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 .
Notice of Pre-AIA or AIA Status
This is the first Non-Final Office Action in response to Application Serial Number: 19/077,535, filed on March 12, 2025. Claims 1-12 are pending in this application and have been rejected below.
Priority
Acknowledgment is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The Examiner has noted the Applicant is claiming priority from Chinese Application No. CN202410294407.3 filed March 14, 2024.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Information Disclosure Statement
The information disclosure statement (IDS) filed on March 12, 2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Here, even though “means for” has not been explicitly recited, claim limitations “a data analysis module comprises a market analysis module, a traffic analysis module, and a price analysis module; the market analysis module comprises a traffic word module and a competitive commodity module, and the traffic word module is configured to monitor a rank change of an e-commerce commodity traffic word; the competitive commodity module is configured to monitor a sale volume change trend of a competitive commodity; the traffic analysis module is configured to analyze a traffic change of the e-commerce commodity, and the price analysis module is configured to analyze a price change trend of the e-commerce commodity and a price change trend of the competitive commodity; the data analysis module is configured to analyze a sale volume change of the e-commerce commodity based on a plurality of influence factors in claim 1, the traffic analysis module comprises an advertisement traffic module for analyzing an advertisement traffic change and a natural traffic module for analyzing a natural traffic change in claims 3, the data analysis module is configured to reduce each importance weight of an external market factor, exposure change influence and price change influence from high to low importance weight in clam 4, the data analysis module is configured to perform step S3.11, and step S3.1 in claim 8, and the data analysis module is configured to perform step S3.23, and step S3.23 and the data analysis module is configured to perform step S3.24, and step S3.24 in claim 9 ” have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “module is configured to” coupled with functional language “data analysis”, “traffic analysis”, “price analysis”, “market analysis”, “traffic word”, “competitive commodity”, “advertisement traffic”, and “natural traffic module” respectively without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1, 3, 4, 8 and 9 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: [0030]-[0032].
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-12 are directed towards a system, which is one of the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite analyzing a sale volume of an e-commerce commodity.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity. Specifically, monitor a rank change of an e-commerce commodity traffic word; monitor a sale volume change trend of a competitive commodity; the e-commerce commodity is a commodity to be analyzed, and the competitive commodity is a commodity belonging to a commodity category corresponding to the commodity to be analyzed; analyze a traffic change of the e-commerce commodity, and analyze a price change trend of the e-commerce commodity and a price change trend of the competitive commodity; analyze a sale volume change of the e-commerce commodity based on a plurality of influence factors, and the plurality of influence factors comprise traffic word rank data of the e-commerce commodity, sale volume data of the competitive commodity, price data of the e-commerce commodity, and price data of the competitive commodity; and the plurality of influence factors further comprise a number of negative reviews at a homepage of the e-commerce commodity, a number of negative reviews at a Review page, and a Rating score constitutes methods based on commercial interactions including advertising, marketing or sales activities or behaviors. The recitation of the data analysis module comprising a market analysis module comprising traffic a word module and competitive commodity module, traffic analysis module and price analysis module does not take the claim out of the certain methods of organizing human activity grouping. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a system comprising a data analysis module comprising a market analysis module comprising traffic a word module and competitive commodity module, traffic analysis module and price analysis module at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a system comprising data analysis module comprising a market analysis module comprising traffic a word module and competitive commodity module, traffic analysis module and price analysis module medium amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); and electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); see MPEP 2106.05(d)(II) (see at least Specification [0032]). Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2, 5, 9, 10 and 12 recite display limitations which are considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g). Claim 3 recites an advertisement module and natural traffic module at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 3-12 recite steps that further narrow the abstract idea. Therefore claims 2-12 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Park et al., U.S. Publication No. 2025/0272711 [hereinafter Park].
