Patent Application 18745459 - UTILIZING MACHINE LEARNING AND A SMART

Title: UTILIZING MACHINE LEARNING AND A SMART TRANSACTION CARD TO AUTOMATICALLY IDENTIFY ITEM DATA ASSOCIATED WITH PURCHASED ITEMS
Application Information

Invention Title: UTILIZING MACHINE LEARNING AND A SMART TRANSACTION CARD TO AUTOMATICALLY IDENTIFY ITEM DATA ASSOCIATED WITH PURCHASED ITEMS
Application Number: 18745459
Submission Date: 2025-05-14T00:00:00.000Z
Effective Filing Date: 2024-06-17T00:00:00.000Z
Filing Date: 2024-06-17T00:00:00.000Z
Examiner Employee Number: 89210
Art Unit: 3621
Tech Center: 3600
Rejection Summary

102 Rejections: 0
103 Rejections: 3
Cited Patents

The following patents were cited in the rejection:
Office Action Text



    DETAILED ACTION

Status of the Application
	Claims 1-20 are pending and currently under consideration for patentability under 37 CFR 1.104.
Priority
The instant application has a filing date of June 17, 2024, and claims priority as a continuation (CON) of US non-provisional Application # 18/318,935 (filed May 17, 2024 and since issued as US patent No. 12,014,391), which claims benefit as a CON of prior-filed non-provisional US Application # 16/881,666 (filed on May 22, 2020 and since issued as US patent No. 11,881,666).
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 .

Information Disclosure Statement
	The information disclosure statement (IDS) submitted on June 26, 2024 has been considered by the examiner.

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.

v	Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.

Step 1:
Claim(s) 1-7 is/are drawn to a method (i.e., a process), claim(s) 8-14 is/are drawn to a device (i.e., a machine/manufacture), and claim(s) 15-20 is/are drawn to a non-transitory computer-readable medium (i.e., a machine/manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention (Step 1: YES).
 
Step 2A - Prong One:
In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether it/they recite(s) a judicial exception.

Claim 1 (representative of independent claim(s) 8 and 15) recites/describes the following steps; 
obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart and customer data identifying the customer; 
obtaining…rewards data identifying rewards associated with the plurality of items;
processing…the item data, the rewards data, and the customer data, with a…model, to identify a reward based on the rewards data associated with the item; 
verifying…a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart; and 
retraining…the…model based on the reward, wherein retraining the…model comprises: performing dimensionality reduction to reduce the item data, the rewards data, and the customer data to a feature set, and training the…model based on the feature set

These steps, under its broadest reasonable interpretation, describe or set-forth a process for identifying a reward for a customer, verifying a completed purchase of an item, and updating a model used to identify the reward. More specifically, these steps, under its broadest reasonable interpretation, describe or set-forth a process for identifying a reward for a customer based on item data (identifying an item, of a plurality of items, placed in a shopping cart) and customer data and rewards data (identifying rewards associated with the plurality of items) and using a model, verifying a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data (including first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart), and retraining the model based on a feature set determined by performing dimensionality reduction to reduce the item data, the rewards data, and the customer data to a feature. This process amounts to a commercial or legal interactions (specifically, an advertising, marketing or sales activity or behavior). These limitations therefore fall within the “certain methods of organizing human activity” subject matter grouping of abstract ideas.

Additionally and/or alternatively, the above-recited steps of “processing…the item data, the rewards data, and the customer data, with a…model, to identify a reward based on the rewards data associated with the item” (one or more evaluations/judgments) and “verifying…a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart” (one or more evaluations/judgments) and “retraining…the…model based on the reward, wherein retraining the…model comprises: performing dimensionality reduction to reduce the item data, the rewards data, and the customer data to a feature set, and training the…model based on the feature set” (one or more evaluations), under their broadest reasonable interpretation, encompass a  human manually (e.g., in their mind, or using paper and pen) performing each of these steps, but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the “mental processes” subject matter grouping of abstract ideas.
Additionally and/or alternatively, the above-recited step of “retraining…the…model based on the reward, wherein retraining the…model comprises: performing dimensionality reduction to reduce the item data, the rewards data, and the customer data to a feature set, and training the…model based on the feature set” describes or sets-forth using math to learn weights/values for one or more algorithms (see paragraphs [0048]-[0059] of Applicant’s published disclosure), which amounts to one or more mathematical relationships, one or more mathematical formulas or equations, one or more mathematical calculations. These limitations therefore fall within the “mathematical concepts” subject matter grouping of abstract ideas. 
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, some steps fall within the mental process grouping of abstract ideas, and some steps fall within the mathematical concepts grouping of abstract ideas. These limitations are considered together as a single abstract idea for further analysis.


As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Independent claim(s) 8 and 15 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
Each of the depending claims likewise recite/describe these steps (by incorporation - and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis. Any element(s) recited in a dependent claim that are not specifically identified/addressed by the Examiner under step 2A (prong two) or step 2B of this analysis shall be understood to be an additional part of the abstract idea recited by that particular claim. The same reasoning is similarly applicable to the limitations in the remaining dependent claims, and their respective limitations are not reproduced here for the sake of brevity.

Step 2A - Prong Two:
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.

