DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed March 4th, 2026, in which claims 1, 11 and 20 have been amended and claims 1-20 are currently pending. No claims have been cancelled nor added. Claims 1, 11, and 20 are independent claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-5 and 13-15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3 and 13 recite the limitation "generating…a profile of the person". It is unclear whether the profile generated is the same profile in which preferences and user interactions are stored in claim 1, line 6, (claim 11, lines 9-10) or a new, separately generated profile. For examination purposes, this limitation has been interpreted as “generating…the [[a]] profile of the person”.
Claims 4, 5, 14, and 15 are rejected for being dependent on a rejected base claim without curing any of the deficiencies.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Claim 1 is directed to [a] computer-implemented method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“associate one or more items identified from the data information with a periodic event based on input data provided by one or more merchants, the input data including products purchased and a corresponding date”, “a determination of a likelihood of the person acquiring an item for a next occurrence of the periodic event”, “upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold, determining…similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item based on the user preferences in the profile of the person” are the abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). A person could receive information about interactions of others, associate items purchased with periodic events, determine a likelihood of a person purchasing an item for the next occurrence of the periodic event, and determine similar items to the determined items and suggest merchants selling those items based on observed knowledge of the person. See MPEP § 2106.04(a)(2)(II)(C).
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…first data comprising information corresponding to previous interactions of a person and user preferences including price range, shipping speed, or geographic location”, “storing, in a database, the user preferences and previous interactions in a profile of the person”, “receiving…from the machine learning model”, “transmitting…an indication to a computing device associated with the person prior to the next occurrence of the periodic event” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “by at least one processor”, “training, by the at least one processor, a machine learning model to”, “the indication is configured to cause a display of the computing device to display interactive text or graphics including the item, the similar items to the item, and the alternative items to the item, and the one or more merchants corresponding to each of the similar items to the item and the alternative items to the item”, “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the at least one processor, the computing device to automatically initiate a phone call with the merchant corresponding to the item, similar item, or available item, based on the interaction”, “updating the machine learning model and the profile of the person based on the interaction by the person” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, “by at least one processor”, “training, by the at least one processor, a machine learning model to”, “the indication is configured to cause a display of the computing device to display interactive text or graphics including the item, the similar items to the item, and the alternative items to the item, and the one or more merchants corresponding to each of the similar items to the item and the alternative items to the item”, “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the at least one processor, the computing device to automatically initiate a phone call with the merchant corresponding to the item, similar item, or available item, based on the interaction”, “updating the machine learning model and the profile of the person based on the interaction by the person”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…first data including information associated with previous interactions of a person”, “receiving…from the machine learning model”, “storing, in a database, the user preferences and previous interactions in a profile of the person”, “transmitting…an indication to a computing device associated with the person prior to the next occurrence of the periodic event” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d or the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claim 2, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“an input from the person correlating the information related to the one or more available items with the periodic event” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “by the at least one processor” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, “by the at least one processor”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Therefore, the claim is ineligible.
Regarding claim 3, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“generating … a profile of the person, wherein the profile includes at least one of the information associated with the previous interactions of the person, demographic information, or preference information” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “by the at least one processor” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 4, the rejection of claim 3 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“adjusting … the one or more available items based on the profile of the person” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “by the at least one processor” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 5, the rejection of claim 3 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“adjusting … the indication to the computing device associated with the person based on the profile of the person” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “by the at least one processor” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 6, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“a feedback from the person related to the one or more items” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “by the at least one processor” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process.
Step 2B: The additional elements, “by the at least one processor”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).See MPEP § 2106.05(f)(2). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Therefore, the claim is ineligible.
Regarding claim 7, the rejection of claim 6 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the determination of the likelihood of the person acquiring the item for the next occurrence of the periodic event is further based on the feedback from the person” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 8, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“associate one or more items identified from additional information of the person” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “training, by the at least one processor, the machine learning model to” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 9, the rejection of claim 8 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the indication … associated with the person prior to the next occurrence of the periodic event is based on the additional information of the person” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “to the computing device” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “to the computing device” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. Therefore, the claim is ineligible.
Regarding claim 10, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“display an interactive user interface indicative of at least one of the one or more items, the information related to the one or more available items, or the periodic event further comprises displaying natural language statements … based on the one or more items, the information related to the one or more available items by the person, or the periodic event” is a continuation of the “managing personal behavior” limitation identified as an abstract idea in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “causing the display of the computing device to”, “generated by the machine learning model” the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Therefore, the claim is ineligible.
Regarding claim 11:
Step 1: Claim 11 is directed to [a] computer system, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“associate one or more items identified from the information with a periodic event”, “a likelihood of the person acquiring an item for a next occurrence of the periodic event”, “upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold, determining similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item based on the user preferences in the profile of the person”, “based on receiving a positive indication from the person, requesting additional input from the person comprising a type or category of the periodic event” are the abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). A person could receive information about interactions of others, associate items purchased with periodic events, determine a likelihood of a person purchasing an item for the next occurrence of the periodic event, and make further decisions if they receive a high likelihood. See MPEP § 2106.04(a)(2)(II)(C).
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “at least one memory having processor-readable instructions stored therein” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h). “at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions”, “training a machine learning model to”, “further tuning algorithms of the machine learning model based on the additional input”, “based on the further tuned machine learning model and prior to the next occurrence of the periodic event cause a display of the computing device to automatically display interactive text or graphics indicative of one or more available items associated with the next periodic event” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “receiving first data compromising information corresponding to previous interactions of a person and user preferences including a product category”, “storing, in a database, the user preferences and previous interactions in a profile of the person”, “receiving, from the machine learning model”, “transmitting a query to a computing device associated with the person to determine whether an acquired item is associated with the periodic event”, “storing the additional input with transaction data of the acquired item and the periodic event in the profile of the person” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The additional elements, “at least one memory having processor-readable instructions stored therein”, “at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions”, “training a machine learning model to”, “further tuning algorithms of the machine learning model based on the additional input”, “based on the further tuned machine learning model and prior to the next occurrence of the periodic event cause a display of the computing device to automatically display interactive text or graphics indicative of one or more available items associated with the next periodic event”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving first data compromising information corresponding to previous interactions of a person and user preferences including a product category”, “storing, in a database, the user preferences and previous interactions in a profile of the person”, “receiving, from the machine learning model”, “transmitting a query to a computing device associated with the person to determine whether an acquired item is associated with the periodic event”, “storing the additional input with transaction data of the acquired item and the periodic event in the profile of the person” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d or Furthermore the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
Regarding claims 12-19
The rejection of claim 11 is further incorporated, the rejection of claims 2-9 are applicable to claims 12-19, respectively.
