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 .
Double Patenting
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.
“A later patent claim is not patentably distinct from an earlier patent claim if the later claim is obvious over, or anticipated by, the earlier claim. In re Longi, 759 F.2d at 896, 225 USPQ at 651 (affirming a holding of obviousness-type double patenting because the claims at issue were obvious over claims in four prior art patents); In re Berg, 140 F.3d at 1437, 46 USPQ2d at 1233 (Fed. Cir. 1998) (affirming a holding of obviousness-type double patenting where a patent application claim to a genus is anticipated by a patent claim to a species within that genus). “ ELI LILLY AND COMPANY v BARR LABORATORIES, INC., United States Court of Appeals for the Federal Circuit, ON PETITION FOR REHEARING EN BANC (DECIDED: May 30, 2001).
Claim 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim1-20 of U.S. Patent No. 12019637 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claim(s) 1-20 of patent # 12019637 contain(s) every element of claim(s) 1-20 of the instant application and as such anticipate(s) claim(s) 1-20 of the instant application.
Application: 18751213
Patent: 12019637
Claim 1
A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform:
receiving in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, wherein a product within the ranked list of the products is in a boost category, and wherein the user search query is received via a graphical user interface that is in a first display state;
analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products; and
in response to determining that the product that in the boost category is to be repositioned within the ranked list of the products, transmitting instructions to modify the graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products, wherein the ranked list of the products is displayed via the graphical user interface in a second display state, the second display state being different than the first display state.
Claim 1
A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform:
receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category, wherein the user search query is received via a graphical user interface that is in a first display state;
determining a respective content model prediction score for each of the products of the ranked list of the products;
determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products based on a respective re-rank model score;
analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and
in response to determining that the product that is part of the boost category is to be repositioned, transmitting instructions to modify the graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products, wherein the ranked listed of the products is displayed via the graphical user interface in a second display state, the second display state to be different than the first display state.
Claim 2
The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further perform:
determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products, wherein analyzing the product further comprises analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for the product.
Claim 1
… determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products based on a respective re-rank model score; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products
Claim 3
The system of claim 1, wherein the in-session user activity information further comprises product information corresponding to the ranked list of the products, a respective ranking score for each of the products of the ranked list of the products, and product interaction information corresponding to the ranked list of the products.
Claim 2
The system of claim 1, wherein the in-session user activity information further comprises product information corresponding to the ranked list of the products, a respective ranking score for each of the products of the ranked list of the products, and product interaction information corresponding to the ranked list of the products.
Claim 4
The system of claim 3, wherein at least one of: the product information comprises at least one or more of: a product type, a product quality, or a product category; or the product interaction information comprises at least one or more of: user examination of the products, user clicks on the products, or user add-to-carts of the products.
Claim 3
The system of claim 2, wherein the product information comprises at least one or more of: a product type, a product quality, or a product category.
Claim 4
The system of claim 2, wherein the product interaction information comprises at least one or more of: user examination of the products, user clicks on the products, or user add-to-carts of the products.
Claim 5
The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further perform: determining a respective content model prediction score for each of the products of the ranked list of the products.
Claim 1
… determining a respective content model prediction score for each of the products of the ranked list of the products;
Claim 6
The system of claim 1, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises determining an alpha value using an equation comprising: α=ĉ*k, where ĉ comprises a predicted click through rate of an item for a query and k comprises a real value corresponding to a number of pseudo-examinations for a query item pair.
Claim 5
The system of claim 1, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises determining an alpha value using an equation comprising: α=ĉ*k, where ĉ comprises a predicted click through rate of an item for a query and k comprises a real value corresponding to a number of pseudo-examinations for a query item pair.
Claim 7
The system of claim 6, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises: determining a posterior distribution using the alpha value; and determining a sample from the posterior distribution, wherein the sample is the respective content model prediction score.
Claim 8
The system of claim 7, wherein the posterior distribution is determined using an equation comprising: posterior distribution=Beta(α+clicks, β+examines−clicks), where clicks comprises a number of times a product has been clicked on from a search results page for a particular query, and examines comprises a number of times a product has been observed in the search results page for the particular query.
Claim 7
The system of claim 6, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises: determining a posterior distribution using the alpha value and the beta value; and determining a sample from posterior distribution, wherein the sample is the respective content model prediction score.
Claim 8
The system of claim 7, wherein the posterior distribution is determined using an equation comprising: posterior distribution=Beta(α+clicks, β+examines−clicks), where clicks comprises a number of times a product has been clicked on from a search results page for a particular query, and examines comprises a number of times a product has been observed in the search results page for the particular query.
Claim 9
The system of claim 1, wherein the computing instructions, when executed on the one or more processors, further perform:
determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products by using an equation comprising
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comprises a number of the products in the ranked list of the products, reli comprises a relevance score of a product at position i.
Claim 9
The system of claim 1,
wherein determining the respective DCG score for each of the products of the ranked list of the products further comprises using an equation comprising:
PNG
media_image1.png
41
142
media_image1.png
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where P comprises a number of the products in the ranked list of the products, reli comprises a relevance score of a product at position i.
