DETAILED ACTION
Status of the Application
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 20, 2025, has been entered.
In response, the Applicant amended claims 1-5, 8-12, and 15-19. Claims 1-20 are pending and currently under consideration for patentability.
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 .
Response to Amendments and Arguments
v Applicant’s arguments, with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered and are not persuasive. The rejections of claims 1-20 under 35 U.S.C. 101 have been maintained accordingly.
Applicant specifically argues that
1) “when analyzed as written, amended claim 1 is directed to a technical
improvement in computer-implemented retail systems and machine-learning infrastructure. Rather than relying on reconstructed SKU-level data after the fact-an approach the specification identifies as resource-intensive and inefficient (Spec. 13)-amended claim 1 recites a concrete method for automatically obtaining item-level data from a sensor associated with the item, verifying a completed purchase using sensor-derived intent data, and retraining a machine-learning model based on dimensionality-reduced features… These are not generic "data gathering" or "apply it on a computer" steps. They define a specific data-acquisition pipeline (sensor-based item identification and sensor-based intent detection) coupled with a specific machine-learning retraining method (dimensionality reduction to a minimum feature set prior to training)…the specification provides factual evidence of the technical problem and technical benefits produced by the claimed recited features…”
Examiner respectfully disagrees with Applicant’s first argument.
The specification identifies the “after-the-fact approach” by stating “When a customer purchases items from a merchant…transaction data for the transaction associated with the purchased items does not typically provide item-level data (e.g., SKU data) associated with the purchased items. Rather, the transaction data typically includes only names of the items, prices of the items, and a merchant identifier. Thus, current techniques may waste computing resources (e.g., processing resources, memory resources, communication resources, and/or the like), networking resources, and/or the like associated with attempting to identify item-level data (e.g., SKU data) associated with the purchased items, determining identified item-level data is correct, requesting item-level data from customers, and/or like.” Although this paragraph asserts that computing resources may be wasted by “current techniques” (e.g., current techniques associated with attempting to identify item-level data, determining if identified item-level data is correct, and/or requesting item-level data from a customer), i) there is no discussion of what these current techniques are and ii) it is unclear how or why these current techniques could be said to waste computing resources. The instant invention is likewise directed to a process for attempting to identify item-level data associated with the purchased item. Applicant’s specification also describes attempting to determine if identified item-level data is correct by requesting item-level data from a customer Furthermore, it is unclear whether the current technique (which involves processing data with a machine learning model, verifying completed purchase of items, retraining the machine learning model) actually uses less computing resources than these unspecified “current techniques”. Per MPEP 2106.04(d)(1) “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” In this case, an improvement to the amount of computing resources utilized would not be apparent to one of ordinary skill in the art. Per MPEP 2106.05(a) “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.”.
The “factual evidence” alleged by Applicant is insufficient. The deficiency of the statement that “convention POS systems lack item-level SKU data and require resource-heavy construction” is insufficient, as discussed above. That the claimed invention enables real-time item identification instead of manual post-hoc inference is merely a result of using general purpose computers to implement the steps rather than performing each step manually (e.g., to detect when a user selects an item, to analyze the data, etc.). The assertion that the platform must handle a lot of data is not reflected in the actual claim language, as the claimed invention may only involve analysis of a small amount of data. Furthermore, there is no discussion of the “dimensionality reduction” technique, this is a data processing step, and this is also a conventional/non-inventive step.
Furthermore, the “specific data-acquisition pipeline” involves receiving item data identifying an item from a sensor (i.e., receiving data from an unspecified/conventional sensor used in its ordinary capacity to sense data) verifying an intent for a customer to purchase the item merely using data (specifically, data indicating that the item was removed from a shelf or placed in a shopping cart- and corresponding transaction data – with no description of how this data was captured or received), use of an unspecified machine learning model to identify a reward, and retraining the model by at least in part performing conventional dimensionality reduction techniques. These additional limitations (i.e., the description of the item data being received from an unspecified/conventional sensor, that the model is a machine learning model, and that the machine learning model is retrained in part using conventional dimensionality reduction techniques) alone and or together amount to insignificant pre-solution activity, provides nothing more than mere instructions to implement an abstract idea on a generic computer, and serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. The underlying process is still directed to an abstract idea.