Regarding claim 20:
Step 1: Claim 20 is directed to [a] computer-implemented method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“associate one or more items identified from the first data with a periodic event”, are the abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). A person could receive information about interactions of others, associate items purchased with periodic events, determine a likelihood of a person purchasing an item for the next occurrence of the periodic event, and make further decisions if they receive a high likelihood. See MPEP § 2106.04(a)(2)(II)(C).
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “receiving…at an issuer system, first data comprising information corresponding to previous interactions of a person and user preferences including merchant type”, “receiving at the issuer computer system, from a computing device associated with the person, user information”, “storing, in a database, the user preferences, previous interactions, and user information in a profile of the person”, “transmitting, by the issuer computer system, an indication to the computing device associated with the person prior to a next occurrence of the periodic event” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “by at least one processor”, “parsing, by the issuer computer system using a trained machine learning model, the first data”, “the indication is configured to cause a display of the computing device to display interactive text or graphics indicative of one or more available items associated with the periodic event, the one or more available items comprising similar items to the one or more items, alternative items to the one or more items, and one or more merchants corresponding to each of the similar one or more items, the one or more available items determined based on the profile of the person”, “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the issuer computer system, the computing device to automatically initiate a purchase with a merchant of the one or more available items”, “further tuning algorithms of the machine learning model” the limitations are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The additional elements, “by at least one processor”, “parsing, by the issuer computer system using a trained machine learning model, the first data”, “the indication is configured to cause a display of the computing device to display interactive text or graphics indicative of one or more available items associated with the periodic event, the one or more available items comprising similar items to the one or more items, alternative items to the one or more items, and one or more merchants corresponding to each of the similar one or more items, the one or more available items determined based on the profile of the person”, “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the issuer computer system, the computing device to automatically initiate a purchase with a merchant of the one or more available items”, “further tuning algorithms of the machine learning model”, taken individually and in combination, do not provide an inventive concept of significantly more than the abstract idea itself for the reasons set forth in step 2A prong 2 above. Further, “receiving…at an issuer system, first data comprising information corresponding to previous interactions of a person and user preferences including merchant type”, “receiving at the issuer computer system, from a computing device associated with the person, user information”, “storing, in a database, the user preferences, previous interactions, and user information in a profile of the person”, “transmitting, by the issuer computer system, an indication to the computing device associated with the person prior to a next occurrence of the periodic event” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d or the additional element is directed to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See MPEP § 2106.05(d)/(II). Therefore, the claim is ineligible.
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, 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.
Claims 1, 3-5, 8, 9, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang et al. (“Time to Shop for Valentine’s Day: Shopping Occasions and Sequential Recommendation in E-commerce”, Wang et al., February 2020) hereinafter Wang in view of Jiang et al. (“Life-stage Prediction for Product Recommendation in E-commerce”, Jiang et al., 2015) hereinafter Jiang in view of Howe et al. (US 20110310891 A1) hereinafter Howe in view of Tiruveedhula (US 20210042657 A1) in view of Yu et al. (US20180285752A1) hereinafter Yu in further view of Brovman et al. ("Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion", Brovman et al., 2016) hereinafter Brovman.
Regarding claim 1:
Wang teaches [a] computer-implemented method for interaction-based indications using machine learning, the method comprising:
receiving, by at least one processor, first data comprising information corresponding to previous interactions of a person (Wang, pages 4-5, col 2-1, section 3.4, ¶1 “For example, a user will often purchase a birthday gift two to three weeks in advance of the birthday. Thus while predicting a user’s preference, we also need to elicit the personal occasion signal by tracing the user’s previous shopping behavior in the neighboring days”)
training, by the at least one processor, a machine learning model to associate one or more items identified from the information with a periodic event (Wang, page 2, col 1, ¶3 “It is important to exploit the linkage between different occasions and shopping behaviors in e-commerce, so that we can: (i) recommend more time or season-aware candidates (like recommending a surfboard in the Summer while recommending snowboard in the Winter), which may alleviate the cold-start problem;”) based on input data provided by one or more merchants, the input data including products purchased and a corresponding date (Wang, page 4, col 1, section 3.2, ¶1 “The neural attention mechanism [2, 14, 25, 31] can be applied to capture the correlation between the target query (recent purchased items or the future timestamp for prediction) and the context contents (purchase history). For different types of attention modules, the input usually consists of a Query, and Key-Value pairs.”);
Wang does not teach “receiving, by the at least one processor, from the machine learning model, a determination of a likelihood of the person acquiring an item for a next occurrence of the periodic event;
upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold,
transmitting, by the at least one processor, an indication to a computing device associated with the person prior to the next occurrence of the periodic event, wherein the indication is configured to cause a display of the computing device to display interactive text or graphics including the item, the similar items to the item, and the alternative items to the item, and the one or more merchants corresponding to each of the similar items to the item and the alternative items to the item transmitting, by the at least one processor, an indication to a computing device associated with the person prior to the next occurrence of the periodic event, wherein the indication is configured to cause a display of the computing device to display interactive text or graphics including the item, similar items to the item, and alternative items to the item;”
However, Jiang teaches receiving, by the at least one processor, from the machine learning model, a determination of a likelihood of the person acquiring an item for a next occurrence of the periodic event (Jiang, page 5, col 1, section 3.5 “To make product recommendations, we propose a model to estimate the probability of a user purchasing a product at a specific age a.”);
upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold (Jiang, page 3, col 2, section 3.2, ¶3 “p(yt|yt−1, dt−1, Xt) is the probability of being in yt at time t given the previous stage yt−1, the previous stage duration dt−1 and the observed behavior sequence Xt.”)