Claim 10
The system of claim 1, wherein analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products further comprises:
identifying a first placement within the ranked list of the products, the first placement corresponding to a first product with a highest rank;
receiving a discounted cumulative gain (DCG) score for the first product with the highest rank;
comparing the DCG score for the first product that is part of the boost category to a boost test threshold, wherein the boost test threshold comprises [(1-s)*DCG for an ith placement]; and
determining that the first product that is part of the boost category is to be repositioned to the first placement when the DCG score for the first product that is part of the boost category is greater than the boost test threshold.
Claim 10
The system of claim 1, wherein analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products further comprises:
identifying a first placement within the ranked list of the products, the first placement corresponding to a first product with a highest rank;
receiving a DCG score for the first product with the highest rank;
comparing the DCG score for the first product that is part of the boost category to a boost test threshold, wherein the boost test threshold comprises [(1−s)*DCG for an ith placement]; and
determining that the first product that is part of the boost category is to be repositioned to the first placement when the DCG score for the first product that is part of the boost category is greater than the boost test threshold.
Claim 11
A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:
receiving in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, wherein a product within the ranked list of the products is in a boost category, and wherein the user search query is received via a graphical user interface that is in a first display state;
analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products; and
in response to determining that the product that in the boost category is to be repositioned within the ranked list of the products, transmitting instructions to modify the graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products, wherein the ranked list of the products is displayed via the graphical user interface in a second display state, the second display state being different than the first display state.
Claim 11
A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:
receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category, wherein the user search query is received via a graphical user interface that is in a first display state;
determining a respective content model prediction score for each of the products of the ranked list of the products;
determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products based on a respective re-rank model score;
analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and
in response to determining that the product that is part of the boost category is to be repositioned, transmitting instructions to modify the graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products, wherein the ranked listed of the products is displayed via the graphical user interface in a second display state, the second display state to be different than the first display state.
Claim 12
The method of claim 11, further comprising: determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products, wherein analyzing the product further comprises analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for the product.
Claim 11
… determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products based on a respective re-rank model score; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products
Claim 13
The method of claim 11, wherein the in-session user activity information further comprises product information corresponding to the ranked list of the products, a respective ranking score for each of the products of the ranked list of the products, and product interaction information corresponding to the ranked list of the products.
Claim 12
The method of claim 11, wherein the in-session user activity information further comprises product information corresponding to the ranked list of the products, a respective ranking score for each of the products of the ranked list of the products, and product interaction information corresponding to the ranked list of the products.
Claim 14
The method of claim 13, wherein at least one of: the product information comprises at least one or more of: a product type, a product quality, or a product category; or the product interaction information comprises at least one or more of: user examination of the products, user clicks on the products, or user add-to-carts of the products.
Claim 13
The method of claim 12, wherein the product information comprises at least one or more of: a product type, a product quality, or a product category.
Claim 14
The method of claim 12, wherein the product interaction information comprises at least one or more of: user examination of the products, user clicks on the products, or user add-to-carts of the products.
Claim 15
The method of claim 11, further comprising: determining a respective content model prediction score for each of the products of the ranked list of the products.
Claim 11
… determining a respective content model prediction score for each of the products of the ranked list of the products;
Claim 16
The method of claim 15, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises determining an alpha value using an equation comprising: α=ĉ*k, where ĉ comprises a predicted click through rate of an item for a query and k comprises a real value corresponding to a number of pseudo-examinations for a query item pair.
Claim 17
The method of claim 16, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises: determining a posterior distribution using the alpha value; and determining a sample from posterior distribution, wherein the sample is the respective content model prediction score.
Claim 15
The method of claim 11, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises determining an alpha value using an equation comprising: α=ĉ*k, where ĉ comprises a predicted click through rate of an item for a query and k comprises a real value corresponding to a number of pseudo-examinations for a query item pair.
Claim 17
The method of claim 16, wherein determining the respective content model prediction score for each of the products of the ranked list of the products further comprises: determining a posterior distribution using the alpha value and the beta value; and determining a sample from posterior distribution, wherein the sample is the respective content model prediction score.
Claim 18
The method of claim 17, wherein the posterior distribution is determined using an equation comprising: posterior distribution=Beta(α+clicks, β+examines−clicks), where clicks comprises a number of times a product has been clicked on from a search results page for a particular query, and examines comprises a number of times a product has been observed in the search results page for the particular query.
Claim 18
The method of claim 17, wherein the posterior distribution is determined using an equation comprising: posterior distribution=Beta(α+clicks, β+examines−clicks), where clicks comprises a number of times a product has been clicked on from a search results page for a particular query, and examines comprises a number of times a product has been observed in the search results page for the particular query.
Claim 19
The method of claim 11, further comprising: determining a respective discounted cumulative gain (DCG) score for positions of each of the products of the ranked list of the products by using an equation comprising:
PNG
media_image1.png
41
142
media_image1.png
Greyscale
comprises a number of the products in the ranked list of the products, reli comprises a relevance score of a product at position i.