The claim(s) recite the additional elements/limitations of
“by a device…by the device…by the device… by the device… by the device” (claim 1)
“a device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to…wherein the one or more processors…are configured to” (claim 8)
“a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to…wherein the one or more instructions, that cause the device…cause the device to” (claim 15)
“with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15)
“causing a server device…to” (claims 5, 12, and 19)
“to a client device” (claims 6 and 13)
“wherein the one or more processors are further configured to” (claims 9-14)
“wherein the one or more instructions further cause the device to” (claims 16-20)

The requirement to execute the claimed steps/functions “by a device…by the device…by the device… by the device… by the device” (claim 1) and/or using “a device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to…wherein the one or more processors…are configured to” (claim 8) and/or “a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to…wherein the one or more instructions, that cause the device…cause the device to” (claim 15) and/or the recitation of  “causing a server device…to” (claims 5, 12, and 19) and/or “wherein the one or more processors are further configured to” (claims 9-14) and/or “wherein the one or more instructions further cause the device to” (claims 16-20) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Applicant’s own disclosure explains that these elements may be embodied as a general-purpose computer (e.g., the following paragraphs of the as-filed specification – [0069]-[0073] “Client device 410 includes one or more devices capable of receiving, generating, storing, processing, and/or providing information, such as information described herein. For example, client device 410 may include a mobile phone (e.g., a smart phone, a radiotelephone, and/or the like), a laptop computer, a tablet computer, a desktop computer, a handheld computer…processing platform 420 may be designed to be modular such that certain software components may be swapped in or out depending on a particular need. As such, processing platform 420 may be easily and/or quickly reconfigured for different uses. In some implementations, processing platform 420 may receive information from and/or transmit information to one or more client devices 410…Computing resource 424 includes one or more personal computers, workstation computers, mainframe devices, or other types of computation and/or communication devices. In some implementations, computing resource 424 may host processing platform 420. The cloud resources may include compute instances executing in computing resource 424, storage devices provided in computing resource 424, data transfer devices provided by computing resource 424, etc. In some implementations, computing resource 424 may communicate with other computing resources 424 via wired connections, wireless connections, or a combination of wired and wireless connections” and [0082]-[0090] “may be implemented within a single device…Device 500 may correspond to client device 410, processing platform 420, computing resource 424”). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The recited additional element(s) of  “with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15) provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The machine learning model is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, these limitations only recite the outcome of “processing…the item data, the rewards data, and the customer data…to identify a reward based on the rewards data” and do not include any details about how the “processing…to identify…” is accomplished. See MPEP 2106.05(f) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis.
The recited additional element(s) of “causing a server device…to” (claims 5, 12, and 19) and/or “to a client device” (claims 6 and 13) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to “to identify a reward" computing environments, such as distributed computing environments and/or the internet, where information is represented digitally, exchanged between computers over a network, and presented using graphical user interfaces. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank  (Fed. Cir. 2015), where the court determined "an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer"). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recitation of “with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element ““with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15)” limits the identified judicial exceptions to processing the data ““with a machine learning model” and retraining  “the machine learning model” (claims 1, 8, and 15), this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the July 2024 Subject Matter Eligibility Examples and corresponding analysis. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)).
The recited element(s) of “obtaining…and based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart and customer data identifying the customer” (claims 1, 8, and 15) and “obtaining… rewards data identifying rewards associated with the plurality of items” (claims 1, 8, and 15), even if treated as “additional” elements for the purpose of the eligibility analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea; mere post-solution activity in conjunction with an abstract idea). The term “extra-solution activity” is understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. The recited additional element(s) do are deemed “extra-solution” because all uses of the recited judicial exceptions require such data gathering, and because such data gathering have long been held to be insignificant pre-solution activity. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h) and (g)).
Furthermore, although the claims recite a specific sequence of computer-implemented functions, and although the specification suggests certain functions may be advantageous for various reasons (e.g., business reasons), the Examiner has determined that the ordered combination of claim elements (i.e., the claims as a whole) are not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. For example, Applicant’s as-filed specification suggests that it is advantageous for advertisers/business to implement the claimed process for identifying a reward for a customer and verifying a completed purchase of an item, because doing so can help identify rewards that are likely to be of use to the customer which can increase customer utility and revenue for the merchant and because doing so can ensure the customer has actually earned the reward (see, for example, paragraphs [0027] & [0035])  of Applicant’s as-filed disclosure). These are non-technical business advantages/improvements. At most, the ordered combination of claim elements is directed to a non-technical improvement to an abstract idea itself (e.g., a an improved process for identifying/redeeming rewards for a customer).
Dependent claims 2-4 and 7 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-4 and 7 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). For example, claim 2 recites “generating a promotion as the reward for the customer based on spending patterns”. This is an abstract limitation which further sets forth the abstract idea encompassed by claim 2. This limitation is not an “additional element”, and therefore it is not subject to further analysis under Step 2A- Prong Two or Step 2B. The same logic applies to each of the other dependent claims, whose limitations are not being repeated here for the sake of brevity and clarity.

The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).