transmitting, by the at least one processor, an indication to a computing device associated with the person prior to the next occurrence of the periodic event, wherein the indication is configured to cause a display of the computing device to display interactive text or graphics including the item, the similar items to the item, and the alternative items to the item, (Jiang, fig 2
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) and the one or more merchants corresponding to each of the similar items to the item and the alternative items to the item (Jiang, page 4, col 2, ¶2 “Product property features: Taobao is a distributed market space where sellers sell products.” Here the sellers associated with the various items on the marketplace can be considered the merchants);
Wang and Jiang are analogous art because both references concern event-based purchases. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wangs’s event-based reminder system to incorporate the product recommendations taught by Jiang. The motivation for doing so would have been as stated in Jiang, abstract “Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user’s current life-stage and recommend products correspondingly.”.
Wang in view of Jiang does not teach “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the at least one processor, the computing device to automatically initiate a phone call with the merchant corresponding to the item, similar item, or available item, based on the interaction”
However, Howe teaches based on interaction with the interactive text or graphics by the person via the computing device, causing, by the at least one processor, the computing device to automatically initiate a phone call with the merchant corresponding to the item, similar item, or available item, based on the interaction (Howe, ¶35 “Potential purchasers who wish to establish a telephone session with a merchant utilizing the free click-to-call feature may click on an advertisement, telephone number, and/or icon including the free click-to-call indicator in step 112.”);
Wang in view of Jiang and Howe are analogous art because both references concern targeted products and e-commerce. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang’s advertisement system to incorporate the automatic calls taught by Howe. The motivation for doing so would have been as stated in Howe, ¶12 “enhancing the predictability, scalability and cost effectiveness of online advertising with voice over IP connectivity and event tracking technologies.”.
Wang in view of Jiang in further view of Howe does not teach “user preferences including price range, shipping speed, or geographic location
updating the machine learning model and the profile of the person based on the interaction by the person”
However, Tiruveedhula teaches user preferences including price range, shipping speed, or geographic location (Tiruveedhula, ¶43 “Demographic data may include age data, gender data, location data, income data, interests, preferences, and/or any other kind of demographic data or combination thereof.” Here, location data can be considered the geographic location. It is noted the claim recites alternative language, and Tiruveedhula teaches at least one of the alternatives.);
updating the machine learning model and the profile of the person based on the interaction by the person (Tiruveedhula, ¶60 “The third-party analytic service may update the machine learning model based on a record of one or more user interaction(s) with the consumer-facing application subsequent to the consumer-facing application presenting the response to the user query. Alternatively or additionally, the third-party analytics service may update the machine learning model based on the user intent associated with the user query.” Here, the updating to add additional records of user interactions can be considered updating the user profile).
Wang in view of Jiang in further view of Howe and Tiruveedhula are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe’s advertisement system to incorporate the automatic updates taught by Tiruveedhula. The motivation for doing so would have been to provide timely and relevant data as stated in Tiruveedhula, ¶60 “In general, in an embodiment, updating the machine learning model updates the machine learning engine's function (e.g., a classification function), which may improve the third-party analytics system's ability to provide timely and relevant data based on subsequent user queries.”
Wang in view of Jiang in view of Howe in further view of Tiruveedhula does not teach "storing, in a database, the user preferences and previous interactions in a profile of the person"
However, Yu teaches storing, in a database, the user preferences and previous interactions in a profile of the person (Yu ¶122 “In this regard, the information about the user 510 may be stored and managed in a user profile database 550. The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510). According to an embodiment, the user profile database 550 may be included in an external server connected with the electronic device 530 via the communication circuit, or may be stored in the information storage module of the electronic device 530.”);
Wang in view of Jiang in view of Howe in further view of Tiruveedhula and Yu are analogous art because both references concern data processing based on a personal profile or database. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula’s advertisement system to incorporate the profile creation taught by Yu. The motivation for doing so would have been to provide personalized and targeted advertisements as stated in Yu, ¶122 “In another example, the goods search module 571 may identify history information for an advertisement provided to the user 510, by using the information about the user 510 and may preferentially search for recently-provided advertisement information or most frequently-provided advertisement information for the specific goods. In this regard, the information about the user 510 may be stored and managed in a user profile database 550.”
Wang in view of Jiang in view of Howe in view of Tiruveedhula in further view of Yu does not teach "determining, by the at least one processor, similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item based on the user preferences in the profile of the person"
However, Brovman teaches determining, by the at least one processor, similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item (Brovman, page 1, col 2, ¶2 “This page shows the details of a seed item, with five recommendations shown above the fold. The recommendations are generated based on both similarity between the seed item and the recommended item, and likelihood of purchase1.”) based on the user preferences in the profile of the person (Brovman, page 3, col 1, section 3 “The training and testing data sets are generated with features derived from{seed item, recommended item} pairs and binary class labels being {0=non-clicked, 1=purchased} from recommendations shown in the past.”);
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in further view of Yu and Brovman are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula/Yu’s advertisement system to incorporate the similar items recommendation based on a profile taught by Brovman. The motivation for doing so would have been to provide accurate alternative items in a scalable architecture as stated in Brovman, page 4, col 2, section 4, ¶1 “In this work, we presented a highly scalable architecture which produces high quality similar item recommendations in a diverse semi-structured marketplace. We developed a widely applicable and interpretable pointwise machine learned ranking model trained on implicit eBay user shopping behavior. The model optimized recommendation rank based on probability of purchase. Although many of our marketplace characteristics are unique, the ranking model and sampling strategy is general enough for most domains which require ranking recommendations against a seed item.”