Referring to Claim 1, Park teaches:
A system for analyzing a sale volume of an e-commerce commodity, comprising:
a data analysis module, and the data analysis module comprises a market analysis module, a traffic analysis module, and a price analysis module (Park, [0045]), “the system and method of the present disclosure, the data store 230 may store computer executable code associated with an assessment module 232, a text analytics module 238, a filtering module 235, a comparison module 234, a recommendation module 236, and a competitivity score generating module 233”; (Park, [0051]), “The present specification further contemplates that any of the assessment module 232, competitivity score generating module 233, comparison module 234, filtering module 235, recommendation module 236, and text analytics module 238 may be distributed amongst a number of computing devices (e.g., computing devices 120 of FIG. 1) and/or amongst any server (e.g., 150 of FIG. 1)”;
the market analysis module comprises a traffic word module and a competitive commodity module, and the traffic word module is configured to monitor a rank change of an e-commerce commodity traffic word (Park, [0045]), “The data store 230 may further include data associated with descriptive terms 241 related to a target product and/or a competing product, relevant descriptive terms 242 associated with either of the target product or a competing product”; (Park, [0106]), “current performance on the search terms related to the target product is shown in FIG. 10. As shown in FIG. 10, the further to the right of the graph any search term (e.g., represented by a circle) is, the search term has a higher volume or appears more often than the other search terms indicating a relatively higher relevance to competing products. Additionally, the further to the left of the graph any search term is, the search term has a lower volume or appears less often than the other search terms indicating a relatively lower relevance to competing products. Also, the further to the top of the graph any search term is, the search term has a higher relevance than the other search terms indicating a relatively higher relevance to competing products. Further as the search term is placed lower on the graph, the search term has a lower relevance than the other search terms indicating a relatively lower relevance to competing products. The most frequently searched and relevant terms may be provided to the comparison module 834 as well and used to further define the sustainability and feasible growth over time of the target product on, for example, the digital marketplace 882”; (Park, [0098]; [0105]; [0120]);
the competitive commodity module is configured to monitor a sale volume change trend of a competitive commodity (Park, [0070]), “a recommendation module 336 may receive this competitivity score 339 along with other data from the digital marketplace 382 hosted by the server 350. Among this other data may include revenue data associated with the organic competing products and the target product (if available). For example, where a click-rate of any given product (e.g., target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module 336”; (Park, [0071]; [0089]);
the e-commerce commodity is a commodity to be analyzed, and the competitive commodity is a commodity belonging to a commodity category corresponding to the commodity to be analyzed; the traffic analysis module is configured to analyze a traffic change of the e-commerce commodity, and the price analysis module is configured to analyze a price change trend of the e-commerce commodity and a price change trend of the competitive commodity (Park, [0065]), “A “competing” product is any product that is similar to the target product but sold by another seller apart from the seller of the target product. The “similarity” of the target product relative to the at least one organic competing product is dependent on the data obtained by the text analytics module 338 and specifically the analysis of descriptive terms 341 associated with each of these types of products”; (Park, [0100]), “The machine learning module 896 may build a number of mathematical models that provide a competitive set report 898 describing a competitive set of products that compete with the target product”; (Park, [0101]), “The machine learning module 896, in some embodiments, may propagate input through the layers of the neural network to project or predict optimal competitive set report 898 based on the known and recorded search term metrics, and compare these projected values to optimal search terms to be presented in the competitive set report 898. Using a back-propagation method, the machine learning module 896, in some embodiments, may then use the difference between the projected values and the known optimal values to adjust weight matrices of the neural network describing the ways in which changes in each of the search term data metrics are likely to affect the optimal search terms to be presented in the competitive set report 898”; (Park, [0131]), “As a target product succeeds on new search terms the competitive products set defined in the competitive set report 898 will shift to be compared to larger and less niche competing products. As the competitive products set defined in the competitive set report 898 shifts, the competitive terms set will shift as well. As reviews, terms, seller ranks, and other attributes shift, the winnability and associated required investment of each term also shifts. With the shift in winnability, new terms are prioritized and the cycle continues iteratively to cause the revenue associated with the targeted product to increase proportionally”; (Park, [0070]-[0071]; [0088]-[008]), “price points”; (Park, [0116]), “a current and historical price for both the target product and competitive products. This historical pricing may be retrieved from one or more digital marketplaces 882 via the execution of the processor 810 and NID 880 as described herein. In this specific example, the processor 810 may cause the NID 880 to access the one or more digital marketplaces 882 either via a wired (wired transmitter/receiver 840) or wireless (wireless transmitter/receiver 850) connection, find instances of the target product and competing products being sold, and retrieve their historic pricing values”; (Park, [0105]; [0113]);
the data analysis module is configured to analyze a sale volume change of the e-commerce commodity based on a plurality of influence factors, and the plurality of influence factors comprise traffic word rank data of the e-commerce commodity, sale volume data of the competitive commodity, price data of the e-commerce commodity, and price data of the competitive commodity (Park, [0079]), “Each of these target product attributes may be requested by the computing device 420 and its assessment module 432 and delivered by the server 452 upon request. Even further, similar attributes related to at least one organic competing product may also be requested by and sent to the computing device 420. These organic product attributes may include competing product ratings 488, competing product review numbers 489, competing product prices 490, competing product content 491, and competing product rank 492. Each of these competing product attributes may be similar to those attributes associated and described herein in connection with the target product”; (Park, [0070]), “a recommendation module 336 may receive this competitivity score 339 along with other data from the digital marketplace 382 hosted by the server 350. Among this other data may include revenue data associated with the organic competing products and the target product (if available). For example, where a click-rate of any given product (e.g., target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module 336. The recommendation module 336 may then provide a recommendation descriptive of the ability (or inability) of the target product to compete with the at least one organic competing product…” (Park, [0112]), “The price of the target product may be, in some embodiments, a suggested retail price by the manufacturer. In some embodiments, the quantitative value of the price in Equation 1 is an average price of the target product, or other brand products resulting from the purchase, across any plurality of digital marketplaces 882 net of any discounts or promotions associated with those sales. This data may be retrieved by the processor 810 by accessing a particular database, accessing a search query website as described herein, or accessing sales data from a database maintained by the manufacturer of the target product”; (Park, [0065]-[0067]; [0081]; [0116]); and