Step 2B:
In step 2B,  the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 134 S. Ct. at 2355, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)

As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions “by a device…by the device…by the device… by the device… by the device” (claim 1) and/or using “a device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to…wherein the one or more processors…are configured to” (claim 8) and/or “a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to…wherein the one or more instructions, that cause the device…cause the device to” (claim 15) and/or the recitation of  “causing a server device…to” (claims 5, 12, and 19) and/or “wherein the one or more processors are further configured to” (claims 9-14) and/or “wherein the one or more instructions further cause the device to” (claims 16-20) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the requirement to execute the claimed steps/functions “with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15)  is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(f)).
As discussed above in “Step 2A – Prong 2”, the recited additional element(s) of “causing a server device…to” (claims 5, 12, and 19) and/or “to a client device” (claims 6 and 13) serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited additional element(s) of “with a machine learning model…the machine learning model…the machine learning model” (claims 1, 8, and 15)  also serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. These limitations therefore do not qualify as “significantly more” (see MPEP 2106.05(g)).
As discussed above in “Step 2A – Prong 2”, the recited element(s) of “obtaining…and based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart and customer data identifying the customer” (claims 1, 8, and 15) and “obtaining… rewards data identifying rewards associated with the plurality of items” (claims 1, 8, and 15), even if treated as “additional” elements for the purpose of the eligibility analysis, would simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea;). These additional element(s), taken individually or in combination, additionally amount to well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, appended to the judicial exception. These additional elements, taken individually or in combination, are well-understood, routine and conventional to those in the field of advertising/marketing. These limitations therefore do not qualify as “significantly more”. (see MPEP 2106.05(d)).This conclusion is based on a factual determination. The determination that receiving data/messages over a network is well-understood, routine, and conventional is supported by Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015);  buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), and MPEP 2106.05(d)(II), which note the well-understood, routine, conventional nature of receiving data/messages over a network. Furthermore, Examiner takes Official Notice that these steps were well-understood, routine, and conventional at the effective filing date of the claimed invention. Furthermore, the lack of technical detail/description in Applicant’s own specification provides implicit evidence that these steps were well-understood, routine, and conventional.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity), and appended with well-understood, routine and conventional activities previously known to the industry.
Dependent claims 2-4 and 7 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims 2-4 and 7 is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea identified by the Examiner to which each respective claim is directed).

The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
	
	
Double Patenting
	The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA  as explained in MPEP § 2159.  See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). 
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed  determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.

v	Claims 1-20 are rejected on the ground of nonstatutory anticipation-type double patenting as being unpatentable over claims 1-20 of US Patent No. 12,014,391 (corresponding to US Application No. 18/318,935). Although the conflicting claims are not identical, they are not patentably distinct from each other. Each of the instant claims is anticipated by at least one claim of US Patent No. 12,014,391. The exact limitations of each of these claims are not being reproduced here for clarity and brevity, as the Examiner believes the anticipation would be self-evident to a PHOSITA. Examiner notes that the instant claims are an exact replica of the claims filed as part of US Application No. 18/318,935, and the only changes that were made as part of the Examiner’s Amendment were additions to the claim language.  It is further noted that Applicant has previously filed a Terminal Disclaimer for each of the previous patents in the family chain.

v	Claims 1-20 are rejected on the ground of nonstatutory anticipation-type double patenting as being unpatentable over claims 1-20 of US Patent No. 11,881,666 (corresponding to US Application No. 16/881,666). Although the conflicting claims are not identical, they are not patentably distinct from each other. Each of the instant claims is anticipated by at least one claim of US Patent No. 11,881,666. The exact limitations of each of these claims are not being reproduced here for clarity and brevity, as the Examiner believes the anticipation would be self-evident to a PHOSITA. It is further noted that Applicant has previously filed a Terminal Disclaimer for each of the previous patents in the family chain.


Claim Rejections - 35 USC § 103
	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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.

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 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.  
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.


v	Claims 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (U.S. PG Pub No. 2019/0272557 September 5, 2019 - hereinafter "Smith”) in view of Ariyibi (U.S. PG Pub No. 2013/0073405, March 21, 2013 - hereinafter "Ariyibi”) in view of Tkachenko et al. (U.S. PG Pub No. 2017/0004472 January 5, 2017)

With respect to claims 1, 8, and 15, Smith teaches a method, a device, and a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to perform the method, comprising;
 one or more memories; and (claim 8) ([0121] “computer-readable medium)
one or more processors, coupled to the one or more memories, configured to (claim 8) ([0121] “executable by one or more processors”)
obtaining, by a device, item data identifying an item, of a plurality of items, placed in a shopping cart ([0072] “, the price management system 102 can detect that a customer has added a product to a shopping cart using a smart cart that reads an RFID tag, code, or other identifier on the product….can then provide, in real-time, information about one or more other products in the product category or related product categories based on the determined interest”, [0110] “provides notifications to a customer indicating that a target product with a better discount price is available than a target product that the customer has selected. In particular, the price management system 102 can determine that a customer has selected a target product by collecting product data from a device such as a smart cart…IoT sensor, or other device….can use a device to detect that a customer has placed the target product in a smart cart…”)

