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 .
DETAILED ACTION
1. This is a first non-final Office Action on the merits for application 18976773. Claims 1-48 are canceled. Claims 49-68 are pending examination.
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 12/11/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 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 49-68 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 49, 57, and 65 is/are drawn to method (i.e., a process). As such, claims 49, 57, and 65 is/are drawn to one of the statutory categories of invention.
Claims 49-68 are directed to receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. Specifically, claim(s) 49, 57, and 65 recite(s) receiving, at a first online session, user input indicating user interest in an item offered by an online merchant; automatically retrieving, from a data store, one or more alternative offers of the item, by: accessing the data store to identify a plurality of offers; applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item; identifying one or more alternative online merchants associated with the identified one or more items; and automatically causing a user device to output a notification including the one or more alternative offers, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) and Mental Processes and is similar to the concept of (concepts performed in the human mind (including an observation, evaluation, judgement, opinion) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and Mental Processes and grouped as following:
receiving, at a first online session, user input indicating user interest in an item offered by an online merchant; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations), and (concepts performed in the human mind (including an observation, evaluation, judgement, opinion),
automatically retrieving, one or more alternative offers of the item, by: accessing the data store to identify a plurality of offers; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations), and (concepts performed in the human mind (including an observation, evaluation, judgement, opinion),
applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations), and (concepts performed in the human mind (including an observation, evaluation, judgement, opinion),
identifying one or more alternative online merchants associated with the identified one or more items; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations), and (concepts performed in the human mind (including an observation, evaluation, judgement, opinion),
identifying, as the one or more alternative offers, a sub-set of the plurality of offers corresponding to the one or more identified alternative online merchants; and automatically causing to output a notification including the one or more alternative offers; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations), and (concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as user device merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the trained machine-learning model, computer, device perform(s) the steps or functions of receiving, at a first online session, user input indicating user interest in an item offered by an online merchant; automatically retrieving, from a data store, one or more alternative offers of the item, by: accessing the data store to identify a plurality of offers; applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item; identifying one or more alternative online merchants associated with the identified one or more items; and automatically causing a user device to output a notification including the one or more alternative offers. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a user device to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. As discussed above, taking the claim elements separately, the user device perform(s) the steps or functions of receiving, at a first online session, user input indicating user interest in an item offered by an online merchant; automatically retrieving, from a data store, one or more alternative offers of the item, by: accessing the data store to identify a plurality of offers; applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item; identifying one or more alternative online merchants associated with the identified one or more items; and automatically causing a user device to output a notification including the one or more alternative offers. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 50-56, 58-64, and 66-68 further describe the abstract idea of receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. Claim(s) 50-56, 58-64, and 66-68 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a trained machine-learning model, computer, device to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. As discussed above, taking the claim elements separately, the trained machine-learning model, computer, device perform(s) the steps or functions of adjusting the one or more alternative offers by at least one applicable coupon code or promotion stored in the data store; wherein applying the predictive algorithm to the plurality of offers, with reference to the item, to identify the one or more items having a positive resolution with the item includes determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item; wherein determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item includes applying the plurality of offers, with reference to the item; trained based on user feedback regarding similarities of items; wherein the method is performed by a software application running in a background relative to a further software application accessing the online merchant; wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of receive user input indicating user interest in an item and retrieve alternate offers of the item with alternative online merchants. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
4. 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.
A. Claim(s) 49-52, 57-60, and 65 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhakwatsalam et al., (U.S. Patent No. 8700490) in view of Or et al., (U.S. Patent Application Publication No. 20140229323).
As to Claim 49, Bhakwatsalam teaches a computer-implemented method, comprising:receiving, at a first online session, user input indicating user interest in an item offered by an online merchant; (claim 17: receive a selection of an item by a customer), and (Fig. 7 customer is interested in boots),automatically retrieving, from a data store, one or more alternative offers of the item, by: (Fig. 7: 632 alternative offers can be different colors and sizes of the boots which is offered by the merchant or different merchants),accessing the data store to identify a plurality of offers; (claim 17: retrieve information from a plurality of sellers having offers for the item from the data store),
identifying one or more alternative online merchants associated with the identified one or more items; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: alternative online merchant can be the merchants that are in Fig. 8 with shoes 660 seller A, seller B, and seller C),identifying, as the one or more alternative offers, a sub-set of the plurality of offers corresponding to the one or more identified alternative online merchants; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: offers can be the offers on shoes with different merchants with different colors and sizes and price which can be found in Fig. 8).