Claim
The method of claim 11,
wherein determining the respective DCG score for each of the products of the ranked list of the products further comprises using an equation comprising:
PNG
media_image1.png
41
142
media_image1.png
Greyscale
where P comprises a number of the products in the ranked list of the products, reli comprises a relevance score of a product at position i.
Claim 20
The method of claim 11, wherein analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products further comprises:
identifying a first placement within the ranked list of the products, the first placement corresponding to a first product with a highest rank;
receiving a discounted cumulative gain (DCG) score for the first product with the highest rank;
comparing the DCG score for the first product that is part of the boost category to a boost test threshold, wherein the boost test threshold comprises [(1-s)*DCG for an ith placement]; and
determining that the first product that is part of the boost category is to be repositioned to the first placement when the DCG score for the first product that is part of the boost category is greater than the boost test threshold.
Claim 20
The method of claim 11, wherein analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products further comprises:
identifying a first placement within the ranked list of the products, the first placement corresponding to a first product with a highest rank;
receiving a DCG score for the first product with the highest rank;
comparing the DCG score for the first product that is part of the boost category to a boost test threshold, wherein the boost test threshold comprises [(1−s)*DCG for an ith placement]; and
determining that the first product that is part of the boost category is to be repositioned to the first placement when the DCG score for the first product that is part of the boost category is greater than the boost test threshold.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5, 11,13, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oztekin (U.S. Pub 2007/0233671 A1)
Claim 1
Oztekin discloses a system comprising (fig. 12):
one or more processors (fig. 12, CPU 1202); and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform (fig. 12, memory 1212):
receiving in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query ([0089], “… the search engine122 receives a search query submitted by a user (910) … identifies a set of documents that match the search query (92), each document having a generic ranking score… the net result is an initial set of search result…” <examiner note: each document is considered as a product because a product is defined as a thing or person that is the result of an action or process. A document is a thing that is the result of an action or process such as creating edit document>), wherein a product within the ranked list of the products is in a boost category ([0091], line 1-3, “… the search result ranker 126 analyzes each identified document to determine one or more boost factors using the group and document profiles (935)…”), and wherein the user search query is received via a graphical user interface that is in a first display state ([0031], line 10-13, “… the website 102 includes webpage 116, which may have an associated search box. From the search box, a visitor … can search… by entering a search query into the search box…” <examiner note: first state user enters a search query into the search box>);
analyzing the product in the boost category to determine if the product is to be repositioned within the ranked list of the products ([0091], line 1-4, “… analyzes each identified document to determine one or more boost factors using the group and document profiles (935) and then assigns the document a group-dependent ranking score…” [0097], “… where the weights (Wterm, Wcategory, Wlink) are assigned so that the value in parentheses in the above equations is equal to about 1.0 if the document is to be neither promoted nor demoted in rank for the group, above 1.0 if the document should be promoted, and below 1.0 if the document should be demoted…”); and
in response to determining that the product that in the boost category is to be repositioned within the ranked list of the products, transmitting instructions to modify the graphical user interface to display the ranked list of the products with the product repositioned within the ranked list of the products, wherein the ranked list of the products is displayed via the graphical user interface in a second display state, the second display state being different than the first display state ([0039], line 14-23, “… The documents are then re-ordered in accordance with their respective group-dependent ranking scores. Next, the search result ranker 126 creates a search result in accordance with the updated order of the documents… The search result, or a portion of the search result (e.g., information identifying the top 10, 15 or 20 information items or documents), is returned to the requesting client 103 and displayed to the user through the client application 132…” <examiner note: the interface is updated with search results that are re-ordered in accordance with group-dependent ranking scores>)
Claim 3
Claim 1 is included, Oztekin discloses wherein the in-session user activity information further comprises product information corresponding to the ranked list of the products, a respective ranking score for each of the products of the ranked list of the products, and product interaction information corresponding to the ranked list of the products ([0046], “… In order to capture the current user interest associated with a particular group, the group profiler 129 may need to have access to the users' search history. The users' search history includes the search queries submitted by users, the search results responsive to the search queries, the users' activities on the search results (e.g., selection of a document link, sometimes called "clicking" on a search result, amount of time spent at a document after selecting the document link, mouse hovering time over a document link, or the like), the current website viewed by the user, the last n websites viewed by the user (where n is a whole number, typically on the order of five to ten), the user's favorite websites, or the like…”)
Claim 5
Claim 1 is included, Oztekin discloses wherein the computing instructions, when executed on the one or more processors, further perform: determining a respective content model prediction score for each of the products of the ranked list of the products ([0091], line 1-4, “… analyzes each identified document to determine one or more boost factors using the group and document profiles (935) and then assigns the document a group-dependent ranking score…” [0097], “… where the weights (Wterm, Wcategory, Wlink) are assigned so that the value in parentheses in the above equations is equal to about 1.0 if the document is to be neither promoted nor demoted in rank for the group, above 1.0 if the document should be promoted, and below 1.0 if the document should be demoted…”)
Claim 11, 13, and 15 are similar to claim 1, 3, and 5. The claims are rejected based on similar reason.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2167
/HAU H HOANG/Primary Examiner, Art Unit 2167
Prosecution Insights