DETAILED ACTION
This communication is a first Office Action Non-Final rejection on the merits. Claims 1-20 as originally filed are currently pending and are considered below.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 23, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 1-20 recite allowable subject matter over the prior art of record.
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 an abstract idea without significantly more.
Regarding claims 1-20, under Step 2A claims 1-20 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites a: obtaining in real-time, via one or more hardware processors, information pertaining to a user shopping cart associated with a user for a context further comprising one or more real-time factors, wherein the user shopping cart comprises one or more items being added therein;
extracting in the real-time, via the one or more hardware processors, a plurality of historical universal shopping carts containing the one or more items being added in the user shopping cart and which is done in a similar context of the user shopping cart; measuring in the real-time, via the one or more hardware processors, similarity of the plurality of historical universal shopping carts based on one or more formats of the plurality of historical universal shopping carts;
determining in the real-time via the one or more hardware processors, during addition of the one or more items in the shopping cart, an improvement in the similarity among the extracted historical universal shopping carts containing the one or more items being added in the user shopping cart for the context;
identifying in the real-time, via the one or more hardware processors, a first set of items amongst the one or more items being added in the user shopping cart based on the improvement in the similarity;
determining in the real-time, via the one or more hardware processors, a context specific expected distribution for the user shopping cart based on a distance distribution of the plurality of historical universal shopping carts containing the first set of items pertaining to the context;
categorizing in the real-time via the one or more hardware processors, in an earlier phase of a self-checkout development, the user shopping cart as a first type or a second type based on a position of the user shopping cart in comparison to dynamic cut off value of the context specific expected distribution for an approval of the user shopper cart;
monitoring correctly categorized via the one or more hardware processors, the first type or the second type of one or more shopping carts through one or more monitoring devices and storing information further comprising at least one context and a format of a plurality of correctly categorized shopping carts;
continually improving an accuracy of the categorization via the one or more hardware processors, in the earlier phase of the self-checkout development, through (i) a self-learning mechanism by using the at least one context, the format, and the dynamic cut off value of the context specific distribution of the plurality of correctly categorized shopping carts, and (ii) by finding (a) an ideal cut off value of the context specific distribution, (b) real time specific ideal formats of the plurality of historical universal shopping carts, and (c) real time specific ideal formats of associated real time factors in an iterative manner;
developing and training, via the one or more hardware processors, a machine learning model in an advanced phase of the self-checkout development, and predicting a user shopping cart for approval based on the trained machine learning model trained using the plurality of correctly categorized user shopping carts and associated context and format of the plurality of correctly categorized user shopping carts to enable categorization in real-time thereof and without extracting the plurality of historical universal shopping carts;
approving in the real-time via the one or more hardware processors, the user shopping cart for a check out predicted as the first type by the trained machine learning model based on a comparison of the predicted probability of the user shopping cart with dynamic cut off value of the predicted probability by the trained machine learning model;
monitoring outcome via the one or more hardware processors, for the first type and the second type through the one or more monitoring devices and storing information pertaining to the at least one context, the format of the plurality of correctly approved user shopping carts and associated predicted probabilities; and
continually improving the accuracy of the approval of the user shopping carts via the one or more hardware processors, in the advanced phase of the self-checkout development through the self-learning mechanism by using the at least one context, the format and the dynamic cut off value of the predicted probability of the plurality of correctly categorized shopping carts and by finding (a) the ideal cut off value of the predicted probability for the approval of the one or more user shopper cart, (b) real time specific ideal formats of the plurality of historical universal shopping carts and (c) real time specific ideal formats of associated real time factors in an iterative manner.
These limitations recite ‘mental processes’, such as by performing an observation or evaluation (see: MPEP 2106.04(a)(2)III). This is because claim 1 sets forth or describes monitoring a customer placing items into a shopping cart prior to proceeding to a self-checkout. This represents the performance of monitoring customers within a retail space, which is an observation or evaluation and falls under mental processes.
Accordingly, under step 2A (prong 1) claim 1 recites an abstract idea because claim 1 recites limitations that fall within the “Mental Processes” grouping of abstract ideas.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, one or more hardware processors, a shopping cart, one or more monitoring devices, "storing information",
"machine learning model", "trained machine learning model", "self-learning mechanism.”
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea).
Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. In addition to the above, storing information comprising at least one context and a format of correctly categorized shopping carts (e.g. data gathering or outputting) are well understood, routine, conventional activity ( see: MPEP 2106.05 (d).
The multiple steps recited are also considered as elements that the courts would have found as well-understood, routine, conventional activity. For example, continually improving an accuracy of the categorization through a self-learning mechanism, developing and training a machine learning model and continually improving the accuracy of customer approval is mere software performing repetitive calculations (see: MPEP 2106.05(d)(II)).
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(II)), including at least:
receiving or transmitting data over a network,
storing or retrieving information from memory,
performing repetitive calculations
Even considered as an ordered combination (as a whole), the additional elements of claim 1do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Regarding independent claim 9 and independent claim 17, claims 9 and 17 recite at least substantially similar concepts and elements as recited in claim 1 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 9 and 17 are rejected under 35 USC 101 for at least similar rationale.
The dependent claims also are patent ineligible. For example, claims 2-3, 10-11 and 18-19 include the step of defining the earlier and advance phases of self-checkout which further describes the
observation and evaluation of a customer and shopping cart within a retail environment.
Claims 4, 12 and 20, further describe the context in which the observation and evaluation is taking place.
Claims 5 and 13 further assign an importance of level of one or more formats of the observed and evaluated items picked.
Claims 6 and 14 recite the step of determining a context specific expected distribution of a user shopping cart which further defines the observation and evaluation of a customer and shopping cart.
Claims 7-8 and 15-16 further describe the abstract idea of observation and evaluation with limitations directed to defining items as a second item and different items.
The claims are patent ineligible.
Applicant is reminded of the existing voluntary option to supply their own evidentiary submission to attempt to overcome Section 101 rejections by filing a Subject Matter Eligibility Declaration (SMED).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Carter et al., U.S. Patent Application Publication 2021/0287013 discusses a trained machine learning model may classify item in a cart as “non-merchandise,” “high theft risk merchandise,” “electronics merchandise,” etc. When a shopping cart approaches a store exit without any indication of an associated payment transaction, the system may use the associated item classification data, optionally in combination with other data such as cart path data, to determine whether to execute an anti-theft action, such as locking a cart wheel or activating a store alarm. The system may also compare the classifications of cart contents to payment transaction records (or summaries thereof) to, e.g., detect underpayment events. Abstract
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/ASHFORD S HAYLES/ Primary Examiner, Art Unit 3627