and customer data identifying the customer; ([0023]-[0024] “generates a customized discount price specific to a given customer based on…one or more products (e.g., a discount price for the customer…the price management system can use customer data for the given customer as input to the trained machine learning model…can use information about the customer's past purchasing habits and other features of the customer's habits or demographics…allows the price management system to generate discount prices for the customer over time based on…the customer's characteristics, purchasing habits, and interests. Likewise, the price management system can use customer data for other customers to determine different tailored pricing models for the other customers…the price management system can collect information ( e.g., location data, browsing data) from a client device of a customer to determine that the customer is interested in at least one product” – therefore the system obtains customer data identifying the customer, [0029] “can utilize a machine-learning model ( e.g., neural network or regression model) to automatically identify significant features based on combined interaction between product data (e.g., data from merchants indicating inventory, sale history, and expiration dates) and customer data (e.g., data from a user data profile such as demographics and purchasing history)…can analyze a variety of flexible data sources to identify these features, including…interaction with shelving, etc.), online behavioral and profile data (e.g., current shopping list, shopping propensities, spending habits), or audience segments. The price management system can train the model to recognize features of individual customers and groups of customers that indicate whether the customers are more likely or less likely to purchase products at certain prices”, [0031] “mobile app…management system can detect a customer's interest based on location data from the customer's client device (e.g., a location within a store relative to a product). Thus, the price management system can provide customized discount prices to a customer when they are most relevant to the customer” – therefore the system obtains customer data (e.g., location data) identifying the customer, [0020] “analyzes historical data, including…for customers…to determine correspondences between product details…and customer preferences…probabilities that one or more customers will purchase the target product at various discount prices…the price management system can analyze…customer history data” – therefore the system obtains customer data identifying the customer, see also [0037] & [0070] for other customer data types)
obtaining, by the device, rewards data identifying rewards associated with the plurality of items; ([0022] “use the trained machine-learned model to generate probabilities indicating whether the customer is likely to purchase the product at a plurality of possible discount price… and then determine a discount price for the customer based on the probabilities” – therefore the system obtains a plurality of possible discount prices (i.e., “rewards data identifying rewards associated with the plurality of items” – consistent with Applicant’s disclosure at Fig 2 and [0026] “rewards data may include…percent discounts…” and [0049] “reward data…percentage off a price, a dollar value…”) for consideration using the ML model, [0065] “identifying…possible prices for the target product”, see also [0079])
processing, by the device, the item data, the rewards data, and the customer data, with a machine learning model, to identify a reward based on the rewards data associated with the item; ([0019]-[0020] “utilize the machine-learning model to analyze customer data associated with one or more customers to further tailor a generated discount price and customized digital product notification to a customer. By generating and utilizing a machine-learning model based on the product data and customer data, the dynamic price management system can determine a discount price for the target product…utilize a machine-learning model trained using the historical data to generate a prediction of a sale of the target product at a discount price…can dynamically generate, for the target product, probabilities that one or more customers will purchase the target product at various discount prices…based on the generated probabilities, the price management system can provide a digital notification of a discount price to a client device of a customer” – therefore the system processes the item data, the rewards data (e.g., potential discount prices), and the customer data, with a machine learning model, to identify a reward based on the rewards data associated with the item (e.g., a discount price having a highest probability of being purchased by the user), [0074]-[0075] “after identifying the product data and the customer data, the price management system 102 can input the product data and/or the customer data into a machine-learning model such as the model described in FIG. 2. The machine-learning model can use the product data and customer data to determine a discount price…probabilities….at a plurality of discount prices”, see also [0079]-[0080] “ identify…plurality of possible discount prices…after identifying the possible discount prices…can use the possible discount prices as input…”, see also [0110], see [0038]-[0040]  for possible types of ML models)
retraining, by the device, the machine learning model based on the reward, ([0021] “trains a machine-learning model using product history data for previously available products and customer history data for a plurality of customers associated with a merchant. The price management system can utilize the machine-learning model ( e.g., neural network or regression model) to output sale predictions for a product…and then train the machine learning model based on a comparison between the output sale predictions and ground truth sales information” – therefore the system iteratively re-trains the model using the previously determined predictions associated with the reward (e.g., discount price) and ground truth observations (e.g., whether or not the customer purchased it), [0030] “can automatically incorporate up-to-date ( e.g., real-time) digital data regarding customers in identifying significant features…detects additional product data (including expiration data) and customer data, the price management system can update training of the machine-learning model to identify any additional, or alternative features…can analyze product sales data (indicating a sale or lack thereof) from a merchant for a previous day before generating customized discount prices for a customer. The price management system can implement such improvements among different products, among different stores, and even among different merchants. Accordingly, the price management system can not only automatically identify significant features for developing pricing models and generating customized discount prices for individual customers (or groups of customers…but can automatically modify which features the system determines to be significant (in real-time) base on additional product data” – therefore the system retrains the machine learning model based on the reward (e.g., sale or lack thereof at the discounted price, see also [0056]-[0061] for positive and negative training examples and loss function used to iteratively retrain the model)
wherein retraining the machine learning model comprises: performing dimensionality reduction to reduce the item data, the rewards data, and the customer data to a feature set, and training the machine learning model based on the feature set ([0058]-[0063] “uses the loss function 210 (e.g., the measure of loss resulting from the loss function 210) to train the machine-learning model 200…to correct parameters that resulted in incorrect predicted values from the predicted sales data 206…modify one or more weights or parameters…reduce the differences between the predicted sales data 206 and the ground truth sales data 208 for the previously available products…. modifying internal parameters of the machine-learning model, the price management system 102 can determine significant features and relationships that accurately predict a sale. For example, for a product, the machine-learning model 200 learns features of, or relationships between, the product history data 202 (including the price(s) of the product and the expiration date of the product) and customer history data 204 to generate an accurate prediction of whether the product sold at the price(s)….can continually update (e.g., fine tune) the machine learning model…at various discount prices….based on product data for the products and customer data for the customer(s)…can use the feedback to update the loss function… can use any type of machine-learning techniques…including…neural networks and/or regression models…dimensionality reduction algorithms” – therefore the system retains the machine learning model using dimensionality reduction algorithms on the product data (item data) and the customer data and the rewards data (e.g., potential discounted prices) to reduce the data to a feature set (what dimensionality algorithms do) and retrains the model using the feature set,  [0038] “machine-learning model can include a model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs…can include…dimensionality reduction algorithms”)
Smith does not appear to disclose,
obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart
verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart
However, Ariyibi discloses 
obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart ([0006]-[0007] “the customer has elected to share customer information with the retailer…offering the enhances shopping experience… while in the store the customers browsing habits and shopping activities are detected and used in providing instant price discounts and other loyalty rewards” – therefore the server receives, by the device and based on the customer joining the rewards program, user’s in-store shopping activities, [0025] “as the customer moves through the store…keep a record of which…items held user interest…used to updated the customer’s profile on the CEM database”,  [0047] “displays sales, discounts, and promotion information that is contextual and relevant to the items located on the nearby shelves and customized to the individual customer…scan the SKU/UPC barcode for items as they are places in the cart. Items placed in the cart are thus automatically detected” – shopping activities include items added to cart, [0021] “customer initiates the process…registering in the retailer’s customer loyalty program…customer enters certain personal data…”, [0033]-[0034] “offering an enhances in-store experience…portal…message to the customer’s mobile device offering personalized service based on the applicable loyalty program…customer may then respond…approving…the offer…authorizing sharing of her customer profile information” [0038])
Ariyibi suggests it is advantageous to include “obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart”, because doing so can ensure the user’s personal and/or in-cart product information is only obtained after receiving customer’s consent(e.g., them joining the rewards program and consenting to share their shopping/browsing activity with the merchant) which can increase customer satisfaction with the system ([0006]-[0007]  [0021] & [0033]-[0034] & [0038]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith to include “obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart”, as taught by Ariyibi, because doing so can ensure the user’s personal and/or in-cart product information is only obtained after receiving customer’s consent(e.g., them joining the rewards program and consenting to share their shopping/browsing activity with the merchant) which can increase customer satisfaction with the system 2Applicant: Jeffrey L. NanusApplication No.: 141593,177 Docket No.: 1377-9Preliminary Amendment.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith to include “obtaining…based on a customer joining a rewards program, item data identifying an item, of a plurality of items, placed in a shopping cart”, as taught by Ariyibi, 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. One of ordinary skill in the art would have recognized that doing so would ensure the user’s information and/or in-cart product information is obtained based at least in part on consent of the customer (e.g., them joining the rewards program and consenting to share their shopping/browsing activity with the merchant) which can increase customer satisfaction and acceptance of the tracking.