Bhakwatsalam does not teach applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item;
automatically causing a user device to output a notification including the one or more alternative offers.
However Or teaches applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items having a positive resolution with the item; (0002: Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer, and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products.), (Examiner notes: prediction of offers are determined based on items bought before by the customer and similar items is sent to consumers that are positive resolution with the item or similar to the items that the consumer bought),automatically causing a user device to output a notification including the one or more alternative offers; (0002: target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns, and sent in an email message to the consumer to entice them to buy more products from the retailer.), (Examiner notes: email can be a notification of similar products that are determined of an interest of the user to be emailed for the user to buy…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhakwatsalam to include automatically causing a user device to output a notification including the one or more alternative offers of Or. Motivation to do so comes from the knowledge well known in the art that automatically causing a user device to output a notification including the one or more alternative offers would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 50, Bhakwatsalam, and Or teach the computer-implemented method of claim 49.
Bhakwatsalam further teaches further comprising:adjusting the one or more alternative offers by at least one applicable coupon code or promotion stored in the data store; (20: different variations of the item may be available with different combinations of attributes from different sellers. For example, seller A may offer the polo shirt in sizes S, M and L and colors yellow and black, while seller B offers the polo shirt in sizes S, M and XL and colors white and blue in some embodiments. The systems and methods disclosed herein compile the variations of the item available from the different sellers and generate a consolidated presentation of the item in a common or single user interface. This presentation is opposed to generating and/or displaying each item available from each seller separately and in multiple user interfaces as is done conventionally.).
As to Claim 51, Bhakwatsalam, and Or teach the computer-implemented method of claim 49.
Or further teaches wherein applying the predictive algorithm to the plurality of offers, with reference to the item, to identify the one or more items having a positive resolution with the item includes determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item; (0002: Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer, and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products.), (Examiner notes: prediction of offers are determined based on items bought before by the customer and similar items is sent to consumers that are positive resolution with the item or similar to the items that the consumer bought).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein applying the predictive algorithm to the plurality of offers, with reference to the item, to identify the one or more items having a positive resolution with the item includes determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item. Motivation to do so comes from the knowledge well known in the art that wherein applying the predictive algorithm to the plurality of offers, with reference to the item, to identify the one or more items having a positive resolution with the item includes determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item would provide a more accurate product or item that the user would be interested in and that would increase the likelihood that the user will review and engage with such offer and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 52, Bhakwatsalam, and Or teach the computer-implemented method of claim 51.
Bhakwatsalam further teaches wherein determining whether each item associated with the plurality of offers is either of identical to or equivalent to the item includes applying a trained machine-learning model to the plurality of offers, with reference to the item; (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: Fig. 8 shows identical offer for shoes but with different size and color).
As to Claim 57, Bhakwatsalam teaches a computer-implemented method, comprising:receiving, from a user device, user input indicating user interest in an item offered by an online merchant; (claim 17: receive a selection of an item by a customer), and (Fig. 7 customer is interested in boots),automatically retrieving, from a data store, one or more alternative offers of the item, by: (Fig. 7: 632 alternative offers can be different colors and sizes of the boots which is offered by the merchant or different merchants),accessing the data store to identify a plurality of offers; (claim 17: retrieve information from a plurality of sellers having offers for the item from the data store),
identifying one or more alternative online merchants associated with the identified one or more items; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: alternative online merchant can be the merchants that are in Fig. 8 with shoes 660 seller A, seller B, and seller C),identifying, as the one or more alternative offers, a sub-set of the plurality of offers corresponding to the one or more identified alternative online merchants; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: offers can be the offers on shoes with different merchants with different colors and sizes and price which can be found in Fig. 8).
Bhakwatsalam does not teach applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items predicted to at least substantially match with the item;
automatically causing the user device to output a notification including the one or more alternative offers.
However Or teaches applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items predicted to at least substantially match with the item; (0002: Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer, and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products.), (Examiner notes: prediction of offers are determined based on items bought before by the customer and similar items is sent to consumers that are positive resolution with the item or similar to the items that the consumer bought),automatically causing the user device to output a notification including the one or more alternative offers; (0002: target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns, and sent in an email message to the consumer to entice them to buy more products from the retailer.), (Examiner notes: email can be a notification of similar products that are determined of an interest of the user to be emailed for the user to buy…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhakwatsalam to include automatically causing the user device to output a notification including the one or more alternative offers of Or. Motivation to do so comes from the knowledge well known in the art that automatically causing the user device to output a notification including the one or more alternative offers would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 58, Bhakwatsalam, and Or teach the computer-implemented method of claim 57.
