Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
This final office action is in response to the communication filed on 8/10/2026. Claims 1, 8, and 15 have been amended. Claims 1-20 are currently pending and have been examined below.
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined 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 § 2146 et seq. 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/patent/patents-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 www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11836755 and claims 1-20 of U.S. Patent No. 12265983. Although the claims at issue are not identical, they are not patentably distinct from each other because independent patent claims 1, 8, and 15 of U.S. Patent No. 11836755 and independent claims 1, 8, and 15 of U.S. Patent No. 12265983 recite all of the limitations of independent claims 1, 8, and 15 of the present claims, in addition to reciting additional imitations that further limit the patented claims. Furthermore, claims 1-20 of U.S. Patent No. 11836755 and claims 1-20 of U.S. Patent No. 12265983 recite limitations similar in scope to all of the limitations of dependent claims 2-7, 9-14, and 16-20 of the present claims. Therefore claims 1-20 of U.S. Patent No. 11836755 and claims 1-20 of U.S. Patent No. 12265983 are in essence “species” of the generic invention of claims 1-20 of the present application. It has been held that a generic invention is "anticipated" by a "species" within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). A representative mapping between claim 1 of US Patent Number 12265983 and claim 1 of the presentation application is provided below.
US Patent Number 12265983 - Claim 1/15 + dependent claim 19
Application Number 19/049,067 – Claim 1
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
one or more memories; and one or more processors, coupled to the one or more memories, configured to:
obtain, from a client device associated with a customer, item data identifying a price of an item to be purchased by the customer and customer data identifying the customer, wherein the item data is received by a transaction card from a price tag of the item and via wireless communication with the price tag
obtain, from a user device, item data identifying a price of an item to be purchased,
wherein the item data is received via wireless communication with a tag of the item;
process the item data, price data, and other data, with a machine learning model, to identify an optimal price for the item relative to multiple prices associated with the item,
process the item data, price data, and other data, with a machine learning model, to identify an optimal price for the item relative to multiple prices associated with the item,
wherein the price data identifies prices associated with a plurality of items
wherein the price data identifies prices associated with a plurality of items,
wherein the price data identifies prices associated with a plurality of items,
wherein the multiple prices are included in the prices associated with the plurality of items,
wherein the other data identifies locations, availabilities, and terms associated with the plurality of items
wherein the other data identifies locations and availabilities associated with the plurality of items,
wherein the machine learning model is trained based on historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations, historical availabilities, and historical terms associated with the plurality of items
wherein the machine learning model is trained based on historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations and historical availabilities associated with the plurality of items
cause, based on identifying the optimal price, an indicator associated with the transaction card to be triggered based on how the price of the item provided on the price tag compares to the optimal price.
cause an indicator to be triggered based on how the price of the item compares to the optimal price
obtaining the price data, wherein obtaining the price data is based on performing a crawl of a data source associated with the plurality of items (NOTE: from dependent claim 19)
wherein the device scrapes information from websites on prices of currently available items at one or more locations
Claim Rejections – 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Per step 1 of the eligibility analysis set forth in MPEP § 2106, subsection III, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, Claim 15 recites specific limitations which fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows:
obtaining, item data identifying a price of an item to be purchased;
processing, the item data, price data, and other data, to identify an optimal price for the item relative to multiple prices associated with the item,
wherein the price data identifies prices associated with a plurality of items,
wherein the multiple prices are included in the prices associated with the plurality of items ,
wherein the other data identifies locations and availabilities associated with the plurality of items,
causing, an indicator to be triggered based on how the price of the item compares to the optimal price.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations recite determining an optimal price and alerting a user based on how the price of an item compares to an optimal price which is a marketing/sales activity hat falls within the certain methods of organizing human activities grouping. Additionally, the steps above also fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Specifically, a human being can mentally (or with pen and paper) identify an optimal price based on item data, price data, and other data, and compare the price of an item to an optimal price. Accordingly claim 1 recites an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Claim 15 recites the additional limitations of:
[obtaining] by a device and from a user device [item data . . .], wherein the item data is received via wireless communication with a tag;
[processing] by the device [the item data, price data and other data] with a machine learning model [to identify an optimal price];
wherein the device scrapes information from websites on prices of currently available items at one or more locations;
wherein the machine learning model is trained based on [historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations and historical availabilities associated with the plurality of items];
[causing] by the device [an] indicator to be triggered.
