DETAILED ACTION
Claims 1-20 are pending in this application. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 an abstract idea without significantly more.
Regarding claims 1-20, under Step 1, the claims recite a process, machine, manufacture, or composition of matter. Under Step 2A claims 1-20 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites: A method, performed at a computer system comprising a processor and a computer-readable medium, comprising: receiving a search query from a device associated with a user of an online system; retrieving a set of candidate search results in response to the search query, each candidate search result in the set of candidate search results associated with a respective item of a plurality of items; accessing a classification computer model of the online system, wherein the classification computer model is trained to compute a probability of classification of each of the plurality of items into each class of a plurality of classes, each class associated with a type of relevance to the search query; applying the classification computer model to generate, based at least in part on the search query and one or more features of each of the plurality of items, a classification score associated with each of the plurality of classes for each of the plurality of items, the classification score being indicative of the probability of classification; classifying, based on the classification score associated with each of the plurality of classes for each of the plurality of items, each of the plurality of items into a corresponding type of relevance to the search query of a plurality of relevance types; selecting, based at least in part on the classification of each of the plurality of items into the corresponding type of relevance, a list of items from the plurality of items; and causing a user interface of the device associated with the user to organize, according to the classification of each item in the list of items, the list of items for recommendation to the user and inclusion into a cart.
The above limitations set forth a procedure for organizing human activity, such as by performing commercial interactions including marketing activity and business relations. This is because the claim recites the steps performed in order to recommend a list of items (Specification ¶0003). Accordingly, under step 2A (prong 1) the claim recites an abstract idea because the claim recites limitations that fall within the “Certain methods of organizing human activity” grouping of abstract ideas. MPEP 2106.04.
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. Claim 1 recites additional elements, including a processor, a device, an online system, and a user interface.
These additional elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as computers or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application 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 the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply or use 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.
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment. MPEP 2106.05.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Dependent claims 2-10 recite limitations which are similarly directed to and elaborate on the judicial exception (abstract idea) of claim 1. 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.
Furthermore, claims 2-10 do not set forth further additional elements. Considered both individually and as a whole, claims 2-10 do not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Lastly, under step 2B, dependent claims 2-10 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). This is again because the claims merely apply the exception on generic computing hardware, generally link the exception to a technological environment, and specified at a high level of generality.
Claims 11-20 are parallel, i.e. recite similar concepts and elements, to claims 1-10, analyzed above, and the same rationale is applied.
In view of the above, claims 1-20 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
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 (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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra et al., US PG Pub 2022/0058504 A1 (hereafter “Malhotra”), in view of Unnikrishnan et al., US PG Pub 2024/0330754 A1 (hereafter “Unnikrishnan”).
Regarding claim 1, Malhotra teaches a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving a search query from a device associated with a user of an online system (¶0024);
accessing a classification computer model of the online system, wherein the classification computer model is trained to compute a probability of classification of each of the plurality of items into each class of a plurality of classes, each class associated with a type of relevance to the search query (¶¶0025-0031, 0052, 0056, and 0073);
applying the classification computer model to generate, based at least in part on the search query and one or more features of each of the plurality of items, a classification score associated with each of the plurality of classes for each of the plurality of items, the classification score being indicative of the probability of classification (¶¶0022, 0029, 0039-0043, and 0052-0055);
classifying, based on the classification score associated with each of the plurality of classes for each of the plurality of items, each of the plurality of items into a corresponding type of relevance to the search query of a plurality of relevance types (¶¶0023-0025, 0032, 0037, 0052, and 0056);
selecting, based at least in part on the classification of each of the plurality of items into the corresponding type of relevance, a list of items from the plurality of items (¶¶0057 and 0073-0075); and
Malhotra does not explicitly teach retrieving a set of candidate search results in response to the search query, each candidate search result in the set of candidate search results associated with a respective item of a plurality of items or causing a user interface of the device associated with the user to organize, according to the classification of each item in the list of items, the list of items for recommendation to the user and inclusion into a cart. Unnikrishnan teaches using machine learning to efficiently promote products including the known techniques retrieving a set of candidate search results in response to the search query, each candidate search result in the set of candidate search results associated with a respective item of a plurality of items (¶¶0019-0024 and 0039-0042) and causing a user interface of the device associated with the user to organize, according to the classification of each item in the list of items, the list of items for recommendation to the user and inclusion into a cart (¶¶0042, 0049, 0063, 0081, and 0100-0101). It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Malhotra, to include a plurality of items and a user interface including a cart as taught by Unnikrishnan, in order to “optmiz[e] the amount of resources necessary to target the consumers most willing to purchase,” as suggested by Unnikrishnan (¶0020).
Further, the claimed invention is merely a combination of old elements in a similar field of endeavor, 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, given the existing technical ability to combine the elements as evidenced by Unnikrishnan, the results of the combination were predictable.
