DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/12/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-10 are directed to a system, which is a machine. Claims 11-18 are directed to a method, which is a process. Claims 19-20 are directed to a medium, which is an apparatus. Therefore, claims 1-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 1 sets forth the following limitations which recite the abstract idea of providing product information:
receive, from a user, an initial search request;
identify relevant products for the initial search request, the relevant products for the initial search request associated with refinement filters for refining the initial search request;
assign scores to the refinement filters based at least on historical search queries of the user or historical interactions of the user;
select one of the refinement filters based on the scores assigned to the refinement filters; and
display a refined search request based on refining the initial search request using the selected refinement filter, wherein the user-interactable component is configured to execute the refined search request upon the user interacting with the user-interactable component.
The recited limitations as a whole set forth the process for providing product information. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner acknowledges that claim 1 does recite additional elements, such as a processor, a memory, a graphical user interface, etc.
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The claim merely includes instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer device to perform the receiving and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-10 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the process for providing product information. Thus, each of claims 2-10 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-10 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claims 11 (method) and 19 (medium), the claim recites substantially similar limitations as set forth in claim 1. As such, claims 11 and 19 and their dependent claims 12-18 and 20 are rejected for at least similar rationale as discussed above.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 11-12 and 19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wu et al. (U.S. Pre-Grant Publication No. 2025/0124483 A1) (“Wu”).
Regarding claims 1, 11 and 19, Wu teaches a system (and related method and medium) for search recommendations, the system comprising:
at least one processor (Fig. 11; para [0086], processors); and
at least one memory comprising computer-readable instructions (Fig. 11; para [0086], memory), the at least one processor, the at least one memory and the computer-readable instructions configured to cause the at least one processor to:
receive, from a user, an initial search request via a graphical user interface (para [0026], in response to a search query or when an item page is presented with similar items);
identify relevant products for the initial search request, the relevant products for the initial search request associated with refinement filters for refining the initial search request (para [0036], search/recommendation system 104 groups items using the pivots into a number of pivot groups; para [0039], pivot generation component 110 accesses information for a number of items, for instance, from the item listing data store; para [0052]);
assign scores to the refinement filters based at least on historical search queries of the user or historical interactions of the user (para [0063], recommended items can be personalized recommendations for a given user based on, for instance, the user's browsing history, purchase history, and other user behavior; para [0066], pivot group ranking could be generated for each pivot group as a function of the item rankings of each item within each pivot group);
select one of the refinement filters based on the scores assigned to the refinement filters (para [0067], subset of pivot groups and/or items to provide to the user device can be selected based on the pivot group rankings and item rankings); and
display, as a user-interactable component on the graphical user interface, a refined search request based on refining the initial search request using the selected refinement filter, wherein the user-interactable component is configured to execute the refined search request upon the user interacting with the user-interactable component (para [0068], user interface component 116 can also provide user interfaces for presenting pivot groups and allowing a user associated with a user device to interact with the pivot groups; para [0069], Each button in the pivot label area 502 comprises a user-selectable user interface element that, when selected, presents the pivot description for the associated pivot group and items within the pivot group).
Regarding claims 2 and 12, Wu teaches the above system and method of claims 1 and 11. Wu also teaches wherein the scores are assigned to the refinement filters based on the historical search queries of the user (para [0063], recommended items can be personalized recommendations for a given user based on, for instance, the user's browsing history, purchase history, and other user behavior; para [0066], pivot group ranking could be generated for each pivot group as a function of the item rankings of each item within each pivot group).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-10, 13-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Tavernier (U.S. Patent No. 10,706,450).
Regarding claims 3 and 13, Wu teaches the above system and method of claims 2 and 12. However, Wu does not explicitly teach wherein the scores assigned to the refinement filters are weighted based on gross merchandises values (GMVs) of relevant products associated with the initial search request.
In a similar field of endeavor, Tavernier teaches wherein the scores assigned to the refinement filters are weighted based on gross merchandises values (GMVs) of relevant products associated with the initial search request (col 3, ln 54-56, user interactions with search refinements 110 (e.g., selecting the $0-$50 price range); col 12, ln 60-64, catalog service adjust (e.g., filter or re-rank) recommendations for display on the detail page based on the identified catalog fields; col 16, ln 15-23, item data can include names, images, brands, prices, descriptions, user reviews (textual or numerical ratings), category/subcategory within a hierarchy of browsable categories of the electronic catalog, high-level category within a general ledger of the electronic catalog, particular services or subscriptions for which the item qualifies, and any metadata associated with specific items of the catalog).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art at the time of filing to include the noted limitations as taught by Tavernier in the system and method of Wu, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, improved systems for e-commerce web sites to provide services for suggesting products to users based on their respective purchase histories, rental histories, product viewing histories, item ratings, and/or other behaviors (see Tavernier; col 1, ln 12-16).