Smith and Ariyibi do not appear to disclose,
verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart
However, Tkachenko discloses 
verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart ([0017] & [0021]-[0022] “packages a pre-transaction image and a post-transaction image for a transaction with related transaction data—such as a date and time of the transaction, a location and/or unique identifier of the kiosk, and payment information provided by the customer (e.g., a credit card number)—once the transaction is completed and upload this package to the transaction system (e.g., executing on a remote computer network or transaction server). The transaction system process the pre- and post-transaction images…can queue transaction data for review… can assemble pre- and post-transaction images corresponding to each shelf”…“once a pre- and post-transaction image pair for a particular shelf in the kiosk for a particular transaction is uploaded from the kiosk to the transaction system, the transaction system can compare the pre-transaction image to the post-transaction image to identify specific regions of the two images that differ…The transaction system (or the kiosk) can also reconcile actual inventory at the kiosk and inventory changes due to transactions at the kiosk—such as at end of each day or when the kiosk is restocked—to determine if a product was not detected or otherwise improperly counted in a transaction occurring at the kiosk since a previous reconciliation event. For example, when the kiosk is restocked or if a difference between actual inventory of a product on a shelf and a predicted inventory of the product on the shelf is determined based on manual or computer vision-based analysis of an image of the shelf, the transaction system can trigger a new reconciliation event. In this example, the transaction system can reprocess all pre- and post-transaction image pairs for transactions occurring between the new and a previous reconciliation event (a “reconciliation period”) to identify a particular transaction for which a product was miscounted (e.g., not counted or double-counted). The transaction system can then return any excess funds paid by a customer for product not actually purchased, and the transaction system can bill a customer for any undercounted product” – therefore the system verifies a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data (specifically, first data indicating that the item has been removed from a shelf), [0035]-[0036] detection that item was removed from shelf may be entirely automated)
Tkachenko suggests it is advantageous to include verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart, because doing so can act as an internal audit to validate the accuracy of purchase records from the POS which can help improve knowledge of actual inventory levels (e.g., as opposed to determinations of inventory levels predicted based on purchase records from the POS alone) and because doing so can help reduce fraud (incidental or otherwise) and/or help ensure a customer obtains a refund if there was a mistake made by overcharging them during a transaction ([0021]-[0022]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi to include verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart, as taught by Tkachenko, doing so can act as an internal audit to validate the accuracy of purchase records from the POS which can help improve knowledge of actual inventory levels (e.g., as opposed to determinations of inventory levels predicted based on purchase records from the POS alone) and because doing so can help reduce fraud (incidental or otherwise) and/or help ensure a customer obtains a refund if there was a mistake made by overcharging them during a transaction2Applicant: Jeffrey L. NanusApplication No.: 141593,177 Docket No.: 1377-9Preliminary Amendment.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi to include verifying, by the device, a completed purchase of the item based on data indicating an intent of the customer to purchase the item and transaction data, wherein the data indicating an intent of the customer to purchase the item includes first data indicating that the item has been removed from a shelf or second data indicating that the item has been placed in the shopping cart, as taught by Tkachenko, 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. One of ordinary skill in the art would have recognized that doing so would act as an internal audit to validate the accuracy of purchase records from the POS which can help improve knowledge of actual inventory levels (e.g., as opposed to determinations of inventory levels predicted based on purchase records from the POS alone) and because doing so can help reduce fraud (incidental or otherwise) and/or help ensure a customer obtains a refund if there was a mistake made by overcharging them during a transaction.