the plurality of influence factors further comprise a number of negative reviews at a homepage of the e-commerce commodity, a number of negative reviews at a Review page, and a Rating score (Park, [0074]-[0076]), “the data request may be a request for attributes regarding the target product…Often, digital marketplaces 482 provide graphical user interfaces (GUIs) to consumers that allows those consumers to rate the products they purchase on the digital marketplace 482… A one-star rating would indicate a poor assessment by the consumer/purchaser of the target product … The assessment module 432 may, therefor, take each star-rating or an average of those star-ratings as input for use in creating the actionable report… A second attribute may include the content 486 of the reviews and description associated with the target product. Again, digital marketplaces 482 often provide a GUI that allow the consumer of the target product to enter text descriptive of the consumers' experiences with the target product. This text may include specific positive keywords or negative keywords that describe the consumers' experience with the target product… A third attribute may be the number of the reviews 484 associated with the target product presented on the digital marketplace 482. The number of reviews 482 may indicate a level of involvement with the target product either for the disparaging of the target product or the approval of the target product. Along with the textual substance of these reviews, the number of reviews associated with the target product may be used to help create the actionable report based on the involvement within the digital marketplace 482 with the target product”; (Park, [0117]), “Digital marketplaces 882 often provide a GUI that allows the consumer of the target product and competing products to enter text descriptive of the consumers' experiences with the target product and competing products as well as a ranked evaluation of those products in the form of a number rating system or star rating system. In this specific example, the processor 810 may cause the NID 880 to access the one or more digital marketplaces 882 either via a wired or wireless connection and find review ratings and review counts associated with the target product and competing products being sold, and provide that review ratings and review counts data to the machine learning module 896”; (Park, [0117]), “…Digital marketplaces 882 often provide a GUI that allows the consumer of the target product and competing products to enter text descriptive of the consumers' experiences with the target product and competing products as well as a ranked evaluation of those products in the form of a number rating system or star rating system. In this specific example, the processor 810 may cause the NID 880 to access the one or more digital marketplaces 882 either via a wired or wireless connection and find review ratings and review counts associated with the target product and competing products being sold, and provide that review ratings and review counts data to the machine learning module 896”; (Park, [0031]; [0065]).
Referring to Claim 2, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 1. Park further teaches:
wherein the plurality of influence factors further comprise page display element change data of the competitive commodity, and the page display element change data comprises title change data for the competitive commodity, main picture change data and BulletPoint adjustment data (Park, [0056]), “the user outputs 270 may present to a user a graphical user interface that, according to the methods described herein, display a listing of relevant descriptive terms 242 of the target product and competitive product as well as display an actionable report that describes a projected performance of the target product in a computer-networked marketplace relative to the at least one organic competing product also presented on the computer-networked marketplace.”; (Park, [0080]), “a computing device 520 that includes a graphic user interface 522 used to enable practice of the disclosure within a client/server architecture. The graphic user interface 522 may be used by a seller of a target product to evaluate the competitivity of the target product as described herein. As described herein, the computing device 520 includes a filtering module 535. The filtering module 535 may be used to filter the descriptive terms 541 to only those relevant descriptive terms 542 that have resulted in the purchase of the target product in the digital marketplace”; (Park, [0081]-[0082]; [0102]).
Referring to Claim 3, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 1. Park further teaches:
wherein: the plurality of influence factors further comprise commodity category demand change data of the e-commerce commodity; and/or the plurality of influence factors further comprise traffic exposure data of the e-commerce commodity; and/or the traffic analysis module comprises an advertisement traffic module for analyzing an advertisement traffic change and a natural traffic module for analyzing a natural traffic change (Park, [0135]), “The computing device 322 may generate a fifth ROAS value for the target product by adding any attributable movement in organic ranking to the fourth ROAS value for the target product. For example, the computing device 322 may identify a click share for each keyword for the target product before and after advertising associated with the target product. If the computing device 322 determines that the rank of the target product has improved over time (e.g., and is likely directly caused by the results of the advertising traffic and purchases), the computing device 322 may determine that the improvement is attributed to advertising, and the computing device 322 generates the fifth ROAS value by adding an organic rank value (e.g., corresponding to additional clicks and sales stemming from the improved rank) to the fourth ROAS value for the target product”; (Park [0137]-[0138]).
Referring to Claim 4, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 1. Park further teaches:
wherein in response to analyzing the plurality of influence factors, the data analysis module is configured to reduce each importance weight of an external market factor, exposure change influence and price change influence from high to low importance weight (Park, [0113]), “any of the impression values, click rate values, conversion rate values, basket size values, and price values in Equation 1 may be augmented by a weight value. In this embodiment, the weight value may accentuate or abate the effect of any one of these values in Equation 1 in order to better determine an increased revenue value or opportunity by the seller of the target product to increase that revenue. Because the actual, real-time data is being used in Equation 1, the seller of the target product or user of the computing device 822 may know, in real-time, whether to take advantage of any instance of increased views or sales of a product in order to increase interest in the target product over any competitors' products”; (Park, [0101]; [0104]; [0114]; [0125]-[0128]).
