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 .
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 instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of the USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. §101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), which is a two-prong inquiry. In prong 1, it must be determined whether the claim recites an abstract idea, a law of nature, or a natural phenomenon, and if so, in prong 2, it must be determined whether the claim recites additional elements that integrate the judicial exception into a practical application. If the claim is determined to be directed to an abstract idea in step 2a, it must additionally be determined in step 2b whether the claim amounts to significantly more than the abstract idea. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. MPEP §2106.04.
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method for recommending bundles of items, as in independent claim 1 and in the claims that depend therefrom. Such methods fall under the statutory category of “process”. Therefore, the claims are directed to a statutory eligibility category.
Step 2A, prong 1. The invention is directed to a method for recommending bundles of items, which is a sales method and, hence, a Certain Method of Organizing Human Activities. MPEP § 2106.04(a). As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“A … method comprising”:
“receiving, via an I…, a historical data of (i) one or more ancillary items, or (ii) one or more bundles purchased by one or more customers as input, wherein each of the one or more bundles include one or more predefined ancillary items”;
“analyzing, via the …, the received historical data to recognize one of (i) a single ancillary item, or (ii) a combination of two or more ancillary items, or (iii) one or more ancillary bundles purchased by the one or more customers”;
“converting, via the …, the historical data into a bundle of one or more ancillary items by assigning a bundle-identification to each of the one or more ancillary bundles if the historical data is recognized as (i) single ancillary item, or (ii) the combination of two or more ancillary items purchased together”;
“identifying, via the …,, one or more existing customers, and one or more new customers of the one or more customers based on the received historical data, wherein each of the one or more existing customers are identified based on a customer identification who has one or more ancillary items, or one or more ancillary bundles purchase history, and wherein the one or more new customers do not have purchase history of one or more ancillary items or one or more ancillary bundles”;
“segmenting, via the …, each of the one or more existing customers into one or more segments based on a Similar Preferred Bundles Customer Segmentation (SPBCS) technique”;
“ranking, via the …, each of the one or more bundles of the one or more ancillary items for each of the one or more segments of one or more existing customers using a Segmented Customer Bayesian Personalized Ranking Learning to Rank technique (SCBPR-LTR) technique and obtain top-k ranked ancillary bundles for each segment of one or more existing customers, wherein top-k ranked ancillary bundles are first k bundles from the list of ranked one or more bundles of ancillary items”;
“pricing, via the …, the top-k ranked bundles of ancillary items for each of the one or more segments of one or more existing customers using a Segmented Customer Optimal Bundle Pricing (SCOBP) technique”;
“constructing top-k ancillary bundle offers by jointly optimizing, via the…, the outputs of the SCBPR-LTR technique and the SCOBP technique considering customer ancillary bundle preferences and revenue maximizing optimal bundle prices for each of the one or more segments of one or more existing customers”; and
“recommending, via the…, the jointly optimized top-k bundles of ancillary items to each of the one or more segments of one or more existing customers”.
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly, the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea. MPEP §2106.04. Thus, under Step 2A, prong 2 of the Mayo framework, the examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" in the related arts.
The instant application includes in Claim 1 additional limitations to those deemed to be abstract ideas. When taken individually, these limitations are
“processor-implemented”;
“nput/Output (I/O) interface”; and
“one or more hardware processors”.