The additional limitations when viewed individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, do not integrate the abstract idea into a practical application because each of the additional elements are recited at high level of generality implementing the abstract idea on a computer (i.e. apply it) or generally linking the use of the judicial exception to a particular technological environment. Specifically:
With respect [obtaining] by a device and from a user device [item data . . .], wherein the item data is received via wireless communication with a tag, Examiner notes that this limitation is recited at a high level of generality receiving data via wireless communication with a tag. Applicant’s specification recites that “communicating wirelessly (e.g., via Bluetooth®, Bluetooth Low Energy (BLE), near-field communication (NFC), WiFi, and/or the like) with other devices, such as client device . . . an NFC attached to a price tag.” Receiving data wirelessly via a variety of generic wirelessly communication standards such as NFC or Bluetooth merely generally links the abstract idea to a particular technological environment and does not integrate the abstract idea into a practical application. Alternatively, this limitation is at most insignificant extra-solution activity (i.e., pre-solution data gathering) gathering item data from a tag using generic wireless technology such as NFC.
With respect to the limitations [processing] by the device [the item data, price data and other data] with a machine learning model [to identify an optimal price]; and wherein the machine learning model is trained based on [historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations and historical availabilities associated with the plurality of items]; Examiner notes that these limitations are recited at a high level of generality. Applicant’s published specification paragraph [0075] recites that “machine learning model may employ a different machine learning algorithm than what is described in connection with FIG. 3, such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm, an a priori algorithm, a k-means algorithm, a support vector machine algorithm, a neural network algorithm (e.g., a convolutional neural network algorithm), a deep learning algorithm, and/or the like.” The independent claims do not recite what type of model is used or how the model is trained beyond specifying at a high level the various inputs used to train the model. Furthermore, no improvements to the underlying machine learning models are disclosed in the claims or specification. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). At this level of generality, the recitation of claim limitations that attempt to cover any solution to an identified problem (i.e. training a machine learning model to identify an optimal [price) merely generally links the abstract idea to a technical field/environment, namely a generic computing environment applying generic machine learning. Further, Examiner notes that Recentive Analytics, Inc. v. Fox Corp. et al., No. 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025) recently held that claims “that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.” Here, Examiner takes the position that utilizing a generic machine learning to train a model to identify an optimal price is the mere application of generic machine learning to a new data environment. Because no improvement to the underlying machine learning models is disclosed, this limitation does not integrate the abstract idea into a practical application.
With respect to wherein the device scrapes information from websites on prices of currently available items at one or more locations, Examiner notes that limitation is recited at a high level of generality and merely generally links the abstract idea to a particular technological environment (i.e., the internet) or at most amounts to insignificant extra solution activity (data gathering).
With respect to [causing] by the device [an] indicator to be triggered, Examiner notes that limitation is recited at a high level of generality causing a generic indicator on a generic device to be triggered (e.g., displaying data on a screen) and therefore merely generally links the abstract idea to a particular technological environment or merely utilizing a computer as a tool to perform the abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and only generally link the use of the judicial exception to a particular technological environment. Thus, the same analysis applies here in 2B, i.e., mere instructions to apply an exception is a particular technological environment cannot provide an inventive concept.
Additionally, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be reevaluated in Step 2B. Here, the limitation with respect to [obtaining] by a device and from a user device [item data . . .], wherein the item data is received via wireless communication with a tag was analyzed as extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. As noted above, Applicant’s specification recites that “communicating wirelessly (e.g., via Bluetooth®, Bluetooth Low Energy (BLE), near-field communication (NFC), WiFi, and/or the like) with other devices, such as client device . . . an NFC attached to a price tag.” The high-level wireless receipt of product data from a price tag (e.g., by using NFC) is well-understood, routine, conventional activity as evidenced by US Patent Application Publication Number 20150225101 (“Van”) paragraph [0071] which recites “NFC technology is well known in the art and there are various NFC tag/reader combinations and systems available.” Accordingly, a conclusion that wirelessly obtaining data from a tag is well-understood, routine, conventional activity is supported under Berkheimer Option 3.