Regarding claim 2, Malhotra in view of Unnikrishnan teaches the method of claim 1, wherein applying the classification computer model comprises: applying the classification computer model to generate, based at least in part on the search query and the one or more features of each of the plurality of items, a first embedding for the search query and a second embedding for each of the plurality of items (Malhotra ¶¶0022-0025 and 0045-0052); and applying the classification computer model to compute, based at least in part on the first and second embeddings, the probability of classification of each of the plurality of items into each of the plurality of classes (Malhotra ¶¶0022, 0029, 0039-0043, and 0052-0055).
Regarding claim 3, Malhotra in view of Unnikrishnan teaches method of claim 2, wherein applying the classification computer model further comprises: applying the classification computer model to generate, based at least in part on the first and second embeddings, for each of the plurality of items, a plurality of classification outputs each associated with a respective class of the plurality of classes (Malhotra ¶¶0040 and 0051); and applying the classification computer model to compute the probability of classification of each of the plurality of items into each of the plurality of classes by applying a defined function to the plurality of classification outputs (Malhotra ¶¶0039-0042 and 0056-0058).
Regarding claim 4, Malhotra in view of Unnikrishnan teaches method of claim 1, further comprising: generating training data including pairs of a search query text and a product description text obtained from engagement data associated with a plurality of users of the online system (Malhotra ¶¶0033-0038); and training the classification computer model using the generated training data (Malhotra ¶¶0038-0044).
Regarding claim 5, Malhotra in view of Unnikrishnan teaches method of claim 1, further comprising: obtaining data that include pairs of search queries and items (Malhotra ¶0042); generating training data based on evaluation of the pairs of search queries and items by a plurality of users of the online system using the plurality of classes (Malhotra ¶¶0027-0028 and 0035-0037); and training the classification computer model using the generated training data (Malhotra ¶¶0038-0044).
Regarding claim 6, Malhotra in view of Unnikrishnan teaches method of claim 1, further comprising: collecting feedback data with information about a conversion by the user of each item in the list of items (Unnikrishnan ¶¶0064, 0071, and 0074-0076); and re-training the classification computer model by updating, based at least in part on the collected feedback data, a set of parameters of the classification computer model (Unnikrishnan ¶¶0064 and 0066-0068). The combination would have been obvious for the reasons stated above with respect to claim 1.
Regarding claim 7, Malhotra in view of Unnikrishnan teaches method of claim 1, further comprising: collecting feedback data with one or more labels provided by the user, the one or more labels associated with one or more pairs of the search query and each item in the list of items (Unnikrishnan ¶¶0064, 0071, and 0074-0076); and re-training the classification computer model by updating, based at least in part on the collected feedback data, a set of parameters of the classification computer model (Unnikrishnan ¶¶0064 and 0066-0068). The combination would have been obvious for the reasons stated above with respect to claim 1.
Regarding claim 8, Malhotra in view of Unnikrishnan teaches method of claim 1, wherein selecting the list of items from the plurality of items comprises: filtering out a subset of items from the plurality of items, each item in the subset of items being classified into a subset of relevance types of the plurality of relevance types (Malhotra ¶¶0025, 0037, 0044, 0052, 0056, and 0069).
Regarding claim 9, Malhotra in view of Unnikrishnan teaches method of claim 1, wherein causing the user interface to organize the list of items comprises: ranking, based at least in part on the corresponding type of relevance to the search query associated with each item in the list of items, each item in the list of items (Unnikrishnan ¶¶0023-0039); determining, based at least in part on the ranking, a displaying order for each item in the list of items (Unnikrishnan ¶¶0039-0048); and causing the device associated with the user to display the user interface with the list of items, each item in the list of items displayed in the determined displaying order (Unnikrishnan ¶0063). The combination would have been obvious for the reasons stated above with respect to claim 1.
Regarding claim 10, Malhotra in view of Unnikrishnan teaches method of claim 1, causing the user interface to organize the list of items comprises: accessing an engagement prediction computer model of the online system, wherein the engagement prediction computer model is trained to compute a probability of engagement by the user for each item in a group of items from the list of items, the group of items associated with a same type of relevance to the search query (Malhotra ¶¶0051-0054); applying the engagement prediction computer model to generate, based at least in part on information about engagement of the user in relation to each item in the group, a ranking score for each item in the group, wherein the ranking score is indicative of the probability of engagement (Malhotra ¶¶0072-0073); determining, based at least in part on the ranking score for each item in the group, a displaying order for each item in the group (Unnikrishnan ¶¶0039-0048); and causing the device associated with the user to display the user interface with the list of items including the group of items, each item in the group displayed in the determined displaying order (Unnikrishnan ¶¶0069-0076). The combination would have been obvious for the reasons stated above with respect to claim 1.
Regarding claims 11-20, all of the limitations in claims 11-20 are closely parallel to the limitations of method claims 1-10, analyzed above, and are rejected on the same bases.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al., US PG Pub 2021/0004919 A1, teaches analysis of intellectual property data in relation to products and services.
Singh, US PG Pub 2023/0385765 A1, teaches a system and method for classification of spend data.
Non-patent literature Shen, Dou, et al. teaches product query classification.
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/CHRISTOPHER B SEIBERT/ Primary Examiner, Art Unit 3688