Regarding claim 3:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 1, further including generating, by the at least one processor, a profile of the person, wherein the profile includes at least one of the information associated with the previous interactions of the person, demographic information, or preference information (Jiang, page 6, col 1, ¶1”For a given user account, we observe a sequence of product purchasing behaviors.” Here, the account can be considered a profile. It is noted the claim recites alternative language, and Jiang teaches at least one of the alternatives.).
Regarding claim 4:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 3, further including adjusting, by the at least one processor, the one or more available items based on the profile of the person (Wang, page 5, col 2, section 3.6, ¶1 “Lastly, we discuss how to balance a user’s intrinsic preferences with occasion signals for personalization? Here we turn to an attention (gating) layer which can control how we assign different weights to each of the components we have developed in the previous sections. The query will be a user-timestamp pair because the status for a user at different timestamps will be different. For example, there are users who have strong personal desire for handcrafted supplies and seldom purchase other items on a site like Etsy.”).
Regarding claim 5:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 3, further including adjusting, by the at least one processor, the indication to the computing device associated with the person based on the profile of the person (Wang, page 5, col 2, section 3.6, ¶1 “Lastly, we discuss how to balance a user’s intrinsic preferences with occasion signals for personalization? Here we turn to an attention (gating) layer which can control how we assign different weights to each of the components we have developed in the previous sections. The query will be a user-timestamp pair because the status for a user at different timestamps will be different. For example, there are users who have strong personal desire for handcrafted supplies and seldom purchase other items on a site like Etsy.”).
Regarding claim 8:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 1, further including training, by the at least one processor, the machine learning model to associate one or more items identified from additional information of the person (Jiang, page 5, col 1-2, section 3.2, ¶3 “To estimate P(p_productj) in E.q 3, we use a large scale logistic regression model:
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where x is a feature vector that represents a purchasing action, which includes scores generated by basic recommendation algorithms such as item-item based collaborative filtering, as well as other features that might be useful. w is learnt by maximizing the likelihood of the training data.”).
Regarding claim 9:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 8, wherein the indication to the computing device associated with the person prior to the next occurrence of the periodic event is based on the additional information of the person (Jiang, page 5, col 1-2, section 3.2, ¶3 “To estimate P(p_productj) in E.q 3, we use a large scale logistic regression model:
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where x is a feature vector that represents a purchasing action, which includes scores generated by basic recommendation algorithms such as item-item based collaborative filtering, as well as other features that might be useful. w is learnt by maximizing the likelihood of the training data.”).
Regarding claim 20:
Wang teaches [a] computer-implemented method for interaction-based indications using machine learning, the method comprising:
receiving, by at least one processor at an issuer computer system, first data comprising information corresponding to previous interactions of a person (Wang, pages 4-5, col 2-1, section 3.4, ¶1 “For example, a user will often purchase a birthday gift two to three weeks in advance of the birthday. Thus while predicting a user’s preference, we also need to elicit the personal occasion signal by tracing the user’s previous shopping behavior in the neighboring days”);
parsing, by the issuer computer system using a trained machine learning model, the first data to associate one or more items identified from the first data with a periodic event (Wang, page 2, col 1, ¶3 “It is important to exploit the linkage between different occasions and shopping behaviors in e-commerce, so that we can: (i) recommend more time or season-aware candidates (like recommending a surfboard in the Summer while recommending snowboard in the Winter), which may alleviate the cold-start problem;”);
Wang does not teach “receiving at the issuer computer system, from a computing device associated with the person, user information;
transmitting, by the issuer computer system, an indication to the computing device associated with the person prior to a next occurrence of the periodic event, wherein the indication is configured to cause a display of the computing device to display interactive text or graphics indicative of one or more available items associated with the periodic event”
However, Jiang teaches receiving at the issuer computer system, from a computing device associated with the person, user information (Jiang, page 4, col 2 ¶1 “For example, a user may search “large-size diaper” or “3 years old children’s garments”. This information is very important for age prediction. Therefore, search queries in E-commerce are also utilized as features” here, user searches can be considered user information from a computing device associated with the person);
transmitting, by the issuer computer system, an indication to the computing device associated with the person prior to a next occurrence of the periodic event, wherein the indication is configured to cause a display of the computing device to display interactive text or graphics indicative of one or more available items associated with the periodic event (Jiang, fig 2
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);
Wang and Jiang are analogous art because both references concern event-based purchases. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wangs’s event-based reminder system to incorporate the product recommendations taught by Jiang. The motivation for doing so would have been as stated in Jiang, abstract “Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user’s current life-stage and recommend products correspondingly.”.
Wang in view of Jiang does not teach “based on interaction with the interactive text or graphics by the person via the computing device, causing, by the issuer computer system, the computing device to automatically initiate a purchase with a merchant of the one or more available items”
However, Howe teaches based on interaction with the interactive text or graphics by the person via the computing device, causing, by the issuer computer system, the computing device to automatically initiate a purchase with a merchant of the one or more available items (Howe, ¶35 “Potential purchasers who wish to establish a telephone session with a merchant utilizing the free click-to-call feature may click on an advertisement, telephone number, and/or icon including the free click-to-call indicator in step 112.” Automatically calling a merchant to imitate a purchase can be considered automatically initiating a purchase with a merchant).
Wang in view of Jiang and Howe are analogous art because both references concern targeted products and e-commerce. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang’s advertisement system to incorporate the automatic calls taught by Howe. The motivation for doing so would have been as stated in Howe, ¶12 “enhancing the predictability, scalability and cost effectiveness of online advertising with voice over IP connectivity and event tracking technologies.”.