Further, the limitation wherein the device scrapes information from websites on prices of currently available items at one or more locations was analyzed as extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. Scraping/crawling item price information from websites is well-understood, routine, conventional activity as evidenced by US Patent Number 8108271 (“Duncan”) [col. 13, lines 40-50] which recites “price of the inventory item is scraped from one or more of the web sites associated with the watch URLs by methods well known to those of skill in the art” and US Patent Application Publication Number 20140358629 (“Shivaswamy”) paragraph [0033] which recites “[t]he crawling module may crawl such product listing pages of a competitor retailer website using any techniques known by those skilled in the art.” Accordingly, a conclusion that scraping/crawling item price information from websites is well-understood, routine, conventional activity is supported under Berkheimer Option 3. All of the additional elements when considered individually or in combination are not significantly more than the abstract idea. Therefore, the independent claims are not patent eligible.
Alice Corp. also establishes that the same analysis should be used for all categories of claims (e.g., product and process claims). Therefore, the device in claim 1 and non-transitory computer readable medium in claim 8 are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 15. The additional limitations in claim 1 (i.e., one or more processors and memories) and the additional limitations of claim 8 (i.e., a non-transitory computer readable medium) add nothing of substance to the underlying abstract idea. The components are merely providing a particular technological environment to implement the abstract idea.
Dependent claims 2-7, 9-14, and 16-20 merely further narrow the abstract idea and/or generally link the abstract idea to a particular technological environment / apply it and therefore do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
Claims 1, 3, 8, 10, 15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication Number 20190272557 (“Smith”) United States Patent Application Publication Number 20140207604 (“Morgan”) in view of US Patent Application Publication Number 20140358629 (“Shivaswamy”).
Claims 1, 8, and 15
As per claims 1, 8, and 15, Smith teaches a method, device, and non-transitory computer readable medium comprising:
one or more memories ([0142] “one or more processors and system memory”; and
one or more processors ([0121] “one or more processors and system memory”) communicatively coupled to the one or more memories, configured to:
obtain, from a user device, item data identifying a price of an item to be purchased ([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.” And, [0110] “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.” And, [0036] “product data can include . . . price information.” And, [0079] “identify an original price of the target product from the product data.” And, [0097] “user interfaces of customer client devices by which customers can receive indications of discount prices for one or more target products.”);
wherein the item data is received via wireless communication with a tag of the item ([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.” And, [0110] “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 . . . The price management system can then make a determination that another target product within the same product category has a lower discount price.” Examiner interprets reading information from an RFID tag as wireless communication.);
process the item data, price data, and other data, with a machine learning model, to identify an optimal price for the item relative to multiple prices associated with the item ([0089] “utilize the machine-learning model to provide different discount prices for different customers or to determine the best price to provide to one or more customers from a plurality of discount prices.” And, [0012] “machine-learning model for dynamically determining price discounts for a target product based on product and customer history data for a customer.” And, [0019] “machine-learning model based on the product data and customer data” where [0029] teaches “product data (e.g., data from merchants indicating inventory, sale history, and expiration dates).” And, [0036] “product data can include . . . a price . . . inventory data, and location/store information.”);
wherein the price data identifies prices associated with a plurality of items ([0065] “price management system can identify product data for a target product” which “can include . . . price information for the target product (e.g., standard price of the products in the product category, possible prices of the target product, cost of the target product to the merchant, prices of related product categories.”);
wherein the multiple prices are included in the prices associated with the plurality of items ([0065] “price management system can identify product data for a target product” which “can include . . . price information for the target product (e.g., standard price of the products in the product category, possible prices of the target product, cost of the target product to the merchant, prices of related product categories.”);
wherein the other data identifies locations and availabilities associated with the plurality of items ([0065] “price management system can identify product data for a target product” which “can include . . . price information for the target product (e.g., standard price of the products in the product category, possible prices of the target product, cost of the target product to the merchant, prices of related product categories.” And, [0115] “the price management system can present the discount price and product information (e.g., size, availability).” And, [0044] “product management system to track inflow and outflow of products in the product inventory as the merchant sells.” And, [0036] “product data can . . . inventory data, and location/store information.”);
wherein the machine learning model is trained based on historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations, historical availabilities, and historical terms associated with the plurality of items ([0020] “utilize a machine-learning model trained using the historical data to generate a prediction of a sale of the target product at a discount price.” And, [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.” And, [0039] “a neural network can train itself using product history data (and in some cases customer history data) to identify whether products with specific expiration dates sold at specific prices.” And, [0036] “product data can include . . . a product category, an expiration date, price information (e.g., current prices of products in the product category, a price history indicating a history of prices for products in the product category), sale/purchase information, inventory data, and location/store information . . . ‘product history data’ refers to historical product data associated with previously available products or previously sold products in a product category, including a purchase history for other products in the product category. Additionally, product history data can refer to global product history data associated with products at a plurality of stores of a merchant (e.g., globally across all stores of the merchant).” And, [0046] “product history data indicating product purchase information, expiration dates, and other information associated with products previously available for purchase from the merchant.”).