Referring to Claim 5, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 1. Park further teaches:
wherein in response to analyzing the sale volume change of the e-commerce commodity, the step of analyzing the sale volume change comprises:
step S1, obtaining sale volume data of the e-commerce commodity on an e-commerce platform; step S2, obtaining preset marketing data, traffic data and price data on the e-commerce platform where the e-commerce commodity is displayed; step S3.1, analyzing the preset marketing data to obtain a commodity category demand change and competitive commodity influence corresponding to the e-commerce commodity, and determining market demand change influence based on the commodity category demand change and the competitive commodity influence (Park, [0112]-[0113]), “The price of the target product may be, in some embodiments, a suggested retail price by the manufacturer. In some embodiments, the quantitative value of the price in Equation 1 is an average price of the target product, or other brand products resulting from the purchase, across any plurality of digital marketplaces 882 net of any discounts or promotions associated with those sales. This data may be retrieved by the processor 810 by accessing a particular database, accessing a search query website as described herein, or accessing sales data from a database maintained by the manufacturer of the target product… any of the impression values, click rate values, conversion rate values, basket size values, and price values in Equation 1 may be augmented by a weight value. In this embodiment, the weight value may accentuate or abate the effect of any one of these values in Equation 1 in order to better determine an increased revenue value or opportunity by the seller of the target product to increase that revenue. Because the actual, real-time data is being used in Equation 1, the seller of the target product or user of the computing device 822 may know, in real-time, whether to take advantage of any instance of increased views or sales of a product in order to increase interest in the target product over any competitors' products”;
step S3.2, analyzing the traffic data to obtain exposure change influence of the e- commerce commodity; step S3.3, analyzing the price data to obtain price change influence of the e-commerce commodity; and step S4, determining a target factor causing sale volume fluctuation of the e-commerce commodity based on the market demand change influence, the exposure change influence and the price change influence of the e-commerce commodity (Park, [0114]), “the value associated with click rate in Equation 1 may significantly shift a decision by a user of the computing device 822 whether to take an action such as provide more advertising supporting the target product. This click rate associated with improving the search rank from the target product's current position on a search term to a potential rank position of a search phrase may be weighted to accommodate for an increase in importance of this value in some embodiments. For example, for a given search term that may improve an organic search rank for any of the search terms from 20th rank to 5th rank will improve the click rate by an estimated 3 times. Some of the improvement in rank may also originate from increased impressions and especially in situations where having an unranked target product on a search term achieves a search rank 10th among the rankings. In this example, this would improve clicks from zero (due to zero impressions) to the associated estimated clicks of 10th rank on that search term. As output, the processor 810 may, via the revenue module 899, provide an increased revenue report 802 describing how to, if at all, increase the revenue related to the sales of the target product”; (Park, [0115]-[0116]).
Referring to Claim 6, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 5. Park further teaches:
wherein step S3.1 comprises:
step S3.11, obtaining search rank data of the e-commerce commodity traffic word on the e-commerce platform; and step S3.12, analyzing the search rank data of the e-commerce commodity traffic word to obtain the commodity category demand change corresponding to the e-commerce commodity (Park, [0068]), “With the relevant descriptive terms 342 being determined, these relevant descriptive terms 342 may be sent to a comparison module 334 to compare those relevant descriptive terms 342 of the target product to those relevant descriptive terms 342 associated with the at least one organic competing product… In a specific example, the top 10 ranked organic competing products may be compared to the target product by the comparison module 334”; (Park, [0064]-[0065]), “A fifth attribute may also include a ranking of the target product relative to at least one organic competing product. This ranking may be a result of an average or accumulative rating of the target product relative to the organic competing product. Often, the digital marketplaces 382 allow purchasers to list organic competing products and the target product by an average rating. By doing so the assessment module 332 may understand the ranking of the target product relative to the at least one organic competing product and use this information to develop the actionable report 337… The assessment module 332 may also determine similar attributes of an at least one organic competing product similar to those attributes discovered by the assessment module 332 for the target product. In the context of the present specification the term “organic competing product” is meant to be understood as any product that, based on consumer reviews, is ranked on the digital marketplace 382. An “organic” competing product is therefore a naturally ranked product based on those reviews provided by past consumers as opposed to those products that may be given “top shelf” preference after payment to achieve such status. This organic ranking nature of products on the digital marketplace 382 is often done to provide potential consumers with evidence that others appreciate that product. A “competing” product is any product that is similar to the target product but sold by another seller apart from the seller of the target product. The “similarity” of the target product relative to the at least one organic competing product is dependent on the data obtained by the text analytics module 338 and specifically the analysis of descriptive terms 341 associated with each of these types of products. In a specific embodiment, the text analytics module 338 may also obtain descriptive data associated with each target product and organic competing product per their listing…”; (Park, [0108]-[0109]), “the impressions may be defined as the search volume of each of those most relevant and most frequent search terms, in some embodiments. In some embodiments, the quantity of impressions may be measured by a number of times an ad associated with the target product is presented to any given user during or after those most relevant and most frequent search terms are entered into a search engine 894. This data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website…the click rate of Equation 1 may be defined as an estimation along a curve of the probabilities of receiving clicks associated with the rank for each of the most relevant and most frequent search terms provided by the actionable report 837…”; (Park, [0079]).
