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 19098229. Claims 22-36 are pending examination.
Double Patenting
2. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 22-36 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1-6 of U.S. Patent No. 12277579. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed to the same subject matter, perform similar method steps and a person of ordinary skill in the art would not be free to practice one of the claimed inventions without infringing upon the other inventions.
Application number: 17693699
22. A system comprising: one or more processing devices configured to:dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information, test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation, determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, anddeliver the optimized recommendation, to a device of the user in real-time, according to the personal contextual information; and a memory operable to:store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context
Patent number: 122775791. A system comprising:
a network comprising a plurality of processing devices, wherein:
the plurality of processing devices comprises a user device of a particular consumer,
the user device is operable to provide a current geographic location of the particular consumer,
the user device is operable to distribute a plurality of executable instructions associated with and executed by one or more of the plurality of processing devices,
at least one test personal context of a plurality of test personal contexts is the current geographic location of the particular consumer,
the one or more of the plurality of processing devices is operable to:
associate a first subset of the plurality of test personal contexts with a first recommendation algorithm,
associate a second subset of the plurality of test personal contexts with a second recommendation algorithm,
dynamically select and adjust the first recommendation algorithm or the second recommendation algorithm according to real-time analysis of the particular consumer's contextual changes,
test the performance of the first recommendation algorithm and the second recommendation algorithm with respect to each test personal context of a plurality of test personal contexts, thereby optimizing the generation of recommendations, and
deliver the optimized recommendation to the user device of the particular consumer in real-time according to updated personal contextual information,
a memory of the user device is operable to:
store the first recommendation algorithm and the second recommendation algorithm for generating product or service recommendations based on personal contextual information, and
store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context,
the first recommendation algorithm generates better optimized recommendations for the first subset of the plurality of test personal contexts,
the second recommendation algorithm generates better optimized recommendations for the second subset of the plurality of test personal contexts,
a relative quality of an optimized recommendation is determined according to a comparison of a plurality of business metrics and financial metrics related to user engagement and conversion rates,
a relative quality of an optimized recommendation is determined according to one or both of a business metric and a financial metric,
the one or more of the plurality of processing devices is operable to test the first recommendation algorithm and the second recommendation algorithm with respect to each test personal context of a plurality of test personal contexts,
the one or more of the plurality of processing devices is operable to deliver the optimized recommendation to the user device of the particular consumer according to personal contextual information stored in the memory,
the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier,
a record of use of the first recommendation algorithm is used to arrange for payment to the first supplier, and
the dynamic selection and adjustment of the recommendation algorithm in response to real-time contextual changes provide a technical improvement to recommendation systems, enhancing accuracy and user satisfaction.
As to the independent claims:
Instant claim 23 is fully disclosed in claim 2 of the copending Patent number 12,227,757.
Instant claim 24 is fully disclosed in claim 3 of the copending Patent number 12,227,757.
Instant claim 25 is fully disclosed in claim 4 of the copending Patent number 12,227,757.
Instant claim 26 is fully disclosed in claim 5 of the copending Patent number 12,227,757.
Instant claim 27 is fully disclosed in claim 6 of the copending Patent number 12,227,757.
Limitations presented in claim 28 is an obvious variation of additional limitations presented in patented claim 1.
Limitations presented in claim 31 is an obvious variation of additional limitations presented in patented claim 1.
Limitations presented in claim 32 is an obvious variation of additional limitations presented in patented claim 1.
Limitations presented in claim 35 is an obvious variation of additional limitations presented in patented claim 1.
It would have been obvious to one having ordinary skill in the art to make the changes above in order to cover slightly broader limitations. Furthermore, the claimed elements perform the same function as before.
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 22-36 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) 22 is/are drawn to a system (i.e., a machine/manufacture). As such, claims 22 is/are drawn to one of the statutory categories of invention.