In the instant case, claim 1 is directed to above mentioned abstract idea. Technical functions such as sending, receiving, displaying and processing data are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
Looking to MPEP §2106.05(d), based on court decisions well understood, routine and conventional computer functions or mere instruction and/or insignificant activity have been identified to include: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321,120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TU Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); O/P Techs., /no., v. Amazon.com, Inc., 788 F,3d 1359, 1363, 115 USPQ2d 1090,1093 (Fed. Cir, 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPG2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink," (emphasis added)}; Insignificant intermediate or post solution activity -See Bilski v. Kappos, 581 U.S. 593, 611 -12, 95 USPQ2d 1001,1010 (2010) (well-known random analysis techniques to establish the inputs of an equation were token extra-solution activity); In Bilski referring to Flook, where Flook determined that an insignificant post-solution activity does not makes an otherwise patent ineligible claim patent eligible. In Bilski, the court added to Flook that pre-solution (such as data gathering) and insignificant step in the middle of a process (such as receiving user input) to be equally ineffective. The specification and Claim does not provide any specific process with respect to the display output that would transform the function beyond what is well understood. Like as found in Electric Power Group, Bilski, the technical process to implement the input and display functions are conventional and well understood.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well-understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for authorizing the timing of a payment and to activate a display screen based on a trigger or camera functions that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device and require no more than a generic computing devices to perform generic functions.
CONCLUSION. It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
Dependent claims 2-10, which impose additional limitations, also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2-10, these dependent claims have also been reviewed with the same analysis as independent claim 1. The dependent claims have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, applicant is invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims 11 recites a “memory” and claim 18 recites a “machine-readable information storage medium”, which are generic elements. Claims 11 and 18 are otherwise similar to claim 1 and are rejected for the same reasons. Claims 12-17 and 19 and 20 depend from claims 11 and 18 respectively, are similar to claims 2-10, and are rejected for the same reasons.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 1-9, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2012/0239515 A1 (hereinafter “Batra”) in view of U.S. Patent Application Publication 2024/0311853 A1 (hereinafter “Rendahl”).
With respect to claim 1, Batra discloses
“A processor-implemented method comprising”: Batra, abstract;
“receiving, via an Input/Output (I/O) interface, a historical data of (i) one or more ancillary items, or (ii) one or more bundles purchased by one or more customers as input, wherein each of the one or more
bundles include one or more predefined ancillary items”; Batra ¶ 0028 (historical transaction data relating to an individual item or a bundle of items is received);
“analyzing, via the one or more hardware processors, the received historical data to recognize one of (i) a single ancillary item, or (ii) a combination of two or more ancillary items, or (iii) one or more ancillary bundles purchased by the one or more customers”; Batra ¶¶ 0028, 0029 (historical transaction data are analyzed to determine one or more bundles comprising one or more ancillary items to recommend to a customer);
“converting, via the one or more hardware processors, the historical data into a bundle of one or more ancillary items by assigning a bundle-identification to each of the one or more ancillary bundles if the
historical data is recognized as (i) single ancillary item, or (ii) the combination of two or more ancillary items purchased together”; Batra ¶¶ 0028, 0029 (bundles are determined; one skilled in the art would recognize that each bundle must be identifiable by some bundle identification);
“identifying, via the one or more hardware processor, one or more existing customers, and one or more new customers of the one or more customers based on the received historical data, wherein each of the
one or more existing customers are identified based on a customer identification who has one or more ancillary items, or one or more ancillary bundles purchase history, and wherein the one or more new customers do not have purchase history of one or more ancillary items or one or more ancillary bundles”; Batra ¶¶ 0028, 0029 (existing customers are identified and offered bundles based on historical transaction data; customers lacking historical transaction data are necessarily identified by such lack of data);
“segmenting, via the one or more hardware processors, each of the one or more existing customers into one or more segments based on a Similar Preferred Bundles Customer Segmentation (SPBCS) technique”; Batra ¶ 0041 (similar preferred bundles are created for particular customer segments);
“ranking, via the one or more hardware processors, each of the one or more bundles of the one or more ancillary items for each of the one or more segments of one or more existing customers using a … technique and obtain top-k ranked ancillary bundles for each segment of one or more existing customers, wherein top-k ranked ancillary bundles are first k bundles from the list of ranked one or more bundles of ancillary items”; Batra ¶¶ 0028-0031 (bundles are scored based on complementarity; one skilled in the art would understand that k top-ranked bundles would be presented to the customer);
“pricing, via the one or more hardware processors, the top-k ranked bundles of ancillary items for each of the one or more segments of one or more existing customers using a Segmented Customer Optimal Bundle Pricing (SCOBP) technique”; Batra ¶¶ 0024, 0028, 0029, 0041 (bundles are priced based on customer data, including customer segment);
“constructing top-k ancillary bundle offers by jointly optimizing, via the one or more hardware processors, the outputs of the SCBPR-LTR technique and the SCOBP technique considering customer ancillary bundle preferences and revenue maximizing optimal bundle prices for each of the one or more segments of one or more existing customers”; Batra ¶¶ 0024, 0028, 0029, 0041 (bundle offers are optimized); and
“recommending, via the one or more hardware processors, the jointly optimized top-k bundles of ancillary items to each of the one or more segments of one or more existing customers”. Batra ¶¶ 0028, 0029, 0041 (bundle offers are provided to customer).