Applicant specifically argues that
2) “claimed steps cannot reasonably be performed as "mental processes." In making rejection, the Office Action asserts that steps of amended claim 1 could allegedly be performed mentally. OA pg. 9-10. Respectfully, this is inconsistent with the actual claim language..”
Examiner respectfully disagrees with Applicant’s second argument.
Applicant’s “actual claim language” is incommensurate with what is actually claimed. The instant claims do not involve sensor data used to detect removal of the item from a shelf or placement in a cart, do not use “synchronized sensor-derived intent data and transaction data”, do not require performing dimensionality reduction “across high-dimensional item, rewards, and customer data”, and do no require training/retraining a model on “reduced features sets comprising thousands or millions of data points”.
Applicant specifically argues that
3) “Second, even if an abstract idea is implicated, amended claim 1 integrates it into a practical application (Step 2A, Prong Two). Amended claim 1 recites limitations that are not generic computer steps but they form a specific, sensor-driven technical architecture for acquiring item-level data, confirming purchase events, and retraining a machine-learning model in a resource-efficient manner…the specification confirms that…”
Examiner respectfully disagrees with Applicant’s third argument.
Applicant argument is again incommensurate with what is actually claimed. The instant claims do not involve sensor data used to detect removal of the item from a shelf or placement in a cart, even if the specification suggests sensors could be used in this context. Furthermore, receiving item data identifying an item from a sensor (i.e., receiving data from an unspecified/conventional sensor, that is external to the scope of the claimed invention and used in its ordinary capacity to sense data) is insignificant pre-solution data gathering activity. The claims similarly do not involve a smart transaction card. As discussed above, that the machine learning model is retrained in part using conventional dimensionality reduction techniques to does mean the claims are directed to a technical improvement. This is non-inventive and described at a high level of generality. Applicant’s argument repeatedly refers to an ordered combination of additional limitations that is incommensurate with what is actually claimed, and is not persuasive.
Applicant specifically argues that
4) “The Examiner asserts that "receiving data/messages over a network" and the other recited elements are "well-understood, routine, and conventional," primarily relying on case law involving generic client/server messaging and then taking Official Notice that the recited steps were conventional. See Office Action, page 15-16. Respectfully, this conclusion is unsupported by the record and is inconsistent with Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018), which holds that whether claim elements or their ordered combination are "well- understood, routine, and conventional" is a question of fact that must be supported by evidence. Here, the Office Action cites no evidence-no prior art, no factual determinations, and no supported rationale-to establish that the claimed combination was conventional..”
Examiner respectfully disagrees with Applicant’s fourth argument.
It is unclear what is meant by “the claimed combination”. The Examiner need not show that the whole claim, or every process/step of the invention was well-understood, routine, and conventional. Novelty or non-obviousness of any of the steps/formulas of the claim are not determinative of eligibility. See Diamond v. Diehr, 450 U.S. 175, 188-89, (1981 - the novelty of a process or its steps is not relevant to determining whether the claimed subject matter is patentable). Elec. Commc’n Techs., LLC v. ShoppersChoice.com, LLC, 958 F.3d 1178, 1183 (Fed. Cir. 2020 - “[E]ven taking as true that claim 11 is ‘unique,’ that alone is insufficient to confer patent eligibility [when] the purported uniqueness of claim 11... is itself abstract.”’); Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 1169 (Fed. Cir. 2019 – “merely reciting an abstract idea by itself in a claim—even if the idea is novel and non-obvious—is not enough to save it from ineligibility’’). The instant claims require “receiving, by the device and from a sensor associated with an item associated with the plurality of items, item data identifying the item”. Receiving item data identifying an item over a network (e.g., from a sensor) and by a device is data gathering, and involves receiving data (item id data) that is transmitted from an external sensor. 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. Applicant has not actually disputed Examiner’s Official Notice. Furthermore, the lack of technical detail/description in Applicant’s own specification provides implicit evidence that this step was well-understood, routine, and conventional.