Wang in view of Jiang in further view of Howe does not teach "further tuning algorithms of the machine learning model"
However, Tiruveedhula teaches further tuning algorithms of the machine learning model (Tiruveedhula, ¶60 “The third-party analytic service may update the machine learning model based on a record of one or more user interaction(s) with the consumer-facing application subsequent to the consumer-facing application presenting the response to the user query. Alternatively or additionally, the third-party analytics service may update the machine learning model based on the user intent associated with the user query.”);
Wang in view of Jiang in further view of Howe and Tiruveedhula are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Palmisano’s advertisement system to incorporate the automatic updates taught by Tiruveedhula. The motivation for doing so would have been to provide timely and relevant data as stated in Tiruveedhula, ¶60 “In general, in an embodiment, updating the machine learning model updates the machine learning engine's function (e.g., a classification function), which may improve the third-party analytics system's ability to provide timely and relevant data based on subsequent user queries.”.
Wang in view of Jiang in view of Howe in further view of Tiruveedhula does not teach "…and user preferences including merchant type;
storing, in a database, the user preferences and previous interactions in a profile of the person"
However, Yu teaches and user preferences including merchant type (Yu ¶122 “The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510).” Here, a preferred brand can be considered a merchant type);
storing, in a database, the user preferences and previous interactions in a profile of the person (Yu ¶122 “In this regard, the information about the user 510 may be stored and managed in a user profile database 550. The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510). According to an embodiment, the user profile database 550 may be included in an external server connected with the electronic device 530 via the communication circuit, or may be stored in the information storage module of the electronic device 530.”);
Wang in view of Jiang in view of Howe in further view of Tiruveedhula and Yu are analogous art because both references concern data processing based on a personal profile or database. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula’s advertisement system to incorporate the profile creation taught by Yu. The motivation for doing so would have been to provide personalized and targeted advertisements as stated in Yu, ¶122 “In another example, the goods search module 571 may identify history information for an advertisement provided to the user 510, by using the information about the user 510 and may preferentially search for recently-provided advertisement information or most frequently-provided advertisement information for the specific goods. In this regard, the information about the user 510 may be stored and managed in a user profile database 550.”
Wang in view of Jiang in view of Howe in view of Tiruveedhula in further view of Yu does not teach "determining, by the at least one processor, similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item based on the user preferences in the profile of the person"
However, Brovman teaches determining, by the at least one processor, similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item (Brovman, page 1, col 2, ¶2 “This page shows the details of a seed item, with five recommendations shown above the fold. The recommendations are generated based on both similarity between the seed item and the recommended item, and likelihood of purchase1.”) based on the user preferences in the profile of the person (Brovman, page 3, col 1, section 3 “The training and testing data sets are generated with features derived from{seed item, recommended item} pairs and binary class labels being {0=non-clicked, 1=purchased} from recommendations shown in the past.”);
Wang in view of Jiang in view of Howe in view of Tiruveedhula in further view of Yu and Brovman are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula/Yu’s advertisement system to incorporate the similar items recommendation based on a profile taught by Brovman. The motivation for doing so would have been to provide accurate alternative items in a scalable architecture as stated in Brovman, page 4, col 2, section 4, ¶1 “In this work, we presented a highly scalable architecture which produces high quality similar item recommendations in a diverse semi-structured marketplace. We developed a widely applicable and interpretable pointwise machine learned ranking model trained on implicit eBay user shopping behavior. The model optimized recommendation rank based on probability of purchase. Although many of our marketplace characteristics are unique, the ranking model and sampling strategy is general enough for most domains which require ranking recommendations against a seed item.”
Claims 2, 6 and 7 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in view of Brovman in further view of Palmisano et al. (“Using Context to Improve Predictive Modeling of Customers in Personalization Applications”, Palmisano et al., 2008) hereinafter Palmisano.
Regarding claim 2:
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 1,
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman does not teach “further including receiving, by the at least one processor, an input from the person correlating the information related to the one or more available items with the periodic event”
However, Palmisano teaches further including receiving, by the at least one processor, an input from the person correlating the information related to the one or more available items with the periodic event (Palmisano, page 6, col 1, ¶1 “The user was asked to specify whether the purchase would be intended for personal purposes or as a gift, for which specific personal purpose, and for whom the gift was intended”).
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman and Palmisano are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang’s advertisement system to incorporate the confirmations and feedback taught by Palmisano. The motivation for doing so would have been to provide context data for better predictive performance as stated in Palmisano, page 7, col 2, ¶1 “Moreover, all the contextual models show a better predictive performance compared to the uncontextual, except for two cases (Cluster 10 in Fig. 6c) where the difference is very small.”.
Regarding claim 6:
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 1
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman does not teach “further including receiving, by the at least one processor, a feedback from the person related to the one or more items”
However, Palmisano teaches further including receiving, by the at least one processor, a feedback from the person related to the one or more items (Palmisano, page 6, col 1, ¶1 “The user was asked to specify whether the purchase would be intended for personal purposes or as a gift, for which specific personal purpose, and for whom the gift was intended”).
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman and Palmisano are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang’s advertisement system to incorporate the confirmations and feedback taught by Palmisano. The motivation for doing so would have been to provide context data for better predictive performance as stated in Palmisano, page 7, col 2, ¶1 “Moreover, all the contextual models show a better predictive performance compared to the uncontextual, except for two cases (Cluster10 in Fig. 6c) where the difference is very small.”.
Regarding claim 7:
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in view of Brovman in further view of Palmisano teaches [t]he computer-implemented method of claim 6, wherein the determination of the likelihood of the person acquiring the item for the next occurrence of the periodic event is further based on the feedback from the person (Palmisano, page 6, col 1, ¶1 “The user was asked to specify whether the purchase would be intended for personal purposes or as a gift, for which specific personal purpose, and for whom the gift was intended”).
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in view of Brovman in further view of Adams (US 10475087 B2).