Batra does not explicitly disclose a Bayesian ranking technique. Rendahl discloses
“ranking, via the one or more hardware processors, each of the one or more bundles of the one or more ancillary items for each of the one or more segments of one or more existing customers using a Segmented Customer Bayesian Personalized Ranking Learning to Rank technique (SCBPR-LTR) technique and obtain top-k ranked ancillary bundles for each segment of one or more existing customers,
wherein top-k ranked ancillary bundles are first k bundles from the list of ranked one or more bundles of ancillary items”. Rehdahl ¶¶ 0025, 0032, 0033, 0063, 0064, 0087, 0092-0102 (products and bundles are ranked based on a customer’s probability of purchase).
Both Batra and Rendahl relate to recommending product bundles. Batra, abstract; Rendahl, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the Bayesian ranking feature as taught by Rendahl in the method of Batra with the motivation of optimizing bundle recommendations. Rendahl ¶¶ 0001-0004.
With respect to claims 2 and 19, Batra discloses
“further comprising: determining, via the one or more hardware processors, a customer feature vector for each of the one or more identified new customers, wherein the customer feature vector is a set of one or more attributes”; Batra ¶¶ 0028, 0031 (new customers are processed using historical transaction data of similar existing customers, which necessarily includes determining customer feature vector similarity);
“mapping, via the one or more hardware processors, the identified one or more new customers to one or more segments using a new customer handling technique and the SPBCS technique”; Batra ¶¶ 0028, 0029, 0031, 0041 (new customers are mapped to segments);
“ranking, via the one or more hardware processors, one or more bundles of ancillary items for the identified one or more new customers by”: Batra ¶¶ 0028-0031 (bundles are scored based on complementarity; one skilled in the art would understand that k top-ranked bundles would be presented to the customer);
“pricing, via the one or more hardware processors, the top-k ranked bundles of ancillary items for one or more group identified for one or more new customer using the SCOBP technique”; Batra ¶¶ 0024, 0028, 0029, 0041 (bundles are priced based on customer data, including customer segment);
“constructing top-k ancillary bundle offers by jointly optimizing, via the one or more hardware processors, the outputs of the SCBPR-LTR technique and the SCOBP technique considering customer ancillary bundle preferences and revenue maximizing optimal bundle prices for each of the one or more groups of the identified one or more new customers”; Batra ¶¶ 0024, 0028, 0029, 0041 (bundle offers are optimized); and
“recommending, via the one or more hardware processors, the jointly optimized top-k bundles of ancillary items to each of one or more groups of the one or more identified new customers”. Batra ¶¶ 0028, 0029, 0041 (bundle offers are provided to customer).
Rendahl discloses
“identifying the one or more bundles of ancillary items and their ranking scores for one or more mapped segments associated with the identified one or more new customers using the SCBPR-LTR technique, and sorting the identified one or more bundles of ancillary items based on the ranking scores and obtain top-k ranked one or more bundles of ancillary items for identified one or more new customers, wherein the top-k ranked one or more bundles is identified as the group for the new customer”. Rehdahl ¶¶ 0025, 0032, 0033, 0063, 0064, 0087, 0092-0102 (products and bundles are ranked based on a customer’s probability of purchase).
