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 and rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/15/2024 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 11-20 are directed to a method, which is a process. 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 recommendations:
determining one or more features associated with a user and also associated with recently viewed items for the user;
determining a respective engagement score for each of the recently viewed items based on one or more first features of the one or more features, wherein:
the one or more first features are determined by a correlation analysis of the one or more features;
ranking the recently viewed items based on the respective engagement score for each of the recently viewed items; and
transmitting the recently viewed items, as ranked, for display.
The recited limitations as a whole set forth the process for providing product recommendations. 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 machine learning model, a user device, 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 recommendations. 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 claim 11 (method), the claim recites substantially similar limitations as set forth in claim 1. As such, claim 11 and its dependent claims 12-20 are rejected for at least similar rationale as discussed above.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pollock (U.S. Pre-Grant Publication No. 2023/0113506 A1) in view of Shanker et al. (U.S. Pre-Grant Publication No. 2024/0242260 A1) (“Shanker”).
Regarding claims 1 and 11, Pollock teaches system (and related method) comprising:
one or more processors (Fig. 1; para [0043]); and
one or more non-transitory computer-readable media storing computing instructions configured to, when run on the one or more processors (Fig. 1; para [0043], facilities are deployed through a machine, service or engine that executes computer software, modules, program codes, and/or instructions on one or more processors), cause the one or more processors to perform:
determining one or more features associated with a user and also associated with recently viewed items for the user (para [0014], one or more content ranking criteria may include at least one of: previous selection of each data object for display in the viewable slot...characteristic relevancy metric computed for each data object with respect to a characteristic identified from a profile associated with the customer.);
determining, at least in part by a machine learning model, a respective engagement score for each of the recently viewed items based on one or more first features of the one or more features (para [0014], [0114], characteristic relevancy metric may be computed by querying data in the customer profile 160, which may be further analyzed (e.g., statistically, or using machine learning algorithms to extract features) to identify characteristics of the customer),
ranking the recently viewed items based on the respective engagement score for each of the recently viewed items (para [0016], content engagement score computed for the respective selected data object for each respective viewable slot...customer-specific slot ranking criterion may be one of: an activity relevancy metric computed for the respective online store assigned to each respective viewable slot); and
transmitting, via a computer network to a user device of the user, the recently viewed items, as ranked, for display on the user device (Fig. 4; para [0078], UI 400 may also display images and/or text representing recently viewed items 420 (e.g., images from recently viewed product pages)).
However, Pollock does not explicitly teach wherein: the one or more first features are determined by a correlation analysis of the one or more features in a training process of the machine learning model.
In a similar field of endeavor, Shanker teaches wherein: the one or more first features are determined by a correlation analysis of the one or more features in a training process of the machine learning model (para [0039], performance monitoring and model training application 112 is configured to monitor the performance of the models and applications within the visual search system; para [0065], nearest neighbor calculation 406 may generate a ranked list of visually similar catalog items (a ranked list of embedding vectors) as initial recommendations).
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 Shanker in the system of Pollock, 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, an improved attribute-based ranking process to rank the top recommended items in a user interface (see Shanker; para [0003]).
Regarding claims 2 and 12, Pollock and Shanker teach the above system and method of claims 1 and 11. Shanker also teaches wherein the computing instructions are further configured to cause the one or more processors to perform: after ranking the recently viewed items based on the respective engagement score, diversifying the recently viewed items across item categories, brands, or colors (para [0038], attribute determination and ranking application 110 is configured to determine attributes of each item detected in the image. Attributes may include features of the item, such as characteristics, shape, type, or color...attributes may be based on the predicted item category).
Regarding claims 3 and 13, Pollock and Shanker teach the above system and method of claims 2 and 12. Shanker also teaches wherein diversifying the recently viewed items comprises: using a second machine learning model trained to re-rank the recently viewed items based on one or more of: item taxonomies, brands, or colors of the recently viewed items (para [0038]; para [0068], perform attribute-based reranking on the ranked/scored list of initial recommendations from the nearest neighbor calculation 406 for determination of a final set of more accurate ordered recommendations).
Regarding claims 4 and 14, Pollock and Shanker teach the above system and method of claims 1 and 11. Pollock also teaches wherein determining the one or more features associated with the user and also associated with the recently viewed items (para [0113], activity relevancy metric) comprises extracting the one or more features from one or more of:
historical behavior of the user in one or more prior sessions (para [0114], purchasing habits);
current behavior of the user in a current session (para [0113], online activity);
one or more propensities of the user;
item statistics of the recently viewed items;
pricing of the recently viewed items; or
one or more promotions for the recently viewed items.