Smith teaches identifying an optimal price but does not explicitly teach the following feature taught by Morgan:
cause an indicator to be triggered based on how the price of the item compares to the optimal price ([0012] “Comparison shopping can even be possible by scanning a particular product in a retail store and searching to find more competitive pricing in nearby stores . . . automatically do a price comparison and alert the user, such as through a pop-up window, that the item can be purchased at a cheaper price elsewhere.” And, [0019] “a message alert is then provided to the consumer. The message alert can be provided in a variety of manners such as a pop-up window on a computer screen, a text message on a mobile phone device or through other interactive manners including sounds.” Examiner interprets a pop-up, message, or sound alert as the indicator triggered by the price comparison).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify Smith to include cause an indicator to be triggered based on how the price of the item compares to the optimal price as taught by Morgan in order to give merchants “a competitive edge over their competitors by offering the best deal possible . . . without having to decrease or increase their price unnecessarily” (Morgan [0002]).
Smith does not explicitly teach but Shivaswamy teaches:
wherein the device scrapes information from websites on prices of currently available items at one or more locations ([0029] “system may then crawl other retailer websites (e.g., competitor retailer websites) to detect and monitor competitor prices for those products that are available for sale.” And [0031] “system may crawl for competitor prices at variable/adjustable time intervals, based on different products in the inventory of the home retailer website. For example, for high demand products on the home retailer website (e.g., the top X % selling products on eBay), prices may be monitored at competitor sites and marked in semi-real time (e.g., every few hours, since many competitors change prices for popular items multiple times a day).” And, [0033] “product listing webpage of a competitor retailer website that describes a particular product P0 (e.g., a smartphone) for sale on the Competitor retailer website, and includes an image of the product P0 and various descriptive information about the product P0 (e.g., title, description, attributes, characteristics, properties, specifications, price, location, etc.). The crawling module 202 may crawl such product listing pages of a competitor retailer website using any techniques known by those skilled in the art.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith and Morgan to include wherein the device scrapes information from websites on prices of currently available items at one or more locations as taught by Shivaswamy in order to “allow inventory items to be compared against each other to determine whether one item really is a ‘special’ relative to other inventory items” (Shivaswamy [0049]).
Claims 3, 10, and 17
As per claims 3, 10, and 17 Smith further teaches:
wherein the tag is a price tag ([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.” And, [0051] “prices to products using printed/physical price tags, digital prices tags.” And, [0118] “dynamic price tags that provide dynamic discount prices at in-store locations . . . [a] dynamic price tag may include a digital price tag in communication with the price management system.”).
Claims 2, 7, 9, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable United States Patent Application Publication Number 20190272557 (“Smith”) United States Patent Application Publication Number 20140207604 (“Morgan”) in view of US Patent Application Publication Number 20140358629 (“Shivaswamy”) as applied to claims 1, 8, and 15 above, and in further view of US Patent Number 10296907 (“Nolte”).
