to a method, claims 11-16 are directed to a system, and claims 17-20 are directed to a non-transitory computer readable medium, all of which are statutory classes of invention.    
6.	Step 2A – Prong 1: Judicial Exception Recited: Nevertheless, independent claims 1, 11, and 17 recite an abstract idea of retail resource management allocation.
The independent claims 1, 11, and 17 recite the following limitations which fall under commercial or legal interactions: 
	  receiving image data of an individual at a location, wherein the location comprises a plurality of…;
processing the image data using a trained neural network, wherein the… analyzes aspects of an appearance or a behavior of the individual as captured in the image data to quantify a likelihood of the individual participating in a checkout procedure at a first…;
obtaining an output from the… indicating a quantification of the likelihood of the individual participating in the checkout procedure at the first…; and   
 	managing resource allocation across the plurality of… based on the output from…   
7.	According to the MPEP, "Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. Clearly, retail resource management allocation falls under sales activities, therefore commercial or legal interactions. If the claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a commercial or legal interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
8.	Step 2A – Prong 2: Practical Application: This judicial exception is not integrated into a practical application because the claim as a whole merely recites retail resource management allocation with generally recited computer elements such as a trained neural network and POS systems, which in these steps are recited at a high-level of generality such that it amounts to more than mere instructions to apply the exception using a generic computer component, and are merely invoked as tools for retail resource management allocation. Accordingly, these elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Simply implementing the abstract idea on a generic computing environment is not a practical application of the abstract idea, and does not take the claim out of the Commercial or Legal Interactions subgrouping of Certain Methods of Organizing Human Activity grouping. The claims are directed to an abstract idea.  
9.	Step 2B – Inventive Concept: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered individually and as an ordered combination, they do not add significantly more (also known as “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of POS systems and a trained neural network to perform these steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered individually and as an ordered combination as there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. The claims are not patent eligible.
10.	Regarding dependent claims 5, 9, 12, and 15 these claims merely narrow the abstract idea of retail resource management allocation, and these claims neither integrate into a practical application nor contain additional elements which amount to significantly more than the abstract idea.
11.	Regarding dependent claims 2-4, 7, 10, 12-14, and 18-20, the claims are directed to limitations which further limit by using a POS system.  Although the claims recite a generally recited POS system, these claims merely narrow the abstract idea of retail resource management allocation, and these claims neither integrate into a practical application nor contain additional elements which amount to significantly more than the abstract idea.
12.	Regarding dependent claims 8-9 and 16, the claims are directed to limitations which further limit by using a trained neural network.  Although the claims recite a generally recited trained neural network, these claims merely narrow the abstract idea of retail resource management allocation, and these claims neither integrate into a practical application nor contain additional elements which amount to significantly more than the abstract idea.
13.	Therefore, the limitations of the claims, when viewed individually and in ordered combination, are directed to ineligible subject matter.