Regarding claims 5 and 15, Pollock and Shanker teach the above system and method of claims 1 and 11. Pollock also teaches wherein the computing instructions are further configured to cause the one or more processors to perform: before transmitting the recently viewed items for display on the user device, filtering out one or more filtered items from the recently viewed items, wherein each of the one or more filtered items is one or more of: out-of-stock, sensitive, or included in one or more user-specified constraints (para [0087], UI content generator 300 also includes a data object creator 320, which creates new content data objects 310. The data object creator 320 may monitor the operational data 139 of a given online store...trigger event may be a change in the operational data...defined change in existing product availability data that satisfies a change rule (e.g., inventory data); para [0112]).
Regarding claims 6 and 16, Pollock and Shanker teach the above system and method of claims 1 and 11. Pollock also teaches wherein: before transmitting the recently viewed items for display on the user device, removing one or more low-ranking items from the recently viewed items, wherein the one or more low-ranking items are ranked lower than a predetermined rank limit in the recently viewed items, as ranked (para [0121], if the selected data object 310 for a given viewable slot 410 has a higher characteristic relevancy metric than another data object 310 selected for another viewable slot 410, then the given viewable slot 410 may be ranked higher).
Regarding claims 7 and 17, Pollock and Shanker teach the above system and method of claims 1 and 11. Pollock also teaches wherein
the recently viewed items were engaged by the user in one or more prior sessions or a current session (para [0112], based on overall customer engagement, which may be quantified by tracking click rate for each data object); and
determining the respective engagement score for each of the recently viewed items comprises, upon determining that a respective prior engagement score determined within an expiration time for each of the recently viewed items exists, using the respective prior engagement score as the respective engagement score (para [0112], content ranking criteria may include: whether a given data object 310 was previously selected for the online store 138, which may be quantified based on the amount of time since the data object 310 was previously selected...which may be quantified based on a timestamp).
Regarding claims 8 and 18, Pollock and Shanker teach the above system and method of claims 1 and 11. Shanker also teaches wherein the computing instructions are further configured to cause the one or more processors to perform: before determining the respective engagement score, training the machine learning model based on a training dataset (para [0096], The model may be trained using a dataset of images included within a retail item catalog of a retail enterprise, where each retail item has a plurality of retail item attributes associated with it.).
Regarding claims 9 and 19, Pollock and Shanker teach the above system and method of claims 8 and 18. Shanker also teaches wherein:
the training dataset comprises historical input data and historical output data (para [0127], Model retraining may be performed on existing, new, or updated/modified dataset.);
the historical input data comprise:
one or more training features associated with customers and historically-engaged items for the customers (para [0127], training datasets may include images and/or attributes of retail items new to an enterprise catalog; para [0105], updated set of customer preference data related to a user interaction with a recommendation is received, and the predetermined attribute weighting is updated based on the second set of customer preference data);
the historical output data comprise whether the customers engaged with the historically-engaged items (para [0105], updated set of customer preference data related to a user interaction with a recommendation is received, and the predetermined attribute weighting is updated based on the second set of customer preference data);
the customers comprise the user (para [0105]); and
the one or more features comprise the one or more training features (para [0127], training datasets may include images and/or attributes of retail items new to an enterprise catalog).
Regarding claims 10 and 20, Pollock and Shanker teach the above system and method of claims 9 and 19. Shanker also teaches wherein the computing instructions are further configured to cause the one or more processors to perform:
after training the machine learning model, performing the correlation analysis of the one or more training features to determine one or more first training features of the one or more training features based on one or more respective weights assigned by the machine learning model to the one or more training features (para [0126], If degradation is detected in one or more of the calculated metrics, a re-training process may be initiated 1212 for the relevant model);
updating the training dataset to include only the one or more first training features in the historical input data (para [0126]); and
re-training the machine learning model based on the training dataset (para [0127]), as updated, wherein:
the training process comprises training the machine learning model and re-training the machine learning model (para [0127], Model retraining may be performed on existing, new, or updated/modified dataset. For example, new datasets may include new images, items, attributes, and/or other data. In an example, training datasets may include images and/or attributes of retail items new to an enterprise catalog).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday through Friday, 9am – 5pm.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689