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 1-7 are directed to a system, which is a machine. Claims 8-14 are directed to a method, which is a process. Claims 15-20 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 a first search request, the first search request including one or more search terms;
identify one or more product categories in response to inputting of the one or more search terms;
identify a first plurality of products that are assigned to the one or more product categories, each product of the first plurality of products identifying a plurality of product titles and a plurality of product short descriptions in a natural language;
apply the plurality of product titles and the plurality of product short descriptions as input to a second machine learning model that is configured to generate a plurality of recommended searches, each recommended search of the plurality of recommended searches including at least one search term;
score each recommended search of the plurality of recommended searches;
select one or more recommended searches of the plurality of recommended searches based on the scoring; and
cause the one or more recommended searches to be displayed as user-interactable components on a graphical user interface, each user-interactable component being configured to execute a second search request upon user interaction 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 machine learning model, 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 2-7 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 2-7 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-7 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 8 (method) and 15 (medium), the claim recites substantially similar limitations as set forth in claim 1. As such, claims 8 and 15 and their dependent claims 9-14 and 16-20 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 1-20 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, 8 and 15, Wu teaches a search recommendations system (and related method and medium) comprising:
at least one processor (Fig. 11; para [0086], processors); and
at least one memory comprising computer-readable instructions, the at least one processor (Fig. 11; para [0086], memory), the at least one memory and the computer-readable instructions configured to cause the at least one processor to:
receive a first search request, the first search request including one or more search terms (para [0026], in response to a search query or when an item page is presented with similar items);
identify one or more product categories as output from a machine learning classification model in response to inputting of the one or more search terms (para [0035], item is associated with one or more categories; para [0052], machine learning);
identify a first plurality of products that are assigned to the one or more product categories, each product of the first plurality of products identifying a plurality of product titles and a plurality of product short descriptions in a natural language (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]);
apply the plurality of product titles and the plurality of product short descriptions as input to a second machine learning model that is configured to generate a plurality of recommended searches, each recommended search of the plurality of recommended searches including at least one search term (para [0039], item information is provided as input to a generative model, which generates a number of pivots based on the item information);
score each recommended search of the plurality of recommended searches (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 or more recommended searches of the plurality of recommended searches based on the scoring (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
cause the one or more recommended searches to be displayed as user-interactable components on a graphical user interface, each user-interactable component being configured to execute a second search request upon user interaction 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).
Regarding claims 2, 9 and 16, Wu teaches the above system, method and medium of claims 1, 8 and 15. Wu also teaches wherein the computer-readable instructions are further configured to cause the at least one processor to train the machine learning classification model to classify natural language input terms to a plurality of product categories, the training causing the machine learning classification model to be configured to receive one or more search terms in a natural language as input, and to generate one or more product categories as output (para [0044], language model can be a tool that determines the probability of a given sequence of words occurring in a sentence (e.g., via NSP or MLM) or natural language sequence. Simply put, it can be a model that is trained to predict the next word in a sentence. A language model is called an LLM when it is trained on enormous amount of data).
Regarding claims 3, 10 and 17, Wu teaches the above system, method and medium of claims 1, 8 and 15. Wu also teaches wherein the second machine learning model is a KeyBERT model that is configured to use the plurality of product titles and plurality of product short descriptions as a natural language-based first input, and one or more natural language patterns as second input, wherein the second machine learning model is configured to generate the each recommended search of the plurality of recommended searches in a format identified by one of the one or more natural language patterns (para [0044], language model is called an LLM when it is trained on enormous amount of data. Some examples of LLMs are GOOGLE's BERT and OpenAI's GPT-3 and GPT-4).
Regarding claims 4, 11 and 18, Wu teaches the above system, method and medium of claims 1, 8 and 15. Wu also teaches wherein the second machine learning model is further configured to generate a semantic similarity score for each recommended search of the plurality of recommended searches, wherein scoring each recommended search of the plurality of recommended searches includes scoring each recommended search of the plurality of recommended searches based on an associated semantic similarity score generated by the second machine learning model (para [0052], natural language processing (NLP) and machine learning, an embedding refers to a numerical representation of text, images, audio, video, or other content in a way that captures their semantic meaning; 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).
Regarding claims 5, 12 and 19, Wu teaches the above system, method and medium of claims 1, 8 and 15. Wu also teaches wherein the computer-readable instructions are further configured to cause the at least one processor to generate one or more backend searches for each recommended search of the plurality of recommended searches, wherein scoring each recommended search of the plurality of recommended searches includes scoring each backend search using a mean reciprocal ratio (MRR) score (para [0039], prompt could be generated to include one or more rules that instruct the generative model on how to generate the pivot names and pivot descriptions. In some aspects, the pivot generation component 110 generates pivots offline and stores information regarding the pivots for use in generating pivot groups online when returning items to a user device; para [0082], pivot groups can be ordered with respect to one another based on their respective pivot rankings.).
Regarding claims 6 and 13, Wu teaches the above system and method of claims 1 and 8. Wu also teaches wherein scoring each recommended search of the plurality of recommended searches includes scoring each recommended search based on one or more of a semantic similarity score and a featured product score (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).
Regarding claims 7 and 14, Wu teaches the above system and method of claims 1 and 8. Wu also teaches wherein scoring each recommended search of the plurality of recommended searches includes scoring each recommended search of the plurality of recommended searches based at least in part on product performance, at particular merchant locations, of one or more products identified by the associated recommended search (para [0082], Item and pivot ranking is performed, as shown at block 1010. Item rankings could be provided for the items based on, for instance, the relevance of the items as recommend items or search results.).
Regarding claim 20, Wu teaches the above medium of claim 15. Wu also teaches wherein scoring each recommended search of the plurality of recommended searches includes scoring each recommended search of the plurality of recommended searches based at least in part on product performance, at particular merchant locations, of one or more products identified by the associated recommended search (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; para [0082], Item and pivot ranking is performed, as shown at block 1010. Item rankings could be provided for the items based on, for instance, the relevance of the items as recommend items or search results).
Conclusion
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689