Referring to Claim 7, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 5. Park further teaches:
wherein step S3.2 comprises:
step S3.21, obtaining each commodity advertisement sale volume corresponding to each advertisement delivery strategy applied to the e-commerce commodity on the e-commerce platform; step S3.22, determining a contribution ratio of the commodity advertisement sale volume to the sale volume fluctuation of the e-commerce commodity, and determining an advertisement delivery strategy with the contribution ratio reaching a preset ratio as a target delivery strategy (Park, [0134]-[0135]), “The computing device 322 may generate a fourth ROAS value for the target product by adding a lifetime revenue of customers acquired via advertising to the ad sales number. For example, the computing device 322 may determine a lifetime revenue value by multiplying a percentage of advertising customers that are first time purchasers of the advertised brand by the lifetime value of shoppers that purchase the advertised product. The computing device 322 may generate the fourth ROAS value for the target product by adding the lifetime revenue value to the third ROAS value for the target product… For example, the computing device 322 may identify a click share for each keyword for the target product before and after advertising associated with the target product. If the computing device 322 determines that the rank of the target product has improved over time (e.g., and is likely directly caused by the results of the advertising traffic and purchases), the computing device 322 may determine that the improvement is attributed to advertising, and the computing device 322 generates the fifth ROAS value by adding an organic rank value (e.g., corresponding to additional clicks and sales stemming from the improved rank) to the fourth ROAS value for the target product”;
step S3.23, determining an advertisement delivery factor causing an advertisement traffic change based on the target delivery strategy ; and step S3.24, determining the exposure change influence based on the advertisement delivery factor (Park, [0138]-[0139]), “where platform reported revenue and/or platform reported advertising spend is received from the marketplace (e.g., or platform) and indicates advertising spend revenue for the target product. Cannibalization is a deduction from ROAS and indicates a percentage of the sales that would have been captured without advertising (e.g., because other advertisements for other products associated with the target product may appear alongside the target product on the marketplace). Cannibalization may be discovered through both experimental and non-experimental sources. Experimental sources may include: (i) A/B tests (e.g., intentional tests to evaluate incremental effects of ads, where inputs include products and advertising timing, and outputs include incremental sales) that output values that are generalized to a larger context; (ii) natural experiments (e.g., discovering naturally occurring scenarios that are similar to A/B tests, which are not put in place intentionally for the purpose of testing and/or scenarios where the advertising budget is changed independent of performance, such as when a brand determines to abruptly shut down or start up advertising for a period) that output results that are leveraged similarly to A/B tests. Non-experimental sources (e.g., indicating whether changes to relevant inputs, such as advertising, result in outputs, such as total sales, that follow the expected modeled path) may include observational impacts based on relevant cannibalization factors, such as product and brand placement in search, market share, and the like, and may include: (i) organic placement by brand by product by search terms (e.g., where products land in organic rank placement on a page of the marketplace); (ii) traffic volume and click estimation models for by search terms, using platform data, model how often the search term is searched per day, and how many total times do users click on products throughout the page; (iii) when available, data from the platform informing the number of clicks a product and/or brand receives on a search term; (iv) clicks per placement on the page, using rank placement from click estimations, and when available, creating a model estimating how many clicks each placement on the page is receiving… cannibalization may also be based on placements for the brand on the page and experimental and non-experimental results, for every brand and/or keyword pair. The computing device 322 may estimate the total number of clicks a brand would likely receive without advertising and compare to the total number of clicks with advertising”; (Park, [0140]), “The computing device 322 may determine a returns value based on: (i) returns reported by the platform and related costs (e.g., indicating a previous advertising sale was returned, which may be directly deducted from the advertising sales); and (ii) returns not reported by the platform (e.g., the platform may not report if advertising sales were returned, or the platform only reports returned advertising sales within a limited time period (e.g., three days). If platform does not report returns, the computing device 322 may estimate the returns value using other sources of data, such as general return rates on the platform and generalize to advertising data. If platform only reports for limited period, the computing device 322 may leverage platform advertising return rates over the period of data that is provided and extrapolate out to the period not reported by the platform (e.g., if the platform only reports returns for the first seven days, the computing device 322 may combine the reported returns value with the total return rate to estimate the general return rate extending out multiple weeks after purchase). The computing device 322 may combine the total returns with the period of advertising sales returns to estimate later returns”.
Referring to Claim 8, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 6. Park further teaches:
wherein: the preset marketing data further comprises category sale volume data of a preset category and sale volume data of the competitive commodity of the preset category, wherein the e-commerce commodity belongs to the preset category (Park, [0032]), “the first product value may be determined based on a comparison between an advertisement sales value and an advertisement spend value for the target product. The first product value may be received from a computer-networked marketplace. The systems and methods described herein may then be configured to determine, for the target product, a cannibalization value based on at least one of experimental data and non-experimental data. The experimental data may be associated with at least one intentional experiment configured to identify advertising data indicating incremental effects of advertisements for products associated with the computer-networked marketplace, at least one natural experiment configured to discover advertising data indicating naturally occurring advertising scenarios of products associated with the computer-networked marketplace, other suitable experiments, or a combination thereof. The non-experimental data may be associated with at least product placement and market share for products associated with the computer-networked marketplace”;
in response to that the data analysis module is configured to perform step S3.11, and step S3.1 further comprises:
step S3.13, comparing the category sale volume data, the sale volume data of the competitive commodity with the sale volume of the e-commerce commodity in terms of a change degree and a change trend to obtain a first comparison result; and step S3.14, determining the competitive commodity influence corresponding to the e- commerce commodity according to the first comparison result (Park, [0071]-[0072]), “the recommendation module 336 may provide additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue. Again, a seller of the target product may not know what appropriate target advertising cost of sale (ACoS) to meet and what price point to sell the target product at in order to see long term gains in lieu of short-term profits. The recommendation module 336 provides this information based on the competitivity score 339 generated by the competitivity score generating module 333 and revenue data received from the digital marketplace 382… This revenue potential of each of the target products and organic competing products may be ranked to determine the placement of the target product within the digital marketplace 382… the recommendation (e.g., the actionable report 337) presented by the recommendation module 336 may be refined by inputting an estimated bid amount from the digital marketplace 382 required to “win” advertising slots for the target product. The digital marketplace 382, along with selling products, may also engage in presenting advertisements to a potential purchaser of one or more products… A return on investment (ROI) may then be calculated by subtracting the investment needed from an investment payoff term and multiplying that by the ad spend potential. Products with no (or low) potential receive suggestion outputs as to why they are not competitive or have bad conversion rates by the recommendation module 336 and its actionable report 337, so that these attributes of the target product can be improved for future potential or the money spent to sell the target product can be reallocated for other uses”; (Park, [0073]).