Claim Rejections - 35 USC § 103
14.	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.  
15.	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.
16.	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.
17.	This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary.  Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
18.	Claims 1-2, 5-9, 12, 15, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al (US 2021/0103941) in view of Liguori et al (US 2023/0169612).
19.	Re Claims 1, 11, 17: Patil discloses comprising:
receiving image data of an individual at a location, wherein the location comprises a plurality of point-of-sale (POS) systems (see [0030] image of each queue of plurality of queues);   
processing the image data using a trained neural network, wherein the trained neural network analyzes aspects of an appearance or a behavior of the individual as captured in the image data to quantify a likelihood of the individual participating in a checkout procedure at a first POS system of the plurality of POS systems (see [0032] uses pre-trained deep learning based recognition model to extract sub-images for each customer);   
and managing resource allocation across the plurality of POS systems based on the output from the trained neural network (see [0058] managing number of counters based on predicted queue wait times).   
Although Patil discloses the following (see [0036] estimate semi-motional state and number of service items of each customer), it fails to explicitly disclose the following. Meanwhile, Liguori teaches:
obtaining an output from the trained neural network indicating a quantification of the likelihood of the individual participating in the checkout procedure at the first POS system (see [0049] predict or estimate resources at future times).
From the teaching of Liguori, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Patil’s predicting wait time with Liguori’s teaching of likelihood of individual participating in order to “… leverage machine learning and artificial intelligence to support smart drive through order pickup or order delivery (see Liguori [0001]).”
20.	Re Claims 2, 12, 18: Patil discloses wherein said managing the resource allocation comprises managing an allocation of checkout-related tasks and non-checkout-related tasks assigned to the plurality of POS systems (see [0058] managing number of counters based on predicted queue wait times). 
21.	Re Claims 5, 15: Patil discloses wherein the checkout-related tasks include tasks associated with at least one of scanning an item, searching for information associated with the item, or weighing the item (see [0048] scanning the item).   
22.	Re Claims 6, 16: Patil discloses wherein the non-checkout-related tasks include running at least a portion of a trained neural network model, wherein the trained neural network model facilitates at least one of produce recognition during a checkout procedure or theft detection (see [0031] items cart recognition feature).   
23.	Re Claim 7: Patil fails to disclose the following. However, Liguori teaches wherein the trained neural network predicts that the individual will participate in the checkout procedure based on a prediction that the individual will interact with the first POS system within a threshold period of time (see [0049] predict or estimate resources at future times). From the teaching of Liguori, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Patil’s predicting wait time with Liguori’s teaching of likelihood of individual participating in order to “… leverage machine learning and artificial intelligence to support smart drive through order pickup or order delivery (see Liguori [0001]).”  
24.	Re Claim 8: Patil discloses wherein the output is a first output, wherein the trained neural network is a first trained neural network, and wherein the method further comprises:   
inputting the image data into a second trained neural network that determines characteristics of items the individual has for checkout; and   
 	obtaining a second output from the second trained neural network, wherein said managing the resource allocation is based on the second output (see [0046] using different trained shopping cart recognition models).   
25.	Re Claim 9: Patil discloses wherein the characteristics comprise a quantity of the items or a size or weight of one or more of the items (see [0031] size of item).  
26.	Claims 3-4, 10, 13-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al (US 2021/0103941) in view of Liguori et al (US 2023/0169612), further in view of Sorensen (US 2020/0043086).
27.	Re Claims 3, 13, 19: However, Patil and Liguori fail to disclose the following. Meanwhile, Sorensen teaches wherein based on the output indicating that the individual is not expected to take part in the checkout procedure at the first POS system, said managing comprises at least one of unassigning a checkout-related task to the first POS system, assigning a non-checkout-related task to the first POS system, or instantiating an isolated execution environment on the first POS system, the isolated execution environment configured to perform the non-checkout-related task (see [0052] configured to detect distance of shopper from depth camera). From the teaching of Sorensen, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Patil’s predicting wait time with Liguori’s teaching of likelihood of individual participating and further with Sorensen’s teaching of non-checkout related tasks in order for “an interactive transaction system… (see Sorensen Abstract).” 
28.	Re Claims 4, 14, 20: However, Patil and Liguori fail to disclose the following. Meanwhile, Sorensen teaches wherein based on the output indicating that the individual is expected to take part in the checkout procedure at the first POS system, said managing includes at least one of assigning a checkout-related task to the first POS system, unassigning a non-checkout-related task to the first POS system, or terminating an isolated execution environment instantiated on the first POS system, the isolated execution environment configured to perform the non-checkout-related task (see [0052] configured to detect distance of shopper from depth camera). From the teaching of Sorensen, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Patil’s predicting wait time with Liguori’s teaching of likelihood of individual participating and further with Sorensen’s teaching of non-checkout related tasks in order for “an interactive transaction system… (see Sorensen Abstract).”   
29.	Re Claim 10: However, Patil and Liguori fail to disclose the following. Meanwhile, Sorensen teaches wherein said managing comprises assigning the first POS system to perform a non-checkout-related task based on the output indicating an expectation of no interactions with the first POS system (see [0052] configured to detect distance of shopper from depth camera). From the teaching of Sorensen, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Patil’s predicting wait time with Liguori’s teaching of likelihood of individual participating and further with Sorensen’s teaching of non-checkout related tasks in order for “an interactive transaction system… (see Sorensen Abstract).”      

Examiner Notes
30.	The Examiner suggests incorporating the elements of claims 5-8 (dependent on claim 2) and 10 together into the independent claims. The Examiner suggests clarifying how the model is trained and re-trained. The Examiner suggests clarifying how to quantify a likelihood of the individual participating in a checkout procedure is calculated. The Examiner suggests clarifying what is meant by managing resource allocation (what resources exactly). The Examiner suggests what or who is exactly assigning these tasks, and how is it communicating with the POS system and machine learning model. Finally, the Examiner suggests incorporating more hardware from the Specification and any unique arrangements of hardware, unique hardware, or unique ways the hardware is communicating. The aforementioned claim suggestions, in combination together, is suggested to help advance prosecution forward, although further search, examination, and consideration is required.
Conclusion
31.	The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Mohammadi et al (Deep Learning for IoT Big Data and Streaming Analytics: A Survey, NPL) is found to be the most pertinent NPL prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FAWAAD HAIDER whose telephone number is (571)272-7178. The examiner can normally be reached Mon-Fri 8 AM to 5 PM.
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, Florian Zeender can be reached on 571-272-6790. 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.



/FAWAAD HAIDER/Examiner, Art Unit 3627             























    
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
    


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