Regarding claims 4 and 14, Wu teaches the above system and method of claims 2 and 12. However, Wu does not explicitly teach wherein the scores assigned to the refinement filters are weighted by based on conversion scores of relevant products associated with the initial search request, the conversion scores of the relevant products based on whether the relevant products were purchased by the user in response to the historical search queries of the user.
In a similar field of endeavor, Tavernier teaches wherein the scores assigned to the refinement filters are weighted by based on conversion scores of relevant products associated with the initial search request, the conversion scores of the relevant products based on whether the relevant products were purchased by the user in response to the historical search queries of the user (col 17, ln 3-6, Item user interaction data repository 434 can, for example in the ecommerce context, include item purchase histories, item viewing histories, item add histories).
The combination would have been obvious for at least the rationale set forth above.
Regarding claim 5, Wu teaches the above system of claim 2. However, Wu does not explicitly teach wherein the scores assigned to the refinement filters are weighted based on a decay function with respect to a length of time that has passed since the historical search queries of the user.
In a similar field of endeavor, Tavernier teaches wherein the scores assigned to the refinement filters are weighted based on a decay function with respect to a length of time that has passed since the historical search queries of the user (col 9, ln 32-34, Some implementations can apply a decay to training data to give greater weight to the most recent data).
The combination would have been obvious for at least the rationale set forth above.
Regarding claims 6 and 15, Wu teaches the above system and method of claims 1 and 11. However, Wu does not explicitly teach wherein the scores are assigned to the refinement filters based on the historical interactions of the user.
In a similar field of endeavor, Tavernier teaches wherein the scores are assigned to the refinement filters based on the historical interactions of the user (col 17, ln 3-6, Item user interaction data repository 434 can, for example in the ecommerce context, include item purchase histories, item viewing histories, item add histories).
The combination would have been obvious for at least the rationale set forth above.
Regarding claims 7 and 16, Wu and Tavernier teach the above system and method of claims 6 and 15. Tavernier also teaches wherein the scores assigned to the refinement filters are weighted based on gross merchandises values (GMVs) of relevant products associated with the initial search request (col 3, ln 54-56, user interactions with search refinements 110 (e.g., selecting the $0-$50 price range); col 12, ln 60-64, catalog service adjust (e.g., filter or re-rank) recommendations for display on the detail page based on the identified catalog fields).
Regarding claim 8 and 17, Wu and Tavernier teach the above system and method of claims 6 and 15. Tavernier also teaches wherein the scores assigned to the refinement filters are weighted by based on conversion scores of relevant products associated with the initial search request, the conversion scores of the relevant products based on whether the relevant products were purchased by the user in response to the historical interactions of the user (col 17, ln 3-6, Item user interaction data repository 434 can, for example in the ecommerce context, include item purchase histories, item viewing histories, item add histories).
Regarding claim 9, Wu and Tavernier teach the above system of claim 6. Tavernier also teaches wherein the scores assigned to the refinement filters are weighted based on a decay function with respect to a length of time that has passed since the historical interactions of the user (col 9, ln 32-34, Some implementations can apply a decay to training data to give greater weight to the most recent data).
Regarding claims 10 and 18, Wu teaches the above system and method of claims 1 and 11. However, Wu does not explicitly teach wherein the scores are assigned to the refinement filters are based on metrics specific to a particular merchant location.
In a similar field of endeavor, Tavernier teaches wherein the scores are assigned to the refinement filters are based on metrics specific to a particular merchant location (col 17, ln 12-16, item interaction event may include an identifier for the item (for example, an item number, stock keeping unit (SKU), etc.), an indication of the type of interaction, or any other suitable information).
The combination would have been obvious for at least the rationale set forth above.
Regarding claim 20, Wu teaches the above medium of claim 19. However, Wu does not explicitly teach wherein the scores are assigned to the refinement filters are based on a combination of product performance specific metrics of a particular merchant location and at least one of the historical search queries of the user or the historical interactions of the user.
In a similar field of endeavor, Tavernier teaches wherein the scores are assigned to the refinement filters are based on a combination of product performance specific metrics of a particular merchant location and at least one of the historical search queries of the user or the historical interactions of the user (col 3, ln 54-56, user interactions with search refinements 110 (e.g., selecting the $0-$50 price range); col 12, ln 60-64, catalog service adjust (e.g., filter or re-rank) recommendations for display on the detail page based on the identified catalog fields; col 16, ln 15-23, item data can include names, images, brands, prices, descriptions, user reviews (textual or numerical ratings), category/subcategory within a hierarchy of browsable categories of the electronic catalog, high-level category within a general ledger of the electronic catalog, particular services or subscriptions for which the item qualifies, and any metadata associated with specific items of the catalog).
The combination would have been obvious for at least the rationale set forth above.
Conclusion
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689