Referring to Claim 9, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 7. Park further teaches:
wherein:
the preset marketing data further comprises category sale volume data of a preset category and sale volume data of the competitive commodity of the preset category, wherein the e- commerce commodity belongs to the preset category (Park, [0110]-[0111]), “The conversion rate in Equation 1 may, in some embodiments, be defined as percentage of those most relevant and most frequent search terms that were clicked and associated with the target product and converted into a sale (e.g., resulted in a sale of the target product). This data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website… the basket size may be defined as the number of units purchased with each conversion. This number may be averaged over a plurality of purchases, in some embodiments. For example, where a number of conversions have been detected, the processor 810 may calculate how many units of the target product were purchased at any one time (e.g., units placed in a “shopping cart” for purchase at the digital marketplace 882). This value may at least be equal to 1 or more. Again, this data may be retrieved by the processor 810 by accessing a particular database or, as described herein, accessing a search query website”; (Park, [0063]; [0065]-[0066]; [0134]-[0135]);
in response to that the data analysis module is configured to perform step S3.23, and step S3.23 comprises:
step S3.231, obtaining each advertisement sale volume contribution rate corresponding to an exposure link, a click link and a conversion link of the target delivery strategy (Park, [0134]-[0135]), “The computing device 322 may generate a fourth ROAS value for the target product by adding a lifetime revenue of customers acquired via advertising to the ad sales number. For example, the computing device 322 may determine a lifetime revenue value by multiplying a percentage of advertising customers that are first time purchasers of the advertised brand by the lifetime value of shoppers that purchase the advertised product. The computing device 322 may generate the fourth ROAS value for the target product by adding the lifetime revenue value to the third ROAS value for the target product… For example, the computing device 322 may identify a click share for each keyword for the target product before and after advertising associated with the target product. If the computing device 322 determines that the rank of the target product has improved over time (e.g., and is likely directly caused by the results of the advertising traffic and purchases), the computing device 322 may determine that the improvement is attributed to advertising, and the computing device 322 generates the fifth ROAS value by adding an organic rank value (e.g., corresponding to additional clicks and sales stemming from the improved rank) to the fourth ROAS value for the target product”;
step S3.232, determining an advertisement delivery factor causing an exposure change of the e-commerce commodity according to the advertisement sale volume contribution rate (Park, [0138]-[0139]), “where platform reported revenue and/or platform reported advertising spend is received from the marketplace (e.g., or platform) and indicates advertising spend revenue for the target product. Cannibalization is a deduction from ROAS and indicates a percentage of the sales that would have been captured without advertising (e.g., because other advertisements for other products associated with the target product may appear alongside the target product on the marketplace). Cannibalization may be discovered through both experimental and non-experimental sources. Experimental sources may include: (i) A/B tests (e.g., intentional tests to evaluate incremental effects of ads, where inputs include products and advertising timing, and outputs include incremental sales) that output values that are generalized to a larger context; (ii) natural experiments (e.g., discovering naturally occurring scenarios that are similar to A/B tests, which are not put in place intentionally for the purpose of testing and/or scenarios where the advertising budget is changed independent of performance, such as when a brand determines to abruptly shut down or start up advertising for a period) that output results that are leveraged similarly to A/B tests. Non-experimental sources (e.g., indicating whether changes to relevant inputs, such as advertising, result in outputs, such as total sales, that follow the expected modeled path) may include observational impacts based on relevant cannibalization factors, such as product and brand placement in search, market share, and the like, and may include: (i) organic placement by brand by product by search terms (e.g., where products land in organic rank placement on a page of the marketplace); (ii) traffic volume and click estimation models for by search terms, using platform data, model how often the search term is searched per day, and how many total times do users click on products throughout the page; (iii) when available, data from the platform informing the number of clicks a product and/or brand receives on a search term; (iv) clicks per placement on the page, using rank placement from click estimations, and when available, creating a model estimating how many clicks each placement on the page is receiving… cannibalization may also be based on placements for the brand on the page and experimental and non-experimental results, for every brand and/or keyword pair. The computing device 322 may estimate the total number of clicks a brand would likely receive without advertising and compare to the total number of clicks with advertising”; (Park, [0140]), “The computing device 322 may determine a returns value based on: (i) returns reported by the platform and related costs (e.g., indicating a previous advertising sale was returned, which may be directly deducted from the advertising sales); and (ii) returns not reported by the platform (e.g., the platform may not report if advertising sales were returned, or the platform only reports returned advertising sales within a limited time period (e.g., three days). If platform does not report returns, the computing device 322 may estimate the returns value using other sources of data, such as general return rates on the platform and generalize to advertising data. If platform only reports for limited period, the computing device 322 may leverage platform advertising return rates over the period of data that is provided and extrapolate out to the period not reported by the platform (e.g., if the platform only reports returns for the first seven days, the computing device 322 may combine the reported returns value with the total return rate to estimate the general return rate extending out multiple weeks after purchase). The computing device 322 may combine the total returns with the period of advertising sales returns to estimate later returns”;