Bhakwatsalam further teaches further comprising:adjusting the one or more alternative offers by at least one applicable coupon code or promotion stored in the data store; (20: different variations of the item may be available with different combinations of attributes from different sellers. For example, seller A may offer the polo shirt in sizes S, M and L and colors yellow and black, while seller B offers the polo shirt in sizes S, M and XL and colors white and blue in some embodiments. The systems and methods disclosed herein compile the variations of the item available from the different sellers and generate a consolidated presentation of the item in a common or single user interface. This presentation is opposed to generating and/or displaying each item available from each seller separately and in multiple user interfaces as is done conventionally.).
As to Claim 59, Bhakwatsalam, and Or teach the computer-implemented method of claim 57.
Or further teaches wherein applying the predictive algorithm to the plurality of offers includes determining whether each item associated with the plurality of offers is either of identical to or substantially matched to the item; (0002: Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer, and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products.), (Examiner notes: prediction of offers are determined based on items bought before by the customer and similar items is sent to consumers that are positive resolution with the item or similar to the items that the consumer bought).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein applying the predictive algorithm to the plurality of offers includes determining whether each item associated with the plurality of offers is either of identical to or substantially matched to the item. Motivation to do so comes from the knowledge well known in the art that wherein applying the predictive algorithm to the plurality of offers includes determining whether each item associated with the plurality of offers is either of identical to or substantially matched to the item would provide a more accurate product or item that the user would be interested in and that would increase the likelihood that the user will review and engage with such offer and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 60, Bhakwatsalam, and Or teach the computer-implemented method of claim 59.
Bhakwatsalam further teaches wherein determining whether each item associated with the plurality of offers is either of identical to or substantially matched to the item includes applying a trained machine-learning model to the plurality of offers, with reference to the item; (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: Fig. 8 shows identical offer for shoes but with different size and color).
As to Claim 65, Bhakwatsalam teaches a computer-implemented method, comprising:determining, based on user interaction with an online merchant via a software application operating on a user device, user interest in an item offered by the online merchant automatically retrieving, from a data store, one or more alternative offers of the item from alternative online merchants, by: ; (claim 17: receive a selection of an item by a customer), and (Fig. 7 customer is interested in boots), (Fig. 7: 632 alternative offers can be different colors and sizes of the boots which is offered by the merchant or different merchants),accessing the data store to identify a plurality of offers from the alternative online merchants; (claim 17: retrieve information from a plurality of sellers having offers for the item from the data store), identifying a subset of the alternative online merchants associated with the identified one or more items; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: alternative online merchant can be the merchants that are in Fig. 8 with shoes 660 seller A, seller B, and seller C),identifying, as the one or more alternative offers, a sub-set of the plurality of offers corresponding to the subset of the alternative online merchants; and (Fig. 8 600: identifying sellers that has the shoes 660 seller A, seller B, and seller C), and (65: the UI can also contain the various sources (i.e., sellers) from which that item is available, along with the sales conditions of the item that each seller is offering. In FIG. 8, the various sellers (A, B, and C) and their sales conditions are shown in area 660. The customer can then, if desired, select one of those sellers from which to purchase the item.), and (claim 1: A method comprising: receiving a selection of an item by a customer; retrieving information from a plurality of sellers having offers for the item; identifying a plurality of attributes for the item; aggregating a plurality of representations for the item based at least in part on the information from the plurality of sellers and the plurality of attributes, wherein the plurality of representations includes one or more images for the item; selecting, by a computer processor, a first representation image for a first attribute from the plurality of attributes to represent the first attribute, wherein the first representation image is selected from the aggregated plurality of representations based upon a first predetermined set of criteria for the first attribute; selecting, by the computer processor, a second representation image for a second attribute from the plurality of attributes to represent the second attribute, wherein the second representation image is selected from the aggregated plurality of representations based upon a second predetermined set of criteria for the second attribute, wherein the second attribute is different from the first attribute; and generating a presentation of the item for sale for the customer, wherein the presentation includes at least the first and second representation images.), (Examiner notes: offers can be the offers on shoes with different merchants with different colors and sizes and price which can be found in Fig. 8).
Bhakwatsalam does not teach applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items predicted to at least substantially match with the item;
automatically causing the user device to output a notification including the one or more alternative offers.