With respect to claims 2, 9, and 16, Smith teaches the method of claim 1, the device of claim 8, and the medium of claim 15;
further comprising: generating a promotion as the reward for the customer based on spending patterns ([0023] “generates a customized discount price specific to a given customer based on…customer data…the customer’s past purchasing habits…tailored pricing model…” & [0029] “exploring and utilizing interactions between a variety of different characteristics to determine different discount prices…shopping propensities, spending habits…” & [0037] “customer data…purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price, consumption rate…” – therefore the system generates a personalized discount (i.e., a promotional price for the customer, which is “a promotion as the reward for the customer”) based on the customers historical spending/purchasing habits (e.g., frequency, recency, price sensitivities – i.e.., “spending patterns”), [0068] “customer data can include purchasing habits in relation to products of the product category or related product categories (e.g., frequency of purchases, recency of purchases), price sensitivity (e.g., an aggregate score of how heavily a customer is influenced by price or brand, whether the customer uses coupons or offers, whether the customer purchases high end products)” – various spending patterns)

Examiner notes that Ariyibi also discloses this limitation at ([0003] “learn a customer’s preferences…anticipating individual preferences and offering customized discounts”,  [0037]-[0041] browsing/transaction data used to determine customer preferences (i.e., spending patterns),  [0044] “customized discounts…customer preferred items…individualized discounts and other preferred customer benefits base be offered automatically based on the customer’s past shopping history…”, CLAIM 1 “customized…customer information being at least…historical…preferences of items and brands purchased by the customer”)


With respect to claims 3, 10, and 17, Smith teaches the method of claim 1, the device of claim 8, and the medium of claim 15;
further comprising: generating a promotion as the reward for the customer based on a relationship of the item to another item  ([0110] “provides notifications to a customer indicating that a target product with a better discount price is available than a target product that the customer has selected. In particular, the price management system 102 can determine that a customer has selected a target product by collecting product data from a device such as a smart cart…IoT sensor, or other device….can use a device to detect that a customer has placed the target product in a smart cart…another target product within the same product category” – therefore the system can generate a personalized discount for another product (i.e., a promotional price for the customer, which is “a promotion as the reward for the customer”) based on the another product being of the same category as the item (i.e., a relationship of the item to another item), see also [0065] & [0072])

With respect to claims 4, 11, and 18, Smith teaches the method of claim 1, the device of claim 8, and the medium of claim 15;
further comprising: modifying the reward after identifying the reward based on determining spending patterns associated with the customer ([0019] “can utilize the machine learning model to analyze customer data…to further tailor a generated discounts price and customized product notification to a customer” – therefore the system can further tailor (i.e., modify) a discounted price (i.e., an identified discount price from identified possible discount prices) based on the customer data,  [0093] “changing a discount price of the target product over time…can…increase c customer’s incentive to purchase the product”, [0023] “generates a customized discount price specific to a given customer based on…customer data…the customer’s past purchasing habits…tailored pricing model…” & [0029] “exploring and utilizing interactions between a variety of different characteristics to determine different discount prices…shopping propensities, spending habits…” & [0037] “customer data…purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price, consumption rate…” – therefore the system based on the customers historical spending/purchasing habits (e.g., frequency, recency, price sensitivities – i.e.., “spending patterns associated with the customer”), [0068] “customer data can include purchasing habits in relation to products of the product category or related product categories (e.g., frequency of purchases, recency of purchases), price sensitivity (e.g., an aggregate score of how heavily a customer is influenced by price or brand, whether the customer uses coupons or offers, whether the customer purchases high end products)” – various spending patterns)