in response to that the data analysis module is configured to perform step S3.24, and step S3.24 comprises:
step S3.241, comparing a page display element of the e-commerce commodity with a preset display standard to obtain a second comparison result; step S3.242, determining natural traffic change influence based on the second comparison result; and step S3.243, determining the exposure change influence based on the advertisement delivery factor and the natural traffic change influence (Park, [0135]), “The computing device 322 may generate a fifth ROAS value for the target product by adding any attributable movement in organic ranking to the fourth ROAS value for the target product. For example, the computing device 322 may identify a click share for each keyword for the target product before and after advertising associated with the target product. If the computing device 322 determines that the rank of the target product has improved over time (e.g., and is likely directly caused by the results of the advertising traffic and purchases), the computing device 322 may determine that the improvement is attributed to advertising, and the computing device 322 generates the fifth ROAS value by adding an organic rank value (e.g., corresponding to additional clicks and sales stemming from the improved rank) to the fourth ROAS value for the target product”; (Park, [0140]), “The computing device 322 may determine a returns value based on: (i) returns reported by the platform and related costs (e.g., indicating a previous advertising sale was returned, which may be directly deducted from the advertising sales); and (ii) returns not reported by the platform (e.g., the platform may not report if advertising sales were returned, or the platform only reports returned advertising sales within a limited time period (e.g., three days). If platform does not report returns, the computing device 322 may estimate the returns value using other sources of data, such as general return rates on the platform and generalize to advertising data. If platform only reports for limited period, the computing device 322 may leverage platform advertising return rates over the period of data that is provided and extrapolate out to the period not reported by the platform (e.g., if the platform only reports returns for the first seven days, the computing device 322 may combine the reported returns value with the total return rate to estimate the general return rate extending out multiple weeks after purchase). The computing device 322 may combine the total returns with the period of advertising sales returns to estimate later returns”; (Park [0137]-[0139]).
Referring to Claim 10, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 9. Park further teaches:
wherein step 53.242 comprises:
step S3.2421, comparing the page display element of the competitive commodity with the preset display standard to obtain a third comparison result; and step 53.2422, determining the natural traffic change influence based on the second comparison result and the third comparison result (Park, [0071]-[0072]), “the recommendation module 336 may provide additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue. Again, a seller of the target product may not know what appropriate target advertising cost of sale (ACoS) to meet and what price point to sell the target product at in order to see long term gains in lieu of short-term profits. The recommendation module 336 provides this information based on the competitivity score 339 generated by the competitivity score generating module 333 and revenue data received from the digital marketplace 382… This revenue potential of each of the target products and organic competing products may be ranked to determine the placement of the target product within the digital marketplace 382… the recommendation (e.g., the actionable report 337) presented by the recommendation module 336 may be refined by inputting an estimated bid amount from the digital marketplace 382 required to “win” advertising slots for the target product. The digital marketplace 382, along with selling products, may also engage in presenting advertisements to a potential purchaser of one or more products… A return on investment (ROI) may then be calculated by subtracting the investment needed from an investment payoff term and multiplying that by the ad spend potential. Products with no (or low) potential receive suggestion outputs as to why they are not competitive or have bad conversion rates by the recommendation module 336 and its actionable report 337, so that these attributes of the target product can be improved for future potential or the money spent to sell the target product can be reallocated for other uses”; (Park, [0073]).
Referring to Claim 11, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 5. Park further teaches:
wherein step S3.3 comprises:
step S3.31, comparing a price trend of the e-commerce commodity with a price trend of the competitive commodity to obtain a fourth comparison result; and step S3.32, determining the price change influence based on the fourth comparison result (Park, [0070]-[0071]; [0088]-[008]), “price points”; (Park, [0116]), “a current and historical price for both the target product and competitive products. This historical pricing may be retrieved from one or more digital marketplaces 882 via the execution of the processor 810 and NID 880 as described herein. In this specific example, the processor 810 may cause the NID 880 to access the one or more digital marketplaces 882 either via a wired (wired transmitter/receiver 840) or wireless (wireless transmitter/receiver 850) connection, find instances of the target product and competing products being sold, and retrieve their historic pricing values”; (Park, [0105]; [0113] [0131]).