Claim 2, 9, and 16
As per claim 2, 9, and 16, Smith does not explicitly teach but Nolte teaches:
wherein a color of the indicator is triggered based on how the price of the item compares to the optimal price ([col. 4, lines 8-11] “the color or the flashing pattern on the LED/LCD lights can change, depending on how good the offer is.” And, [Col. 2, lines 33-37] “the visual indication mechanism (e.g., based on display screen or flashing lights) on the user's card can notify the user of future offers and deals” and that [Col. 3, lines 35-40] “user can be notified of the redeemable deal by flashing lights or a message on the credit card. Such notification can result in greater utilizations of the financial instrument by users who wish to avail themselves of these offers.").
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith, Morgan, and Shivaswamy to include wherein a color of the indicator is triggered based on how the price of the item compares to the optimal price as taught by Nolte in order to “result in greater utilizations of the financial instrument by users who wish to avail themselves of these offers" (Col. 3, lines 35-40) leading to increased sales.
Claims 7 and 14
As per claims 7 and 14, Smith does not explicitly teach but Nolte teaches:
wherein the indicator is triggered based on whether a rebate is available for the item ([col. 12, lines 15-20] “GUI that displays various promotional offers available for a user. Information relating to these offers can come up from one or more merchant locations. For example, region shows a rebate of $10.88. Region shows an offer from a merchant location ‘Ross Stores.’” And, [Col. 2, lines 33-37) “the visual indication mechanism (e.g., based on display screen or flashing lights) on the user's card can notify the user of future offers and deals” and that (Col. 3, lines 35-40) “user can be notified of the redeemable deal by flashing lights or a message on the credit card. Such notification can result in greater utilizations of the financial instrument by users who wish to avail themselves of these offers.”).
Therefore, it would have been obvious to modify the combination of Smith, Morgan, and Shivaswamy to include wherein the indicator is triggered based on whether a rebate is available for the item as taught by Nolte in order to “result in greater utilizations of the financial instrument by users who wish to avail themselves of these offers" (Col. 3, lines 35-40) leading to increased sales.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable United States Patent Application Publication Number 20190272557 (“Smith”) United States Patent Application Publication Number 20140207604 (“Morgan”) in view of US Patent Application Publication Number 20140358629 (“Shivaswamy”) as applied to claims 1, 8, and 15 above, and in further view of US Patent Application Publication Number 20100250333 (“Agrawal”).
Claim 4, 11, and 18
As per claim 4, 11, and 18, Smith further teaches:
receive, from the user device, transaction data indicating a purchase of the item ([0037] “local customer history data associated with customers.” And, [0046] “obtain customer history data indicating past customer purchase habits of a plurality of customers.” And, [0036] “a history of purchases of products in a product category by one or more customers.” And, [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).”);
receive [discount] data identifying [discounts] associated with a plurality of items and process the item data and the [discount data], with the machine learning model, to identify an optimal [discount] for the item relative to multiple [discounts] associated with the item ([0089] “utilize the machine-learning model to provide different discount prices for different customers or to determine the best price to provide to one or more customers from a plurality of discount prices.” And, [0012] “machine-learning model for dynamically determining price discounts for a target product based on product and customer history data for a customer.” And, [0019] “machine-learning model based on the product data and customer data” where [0029] teaches “product data (e.g., data from merchants indicating inventory, sale history, and expiration dates).” And, [0036] “product data can include . . . a price . . . inventory data, and location/store information.”);
wherein the multiple [discounts] are included in the [discounts] associated with the plurality of items ([0065] “price management system can identify product data for a target product” which “can include . . . price information for the target product (e.g., standard price of the products in the product category, possible prices of the target product, cost of the target product to the merchant, prices of related product categories.”);
process a request for the optimal [discount] to generate a completed [discount] for the item ([0110] “The [PMS] system can then allow the customer to select the best target product to meet the customer's desires based on the expiration date(s) and discount price(s).” And, [0116] “the discount section can also include an option to add the target product at the preferred store location to a shopping cart” and “adding the target product to the shopping cart and purchasing the target product.” And, [0061] “The [PMS] can also receive actual sales data for the products after the products are sold . . . When the [PMS] receives feedback corresponding to the predictions for the products, the machine-learning model can use the feedback to update the loss function.”).