With respect to claims 3, 13, and 20, Batra discloses
“wherein the SPBCS technique involves a segmentation of the one or more customers into one or more segments comprises: analyzing, via the one or more hardware processors, the one or more bundles historically purchased by the one or more customers to determine a unique set of ordered bundles purchased and a unique set of customer feature vectors for each of the one or more customers; finding, via the one or more hardware processors, one or more customers having a similar unique set of ordered bundles purchased by forming key-value pairs with segment as key and customers in the segment as value, wherein the segment is the unique set of ordered bundles historically purchased; finding, via the one or more hardware processors, a unique set of customer feature vectors associated with the segments by forming the key-value pairs with the segment as key and the unique set of customer feature vectors associated with the segment as value; assigning, via the one or more hardware processors, a segment-identification to each of the identified list of segments and associate this segment identification with the customers in the segment, unique set of ordered bundles purchased in the segment and unique set of customer feature vectors associated with the segments; and btaining, via the one or more hardware processors, one or more segment side features for each of the one or more segments to be used in the SCBPR-LTR technique”. Batra ¶¶ 0024, 0028, 0029, 0041 (items and bundle offers are analyzed based on customer similarity and value; bundle offers are determined based on such analysis).
Rendahl discloses
“storing, via the one or more hardware processors, the output of the SPBCS technique comprises of segment side features, segment-identification, customers in the segments and unique set of ordered
bundles purchased associated with the segment into the ancillary bundled offers recommendation system database”. Rehdahl ¶¶ 0025, 0032, 0033, 0063, 0064, 0087, 0092-0102 (products and bundles are ranked based on a customer’s probability of purchase).
With respect to claims 4 and 14, Batra discloses
“ wherein the new customer handling technique comprises identifying an existing customer feature vector that matches with the new customer feature vector using … similarity and then the existing customer feature vector to segments mapping, and wherein the existing customer feature vector to segment mapping involves identifying one or more existing customers that matches with identified existing customer feature vector and then mapping identified existing customers to one or more segments obtained using … technique”. Batra ¶¶ 0024, 0028, 0029, 0041 (customers are mapped to segments based on similarity).
Rendahl discloses “the SPBCS technique”. Rehdahl ¶¶ 0025, 0032, 0033, 0063, 0064, 0087, 0092-0102 (products and bundles are ranked based on a customer’s probability of purchase).
While Batra does not explicitly disclose what technique is to be used to determine similarity, cosine similarity is one of a limited number of known techniques for determining similarity. It would have been obvious to try to one skilled in the art before the effective filing date of the claimed invention cosine similarity.
With respect to claim 5, Batra discloses
“wherein the SCBPR-LTR technique provides an ancillary bundle ranking for each segment without price”. Batra ¶¶ 0024, 0028, 0029, 0041 (bundles are ranked based on non-price factors including value).
With respect to claims 6 and 15, Batra discloses
“wherein the SCBPR-LTR technique comprises of a segment relative preference bundle data generation, an optimization criterion SCBPR-OPT, a recommendation model (Factorization machine), optimization model (Stochastic gradient descent), a trained model evaluation and storing the optimally ranked one or more bundles of ancillary items for each segment of customers into ancillary bundled offers recommendation system database, wherein the trained model evaluation involves evaluating the recommendation model that is trained using ranking evaluation metrics using segment relative preference test data”. Batra ¶¶ 0024, 0028, 0029, 0041 (bundles are optimized and ranked).
With respect to claims 7 and 16, Batra discloses
“wherein the segment relative preference bundle data generation includes generation of a segment of customers and a pair of bundles where the first bundle, referred to as the positive bundle, is chosen from the segment's positive feedback (purchased bundle), and the second bundle, referred to as the negative bundle, is sampled from an unobserved interaction (non-purchased bundle), wherein this data is divided into segment relative preference bundle training data and test data and stored into ancillary bundled offers recommendation system database”. Batra ¶¶ 0024, 0028, 0029, 0041 (purchased bundles are treated separately from non-purchased bundles, which receive lower ranking).