Regarding claim 10:
Wang in view of Jiang in view of Howe in view of Tiruveedhula in view of Yu in further view of Brovman teaches [t]he computer-implemented method of claim 1,
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman does not teach “wherein the causing the display of the computing device to display an interactive user interface indicative of at least one of the one or more items, the information related to the one or more available items, or the periodic event further comprises displaying natural language statements generated by the machine learning model based on the one or more items, the information related to the one or more available items by the person, or the periodic event”
However, Adams teaches wherein the causing the display of the computing device to display an interactive user interface indicative of at least one of the one or more items, the information related to the one or more available items, or the periodic event further comprises displaying natural language statements generated by the machine learning model based on the one or more items, the information related to the one or more available items by the person, or the periodic event (Adams, col 11, lines 47-53 “FIG.5B illustrates a gift suggestion 508 including text identifying an event inferred by the social networking system and a domain wherein a viewing user may purchase a gift to give to the targeting user. The gift suggestion 508 may include one or more links to an external site, wherein each link is for a gift item that the targeting user may be interested in.”).
Wang in view of Jiang in view of Howe in further view of Tiruveedhula in view of Yu in further view of Brovman and Adams are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula’s advertisement system to incorporate the natural language graphic display taught by Adams. The motivation for doing so would have been to allow the sending of content to users as stated in Adams, page 7, col 2, lines 57-60 “Additionally, the selected content may be displayed to friends of the users within the social networking system wherein the friends can send the content to the user..”.
Claims 11-19 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang in view of Jiang in view of Palmisano in view of Tiruveedhula in view of Yu in further view of Brovman .
Regarding claim 11:
Wang teaches [a] computer system for interaction-based indications using machine learning, the computer system comprising:
at least one memory having processor-readable instructions stored therein; and at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions (Wang, page 7, col 1, section 4.3, ¶3 “Comparing the recent neural-based sequential models, we find that GRU4Rec+ works slightly better than TCN, which is based on dilated CNN. And HPMN utilizing hierarchical multi-layer memory networks outperforms GRU4Rec+ in both data, which proves that there are periodic pattern in users’ shopping behaviors”), including functions for:
receiving first data comprising information corresponding to previous interactions of a person (Wang, pages 4-5, col 2-1, section 3.4, ¶1 “For example, a user will often purchase a birthday gift two to three weeks in advance of the birthday. Thus while predicting a user’s preference, we also need to elicit the personal occasion signal by tracing the user’s previous shopping behavior in the neighboring days”)
training a machine learning model to associate one or more items identified from the information with a periodic event (Wang, page 2, col 1, ¶3 “It is important to exploit the linkage between different occasions and shopping behaviors in e-commerce, so that we can: (i) recommend more time or season-aware candidates (like recommending a surfboard in the Summer while recommending snowboard in the Winter), which may alleviate the cold-start problem;”);
Wang does not teach “receiving, from the machine learning model, a likelihood of the person acquiring an item for a next occurrence of the periodic event;
based on the further tuned machine learning model, cause a display of the computing device to automatically display interactive text or graphics indicative of one or more available items associated with the next periodic event”
However, Jiang teaches receiving, from the machine learning model, a likelihood of the person acquiring an item for a next occurrence of the periodic event (Jiang, page 5, col 1, section 3.5 “To make product recommendations, we propose a model to estimate the probability of a user purchasing a product at a specific age a.”);
based on the further tuned machine learning model, cause a display of the computing device to automatically display interactive text or graphics indicative of one or more available items associated with the next periodic event (Jiang, fig 2
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Wang and Jiang are analogous art because both references concern event-based purchases. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wangs’s event-based reminder system to incorporate the product recommendations taught by Jiang. The motivation for doing so would have been as stated in Jiang, abstract “Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user’s current life-stage and recommend products correspondingly.”.
Wang in view of Jiang does not teach “upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold…transmitting a query to a computing device associated with the person to determine whether an acquired item is associated with the periodic event;
based on receiving a positive indication from the person, requesting additional input from the person comprising a type or category of the periodic event”
However, Palmisano teaches upon receiving a determination that the likelihood is equal to or exceeds a predetermined likelihood threshold, transmitting a query to a computing device associated with the person to determine whether an acquired item is associated with the periodic event (Palmisano, page 6, col 1, ¶1 “The user was asked to specify whether the purchase would be intended for personal purposes or as a gift, for which specific personal purpose, and for whom the gift was intended”);
based on receiving a positive indication from the person, requesting additional input from the person comprising a type or category of the periodic event (Palmisano, page 6, col 1, ¶1 “The user was asked to specify whether the purchase would be intended for personal purposes or as a gift, for which specific personal purpose, and for whom the gift was intended” here, the person whom the gift was intended can be considered a category of event);
Wang in view of Jiang and Palmisano are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang’s advertisement system to incorporate the confirmations and feedback taught by Palmisano. The motivation for doing so would have been to provide context data for better predictive performance as stated in Palmisano, page 7, col 2, ¶1 “Moreover, all the contextual models show a better predictive performance compared to the uncontextual, except for two cases (Cluster10 in Fig. 6c) where the difference is very small.”.
Wang in view of Jiang in further view of Palmisano does not teach “further tuning algorithms of the machine learning model based on the additional input”
However, Tiruveedhula teaches further tuning algorithms of the machine learning model based on the additional input (Tiruveedhula, ¶60 “The third-party analytic service may update the machine learning model based on a record of one or more user interaction(s) with the consumer-facing application subsequent to the consumer-facing application presenting the response to the user query. Alternatively or additionally, the third-party analytics service may update the machine learning model based on the user intent associated with the user query.”);
Wang in view of Jiang in further view of Palmisano and Tiruveedhula are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Palmisano’s advertisement system to incorporate the automatic updates taught by Tiruveedhula. The motivation for doing so would have been to provide timely and relevant data as stated in Tiruveedhula, ¶60 “In general, in an embodiment, updating the machine learning model updates the machine learning engine's function (e.g., a classification function), which may improve the third-party analytics system's ability to provide timely and relevant data based on subsequent user queries.”.