Applicant proceeds to repeatedly refers to suggested embodiments discussed in the specification. However, limitations are not to be read into the claims, and the eligibility analysis is concerned with the claim language.
The element of “performing dimensionality reduction” is not an “additional” element in the claims. This is a data processing step, and one that a human being is capable of performing. Examiner need not cite evidence that this step was well-understood, routine, conventional (even though it was). That the dimensionally-reduced data is used to retain a model that is required to be some unspecified machine learning model provides nothing more than mere instructions to implement an abstract idea on a generic computer, and serves merely to generally link the use of the judicial exception to a particular technological environment or field of use.
v Applicant’s arguments, with respect to the rejection of claims 1-20 under 35 U.S.C. 101 for Double Patenting have been considered, but are not persuasive. The amended claims are still anticipated by at least one claim of US Patent No. 12,014,391 and by at least one claim of US Patent No. 11,676,167. See the updated rejection(s) below. The rejections of claims 1-20 under 35 U.S.C. 101 for Double Patenting have been maintained.
v Applicant’s arguments, with respect to the rejection of claims 1-20 under 35 U.S.C. §112(b) have been fully considered and are persuasive. There is now sufficient antecedent basis for the aforementioned claim elements. The rejections of claims 1-20 under 35 U.S.C. §112(b) have been withdrawn accordingly.
v Applicant’s arguments, with respect to the rejection of amended claims 1, 8, and 15 (as well as each of the dependent claims) under 35 U.S.C. §103 have been considered, but are not persuasive. Applicant again stated that “(t)he Examiner agreed that the proposed amendments, substantially included in this response, overcome the applied references”. This is not correct. The Examiner indicated that additional reconsideration would be required to determine, with certainty, whether the propose amendments would overcome the cited prior art.
Applicant further argues that “SMITH, ARIYIBI, and TKACHENKO do not disclose at least "modifying, by the device, the reward, based on a spending pattern associated with the customer, to generate a modified reward," as recited in amended claim 1. In rejecting claim 4 (which is partly incorporated into claim 1), the Office Action asserts that SMITH's paragraph 19 recites: "can utilize the machine learning model to analyze customer data ... to further tailor a generated discounts price and customized product notification to a customer." See Office Action, page 32. Accordingly, the Office Action asserts that "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." Id. Accordingly, at most, as discussed during the interview, SMITH may disclose generating rewards - e.g., discounted prices - based on customer data. However, as agreed during the interview, SMITH does not disclose modifying an identified reward of the customer, much less doing so based on a spending pattern of the customer.”
Examiner respectfully disagrees. Applicant has focused on certain embodiments disclosed by Smith, and ignored. Others. Furthermore, under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. Applicant’s specification explains that rewards data identifying rewards may include different percent discounts for purchases ([0027] of the as-file specification). Processing item data, the rewards data, and the customer data using the ML model to identify a reward may comprise identifying one of these rewards (e.g., a particular percent discount) – [0027]-[0028] of the as-file specification). Other embodiments appear to suggest dynamically generating a reward value (as does Smith) based on historical rewards value in general (i.e., where the rewards data identifying the rewards is simply historic rewards/values that were used for an item, not a list of presently available rewards for a particular item). Finally, Applicant’s specification merely suggests, at a high level, that an identified reward may be “modified”, such that the value of the reward is increased or decreased relative to an initial value ([0035], [0129]). This is based, in some way, on the customers spending patterns. There are no details regarding the modification process. The claims further do not limit how the modification is performed. A broadest reasonable interpretation of modifying an identified reward comprises changing, in some way, the identified reward (e.g., the value of the discount). This may comprise updating the identified reward (e.g., the percent discount) in some way (e.g., by selecting a new/updated value for the identified reward that is higher or lower than the initial value). Smith discloses this embodiment.