Claims 22-36 are directed to generate a product or service recommendation based on test personal contexts. Specifically, claim(s) 22 recite(s) dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information, test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation, determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, and deliver the optimized recommendation, to the user in real-time, according to the personal contextual information; and a operable to: store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context, 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) 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 grouped as following:
dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, and deliver the optimized recommendation, to the user in real-time, according to the personal contextual information; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
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 system, devices, device, memory 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 system, devices, device, memory perform(s) the steps or functions of dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information, test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation, determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, and deliver the optimized recommendation, to the user in real-time, according to the personal contextual information; and a operable to: store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context. 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 system, devices, device, memory to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of generate a product or service recommendation based on test personal contexts. As discussed above, taking the claim elements separately, the system, devices, device, memory perform(s) the steps or functions of dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information, test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation, determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, and deliver the optimized recommendation, to the user in real-time, according to the personal contextual information; and a operable to: store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context. 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 generate a product or service recommendation based on test personal contexts. 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 23-36 further describe the abstract idea of generate a product or service recommendation based on test personal contexts. Claim(s) 23-36 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 system, devices, device, memory, network interface to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of generate a product or service recommendation based on test personal contexts. As discussed above, taking the claim elements separately, the system, devices, device, memory, network interface perform(s) the steps or functions of wherein a request for a product or service recommendation originates in an interaction with an Internet web page; wherein a request for a product or service recommendation is generated as part of production of promotional or marketing communication for the user; wherein the personal contextual information comprises personal financial data for the user, personal product preference data for the user, and data representative of non-financial interactions between the merchant and the user; wherein customization of a webpage comprises: generation of a web page that displays a generated product or service recommendation according to one of the first recommendation algorithm and the second recommendation algorithm, and delivery of the web page; wherein operable to deliver a promotional communication of the user according to a merchant selecting a particular user; wherein the relative quality of an optimized recommendation is determined according to one or both of a business metric and a financial metric; wherein the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier; wherein a record of use of the first recommendation algorithm is used to arrange for payment to the first supplier; associate a first subset of the plurality of test personal contexts and associate a second subset of the plurality of test personal contexts; wherein the first recommendation generates better optimized recommendations for the first subset of the plurality of test personal contexts, and the second recommendation generates better optimized recommendations for the second subset of the plurality of test personal contexts; provide a current geographic location of the user; distribute a plurality of executable instructions; wherein at least one test personal context of the plurality of test personal contexts is a geographic location of the use. 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 generate a product or service recommendation based on test personal contexts. 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) 22, 28, 31-33, and 35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., (U.S. Patent Application Publication No. 20160162974) in view of Dhawan et al., (U.S. Patent Application Publication No. 20140274362) in view of Libby et al. (U.S. Patent Application Publication No. 20110055004).
As to Claim 21, Lee teaches a system comprising: one or more processing devices configured to:dynamically select and adjust a first recommendation algorithm or a second recommendation algorithm according to a real-time analysis of personal contextual information, (0075: selectively adjusts the single recommendation algorithms according to the category and the characteristic of the recommendation and 0076: recommending a product for each customer by considering characteristics of each customer),determine a relative quality of the optimized recommendation according to a comparison of a plurality of metrics related to user engagement and conversion rates, and (Fig. 5: ranking recommendation of products based on sale rate ranks.), (0044: Referring to FIG. 3, the first recommendation result 211 and the first verifying result 212 obtained using the selected single recommendation algorithm 210 are illustrated. Three algorithms of the CF algorithm, the AR algorithm, and the PP product recommendation algorithm may be selected as the single recommendation algorithm 210. That is, FIG. 3 (a) is the first recommendation result 211 and the first verifying result 212 showing the first recommendation product and a first recommendation ranking of each customer obtained using the CF algorithm. Here, the first recommendation result 211 may be a data result with respect to the first learning data, and the first verifying result 212 may be a data result with respect to the first verifying data.),deliver the optimized recommendation, to a device of the user in real-time, according to the personal contextual information; and (0039: selecting the customer recommendation product may include the single recommendation algorithm 210 and the hybrid recommendation algorithm 220, and select a product having a high hit rate as the customer recommendation product… abstract: and providing a personalized recommendation of products.).