Wang in view of Jiang in view of Palmisano in further view of Tiruveedhula does not teach "…user preferences including a product category;
storing, in a database, the user preferences and previous interactions in a profile of the person
storing the additional input with transaction data of the acquired item and the periodic event in the profile of the person"
However, Yu teaches and user preferences including a product category (Yu ¶122 “The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510).” Here, a preferred brand can be considered a preferred product category);
storing, in a database, the user preferences and previous interactions in a profile of the person (Yu ¶122 “In this regard, the information about the user 510 may be stored and managed in a user profile database 550. The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510). According to an embodiment, the user profile database 550 may be included in an external server connected with the electronic device 530 via the communication circuit, or may be stored in the information storage module of the electronic device 530.”);
storing the additional input with transaction data of the acquired item and the periodic event in the profile of the person (Yu ¶122 “In this regard, the information about the user 510 may be stored and managed in a user profile database 550. The user profile database 550 may store various types of information about the user 510, for example, the age, gender, physical information, preferred brand, or advertisement provision history of the user 510, as well as identification information of the user 510 (e.g., the name, telephone number, or ID of the user 510). According to an embodiment, the user profile database 550 may be included in an external server connected with the electronic device 530 via the communication circuit, or may be stored in the information storage module of the electronic device 530.”)
Wang in view of Jiang in view of Palmisano in view of Tiruveedhula in further view of Yu does not teach "determining similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item based on the user preferences in the profile of the person"
However, Brovman teaches determining similar items to the item, alternative items to the item, and one or more merchants corresponding to each of the similar items to the item and the alternative items to the item (Brovman, page 1, col 2, ¶2 “This page shows the details of a seed item, with five recommendations shown above the fold. The recommendations are generated based on both similarity between the seed item and the recommended item, and likelihood of purchase1.”) based on the user preferences in the profile of the person (Brovman, page 3, col 1, section 3 “The training and testing data sets are generated with features derived from{seed item, recommended item} pairs and binary class labels being {0=non-clicked, 1=purchased} from recommendations shown in the past.”);
Wang in view of Jiang in view of Palmisano in further view of Tiruveedhula in further view of Yu and Brovman are analogous art because both references concern advertisements and targeted products. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Wang/Jiang/Howe/Tiruveedhula/Yu’s advertisement system to incorporate the similar items recommendation based on a profile taught by Brovman. The motivation for doing so would have been to provide accurate alternative items in a scalable architecture as stated in Brovman, page 4, col 2, section 4, ¶1 “In this work, we presented a highly scalable architecture which produces high quality similar item recommendations in a diverse semi-structured marketplace. We developed a widely applicable and interpretable pointwise machine learned ranking model trained on implicit eBay user shopping behavior. The model optimized recommendation rank based on probability of purchase. Although many of our marketplace characteristics are unique, the ranking model and sampling strategy is general enough for most domains which require ranking recommendations against a seed item.”
Regarding claims 12-19:
Claims 12-19 are rejected under the same rationale as claims 2-9.
Response to Arguments
Applicant's arguments filed March 4th, 2026 (hereinafter “Remarks”) have been fully considered but they are not persuasive.
Applicant’s arguments regarding the 35 U.S.C. 112(b) rejections of the previous office action have been fully considered, and are persuasive. The rejections have been withdrawn due to claim amendments. However, the amendments have required additional indefiniteness rejections to be made in this action.
Applicant’s arguments with respect to the 35 U.S.C. § 103 rejections of claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Rejections under 35 U.S.C. § 101:
Argument 1:
“The claims are directed to specific technological solutions to technological problems in the field of computerized recommendation systems and user interaction processing, and integrate the alleged abstract idea into a practical application that yields concrete technological improvements.” (Remarks, page 11).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “It is important to note, the judicial exception alone cannot provide the improvement.” See MPEP § 2106.05(a). The recommendation system and user interaction processing embody an abstract idea as identified in Step 2A Prong 1 above, and the computers and processing involved are identified in Step 2A Prong 2 and Step 2B as amounting to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Argument 2:
“…when the claims are properly evaluated as a whole and considering the specific limitations as amended, they do not recite an abstract idea. Rather, the claims recite specific computerized processes that utilize machine learning models to perform technical functions that cannot be performed
mentally or manually.” (Remarks, pages 11-12).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.” See MPEP § 2106.05(a)(II). Here, the claims recite the abstract idea of organized human activity, while the recitation of computer parts can be considered amounting to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Argument 3:
“These claims are fundamentally technological in character. They address the technical problem of how to efficiently process large volumes of user interaction data and user preference data to generate personalized recommendations and automatic system actions. The solution involves specific technical implementations including storing user preference data and interaction data in structured database profiles, training machine learning models with merchant-provided input data, processing profile data through trained models to determine likelihood values, determining items and merchants based on database profile queries, generating and transmitting interactive interfaces, automatically initiating communications or transactions based on user interactions, and dynamically updating both machine learning models and user profiles.” (Remarks, page 14).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “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. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.” See MPEP § 2106.04(d)(1). Further, the use of a computer to perform the judicial exception does not amount to an improvement to a technology. “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.” See MPEP § 2106.05(a)(II).
Argument 4:
“The 2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG") explains that a key consideration in determining whether a claim recites an abstract idea is whether the claimed process can be performed wholly in the human mind or by a human using pen and paper. The 2019 PEG clarifies that if a claim requires a computer or other technical device, and the claim limitations cannot practically be performed in the human mind, then the claim does not recite a mental process.” (Remarks, page 14).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.” See MPEP § 2106.04(a)(2)(III). Here, the acts of receiving information about interactions of others, associating items purchased with periodic events, determining a likelihood of a person purchasing an item for the next occurrence of the periodic event, and determining similar items to the determined items and suggest merchants selling those items based on observed knowledge of the person, have been identified the abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), not as a mental process. See MPEP § 2106.04(a)(2)(II)(C).
