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 .
Election/Restrictions
Applicant’s election without traverse of Species I, claims 2-6, in the reply filed on 7/20/2026 is acknowledged.
Claims 8-11 and 13-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, there being no allowable generic or linking claim.
Claims 1-7 and 12 are elected.
Claims 8-11 and 13-20 are withdrawn.
Claims 1-7 and 12 are pending and rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Double Patenting
Non-Statutory 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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-7 and 12 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of copending Application No. 19/015,882.
Although the claims are not completely identical, they are not patentably distinct from each other because each limitation of the instant claims is fully defined by the claims in Application No. 19/015,882. Specifically, independent claims 1, 7 and 12 of the instant application would be anticipated by at least claims 1, 10 and 17 of Application No. 19/015,882.
Claims 1, 10 and 17 of the ‘882 application teach a system (and related method and medium) comprising: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions to: receive interaction data indicative of an interaction with an information item associated with an anchor item in a first category; in accordance with a determination that the first category is associated with a plurality of themes of a second category, apply at least one type selection model to determine a set of item types associated with the plurality of themes of the second category; generate an ordered list of recommended items of the second category based on the set of item types; and in response to the interaction data, enable display of the ordered list of recommended items of the second category on a display of a client device.
These claims anticipate the independent claims of the instant application. See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998).
This is a provisional nonstatutory double patenting rejection as the copending application has not yet been patented.
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 1-7 and 12 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 1-6 are directed to a system, which is a machine. Claim 7 is directed to a non-transitory computer-readable storage medium, which is an apparatus. Claim 12 is directed to a method, which is a process. Therefore, claims 1-7 and 12 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Representative claim 1 sets forth the following limitations which recite the abstract idea of providing product recommendations:
obtain historic interaction data associated with past interactions of a plurality of users with a plurality of information items, each information item corresponding to a respective item type;
generate a first set of item types including item types involved in the past interactions jointly with an anchor item type based on the historic interaction data of the plurality of users;
generate a second set of item types using a type selection model configured to identify item types semantically associated with the anchor item type;
combine the first set of item types and the second set of item types to generate a list of target item types;
generate a list of recommended information items based on the list of target item types; and
in response to detection of a user interaction with the anchor item type by a first user, enable display of at least a subset of information items in the list of recommended information items to the first user on an electronic device associated with the first user.
The recited limitations above set forth steps to provide 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 notes that representative claim 1 recites additional elements such as a memory, a processor, etc.
When taken individually and as a whole, the additional elements of claim 1 do 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 processor to perform the 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 if 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 2-6 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the steps for providing product recommendations. Thus, each of claims 2-6 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-6 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 7 (medium) and 12 (method), the claims recite substantially similar limitations as set forth in claim 1. As such, claims 7 and 12 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 1-3, 5-7 and 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mantha et al. (U.S. Pre-Grant Publication No. 2021/0398192 A1) (“Mantha”).
Regarding claims 1, 7 and 12, Mantha teaches a system (and related medium and method), comprising:
a non-transitory memory having instructions stored thereon (Fig. 1; para [0021]); and
at least one processor operatively coupled to the non-transitory memory (Fig. 1; para [0021]), and configured to read the instructions to:
obtain historic interaction data associated with past interactions of a plurality of users with a plurality of information items, each information item corresponding to a respective item type (para [0049], the triple embeddings model can be trained end-to-end for 100 epochs using 500 million triplets, using a past purchase data set over a one year time frame with 800 million user-item interactions, with 3.5 million users, and 90 thousand items, in which frequency threshold-based user level and item level filters were used to remove cold start users and items from the training.; para [0085], triple embeddings model can be trained as describe above, based on past purchase history, using triplets of (user, first item, second item), in which the first item and the second item were selected (e.g., purchased) in the same basket by the user);
generate a first set of item types including item types involved in the past interactions jointly with an anchor item type based on the historic interaction data of the plurality of users (para [0056], complementary category filtering technique can be performed at the L4 subcategory level, by considering other subcategories that are complementary to the subcategory of the anchor item, and boosting the scores for items in those subcategories);
generate a second set of item types using a type selection model configured to identify item types semantically associated with the anchor item type (para [0045], Item representation learning approaches based on a skip-gram framework generally seek to find item representations that are useful for predicting contextual (e.g., related) items or users, by defining different “context windows.” These context windows can be implemented in various different instantiations on a heterogeneous graph, with nodes that represent items, users, or baskets.);
combine the first set of item types and the second set of item types to generate a list of target item types (para [0047], dual set of embedding vectors (pi, qj) for the item pair (i, j). These embeddings can be modeled by taking a dot product between each of the embedding vectors, such that a cohesion score si,j,u for a triplet can be defined; para [0108]);
generate a list of recommended information items based on the list of target item types (para [0050], Once the triple embeddings model is trained, matrix P and matrix Q can store the two sets of trained item embeddings for the catalog of items, such that matrices P and Q are each real-valued matrices having a number of rows equal to the number of items in the item catalog); and
in response to detection of a user interaction with the anchor item type by a first user, enable display of at least a subset of information items in the list of recommended information items to the first user on an electronic device associated with the first user (para [0051], For a given “anchor” item j and a given user u, as inputs, the trained item matrices P and Q and trained user matrix H can be used to compute the cohesion score for each of the items i, to determine a score that indicates how complementary item i is to anchor item j for user u; para [0098], sending instructions to display at least a portion of the list of personalized recommended items to the user).