Smith teaches generating an optimal discount for an item but does not teach the discount is in the form of a rebate as taught by Agrawal ([0005] “The optimal sales rebate rate is then determined for each of the products/services.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith, Morgan, and Shivaswamy to include that the discount is in the form of a rebate as taught Agrawal by in order to use "feedback data from cashback operations [as] a means to maximize sales or profits" (Agrawal [0006]).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable United States Patent Application Publication Number 20190272557 (“Smith”) United States Patent Application Publication Number 20140207604 (“Morgan”) in view of US Patent Application Publication Number 20140358629 (“Shivaswamy”) in view of US Patent Application Publication Number 20100250333 (“Agrawal”) as applied to claims 4, 11, and 18 above, and in further view of US Patent Publication Number 10206098 (“Sivaganesh”).
Claims 5, 12, and 19
As per claim 5, 12, and 19, Smith further teaches:
receive, from the user device, information identifying a network address of the user device ([0113] “price management system can detect the customer location (e.g., based on IP address or user profile information).”).
Smith does not explicitly teach but Agrawal teaches:
process the request for the optimal rebate using the customer data ([0016] “determine optimal sales rebates.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith, Morgan, and Agrawal to include process the request for the optimal rebate using the customer data as taught Agrawal by in order to use "feedback data from cashback operations [as] a means to maximize sales or profits" (Agrawal [0006]).
Smith does not explicitly teach but Sivaganesh teaches:
obtain, from a data structure and using the network address, customer data associated with a customer, wherein the customer data includes contact information for the customer ([col. 2, lines 20-25] “identify an MDN of the mobile device based on an IP address contained in the IP packet.” And, [col. 3, lines 40-50] “[t]he customer account record database finds the mobile subscriber account record based on the mobile subscriber account record identifier and responds to the query with information from the account record for the owner of the mobile device . . . information includes one or more of: a first and last name of the owner of the mobile device, a street address of the owner, a phone number of the owner and/or the last four digits of the social security number of the owner.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith, Morgan, Shivaswamy, and Agrawal to include obtain, from a data structure and using the network address, customer data associated with a customer, wherein the customer data includes contact information for the customer as taught by Sivaganesh so that “relevant data for the originator may then be automatically populated to assist in completion of an e-commerce transaction” (Sivaganesh [col 2, lines 8-11]).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable United States Patent Application Publication Number 20190272557 (“Smith”) United States Patent Application Publication Number 20140207604 (“Morgan”) in view of US Patent Application Publication Number 20140358629 (“Shivaswamy”) in view of US Patent Application Publication Number 20100250333 (“Agrawal”) as applied to claims 4, 11, and 18 above, and in further view of US Patent Application Publication Number 20100161399 (“Posner”).
Claims 6, 13, and 20
As per claim 6, 13, and 20, Smith further teaches does not explicitly teach but Posner teaches:
provide, to the user device, the completed rebate and a link to a network location to which the completed rebate is submitted ([0052] “the rebate request was submitted into the system.” And, [0055] “incentive server 402 then records the rebate transaction as complete.” And, [0057] “create (a new) or access (an existing) cashback account to link this transaction and user to that account so that this rebate can be paid out through such account.” And, [0056] “user may receive a number of rebates before clicking on the link to link the two required accounts together.”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of applicant's claimed invention to modify the combination of Smith, Morgan, Shivaswamy, and Agrawal to include obtain, from a data structure and using the network address, customer data associated with a customer, wherein the customer data includes contact information for the customer as taught by Posner in order to “affirm that the account also exists and is a true valid account (e.g., not fictitious or fraudulent)” (Posner [0076]).
Response to Arguments
Double Patenting
Applicant's arguments, see page 10, filed 8/10/2026 with respect to the rejection(s) of claims 1-20 under non-statutory obviousness-type double patenting been fully considered but are not persuasive.
Examiner notes that the claim amendments are of similar scope to the subject matter in dependent claims 3 and 19 of US Patent Number 1836755 and US Patent Number 12265983 respectively. Therefore the argument is respectfully found to be unpersuasive.