Lee does not teach test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation.
However Dhawan teaches test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation, (0078: The recommendation algorithm then matches the attributes of the players retrieved from the online game data store with the player selection parameters of the request to identify a subset of players whose attributes match the player selection parameters, as illustrated in operation 730. The recommendation algorithm performs the match by first analyzing the player selection parameters to determine user attributes and player attributes desired by the user. The algorithm then retrieves categorized information related to player attributes of plurality of players maintained within a player attribute bucket data store. The recommendation algorithm then matches player selection parameters of the request with the corresponding player attributes of the plurality of players provided within the categorized information to identify a subset of the players), (Examiner interpretation: generating better optimized recommendations for the first subset as mentioned in paragraph 0079 and Fig. 5A-5D can be the other players that has the same level and almost same amount of chips as Ronnie, the recommendation algorithm performs the match by first analyzing the player selection parameters to determine user attributes and player attributes desired by the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee to include test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation of Dhawan. Motivation to do so comes from the knowledge well known in the art that test a performance of the first recommendation algorithm and the second recommendation algorithm with respect to a plurality of test personal contexts, thereby generating an optimized recommendation would provide a more accurate recommendation that the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
Lee does not teach a memory operable to: store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context.
However Libby teaches a memory operable to:store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context; (0077: The recommendation algorithm may query online game data repository, which stores dynamically changing attributes of the players captured during online game play, to obtain the player attributes of the different players), (0078: The recommendation algorithm then matches the attributes of the players retrieved from the online game data store with the player selection parameters of the request to identify a subset of players whose attributes match the player selection parameters, as illustrated in operation 730. The recommendation algorithm performs the match by first analyzing the player selection parameters to determine user attributes and player attributes desired by the user. The algorithm then retrieves categorized information related to player attributes of plurality of players maintained within a player attribute bucket data store. The recommendation algorithm then matches player selection parameters of the request with the corresponding player attributes of the plurality of players provided within the categorized information to identify a subset of the players), (Examiner interpretation: generating better optimized recommendations for the first subset as mentioned in paragraph 0079 and Fig. 5A-5D can be the other players that has the same level and almost same amount of chips as Ronnie, the recommendation algorithm performs the match by first analyzing the player selection parameters to determine user attributes and player attributes desired by the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lee to include store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context of Libby. Motivation to do so comes from the knowledge well known in the art that store data representing the dynamic adjustment of recommendation algorithms in response to changes in the consumer's context would help in determining a more accurate recommendation based on previously stored data and that would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 28, Lee, Dhawan, and Libby teach the system of claim 22.
Dhawan further teaches wherein the relative quality of an optimized recommendation is determined according to one or both of a business metric and a financial metric; (Fig. 5A-5D: financial metric with quantity of an optimized recommendation can be the number of chips each player has).
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 relative quality of an optimized recommendation is determined according to one or both of a business metric and a financial metric. Motivation to do so comes from the knowledge well known in the art that wherein the relative quality of an optimized recommendation is determined according to one or both of a business metric and a financial metric would help in determining a more accurate recommendation based on metrics data and that would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 32, Lee, Dhawan, and Libby teach the system of claim 31.
Dhawan further teaches wherein the first recommendation algorithm generates better optimized recommendations for the first subset of the plurality of test personal contexts, and the second recommendation algorithm generates better optimized recommendations for the second subset of the plurality of test personal contexts; (Fig. 7 720: gathering player attributes of the plurality of players in substantial real-time, the player attributes analyzed and categorized into a plurality of attribute buckets), and (0077: the recommendation algorithm collects player attribute information, in substantial real time, of each of the players currently playing a plurality of online games).