For example, Smith discloses an embodiment where the system obtains rewards data identifying rewards (e.g., a plurality of possible discount values) associated with items ([0079]-[0080] “ the machine-learning model can utilize the product data to determine the plurality of discount prices for the target product to use as input. To illustrate, the price management system 102 can identify an original price of the target product from the product data and then analyze a plurality of possible discount prices based on the original price. The price management system 102 can select the possible discount prices by selecting common price points (e.g., historical prices of products in the product category), specific increments of price points lower than the original price, or other criteria for minimizing the number of possible discount prices to analyze. After identifying the possible discount prices, the price management system 102 can use the possible discount prices as input to the machine-learning model to”). The system them processes the item data (i.e., data identifying an item), the rewards data (e.g., the plurality of possible discount values used as input), and the customer data (e.g., as customer-specific demographic input, or as merchant-level demographic information that is generated using the customer-specific demographic data) to identify a reward (e.g., a a discount value from the possible discount values having the highest probability of acceptance, or resulting in the lowest predicted loss) ([0080]-[0083] “After identifying the possible discount prices, the price management system 102 can use the possible discount prices as input to the machine-learning model to generate probabilities for each of the possible discount prices. In particular, the price management system 102 can use the machine-learning model to perform a separate analysis for each discount price to generate a probability of sale at the discount price. To illustrate, the price management system 102 can perform a first analysis on “Price 1” to generate a first probability of sale corresponding to “Price 1.” The price management system 102 can then perform a second analysis on “Price 2” to generate a second probability of sale corresponding to “Price 2.” The price management system 102 can similarly perform as many analyses as there are possible prices…the machine-learning model can learn that customers with larger families who have a higher purchasing frequency of milk are more likely to be interested in purchasing a gallon of milk that is closer to expiration than customers with smaller families and lower purchasing frequency of milk. The machine-learning model can use that information, to determine a probability that a gallon of milk with a given expiration date is likely to sell at a possible discount price. Additionally, the machine-learning model can use information about demographics of the merchant (e.g., whether the merchant has a higher number of customers with large families than customers with small families) to determine the probability…the machine-learning model outputs probabilities for different customer demographics to allow the price management system 102 to target different groups of customers with different discount prices. For example, the price management system 102 can use the machine-learning model to output a first probability for a first customer demographic and a second probability for a second customer demographic. The price management system 102 can use the different probabilities to target the first customer demographic with a first discount price and the second customer demographic with a second discount price. Thus, the price management system 102 can target a first group of customers with a first discount price and a second group of customers with a second discount price based on purchase habits of the different groups….In one or more embodiments, the machine-learning model outputs probabilities for different times of day or seasons based on the customer demographics that visit the merchant at those times. For instance, the price management system 102 can utilize shopping time as the customer data for training and applying a machine learning model and then generate a discount price based on the shopping time. Accordingly, the price management system can generate discount prices corresponding to daypart (e.g., morning, afternoon, evening), season, etc. In particular, the price management system 102 can generate different discount prices for the different groups based on daypart. Thus, the price management system 102 can flexibly change the discount price for a target product based on the time of day or time of year according to the customers that purchase product from the merchant during different times and that purchase products in the product category with certain shelf lives.”, see also [0086]-[0088] “the price management system 102 can use the probabilities and loss values to determine a discount price for the target product. The price management system 102 can thus attempt to minimize actual losses incurred by the merchant for listing the target product at discount price according to the probability of selling the target product at the discount price….the price management system 102 can utilize the machine-learning model to generate a plurality of possibilities for a plurality of possible discount prices. The price management system 102 can also predict losses associated with the possible discount prices based on the probabilities. The discount prices (as a percentage of the original listing price), example probabilities based on the discount prices, and predicted loss are shown in Table 1…the price management system 102 can determine a probability for each possible discount price. As one can appreciate, while Table 1 illustrates a plurality of probabilities, the probabilities shown are merely examples and can be different values according to product data, customer data, and training of the machine-learning model. The price management system 102 can then use the probability for each discount price to determine a loss that is based on the cost, probability, and discount price. Based on the losses for the discount prices, the price management system 102 can select a discount price for the target product that minimizes the loss to the merchant (e.g., a discount price of 60% in Table 1 with a loss of $200).” – therefore the system identifies a reward based on the rewards data associated with the item).