35 U.S.C. 103
Applicant's arguments, see pages 11-15, filed 8/10/2026, with respect to the rejection(s) of claims
1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore the rejections have been withdrawn. However, a new ground of rejection has been made in view of Smith, Morgan, and Shivaswamy.
35 U.S.C. 101
Applicant's arguments, see pages 11-15, filed 8/10/2026 with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
First, Applicant argues:
Applicant respectfully submits that the Examiner's characterization of the claims reciting "a method of organizing a human activity" is incorrect. For example, Applicant respectfully submits that the claims themselves do not a method of organizing a human activity, at least because claim 1 recites a variety of non-human activities including "one or more processors, coupled to the one or more memories, configured to:" "obtain, from a user device, item data identifying a price of an item to be purchased, wherein the item data is received via wireless communication with a tag of the item," "process the item data, price data, and other data, with a machine learning model, to identify an optimal price for the item relative to multiple prices associated with the item, wherein the price data identifies prices associated with a plurality of items, wherein the multiple prices are included in the prices associated with the plurality of items, wherein the other data identifies locations and availabilities associated with the plurality of items, wherein the device scrapes information from websites on prices of currently available items at one or more locations, and wherein the machine learning model is trained based on historical item data identifying the plurality of items, historical price data identifying historical prices associated with the plurality of items, and historical other data identifying historical locations and historical availabilities associated with the plurality of items," "cause an indicator to be triggered based on how the price of the item compares to the optimal price." Accordingly, for at least the reasons provided above, amended claim 1 does not recite a method of organizing a human activity (remarks page 11-12).
Examiner respectfully disagrees and replies that the independent claims recites the following limitations that fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) as follows: obtaining, item data identifying a price of an item to be purchased; processing, the item data, price data, and other data, to identify an optimal price for the item relative to multiple prices associated with the item, wherein the price data identifies prices associated with a plurality of items, wherein the multiple prices are included in the prices associated with the plurality of items , wherein the other data identifies locations and availabilities associated with the plurality of items, causing, an indicator to be triggered based on how the price of the item compares to the optimal price.
As noted above, these limitations fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106.04(a)(2). Specifically, these limitations fall within the group Certain Methods of Organizing Human Activity (i.e., fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). That is – the limitations recite determining an optimal price and alerting a user based on how the price of an item compares to an optimal price which is a marketing/sales activity hat falls within the certain methods of organizing human activities grouping. Additionally, the steps above also fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Specifically, a human being can mentally (or with pen and paper) identify an optimal price based on item data, price data, and other data, and compare the price of an item to an optimal price. Accordingly claim 1 recites an abstract idea. The limitations recited by Applicant have been analyzed under Step 2A, prong 2.
Second, Applicant recites paragraph [0015] and [0016] of the specification argues that the specification recites that “the conventional technological processing fails to identify optimal prices during shopping” (remarks page 13). Examiner respectfully disagrees and replies that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Here, while automating price comparison by gathering data and analyzing it with a computer may be more efficient than in the in-person comparison techniques described, simply gathering data, analyzing the data to compare prices, and alerting the user based on the comparison merely generally links the abstract idea to a particular technological environment or uses the computer as a tool to perform the abstract idea. This does not to a technological solution to a technological problem.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Number 20170318140 (“Sinha”) teaches in response to a user rejecting an offer, provided the user with a next best offer determined based on an algorithm
US Patent Application Publication Number 20140278804 (“Lanxner”) teaches determining
an optimal price based on rules)
US Patent Number 10855835 (“Fontana”) discloses teaches comparing the potential savings at a particular location with the cost of travel to the location and determining whether it is worth traveling to a location based on the cost of travel.
US Patent Application Publication Number 20120296565 (“Liu”) discloses calculating the extra travel cost needed to travel from a consumer location to a competitor's location and displaying the extra traveling cost to help consumers decide if traveling to another store or location for the competitor's offer is cost efficient compared to the initial offer.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALLAN J WOODWORTH, II/Primary Examiner, Art Unit 3622
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