As to Claim 33, Lee, Dhawan, and Libby teach the system of claim 22.
Lee further teaches wherein the one or more processing devices comprise the device of the user; (Fig. 1 Device of the user).
As to Claim 35, Lee, Dhawan, and Libby teach the system of claim 22.
Dhawan further teaches wherein the device of the user is operable to distribute a plurality of executable instructions associated with and executed by the one or more processing devices; (Fig. 7 720: gathering player attributes of the plurality of players in substantial real-time, the player attributes analyzed and categorized into a plurality of attribute buckets), and (0077: the recommendation algorithm collects player attribute information, in substantial real time, of each of the players currently playing a plurality of online games).
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 device of the user is operable to distribute a plurality of executable instructions associated with and executed by the one or more processing devices. Motivation to do so comes from the knowledge well known in the art that wherein the device of the user is operable to distribute a plurality of executable instructions associated with and executed by the one or more processing devices would help in determining a more accurate recommendation and that would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
B. Claim(s) 23-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., (U.S. Patent Application Publication No. 20160162974) in view of Dhawan et al., (U.S. Patent Application Publication No. 20140274362) in view of Libby et al., (U.S. Patent Application Publication No. 20110055004) in view of Dicker et al. (U.S. Patent No. US7720723B2).
As to Claim 23, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein a request for a product or service recommendation originates in an interaction with an Internet web page.
However Dicker teaches wherein a request for a product or service recommendation originates in an interaction with an Internet web page; (col. 3 and 4 lines 19-45: product viewing histories of users are recorded and analyzed to identify items that tend to be viewed in combination (e.g., products A and B are similar because a significant number of those who viewed A also viewed B during the same browsing session), And (claim 23: providing item recommendations for a user, said method performed by a machine that comprises one or more physical computers, the method comprising: executing a first recommendation algorithm to generate a first set of item recommendations for the user based on a first set of information reflective of item preferences of the user, said first set of item recommendations comprising identifiers of a first plurality of recommended items; executing a second recommendation algorithm to generate a second set of item recommendations for the user based on a second set of information reflective of item preferences of the user, said second set of item recommendations comprising identifiers of a second plurality of recommended items; and generating a page for presentation to the user, wherein generating the page comprises (1) populating a first recommendations section of the page with a representation of the first set of item recommendations such that each recommended item is presented with an associated control for adding the respective item to a shopping cart, (2) populating a second recommendations section of the page with a representation of the second set of item recommendations, and (3) generating a shopping cart section that identifies one or more items represented in the shopping cart and which includes a selectable control for proceeding to checkout, said first recommendations section, second recommendations section and shopping cart section being visually demarcated).
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 a request for a product or service recommendation originates in an interaction with an Internet web page. Motivation to do so comes from the knowledge well known in the art that wherein a request for a product or service recommendation originates in an interaction with an Internet web page would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 24, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein a request for a product or service recommendation is generated as part of production of promotional or marketing communication for the user.
However Dicker teaches wherein a request for a product or service recommendation is generated as part of production of promotional or marketing communication for the user; (claim 23: generate a first set of item recommendations for the user based on a first set of information reflective of item preferences of the user), and (col. 4 lines 37-54: creating process, each user builds a personal profile of his or her preferences. To generate recommendations for a particular user, the user's profile is compared to the profiles of other users to identify one or more "similar users." Items that were rated highly by these similar users, but which have not yet been rated by the user, are then recommended to the user), (Examiners interpretation: generating the promotion recommendation is based on preferences and is being generated as part of the product of the promotion).
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 a request for a product or service recommendation is generated as part of production of promotional or marketing communication for the user. Motivation to do so comes from the knowledge well known in the art that wherein a request for a product or service recommendation is generated as part of production of promotional or marketing communication for the user would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 25, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein the personal contextual information comprises personal financial data for the user, personal product preference data for the user, and data representative of non-financial interactions between the merchant and the user.