However Or teaches applying a predictive algorithm to the plurality of offers, with reference to the item, to identify one or more items predicted to at least substantially match with the item; (0002: Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer, and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products.), (Examiner notes: prediction of offers are determined based on items bought before by the customer and similar items is sent to consumers that are positive resolution with the item or similar to the items that the consumer bought),automatically causing the user device to output a notification including the one or more alternative offers; (0002: target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns, and sent in an email message to the consumer to entice them to buy more products from the retailer.), (Examiner notes: email can be a notification of similar products that are determined of an interest of the user to be emailed for the user to buy…).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bhakwatsalam to include automatically causing the user device to output a notification including the one or more alternative offers of Or. Motivation to do so comes from the knowledge well known in the art that automatically causing the user device to output a notification including the one or more alternative offers would increase the likelihood that the user will review and engage with such advertisement and that would promote an increase in the sales and would therefore make the method/system more profitable.
B. Claim(s) 53, and 61 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhakwatsalam et al., (U.S. Patent No. 8700490) in view of Or et al., (U.S. Patent Application Publication No. 20140229323) in view of Henderson et al., (U.S. Patent Application Publication No. 20150302009).
As to Claim 53, Bhakwatsalam, and Or teach the computer-implemented method of claim 52.
Bhakwatsalam, and Or do not teach wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items.
However Henderson teaches wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items; (0045: The training input for the machine learning system used by the media selector 110 may be, for example, the application state and user data in the user feedback data 170 correlated with direct user feedback, or may be mock application state and user data. The direct user feedback in the user feedback data 170 may be used to determine the training answers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items. Motivation to do so comes from the knowledge well known in the art that wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items would provide a more accurate product or item that the user would be interested in and that would increase the likelihood that the user will review and engage with such offer and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 61, Bhakwatsalam, and Or teach the computer-implemented method of claim 60.
Bhakwatsalam, and Or do not teach wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items.
However Henderson teaches wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items; (0045: The training input for the machine learning system used by the media selector 110 may be, for example, the application state and user data in the user feedback data 170 correlated with direct user feedback, or may be mock application state and user data. The direct user feedback in the user feedback data 170 may be used to determine the training answers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items. Motivation to do so comes from the knowledge well known in the art that wherein the trained machine-learning model has been trained based on user feedback regarding similarities of items would provide a more accurate product or item that the user would be interested in and that would increase the likelihood that the user will review and engage with such offer and that would promote an increase in the sales and would therefore make the method/system more profitable.
C. Claim(s) 54-56, 62-64, and 66-68 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bhakwatsalam et al., (U.S. Patent No. 8700490) in view of Or et al., (U.S. Patent Application Publication No. 20140229323) in view of DEsposito, (U.S. Patent No. 8972569).
As to Claim 54, Bhakwatsalam, and Or teach the computer-implemented method of claim 52.
Bhakwatsalam, and Or do not teach wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant; (55: the item may be added to the consumer's shopping cart.
However DEsposito teaches wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant; (55: the item may be added to the consumer's shopping cart (27: adding items to a shopping cart and checking out.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant would provide a faster way for the customer to check out and that would therefore make the method/system more user friendly.
As to Claim 55, Bhakwatsalam, and Or teach the computer-implemented method of claim 49.
Bhakwatsalam, and Or do not teach wherein the computer- implemented method is performed by a software application running in a background of a user device relative to a further software application accessing the online merchant.
However DEsposito teaches wherein the computer- implemented method is performed by a software application running in a background of a user device relative to a further software application accessing the online merchant; (27: The mobile application is installed on the mobile device(s) 101 either manually via a direct connection or through an online store application, the mobile application can be run in the background on a mobile device 101.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the computer- implemented method is performed by a software application running in a background of a user device relative to a further software application accessing the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the computer- implemented method is performed by a software application running in a background of a user device relative to a further software application accessing the online merchant would provide a faster way for the system to communicate and that would therefore make the method/system more efficient.
As to Claim 56, Bhakwatsalam, and Or teach the computer-implemented method of claim 55.
Bhakwatsalam, and Or do not teach wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring.
However DEsposito teaches wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring; (abstract: a mobile device receives instruction sets for executing and monitoring business transactions over networks.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring. Motivation to do so comes from the knowledge well known in the art that wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring would provide a faster way for the system to monitor data and that would therefore make the method/system more efficient and more accurate.
As to Claim 62, Bhakwatsalam, and Or teach the computer-implemented method of claim 57.
Bhakwatsalam, and Or do not teach wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant.
However DEsposito teaches wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant (27: adding items to a shopping cart and checking out.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the user input includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant would provide a faster way for the customer to check out and that would therefore make the method/system more user friendly.
As to Claim 63, Bhakwatsalam, and Or teach the computer-implemented method of claim 57.
Bhakwatsalam, and Or do not teach wherein the computer- implemented method is performed by a software application running in a background of the user device relative to a further software application accessing the online merchant.