Furthermore, Smith suggests an embodiment where the system can update the identified reward based on changes to customer data ([0095] “ if a customer's purchase habits change during the duration of the pricing model (e.g., prior to the expiration date of the target product), the price management system 102 can input the new customer data into the machine-learning model to update the probabilities and select new discount price(s), if applicable.”), which reads on a broadest reasonable interpretation of modifying the identified reward (e.g., because the discount price is updated such that it is increased or decreased relative to the initially determined amount), based on new customer data associated with the customer. Smith further clarifies that the customer data may include spending patterns ([0037] “As used herein, the term “customer data” refers to descriptive information of a customer associated with a merchant. Specifically, customer data can include, but is not limited to, purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price), consumption rate…”). As such, Smith suggests an embodiment where the system modifies the identified reward (e.g., by updating the discount price such that it is increased or decreased relative to the initially determined amount), based on new customer spending pattern data associated with the customer.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 11, 2025 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…rewards data identifying rewards associated with the plurality of items;
obtaining…and based on a customer joining a rewards program, customer data identifying a customer;
receiving…item data identifying the item
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;
modifying…the reward, based on a spending pattern associated with the customer, to generate a modified reward
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 the 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 modified 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, modifying the reward, 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) and customer data and rewards data (identifying rewards associated with the plurality of items) and using a model, modifying the reward based on a spending pattern associated with the customer, 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 “modifying…the reward, based on a spending pattern associated with the customer, to generate a modified reward” (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 and from a sensor associated with an item associated with the plurality of items… 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)
“from a sensor associated with an item associated with the plurality of items” (claims 8 and 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 and from a sensor associated with an item associated with the plurality of items… 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 “from a sensor associated with an item associated with the plurality of items” (claims1, 8 and 15) and/or “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 computing environments, such as distributed computing environments and/or the internet, where information is represented digitally, exchanged between computers/sensors 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…rewards data identifying rewards associated with the plurality of items” (claims 1, 8, and 15) and/or “obtaining…and based on a customer joining a rewards program, customer data identifying a customer” (claims 1, 8, and 15) and/or “receiving…item data identifying the item” (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 and from a sensor associated with an item associated with the plurality of items… 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 “from a sensor associated with an item associated with the plurality of items” (claims1, 8 and 15) and/or “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…rewards data identifying rewards associated with the plurality of items” (claims 1, 8, and 15) and/or “obtaining…and based on a customer joining a rewards program, customer data identifying a customer” (claims 1, 8, and 15) and/or “receiving…item data identifying the item” (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).
For the sake of expediting prosecution, and as indicated in the interview held on November 6, 2025, Examiner recommends positively reciting a specific configuration of sensors/devices (e.g., shelf sensor, cart sensor, smart card, user mobile device, server, etc.) and using this particular configuration of sensors/devices to obtain the data that is then used/analyzed to generate the rewards and to train/retrain the model.
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. 11,676,167 (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. 11,676,167. 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, rewards data identifying rewards associated with the plurality of items; (([0079]-[0080] “ the machine-learning model can utilize the product data to determine the plurality of discount prices for the target product to use as input. To illustrate, the price management system 102 can identify an original price of the target product from the product data and then analyze a plurality of possible discount prices based on the original price. The price management system 102 can select the possible discount prices by selecting common price points (e.g., historical prices of products in the product category), specific increments of price points lower than the original price, or other criteria for minimizing the number of possible discount prices to analyze. After identifying the possible discount prices, the price management system 102 can use the possible discount prices as input to the machine-learning model to” – therefore Smith discloses an embodiment where the system obtains rewards data identifying rewards (e.g., a plurality of possible discount values) associated with items, see also [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”)
obtaining, by the device, 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 server/device 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)
receiving, by the device and from a sensor associated with an item associated with the plurality of items, item data identifying the item, of a plurality of items ([0110] “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, checkout scanner, IoT sensor, or other device. For example, the price management system 102 can use a device to detect that a customer has placed the target product in a smart cart…by utilizing a checkout scanner or IoT sensor…” – therefore the device (e.g., price management system device) receives from a sensor (e.g., IoT sensor, smart cart sensor) associated with an item associated with the plurality of items (e.g., because it senses the item, consistent with Applicant’s own published disclosure at [0019]-[0020] where a shelf/cart sensor can send the item data and is “associated with the item” insofar as it is used to sense the item) item data identifying the item of the plurality of items, [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” – smart cart instrument used to detect the RFID tag/code is a sensor used to obtain the item data identifying the item which is then sent to the system device/server, [0107] “the price management system 102 can use precise location data from the customer client device 500 (e.g., via Bluetooth location detection, cameras, wireless sensors, beacons, IoT devices, or other sensors that allow the price management system 102 to track the location of the customer within the store via the customer client device 500 or a smart cart) to identify products that are within a geo-fence of the customer)