However Dicker teaches wherein the personal contextual information comprises personal financial data for the user, personal product preference data for the user (claim 23: information reflective of item preferences of the user), and data representative of non-financial interactions between the merchant and the user; (col. 7 lines 5-22: user's liking of or affinity for an item).
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 personal contextual information comprises personal financial data for the user, personal product preference data for the user, and data representative of non-financial interactions between the merchant and the user. Motivation to do so comes from the knowledge well known in the art that wherein the personal contextual information comprises personal financial data for the user, personal product preference data for the user, and data representative of non-financial interactions between the merchant and the user would provide more data in the recommendation determination and that would provide a more accurate recommendation and encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 26, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein customization of a webpage comprises: generation of a web page that displays a generated product or service recommendation according to one of the first recommendation algorithm and the second recommendation algorithm, and delivery of the web page via a network interface.
However Dicker teaches wherein customization of a webpage comprises: generation of a web page that displays a generated product or service recommendation according to one of the first recommendation algorithm and the second recommendation algorithm, and (col. 3 and 4 lines 19-45: product viewing histories of users are recorded and analyzed to identify items that tend to be viewed in combination (e.g., products A and B are similar because a significant number of those who viewed A also viewed B during the same browsing session), And (claim 23: providing item recommendations for a user, said method performed by a machine that comprises one or more physical computers, the method comprising: executing a first recommendation algorithm to generate a first set of item recommendations for the user based on a first set of information reflective of item preferences of the user, said first set of item recommendations comprising identifiers of a first plurality of recommended items; executing a second recommendation algorithm to generate a second set of item recommendations for the user based on a second set of information reflective of item preferences of the user, said second set of item recommendations comprising identifiers of a second plurality of recommended items; and generating a page for presentation to the user, wherein generating the page comprises (1) populating a first recommendations section of the page with a representation of the first set of item recommendations such that each recommended item is presented with an associated control for adding the respective item to a shopping cart, (2) populating a second recommendations section of the page with a representation of the second set of item recommendations), delivery of the web page via a network interface (abstract: An improved user interface and method are provided for presenting recommendations to a user when the user), and (Fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generation of a web page that displays a generated product or service recommendation according to one of the first recommendation algorithm and the second recommendation algorithm, and delivery of the web page via a network interface. Motivation to do so comes from the knowledge well known in the art that generation of a web page that displays a generated product or service recommendation according to one of the first recommendation algorithm and the second recommendation algorithm, and delivery of the web page via a network interface would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 27, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein the one or more of the plurality of processing devices is operable to deliver a promotional communication to the device of the user according to a merchant selecting a particular user.
However Dicker teaches wherein the one or more of the plurality of processing devices is operable to deliver a promotional communication to the device of the user according to a merchant selecting a particular user; (col. 3 and 4 lines 19-45: product viewing histories of users are recorded and analyzed to identify items that tend to be viewed in combination (e.g., products A and B are similar because a significant number of those who viewed A also viewed B during the same browsing session), And (claim 23: providing item recommendations for a user, said method performed by a machine that comprises one or more physical computers, the method comprising: executing a first recommendation algorithm to generate a first set of item recommendations for the user based on a first set of information reflective of item preferences of the user, said first set of item recommendations comprising identifiers of a first plurality of recommended items; executing a second recommendation algorithm to generate a second set of item recommendations for the user based on a second set of information reflective of item preferences of the user, said second set of item recommendations comprising identifiers of a second plurality of recommended items; and generating a page for presentation to the user, wherein generating the page comprises (1) populating a first recommendations section of the page with a representation of the first set of item recommendations such that each recommended item is presented with an associated control for adding the respective item to a shopping cart, (2) populating a second recommendations section of the page with a representation of the second set of item recommendations).