However DEsposito teaches wherein the computer- implemented method is performed by a software application running in a background of the user device relative to a further software application accessing the online merchant; (27: The mobile application is installed on the mobile device(s) 101 either manually via a direct connection or through an online store application, the mobile application can be run in the background on a mobile device 101.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the computer- implemented method is performed by a software application running in a background of the user device relative to a further software application accessing the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the computer- implemented method is performed by a software application running in a background of the user device relative to a further software application accessing the online merchant would provide a faster way for the system to communicate and that would therefore make the method/system more efficient.
As to Claim 64, Bhakwatsalam, and Or teach the computer-implemented method of claim 63.
Bhakwatsalam, and Or do not teach wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring.
However DEsposito teaches wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring; (abstract: a mobile device receives instruction sets for executing and monitoring business transactions over networks.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring. Motivation to do so comes from the knowledge well known in the art that wherein: the software application is configured to monitor electronic communication traffic of the further software application; and the user input is received via the monitoring would provide a faster way for the system to monitor data and that would therefore make the method/system more efficient and more accurate.
As to Claim 66, Bhakwatsalam, and Or teach the computer-implemented method of claim 65.
Bhakwatsalam, and Or do not teach wherein the user interaction includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant.
However DEsposito teaches wherein the user interaction includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant (27: adding items to a shopping cart and checking out.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the user interaction includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the user interaction includes one or more of adding the item to an electronic shopping cart or initiating a checkout procedure with the online merchant would provide a faster way for the customer to check out and that would therefore make the method/system more user friendly.
As to Claim 67, Bhakwatsalam, and Or teach the computer-implemented method of claim 65.
Bhakwatsalam, and Or do not teach wherein the computer- implemented method is performed by a further software application running in a background of the user device relative to the software application accessing the online merchant.
However DEsposito teaches wherein the computer- implemented method is performed by a further software application running in a background of the user device relative to the software application accessing the online merchant; (27: The mobile application is installed on the mobile device(s) 101 either manually via a direct connection or through an online store application, the mobile application can be run in the background on a mobile device 101.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein the computer- implemented method is performed by a further software application running in a background of the user device relative to the software application accessing the online merchant. Motivation to do so comes from the knowledge well known in the art that wherein the computer- implemented method is performed by a further software application running in a background of the user device relative to the software application accessing the online merchant would provide a faster way for the system to communicate and that would therefore make the method/system more efficient.
As to Claim 68, Bhakwatsalam, and Or teach the computer-implemented method of claim 67.
Bhakwatsalam, and Or do not teach wherein: the further software application is configured to monitor electronic communication traffic of the software application; and the user interest is determined based on the monitoring.
However DEsposito teaches wherein: the further software application is configured to monitor electronic communication traffic of the software application; and the user interest is determined based on the monitoring; (abstract: a mobile device receives instruction sets for executing and monitoring business transactions over networks.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include wherein: the further software application is configured to monitor electronic communication traffic of the software application; and the user interest is determined based on the monitoring. Motivation to do so comes from the knowledge well known in the art that wherein: the further software application is configured to monitor electronic communication traffic of the software application; and the user interest is determined based on the monitoring would provide a faster way for the system to monitor data and that would therefore make the method/system more efficient and more accurate.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Referral Marketing Software for Ecommerce Brands” describes “Referral marketing is a customer acquisition strategy that works by turning existing customers into a customer acquisition channel. A customer recommends your brand to a friend using a referral link, code, or invite. If that friend makes a purchase, the referral is tracked and the customer receives a reward. Because the recommendation comes from someone the buyer already knows, referral traffic usually arrives with more trust and less friction than traffic from ads. For ecommerce brands, that can lead to lower customer acquisition costs, higher conversion rates, and more customers who go on to refer others.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference# 8577749 teaches similar invention which describes Systems and methods for facilitating an on-line purchase of at least one item on behalf of a consumer are disclosed. A consumer may shop and purchase at least one item on a consolidated shopping (CS) website displaying one or more items available for purchase on one or more different merchant websites. The CS website provides consumers with advanced searching that takes into consideration a personal profile of the consumer, the consumer's previous shopping history, transactional data relating to a group of similar consumers, and the like. A host computer providing the CS website may access a merchant website selling the requested item, and order the requested item on behalf of the consumer, and charging a transaction fee, listing fee, receiving a rebate and/or offering a rebate for performing such. One embodiment allows a consumer to purchase/order multiple items from multiple websites in a single purchase request to the CS website.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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