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; ([0080]-[0083] “After identifying the possible discount prices, the price management system 102 can use the possible discount prices as input to the machine-learning model to generate probabilities for each of the possible discount prices. In particular, the price management system 102 can use the machine-learning model to perform a separate analysis for each discount price to generate a probability of sale at the discount price. To illustrate, the price management system 102 can perform a first analysis on “Price 1” to generate a first probability of sale corresponding to “Price 1.” The price management system 102 can then perform a second analysis on “Price 2” to generate a second probability of sale corresponding to “Price 2.” The price management system 102 can similarly perform as many analyses as there are possible prices…the machine-learning model can learn that customers with larger families who have a higher purchasing frequency of milk are more likely to be interested in purchasing a gallon of milk that is closer to expiration than customers with smaller families and lower purchasing frequency of milk. The machine-learning model can use that information, to determine a probability that a gallon of milk with a given expiration date is likely to sell at a possible discount price. Additionally, the machine-learning model can use information about demographics of the merchant (e.g., whether the merchant has a higher number of customers with large families than customers with small families) to determine the probability…the machine-learning model outputs probabilities for different customer demographics to allow the price management system 102 to target different groups of customers with different discount prices. For example, the price management system 102 can use the machine-learning model to output a first probability for a first customer demographic and a second probability for a second customer demographic. The price management system 102 can use the different probabilities to target the first customer demographic with a first discount price and the second customer demographic with a second discount price. Thus, the price management system 102 can target a first group of customers with a first discount price and a second group of customers with a second discount price based on purchase habits of the different groups….In one or more embodiments, the machine-learning model outputs probabilities for different times of day or seasons based on the customer demographics that visit the merchant at those times. For instance, the price management system 102 can utilize shopping time as the customer data for training and applying a machine learning model and then generate a discount price based on the shopping time. Accordingly, the price management system can generate discount prices corresponding to daypart (e.g., morning, afternoon, evening), season, etc. In particular, the price management system 102 can generate different discount prices for the different groups based on daypart. Thus, the price management system 102 can flexibly change the discount price for a target product based on the time of day or time of year according to the customers that purchase product from the merchant during different times and that purchase products in the product category with certain shelf lives.” – therefore the system processes the item data (i.e., data identifying an item), the rewards data (e.g., the plurality of possible discount values used as input), and the customer data (e.g., as customer-specific demographic input, or as merchant-level demographic information that is generated using the customer-specific demographic data) to identify a reward (e.g., a a discount value from the possible discount values having the highest probability of acceptance, or resulting in the lowest predicted loss), [0086]-[0088] “the price management system 102 can use the probabilities and loss values to determine a discount price for the target product. The price management system 102 can thus attempt to minimize actual losses incurred by the merchant for listing the target product at discount price according to the probability of selling the target product at the discount price….the price management system 102 can utilize the machine-learning model to generate a plurality of possibilities for a plurality of possible discount prices. The price management system 102 can also predict losses associated with the possible discount prices based on the probabilities. The discount prices (as a percentage of the original listing price), example probabilities based on the discount prices, and predicted loss are shown in Table 1…the price management system 102 can determine a probability for each possible discount price. As one can appreciate, while Table 1 illustrates a plurality of probabilities, the probabilities shown are merely examples and can be different values according to product data, customer data, and training of the machine-learning model. The price management system 102 can then use the probability for each discount price to determine a loss that is based on the cost, probability, and discount price. Based on the losses for the discount prices, the price management system 102 can select a discount price for the target product that minimizes the loss to the merchant (e.g., a discount price of 60% in Table 1 with a loss of $200).” – therefore the system identifies a reward based on the rewards data associated with the item, see also [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 [0110], see [0038]-[0040] for possible types of ML models)
modifying, by the device, the reward, based on a spending pattern associated with the customer, to generate a modified reward ([0095] “ if a customer's purchase habits change during the duration of the pricing model (e.g., prior to the expiration date of the target product), the price management system 102 can input the new customer data into the machine-learning model to update the probabilities and select new discount price(s), if applicable.” – therefore Smith suggests an embodiment where the system can update the identified reward based on changes to customer data which reads on a broadest reasonable interpretation of modifying the identified reward (e.g., because the discount price is updated such that it is increased or decreased relative to the initially determined amount), based on new customer data associated with the customer. Smith further clarifies that the customer data may include spending patterns at [0037] “As used herein, the term “customer data” refers to descriptive information of a customer associated with a merchant. Specifically, customer data can include, but is not limited to, purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price), consumption rate…”). As such, Smith suggests an embodiment where the system modifies the identified reward (e.g., by updating the discount price such that it is increased or decreased relative to the initially determined amount), based on new customer spending pattern data associated with the customer.)