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 one or more of the plurality of processing devices is operable to deliver a promotional communication to the device of the user according to a merchant selecting a particular user. Motivation to do so comes from the knowledge well known in the art that wherein the one or more of the plurality of processing devices is operable to deliver a promotional communication to the device of the user according to a merchant selecting a particular user would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
C. Claim(s) 29 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., (U.S. Patent Application Publication No. 20160162974) in view of Dhawan et al., (U.S. Patent Application Publication No. 20140274362) in view of Libby et al., (U.S. Patent Application Publication No. 20110055004) in view of Chau et al. (U.S. Patent No. US9665874B2).
As to Claim 29, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier.
However Chau teaches wherein the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier; (claim 1: determining, by a tailoring marketing computer-based system, that a consumer is eligible to receive an offer, wherein the consumer is not eligible in response to the consumer having a transaction account affiliated with a first merchant and the offer is offered by a second merchant, wherein the second merchant is a competitor of the first merchant; determining, by the computer-based system and in response to the consumer being eligible, a consumer relevance value associated with the offer based upon content of the offer, an industry of the offer, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data, consumer feedback data, a collaborative filtering algorithm and a plurality of offer to offer similarity values, wherein the offer to offer similarity value is based on pairings of offers at least one of occurring most often or are most strongly correlated, wherein the offer to offer similarity value is determined based on at least one of a co-occurrence method or a cosine method, wherein the offer to offer similarity value is between a plurality of merchants, wherein the offer to offer similarity value is determined by comparing record of charges (ROCS) of a plurality of consumers at the plurality of merchants, and wherein the consumer relevance value is determined for the consumer; storing, by the computer-based system, data sets of the consumer relevance value in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset; partitioning, by the computer-based system and using a key field, the database according to a class of objects defined by the key field to speed searching for the consumer relevance value; linking, by the computer-based system, data tables based on the type of data in the key fields; annotating, by the computer-based system, the data sets to include security information establishing access levels; obtaining, by the computer-based system, the consumer relevance value from the database; generating, by the computer-based system and using the consumer relevance value, an offer matrix having coefficients indicating that the offers are associated, wherein each of the coefficients of the offer matrix comprises a record associated with one or more offers; adjusting, by the computer-based system, the consumer relevance value based on the coefficients, a merchant goal of the first merchant and a business rule, wherein the merchant goal includes one or more of acquiring only new customers, tailoring existing customers of the first merchant, and tailoring all consumers, wherein the business rule includes one or more of a holiday, a particular time of day, a determination that the consumer is traveling based on a consumer device, a determination that the offer is associated with the first merchant that is a particular distance away from a consumer location, and a consumer preference not to receive the offer; associating, by the computer-based system, criteria with the offer for the first merchant; obtaining, by the computer-based system, social data from a social media website about the first merchant; comparing, by the computer-based system and based on matching rules, the criteria with the social data from the social media website about the first merchant to determine a social media association between the criteria for the offer and the social media data about the first merchant, wherein the offer is for use at the first merchant; providing, by the computer-based system, a higher ranking for the offer based on the social media association and the consumer relevance value; more prominently displaying, by the computer-based system, the offer based on the social media association and the consumer relevance value; associating, by the computer-based system, a merchant identifier with the consumer relevance value; ranking, by the computer-based system, the offer among a plurality of offers, wherein the consumer relevance value for each of the plurality of offers is unique for the consumer, and transmitting, by the computer-based system and in response to the ranking, a first ranked list of the plurality of offers to the consumer device; monitoring, by the computer-based system, real time transaction information associated with the consumer; receiving, by the computer-based system, a request for the plurality of offers from the consumer device; adjusting, by the computer-based system, the consumer relevance value of the offer based on the real time transaction information and in response to the request for the recommendation; re-ranking, by the computer-based system, the first ranked list based on the real time transaction information to create a second ranked list of the plurality of offers; and transmitting, by the computer-based system, the second ranked list in real time and in response to the request for the recommendation).