Regarding claim 2, Mantha teaches the above system of claim 1. Mantha also teaches wherein the type selection model includes at least a feature extraction model, and the instructions to generate the second set of item types further comprises instructions to apply the feature extraction model to:
process a first query for identifying item types associated with the anchor item type to determine a query embedding (para [0123], generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective lookup call to the approximate nearest neighbor index using a query vector associated with the user and the respective anchor item); and
process a second query for identifying a collection of item types to determine a plurality of item type embeddings (para [0123], query vector can be similar or identical to the query vector described above. In many embodiments, the query vector can be generated for the user and the respective anchor item using the two sets of item embeddings and the set of user embeddings);
determine a respective similarity level between each of the plurality of item type embeddings with the query embedding (para [0123], Block 925 can be similar to group of blocks 530 (FIG. 5) and/or block 625 (FIG. 6), but can involve an approximate inferencing approach using the ANN index); and
based on the respective similarity levels, select a plurality of candidate item types corresponding to the highest similarity levels among the collection of item types (para [0123], items in the list of complementary items can be a top k items based on the given anchor item and the user).
Regarding claim 3, Mantha teaches the above system of claim 1. Mantha also teaches wherein the second set of item types is semantically associated with the anchor item type for a user class, the system further comprising instructions to determine that the first user is included in the user class in response to detection of the user interaction with the anchor item type by the first user (para [0045], Item representation learning approaches based on a skip-gram framework generally seek to find item representations that are useful for predicting contextual (e.g., related) items or users, by defining different “context windows.” These context windows can be implemented in various different instantiations on a heterogeneous graph, with nodes that represent items, users, or baskets.).
Regarding claim 5, Mantha teaches the above system of claim 1. Mantha also teaches further comprising instructions to:
classify the first user to a user class based on a subset of the historic interaction data associated with the first user (para [0119], approximate nearest neighbor index can be periodically precomputed using the ANN index library and/or similarity search library...trained model with ANN index can be deployed in real-time inference engine 713 (FIG. 7), and the user embeddings can be deployed in embedding lookup cache); and
identify the anchor item type based on the user class, the anchor item type corresponding to one or more items associated with one or more information items (para [0119]-[0120], In many embodiments, the set of user embeddings for the users are loaded into a memory cache, such as embedding lookup cache 714 (FIG. 7), before the respective lookup calls are made. In several embodiments, method 900 also further include a block 915 of receiving a basket comprising basket items selected by a user from the item catalog).
Regarding claim 6, Mantha teaches the above system of claim 1. Mantha also teaches the instructions to generate the first set of item types further comprising instructions to:
for each of the first set of item types, determine correlation information of the anchor item type and a respective item type, wherein the correlation information includes a support parameter, a confidence parameter, and a lift parameter (para [0053]-[0055], for the anchor item j and user u, given as inputs, the top k complementary items (i) can be determined as outputs by iterating through the items (i) in matrix P and computing the cohesion score, and selecting the top k items (i));
determine that the support parameter of each of the first set of item types is greater than a first threshold and the confidence parameter of each of the first set of item types is greater than a second threshold (para [0055], item-to-item model additionally can include a complementary category filtering technique, which can filter out items that are recommended due to being popular overall items. For example, in online grocery shopping, bananas, milk, eggs, and bath tissue are very popular items. These items would often be included as recommendations from the item-to-item model, merely due to their popularity in most carts, despite not being particularly complementary to a given anchor item j, such as specific type of dry pasta, for a particular user u; para [0056]-[0057]); and
organize the first set of item types based on the lift parameter of each item type (para [0056], complementary category filtering technique can be based on subcategories that are complementary to the subcategory of the anchor item; para [0057]).
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mantha in view of Wang et al. (U.S. Pre-Grant Publication No. 2024/0241897 A1) (“Wang”).
Regarding claim 4, Mantha teaches the above system of claim 1, however Wang does not explicitly teach wherein the type selection model includes a large language model (LLM) provided by a third-party server, and the instructions to generate the second set of item types further comprises instructions to: send a prompt for identifying the second set of item types associated with the anchor item type to the third-party server, the prompt including information of one or more of: the anchor item type, a user class, and a plurality of candidate item types; and receive, from the third-party server, a response including the second set of item types selected from the candidate item types.
In a similar field of endeavor, Wang teaches wherein the type selection model includes a large language model (LLM) provided by a third-party server (para [0043], LLM is able to perform various tasks and synthesize and formulate output responses based on information extracted from the training data), and the instructions to generate the second set of item types further comprises instructions to:
send a prompt for identifying the second set of item types associated with the anchor item type to the third-party server, the prompt including information of one or more of: the anchor item type, a user class, and a plurality of candidate item types (para [0048], generate a prompt that is based in part on the search query. The online concierge system 140 may provide the generated prompt to the search and recommendation model which generates one or more item predictions (that include item identifiers) associated with the prompt); and
receive, from the third-party server, a response including the second set of item types selected from the candidate item types (para [0048], Item information is information associated with the recommended item. Item information may include, e.g., item data (name, price, brand, size, icon, etc.) for the recommended item in the online catalog, and some embodiments may also include, customer data associated with the recommended item, some other information associated with the recommended item, or some combination thereof).
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 before the effective filing date of the claimed invention to include the noted limitations as taught by Wang in the system of Mantha, 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 improvement in using machine learned models for search and recommendation in e-commerce to convert unstructured data about products into a structured format (See Wang: para [0003]-[0004]).
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
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