retraining, by the device, the machine learning model based on the modified 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 and or (where applicable) modified reward (e.g., original/updated 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 minimum feature set, and training the machine learning model based on the minimum 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 minimum feature set (what dimensionality algorithms do) and retrains the model using the minimum 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, customer data identifying the customer;
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, customer data identifying the customer ([0006]-[0007] “the customer has elected to share customer information with the retailer…offering the enhances shopping experience. Customer information from the database is then made accessible… 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 device/server receives customer information based on the customer joining a rewards program (e.g., because they earn price discounts and other loyalty rewards for electing to share), [0020]-[0023] “customer is issued a loyalty card…able to retrieve individual customer information such as the customer's name, size, favorite colors, personal style preferences, etc….customer initiates the process by registering in a retailer’s customer loyalty program…enters certain personal data ad shopping preferences…customer loyalty service identifier” therefore the device/server receives customer information based on the customer joining a rewards program , [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…”, [0029]-[0030] “ The CLSI (RFID based personal identifier card, device or other form factor) is equivalent to a web browser cookie and single sign-on customer profile; the CLSI stores individual shopping preferences, facilitating instant customer recognition and multi platform loyalty rewards and personalized shopping experience and benefits After registering, the customer may receive customized alerts and links pointing to sales and events relevant to her shopping preferences. In an embodiment, the customer may also input shopping lists which will be transmitted to participating stores that may be able to fulfill the request”, [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, customer data identifying the customer”, because doing so can ensure the user’s personal data 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, customer data identifying the customer”, 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, customer data identifying the customer”, 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 modified reward for the customer based on spending patterns (([0095] “ if a customer's purchase habits change during the duration of the pricing model (e.g., prior to the expiration date of the target product), the price management system 102 can input the new customer data into the machine-learning model to update the probabilities and select new discount price(s), if applicable.” – therefore Smith suggests an embodiment where the system can update the identified reward based on changes to customer data which reads on a broadest reasonable interpretation of modifying the identified reward (e.g., because the discount price is updated such that it is increased or decreased relative to the initially determined amount), based on new customer data associated with the customer. Smith further clarifies that the customer data may include spending patterns at [0037] “As used herein, the term “customer data” refers to descriptive information of a customer associated with a merchant. Specifically, customer data can include, but is not limited to, purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price), consumption rate…”). As such, Smith suggests an embodiment where the system modifies the identified reward (e.g., by updating the discount price such that it is increased or decreased relative to the initially determined amount), based on new customer spending pattern data associated with the customer., see also [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 modified 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: determining the spending pattern associated with the customer (
([0037] “As used herein, the term “customer data” refers to descriptive information of a customer associated with a merchant. Specifically, customer data can include, but is not limited to, purchasing habits (e.g., frequency, recency), price sensitivity (i.e., how heavily a customer is influenced by price), consumption rate…”. As such, Smith suggests determining the customers spending habits/frequency/recency/sensititvity (i.e., 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…”, [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 modified 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 modified 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 modified 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 modified 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 modified 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