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 first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier. Motivation to do so comes from the knowledge well known in the art that wherein the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 30, Lee, Dhawan, and Chau Libby teach the system of claim 29.
Chau further teaches wherein a record of use of the first recommendation algorithm is used to arrange for payment to the first supplier; (claim 1: determining, by a tailoring marketing computer-based system, that a consumer is eligible to receive an offer, wherein the consumer is not eligible in response to the consumer having a transaction account affiliated with a first merchant and the offer is offered by a second merchant, wherein the second merchant is a competitor of the first merchant; determining, by the computer-based system and in response to the consumer being eligible, a consumer relevance value associated with the offer based upon content of the offer, an industry of the offer, a consumer profile, a transaction history associated with the consumer, social data, demographic data, clickstream data, consumer feedback data, a collaborative filtering algorithm and a plurality of offer to offer similarity values, wherein the offer to offer similarity value is based on pairings of offers at least one of occurring most often or are most strongly correlated, wherein the offer to offer similarity value is determined based on at least one of a co-occurrence method or a cosine method, wherein the offer to offer similarity value is between a plurality of merchants, wherein the offer to offer similarity value is determined by comparing record of charges (ROCS) of a plurality of consumers at the plurality of merchants, and wherein the consumer relevance value is determined for the consumer; storing, by the computer-based system, data sets of the consumer relevance value in a database as ungrouped data elements formatted as a block of binary (BLOB) via a fixed memory offset; partitioning, by the computer-based system and using a key field, the database according to a class of objects defined by the key field to speed searching for the consumer relevance value; linking, by the computer-based system, data tables based on the type of data in the key fields; annotating, by the computer-based system, the data sets to include security information establishing access levels; obtaining, by the computer-based system, the consumer relevance value from the database; generating, by the computer-based system and using the consumer relevance value, an offer matrix having coefficients indicating that the offers are associated, wherein each of the coefficients of the offer matrix comprises a record associated with one or more offers; adjusting, by the computer-based system, the consumer relevance value based on the coefficients, a merchant goal of the first merchant and a business rule, wherein the merchant goal includes one or more of acquiring only new customers, tailoring existing customers of the first merchant, and tailoring all consumers, wherein the business rule includes one or more of a holiday, a particular time of day, a determination that the consumer is traveling based on a consumer device, a determination that the offer is associated with the first merchant that is a particular distance away from a consumer location, and a consumer preference not to receive the offer; associating, by the computer-based system, criteria with the offer for the first merchant; obtaining, by the computer-based system, social data from a social media website about the first merchant; comparing, by the computer-based system and based on matching rules, the criteria with the social data from the social media website about the first merchant to determine a social media association between the criteria for the offer and the social media data about the first merchant, wherein the offer is for use at the first merchant; providing, by the computer-based system, a higher ranking for the offer based on the social media association and the consumer relevance value; more prominently displaying, by the computer-based system, the offer based on the social media association and the consumer relevance value; associating, by the computer-based system, a merchant identifier with the consumer relevance value; ranking, by the computer-based system, the offer among a plurality of offers, wherein the consumer relevance value for each of the plurality of offers is unique for the consumer, and transmitting, by the computer-based system and in response to the ranking, a first ranked list of the plurality of offers to the consumer device; monitoring, by the computer-based system, real time transaction information associated with the consumer; receiving, by the computer-based system, a request for the plurality of offers from the consumer device; adjusting, by the computer-based system, the consumer relevance value of the offer based on the real time transaction information and in response to the request for the recommendation; re-ranking, by the computer-based system, the first ranked list based on the real time transaction information to create a second ranked list of the plurality of offers; and transmitting, by the computer-based system, the second ranked list in real time and in response to the request for the recommendation).