With respect to claims 5, 12, and 19, Smith, Ariyibi, and Tkachenko teach the method of claim 1, the device of claim 8, and the medium of claim 15. Smith does not appear to disclose,
further comprising: causing a server device, associated with a financial institution managing a transaction card associated with the customer, to provide the reward to the transaction card
However, Ariyibi discloses 
causing a server device, associated with a financial institution managing a transaction card associated with the customer, to provide the reward to the transaction card ([0004]-[0005]& [0022] & [0026]-[0029] & [0031] & [0040]-[0041] user’s card/credit card may include multiple rfid tags and may be a “smart” transaction card and the card may be encoded with the user’s loyalty CLSI and rewards/promotions may be added to the user’s loyalty account for subsequent communication to the POS to enable loyalty discounts to be redeemed during a purchase – therefore the discounts/rewards are provided “to the transaction card” (interpretation of providing “to the transaction card” consistent with Applicant’s own specification and the description of automatically providing the reward “to the transaction card” found within Applicant’s specification), [0026] &  [0029] card may be credit card and therefore the server used to provide the reward to the card is a “server device, associated with a financial institution managing a transaction card associated with the customer” )
Ariyibi suggests it is advantageous to include causing a server device, associated with a financial institution managing a transaction card associated with the customer, to provide the reward to the transaction card, because doing so can provide an efficient and effective way to link the reward to the customer so that the customer can redeem the customized promotional price ([0004]-[0005]& [0022] & [0026]-[0029] & [0031] & [0040]-[0041]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith to include causing a server device, associated with a financial institution managing a transaction card associated with the customer, to provide the reward to the transaction card, as taught by Ariyibi, because doing so can provide an efficient and effective way to link the reward to the customer so that the customer can redeem the customized promotional price 2Applicant: Jeffrey L. NanusApplication No.: 141593,177 Docket No.: 1377-9Preliminary Amendment.
Furthermore, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the mechanism for providing the identified reward to the customer of Ariyibi (i.e., causing a server device, associated with a financial institution managing a transaction card associated with the customer, to provide the reward to the transaction card) for that of Smith. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.


v	Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Smith in view of Ariyibi in view of Tkachenko, as applied to claims 1 and 8 above, and further in view of Darragh (U.S. PG Pub No. 2012/0253905 October 4, 2012 – hereinafter “Darragh”)

With respect to claims 6 and 13, Smith, Ariyibi, and Tkachenko the method of claim 1 and the device of claim 8. Smith does not appear to disclose,
further comprising: providing, to a client device, a request for feedback as to why the item was purchased
However, Darragh discloses 
providing, to a client device, a request for feedback as to why the item was purchased ([0050] “mobile communication device 14 is also configured to receive purchasing questions from the retail shopping store 12. Non-limiting examples of purchasing questions that the retail shopping store 12 may ask the viewer/consumer of the mobile communication device 14, may be: "Would you like to purchase this item?"; "Why did you not purchase this item?"; "Why did you purchase this item?"; "Please rate the following item"; "Would you purchase this if it was discounted by 15%?"; and etc. and the like and combinations thereof. Such questions may be triggered dependent on one or more factors, including but not limited to answers to previous questions; an amount of passage of time between purchases, non-purchases, product viewings, store visits and the like and combinations thereof; the success/failure rate of such and/or similar questions in securing desired behavior such as but not limited to inducing and/or influencing additional store visits, purchases, item reviews, answers to questions asked and the like and combinations thereof; previous offers made; potential offers to be made; random selection; split-testing processes; and the like and combinations there”)
Darragh suggests it is advantageous to include providing, to a client device, a request for feedback as to why the item was purchased, because doing so can help the merchant/store to better understand customer preferences which can help them to improve subsequent effectiveness of their product marketing/promotions ([0006] & [0015]-[0016] & [0050])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi in view of Tkachenko to include generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items, as taught by Darragh, because doing so can help the merchant/store to better understand customer preferences which can help them to improve subsequent effectiveness of their product marketing/promotions 2Applicant: Jeffrey L. NanusApplication No.: 141593,177 Docket No.: 1377-9Preliminary Amendment.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi in view of Tkachenko to include generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items, 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. One of ordinary skill in the art would have recognized that doing so would help the merchant/store to better understand customer preferences which can help them to improve subsequent effectiveness of their product marketing/promotions.


v	Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith in view of Ariyibi in view of Tkachenko, as applied to claims 1, 8, and 15 above, and further in view of Spiro (U.S. PG Pub No. 2016/0210585 July 21, 2016 – hereinafter “Spiro”)