Argument 5:
“Training a machine learning model requires computational processing of data sets to iteratively adjust model parameters-a process that is inherently computational. Storing data in database profiles with the specificity recited (including price range, shipping speed, geographic location, product category, merchant type) requires database management systems. Determining similar and alternative items and corresponding merchants based on querying user preference profiles requires programmatic database operations. Automatically initiating phone calls or purchases based on user interactions requires computer-controlled communication systems.” (Remarks, page 15).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more…Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” See MPEP § 2106.5(f). Here, the claimed speed and computational efficiency and storing and retrieving of data do not integrate a judicial exception into a practical application or provide an inventive concept. Further, the determination of merchants for determined items can be considered both a mental process or certain methods of organizing human activity. Lastly, the automatic initiation of phone calls the limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Argument 6:
“Moreover, the volume and complexity of data processing required by the claims-processing previous interactions, user preferences across multiple dimensions, merchant input data, and real-time likelihood determinations-cannot practically be performed manually. The claims are necessarily rooted in computer technology.” (Remarks, page 15).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more…Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).” See MPEP § 2106.5(f).
Argument 7:
“The amended claims provide specific improvements to computer functionality and database technology, including an improved machine learning model functionality through specific training and updating processes. For example, claim 1 recites "training a machine learning model to associate one or more items identified from the data information with a periodic event based on input data provided by one or more merchants, the input data including products purchased and a corresponding date." This training process configures the machine learning model to perform the specific technical function of associating items with periodic events based on temporal patterns in merchant transaction data.” (Remarks, page 16).
Examiners Response:
Examiner respectfully disagrees, the MPEP states “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016).” See MPEP § 2106.05(I). furthermore “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” See MPEP § 2106.05(a)(II). Here, the improvement to the organization and recommendation of products and merchants may be an improvement in an abstract idea, but improves neither the technological field nor the functioning of a computer, as a computer.
Argument 8:
“Furthermore, the claims recite "updating the machine learning model and the profile of the person based on the interaction by the person" (claim 1). This dynamic updating improves the model's accuracy and performance over time by incorporating user feedback from interactions with the system-generated displays…Thus, the training and additional tuning of the machine learning model is a technical improvement that extends beyond generic computing within a technical environment.” (Remarks, pages 16-17).
Examiners Response:
Examiner respectfully disagrees, as in example 48 of the Al-related SME examples 47-49 issued in 2024, which uses a generic deep neural network to apply a judicial exception of a mathematical process and was found to fall under MPEP § 2106.05(f)(2), as well as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, the use of a generic machine learning model, as well as further tuning of said model, without details on how this the claimed improvement is accomplished also can be considered to fall under MPEP § 2106.05(f)(2). While an improvement to the training of a machine learning model can in certain cases be considered an improvement, the generic act of training and additional retraining cannot be considered a technical improvement.
Argument 9:
“The USPTO's Subject Matter Eligibility Example 47 (Anomaly Detection) is instructive. In Example 47, Claim 3 was found eligible because it integrated the abstract idea into a practical application by improving network security through specific remedial actions-specifically, adjusting and optimizing the parameters of a machine learning model reflect an improvement to how the model itself operates (not an abstract idea) and thus satisfy the practical application inquiry the trained ANN output to take specific actions including "dropping the one or more malicious network packets in real time" and "blocking future traffic from the source address." The present claims are analogous: just as Claim 3 in Example 47 was eligible because it used the trained neural network output to take specific remedial actions, the present claims use the trained machine learning model output to take specific actions (automatically initiating phone calls or purchases based on user interactions, and dynamically updating both the machine learning model and user profiles based on those interactions).” (Remarks, pages 17-18).
Examiners Response:
Examiner respectfully disagrees, the claim is more closely drawn to USPTO's Subject Matter Eligibility Example 47 (Anomaly Detection) claim 2, which was found to be recited at a high level of generality and as such could be considered to be directed to mental processes as well as mathematical formulas, with additional elements such as the use of a computer amounting to adding the words “apply it” (or an equivalent) with the judicial exception, or merely using a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2), further finding that the output of the trained ANN could be considered mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Likewise, claim 1 amounts to the abstract idea of managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). A person could receive information about interactions of others, associate items purchased with periodic events, determine a likelihood of a person purchasing an item for the next occurrence of the periodic event, and determine similar items to the determined items and suggest merchants selling those items based on observed knowledge of the person. See MPEP § 2106.04(a)(2)(II)(C). 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. See MPEP § 2111. Further, the additional elements are recited at a high level of generality, and even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application
Argument 10:
“The Appeals Review Panel recognized in Ex Parte Desjardins, claim features directed to adjusting and optimizing the parameters of a machine learning model reflect an improvement to how the model itself operates (not an abstract idea) and thus satisfy the practical application inquiry. The feedback loop recited in the independent claims is analogous as the claims recite a particular, unconventional mechanism for keeping the machine learning model aligned with evolving real-world data, thereby enhancing its predictive accuracy and reliability in a manner that would not be possible through conventional, static model deployment.” (Remarks, page 18).
Examiners Response:
Examiner respectfully disagrees, as in example 48 of the Al-related SME examples 47-49 issued in 2024, which uses a generic deep neural network to apply a judicial exception of a mathematical process and was found to fall under MPEP § 2106.05(f)(2), as well as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers, the use of a generic machine learning model without details on how this the claimed improvement is accomplished also can be considered to fall under MPEP § 2106.05(f)(2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Freno et al. ("One-Pass Ranking Models for Low-Latency Product Recommendations", Freno et al., 2015) discloses using purchase logs collected in e-commerce platforms provide rich information about customer preferences.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB Z SUSSMAN MOSS whose telephone number is (571) 272-1579. The examiner can normally be reached Monday - Friday, 9 a.m. - 5 p.m. ET. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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