D. Claim(s) 34 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al., (U.S. Patent Application Publication No. 20160162974) in view of Dhawan et al., (U.S. Patent Application Publication No. 20140274362) in view of Libby et al., (U.S. Patent Application Publication No. 20110055004) in view of Burgess et al. (U.S. Patent Application Publication No. 20110055004).
As to Claim 34, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein the first recommendation algorithm is submitted by a first supplier and the second recommendation algorithm is submitted by a second supplier that is different than the first supplier.
However Burgess teaches wherein the device of the user is operable to provide a current geographic location of the user; (0044: The first component in the exemplary predictive analysis approach shown in FIG. 1 is performing a context analysis 302. Context analysis 302 examines the circumstances and activities of the customer to optimize the presentation and content of information and to improve the accuracy of the predictive analysis. The inputs to the context analysis include, among other data, the geographic location of the customer, the indoor location of the customer, and prior-behavior context triggers. The geographic location of the customer can be determined using a GPS device integrated with the consumer computing device 101, and the indoor location of a customer can be determined using an indoor positioning system integrated with the provider's computer system 100. Prior-behavior context triggers can be determined by examining customer account information, such as the frequency, type, and circumstances of prior transactions).
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 device of the user is operable to provide a current geographic location of the user. Motivation to do so comes from the knowledge well known in the art that wherein the device of the user is operable to provide a current geographic location of the user would help provide a more accurate recommendation based on current geographic location of the user and that would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
As to Claim 36, Lee, Dhawan, and Libby teach the system of claim 22.
Lee, Dhawan, and Libby do not teach wherein at least one test personal context of the plurality of test personal contexts is a geographic location of the user.
However Burgess teaches wherein at least one test personal context of the plurality of test personal contexts is a geographic location of the user; (0044: The first component in the exemplary predictive analysis approach shown in FIG. 1 is performing a context analysis 302. Context analysis 302 examines the circumstances and activities of the customer to optimize the presentation and content of information and to improve the accuracy of the predictive analysis. The inputs to the context analysis include, among other data, the geographic location of the customer, the indoor location of the customer, and prior-behavior context triggers. The geographic location of the customer can be determined using a GPS device integrated with the consumer computing device 101, and the indoor location of a customer can be determined using an indoor positioning system integrated with the provider's computer system 100. Prior-behavior context triggers can be determined by examining customer account information, such as the frequency, type, and circumstances of prior transactions).
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 at least one test personal context of the plurality of test personal contexts is a geographic location of the user. Motivation to do so comes from the knowledge well known in the art that wherein at least one test personal context of the plurality of test personal contexts is a geographic location of the user would help provide a more accurate recommendation based on current geographic location of the user and that would encourage the user would review and engage with such recommendation and that would promote an increase in the sales and would therefore make the method/system more profitable.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “System Architectures for Personalization and Recommendation” describes in our previous posts about Netflix personalization, we highlighted the importance of using both data and algorithms to create the best possible experience for Netflix members. We also talked about the importance of enriching the interaction and engaging the user with the recommendation system. Today we’re exploring another important piece of the puzzle: how to create a software architecture that can deliver this experience and support rapid innovation. Coming up with a software architecture that handles large volumes of existing data, is responsive to user interactions, and makes it easy to experiment with new recommendation approaches is not a trivial task. In this post we will describe how we address some of these challenges at Netflix.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20150286937 teaches similar invention which describes technology is directed to text message based concierge services (“the technology”). A user interacts with a concierge service (CS) via text messages to obtain a specific concierge service. For example, the user can send a text message to the CS, e.g., to a contact number provided by the CS, requesting for a recommendation of a restaurant, and the CS can respond by sending the recommendation as a text message. The CS determines a context of the request and generates recommendations that are personalized to the user and is relevant to the context. The CS can use various techniques, e.g., artificial intelligence, machine learning, natural language processing, to determine a context of the request and generate the recommendations accordingly. The CS can also receive additional information from a person associated with the CS, such as a concierge, to further customize or personalize the recommendations to the user.
Conclusion
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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