With respect to claims 8 and 17, Rendahl discloses
“wherein the optimization criterion SCBPR-Opt is derived by maximizing the posterior probability of Bayesian analysis of the pairwise ranking and training the SCBPR-LTR involves optimization done based on optimization model that is stochastic gradient descent with graph sampling to optimize the factorization machine model that provides ranking score used in ranking one or more bundles of ancillary items with respect to the SCBPR-optimization criterion (SCBPR-Opt) to arrive at the optimal personalized ranking for all segments of customers”. Rehdahl ¶¶ 0025, 0032, 0033, 0063, 0064, 0087, 0092-0102 (bundles are optimized based on likelihood of purchase).
With respect to claim 9, Batra discloses
“wherein the SCOBP technique learns a bundle purchase probability, an ancillary bundle revenue and a revenue maximizing-optimal bundle prices for segmented customers using historical purchased data of (i) one or more ancillary items, or (ii) one or more bundles purchased by one or more customers and relevant segments from SPBCS technique as input”. Batra ¶¶ 0024, 0028, 0029, 0041 (bundle prices are determined based in part on historical transaction data).
With respect to claim 11, Batra discloses
“a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to”. Batra ¶¶ 0043, 0046.
Claim 11 is otherwise rejected on the same basis as claim 1.
With respect to claim 18, Batra discloses
“One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause”. Batra ¶¶ 0043, 0046.
Claim 18 is otherwise rejected on the same basis as claim 1.
Claims 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Batra in view of Rendahl as applied to claims 1-9, 11, and 13-20 above, and further in view of U.S. Patent Number 11,250,461 B2 (hereinafter “Song”).
With respect to claim 10, Batra discloses
“wherein the SCOBP technique comprising: training, via the one or more hardware processors, a … model based on the received historical purchased data and relevant segments from the SPBCS technique to estimate pricing parameters of the (i) one or more ancillary items, or (ii) one or more bundles purchased by one or more segmented customers; determining, via the one or more hardware processors, an optimal price that maximizes revenue of one or more ranked ancillary bundles recommended for one or more segments of customer over the range of price using estimated pricing parameters and a standard optimization technique, wherein optimal price is determined for one or more ancillary bundle combination recommended considering the relative comparison between bundles when using one or more bundles purchase data and optimal price is determined for one or more ancillary bundle recommended when using one or more ancillary items purchase data; and storing, via the one or more hardware processors, the optimal price of one or more ranked bundles of ancillary items for each segmented customer in Ancillary bundled offers recommendation system database”. Batra ¶¶ 0024, 0028, 0029, 0041 (model is trained to optimize bundle prices).
Song discloses a “Multi-Layer Perceptron (MLP) classifier model”. Song, claims 1, 7, 13.
Batra and Song both relate to offer recommendations. Batra, abstract; Song, abstract. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the MLP feature as taught by Song in the method of Batra with the motivation of reducing computational burdens. Song 1:23-2:25.
Claim 12 is rejected on the same basis as claims 2 and 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 2019/0362407 A1 (hereinafter “Garg”) discloses determining product bundles based on price and value. Garg, abstract.
U.S. Patent Application Publication 2017/0278173 A1 (hereinafter “Ettl”) discloses personalized bundle recommendations. Ettl, abstract, ¶¶ 0052, 0064, 0076.
U.S. Patent Application Publication 2014/0310065 A1 (hereinafter “Chowdhary”) discloses bundle pricing. Chowdhary, abstract, ¶ 0062.
U.S. Patent Application Publication 2014/0058872 A1 (hereinafter “Sandholm”) discloses bundle pricing. Sandholm, abstract, ¶¶ 0007, 0027.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETHAN D CIVAN whose telephone number is (571)270-3402. The examiner can normally be reached Monday-Thursday 8-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A Smith can be reached at (571) 272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ETHAN D. CIVAN
Primary Examiner
Art Unit 3688
/ETHAN D CIVAN/Primary Examiner, Art Unit 3688
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