With respect to claims 7, 14, and 20, Smith, Ariyibi, and Tkachenko the method of claim 1, the device of claim 8, and the medium of claim 15. Smith does not appear to disclose,
further comprising: generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items
However, Spiro discloses 
generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items ([0033] “configured to report its location based on the X, Y, and Z-coordinate system, whereby a three dimensional map is generated showing not only where a product is in relation to its X and Y coordinates, but also a height. For example, multiple products (and their display devices) may be stacked on top of each other such that locating a specific product may require the additional Z-coordinate. The display device 100 may broadcast and/or otherwise transmit its location to another system or device such as a navigation device 203” and [0054]-[0058] “the navigation device 203 may be configured to be disposed to another object such as a shopping cart or as a software application on an electronic device such as a cell phone… multidimensional map may be maintained by the centralized server 210 and communicates to the navigational device 203…the navigation device 203 may be configured to dynamically generate a floorplan layout of a space based on the sensed locations of a plurality of display devices 100. For example, it may be the case that the defined space does not have a predetermined layout, or the location may have a floorplan that is dynamic and always changing…the navigation device 203 may be configured to automatically generate the multidimensional map based on the sensed locations of a plurality of display devices 100 within the defined space…this process may repeat periodically” – therefore the item data is used to generate a map of the store including the location of the products including their height )
Spiro suggests it is advantageous to include generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items, because doing so can iteratively update a map of the retail store to enable the system to provide accurate navigation information to customers and because doing so can verify products are located where they are supposed to be ([0033] & [0054]-[0058] & [0066] & [0069])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi in view of Tkachenko to include generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items, as taught by Spiro, because doing so can iteratively update a map of the retail store to enable the system to provide accurate navigation information to customers and because doing so can verify products are located where they are supposed to be2Applicant: Jeffrey L. NanusApplication No.: 141593,177 Docket No.: 1377-9Preliminary Amendment.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, device, and medium of Smith in view of Ariyibi in view of Tkachenko to include generating a map of item locations based on the item data, wherein the map includes data indicating a height associated with particular items, 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. One of ordinary skill in the art would have recognized that doing so would enable the system to provide accurate navigation information to customers and because doing so can verify products are located where they are supposed to be.


Prior Art of Record
	The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.

Lavu et al. (U.S. PG Pub No. 2016/0283925 September 29, 2016) discloses use of a client device while in a merchant’s store to provide an indication of items that have been added to the user’s shopping cart to a remote server and providing in-store offers/rewards to the user device based on these items

Zalewski et al. (U.S. Patent No. 9,911,290 March 6, 2018) discloses use of a client device while in a merchant’s store to provide an indication of items that have been added to the user’s shopping cart to a remote server and providing in-store offers/rewards to the user device based on these items

Veettil (U.S. PG Pub No. 2021/0295364, September 23, 2021) discloses training a machine learning model based on historical item data, historical rewards data, and historical customer data, to identify a particular award, from a plurality of rewards, associated with an item for a particular customer, and processing item data, rewards data, and customer data, with the machine learning model, to determine a predicted value, for a target value of a reward for the customer associated with the item based on a need to sell the item and a likelihood that the customer will purchase the item; and utilizing the predicted value as the reward for purchasing the item or another item.

Moreau (U.S. PG Pub No.2017/0228811 August 10, 2017) discloses performing a crawl of a data source associated with items to receive rewards data identifying rewards associated with the items.

Lissick et al. (U.S. PG Pub No. 2020/0314598 October 1, 2020) discloses generating a map of item locations based on item data and location information associated with a client device in a merchant’s store.

Nemati et al. (U.S. PG Pub No. 2020/0034812, January 30, 2020) teaches wherein item data is received based on: a shelf sensor wirelessly communicating  data indicating that the item has been removed from a shelf, to a client device, and a shopping cart sensor wirelessly communicating data, indicating that the item has been placed in the shopping cart, to the client device.

Regmi et al. (U.S. PG Pub No. 2009/0271275, October 29, 2009) teaches performing a crawl of a data source associated with items to receive rewards data identifying rewards associated with the items.

Reichert (U.S. PG Pub No. 2015/0278849, October 1, 2015) teaches analyzing user data and reward data and item data to determine dynamic/personalized promotions/rewards/discounts and using a machine learning model.

Vangala et al. (U.S. PG Pub No. 2017/0068982, March 9, 2017) teaches analyzing user data and reward data and item data to determine dynamic/personalized promotions/rewards/discounts and using a machine learning model and wherein receiving the rewards data identifying the rewards associated with the plurality of items comprises: performing a crawl of a data source associated with the plurality of items; and receiving the rewards data identifying the rewards associated with the plurality of items based on performing the crawl of the data source.

Singh et al. (U.S. PG Pub No. 2018/0197197, July 12, 2018) teaches wherein item data is received based on: a shelf sensor wirelessly communicating  data indicating that the item has been removed from a shelf, to a client device, and a shopping cart sensor wirelessly communicating data, indicating that the item has been placed in the shopping cart, to the client device.

O’Shea et al. (U.S. PG Pub No. 2005/0149391) teaches wherein item data is received based on: a shelf sensor wirelessly communicating  data indicating that the item has been removed from a shelf, to a client device, and a shopping cart sensor wirelessly communicating data, indicating that the item has been placed in the shopping cart, to the client device.

Douglas et al. (U.S. PG Pub No. 2014/0067531 March 6, 2014) discloses verifying a transaction was completed based on transaction data and data indicating that a product had previously been placed in the user’s shopping cart.

“A Review of Dimensionality Reduction Techniques for Efficient Computation” (Vekkuabgiri, S. et al. Available online 27 February 2020, Version of Record 27 February 2020. Procedia Computer Science Volume 165, 2019, Pages 104-111) discloses techniques for pre-processing data using dimensionality reduction prior to training. 

Conclusion
	No claim is allowed

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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621