Referring to Claim 12, Park teaches the system for analyzing the sale volume of the e-commerce commodity according to claim 5. Park further teaches:
wherein the step of analyzing the sale volume change further comprises:
step S5.1, sorting and displaying the target factor according to each priority of the market demand change influence, the exposure change influence, and the price change influence; and step S5.2, generating a corresponding operation suggestion based on a sorting and displaying result of the target factor (Park, [0129]-[0131]), “… A target ACoS may be determined and set by the seller based on available capital or may be set by the seller based on the fraction of the revenue received thus far from the sale of the target product on the digital marketplace 382 and costs of manufacturing. Ad spend potential may then be calculated by multiplying monthly opportunity units (OU) by the spend margin. The monthly OUs may be calculated as a result of the conversion rate of clicks to the target product that is the result of sales of the target product after a purchaser has viewed the product. The revenue potential may then be calculated by multiplying the OU with the price of the target product. This revenue potential of each of the target products may be ranked to determine the placement of the target product within the digital marketplace 882. The search terms presented in the winnability report 804 may be sorted by revenue potential to determine the target product's best opportunities for revenue growth. In order to refine a recommendation, the process may continue with inputting estimated bid amounts from the digital marketplaces 882 required to win advertising slots for these keywords. In this manner, the execution of the processor 810 may initiate these calculations in order to predict a number of clicks and a cost necessary to achieve the potential growth… As highly winnable terms are targeted in this process with both advertising and search engine optimization techniques, increasing the associated impressions, clicks, and conversions, the processing applied to the target product may continually adapt. As a target product succeeds on new search terms the competitive products set defined in the competitive set report 898 will shift to be compared to larger and less niche competing products. As the competitive products set defined in the competitive set report 898 shifts, the competitive terms set will shift as well. As reviews, terms, seller ranks, and other attributes shift, the winnability and associated required investment of each term also shifts. With the shift in winnability, new terms are prioritized and the cycle continues iteratively to cause the revenue associated with the targeted product to increase proportionally”; (Park, [0070]-[0071]), “a recommendation module 336 may receive this competitivity score 339 along with other data from the digital marketplace 382 hosted by the server 350. Among this other data may include revenue data associated with the organic competing products and the target product (if available). For example, where a click-rate of any given product (e.g., target product or organic competing product) results in a purchase, this conversion rate data along with the pricing data of the products may be passed to the recommendation module 336. The recommendation module 336 may then provide a recommendation descriptive of the ability (or inability) of the target product to compete with the at least one organic competing product. In some embodiments, a threshold competitivity score may be set such that the report provided by the recommendation module 336 indicates to the seller of the target product whether to proceed to sell that product on the digital marketplace 382…Where the threshold competitivity score is reached, the recommendation module 336 may provide additional economic data descriptive of price points and ACoS statistics to use in order to increase revenue. Again, a seller of the target product may not know what appropriate target advertising cost of sale (ACoS) to meet and what price point to sell the target product at in order to see long term gains in lieu of short-term profits. The recommendation module 336 provides this information based on the competitivity score 339 generated by the competitivity score generating module 333 and revenue data received from the digital marketplace 382. In a specific example, the revenue potential of the target product may be determined by the recommendation module 336 calculating an ad spend margin, an ad spend potential, and a revenue potential. The ad spend margin may be calculated by multiplying a target ACoS by the price of the target product. A target ACOS may be determined and set by the seller based on available capital or may be set by the seller based on the fraction of the revenue received thus far from the sale of the target product on the digital marketplace 382 and costs of manufacturing. Ad spend potential may then be calculated by multiplying monthly opportunity units (OU) by the spend margin. The monthly OUs may be calculated as a result of the conversion rate of clicks to the target product that is the result of sales of the target product after a purchaser has viewed the product. The revenue potential may then be calculated by multiplying the OU with the price of the target product. This revenue potential of each of the target products and organic competing products may be ranked to determine the placement of the target product within the digital marketplace 382”; (Park, [0036]; [0056]; [0072]; [0084]; [0094]-[0095]), “actionable report” (Park, [0085]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith et al. (US 20220122100 A1) – The present invention relates generally to commerce systems and methods, and more specifically, to a product evaluation system that collects and extrapolates data about a product to produce a measurement of its viability and competitiveness in a market.
Bowman-Amuah (WO 0116851 A2) – A system, method, and article of manufacture are provided for manipulating data about a customer in an e-Commerce environment. An e-Commerce application is provided which allows the purchase of products or services. Information about a customer is received from the e-Commerce application and analyzed. This information includes an amount of purchases made by the customer and times at which the purchases occurred. The analysis of the information about the customer is stored and a decision support service for managing the e-Commerce application is provided based on the analysis of the information about the customer.
Li et al. (Market Competition and Pricing Model of E-Commerce under the Background of Digitalization) – The e-commerce industry is growing rapidly as the Internet is widely used. On major e-commerce platforms, enterprises can use consumer behavior preference analysis to understand key information such as customer demand, market demand and product characteristics, so as to formulate corresponding strategies to meet the needs of target customers and specific groups. Based on this idea, this paper proposes a pricing model of business competition under the digital background, and tests the performance of this model in all aspects. These research results also provide useful references for various stakeholders in the development process of e-commerce, and have a positive impact on the marketing activities of merchants on e-commerce platforms. The result of e-commerce market competition and pricing models in the digital context is to increase the efficiency of market competition and supply-demand matching, promote the application of dynamic pricing models, and increase price transparency and pricing elasticity. These results require companies to observe the market and consumer demand more keenly, and flexibly adjust their pricing strategies to gain a competitive advantage.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Crystol Stewart whose telephone number is (571)272-1691. The examiner can normally be reached 9:00am-5:00pm.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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