Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 12/23/2025.
Claims 1-20 are currently pending and have been examined. Claims 1, 9, and 17 have been amended. The prior art rejection has been overcome.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/23/2025 has been entered.
Claim Rejection - 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.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-8 are directed to a machine, claims 9-16 are directed to a process, and claims 17-20 are directed to an article of manufacture. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the eligibility analysis as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1, 9, and 27 recite at least the following limitations that are believed to recite an abstract idea:
Perform configuring of an item intent model using at least a portion of item data and, based on the configuring, determine a value of each of a plurality of weights of the item intent model;
input the item data for each of a plurality of items to the item intent model that applies the plurality of first weights to the input item data and, in response, generates an item intent value for each of the plurality of items, wherein each item intent value characterizes a first level of consideration associated with the corresponding item;
store the item intent values for the plurality of items in a storage;
receive, via a user, an indication of an event occurring from the user;
determine, based on the indication, a set of parameters associated with the event, the set of parameters including a set of items of the plurality of items;
retrieve, via the storage and from the stored item intent values, a set of item intent values corresponding to the set of items;
input at least one parameter of the set of parameters to a user intent model that applies a plurality of second weights to the at least one parameter and, in response, generates a first user intent value characterizing a second level of consideration associated with the user;
determine, using a procedure, a classification of the event by inputting the set of item intent values and the user intent value as features into the procedure,
wherein the set of item intent values and the user intent value are assigned respective weights, and the classification is one of a:(i) low consideration event indicative of a first set of historical circumstances associated with a first level of consideration for the set of items, and(ii) high consideration event indicative of a second set of historical circumstances associated with a second level of consideration for the set of items, wherein the second level of consideration is higher than the first level of consideration;
based on the classification, identify a set of recommendation models;
generate a set of recommended item identifiers by implementing at least one recommendation model of the set of recommendation models; and
transmit the set of recommended item identifiers to the user for display.
The above limitations recite the concept of personalized product recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1, 9, and 17 recites an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
A system comprising a processor and a non-transitory memory storing processor-executed instructions
Hyperparameter tuning
A database communicably couple with the processor
A user device communicatively coupled to the processor
A clustering machine learning model
A user interface
A non-transitory computer-readable medium having instructions thereon executed by at least one processor to cause a device to perform operations
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or 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, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or 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, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or 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, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 2-5, 7-8, 10-13, 15-16, and 18-20 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. As for claims 6 and 14, these claims are similar to the independent claims except that they recite the further additional elements of further hyperparameter tuning. These additional elements are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or 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, such that the claim as a whole is more than a drafting effort to monopolize the exception. Therefore the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A system comprising a processor and a non-transitory memory storing processor-executed instructions
Hyperparameter tuning
A database communicably couple with the processor
A user device communicatively coupled to the processor
A clustering machine learning model
A user interface
A non-transitory computer-readable medium having instructions thereon executed by at least one processor to cause a device to perform operations
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Allowable over Prior Art of Record
Claims 1-20 are allowable over prior art though rejected on other grounds (e.g. 101) as discussed above. The combination of elements of the claim as a whole are not found in the prior art.
Claims 1-20 would be allowable over prior art if rewritten to overcome the rejections above and to include all of the limitations of the base claim and any intervening claims. Upon review of the evidence at hand, it is hereby concluded that the totality of the evidence, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
In the present application, claims 1-20 are allowable over prior art. The most related prior art patent of record include Liu et al (US 20120089621 A1), hereinafter Liu, in view of Zhou et al (US 11507996 B1), hereinafter Zhou, Makhijani et al (US 11080596 B1), hereinafter Makhijani, and Sherman et al (US 20150120386 A1), hereinafter Sherman.
Liu discloses system for processing user content browsing history comprising content items and associated timestamps to track and weight users’ long term interests as vectors [0031]. This interest profile is stored [0040], and when the user views a current page, the system determines short term topic interests of the user [0026]. These are represented as a topic distribution vector [0028], which represents the relevance of content items to users’ short and long term browsing interests [0027]. A probabilistic topic distribution vector is generated and used to calculate interest weights representing both short & long term interests [0026], and yields a set of topics which represent the strength of interest in each topic [0034-0035]. A determination is made if the interest in each topic is significant enough, e.g. above or below a threshold; significant interests cause the system to retrieve content items associated therewith, while insignificant interests are added to a secondary group for additional group interest retrieval [0036-0037]. The two recommendation sets are then merged and de-duplicated [0038], and presented to the user on a page [0040-0042].
Zhao teaches item recommendations [Col. 2], where objects are classified using machine learning, such as a clustering model [Col. 6, Col. 11], to generate similarity between items in real time [Col. 4]. This is done to determine brand affinity for a user for different product brands [Col. 10], and allows for the model to be fine-tuned to classify items to best predict items [Col. 11].
Additionally, Makhijani teaches clustering machine learning techniques for item recommendations based on user-vector and item-vector comparison, and Sherman teaches systems for identifying purchase intent and providing relevant messaging based thereon.
However, each of these limitations fail to disclose or render obvious at least the limitations to: perform hyperparameter tuning of an item intent module using at least a portion of item data and, based on the hyperparameter tuning, determine a value for each of the plurality of first weights of the item intent model; input at least one parameter of the set of parameters to a user intent model that applies a plurality of second weights to the at least one parameter and, in response, generates a first user intent value characterizing a second level of consideration associated with the user; input at least one parameter of the set of parameters to a user intent model that applies a plurality of second weights to the at least one parameter and, in response, generates a first user intent value characterizing a second level of consideration associated with the user; determine, using a clustering machine learning algorithm, a classification of the event by inputting the set of item intent values and the user intent value as features into the clustering machine learning algorithm, wherein the set of item intent values and the user intent value are assigned respective weights, and the classification is one of a:(i) low consideration event indicative of a first set of historical circumstances associated with a first level of consideration for the set of items, and(ii) high consideration event indicative of a second set of historical circumstances associated with a second level of consideration for the set of items, wherein the second level of consideration is higher than the first level of consideration; based on the classification, identify a set of recommendation models.
Each of these references fail to disclose or render obvious the combination of limitations in the independent claims 1, 9, and 17, alone or in obvious combination. Therefore, at least for the combination of elements recited in the independent claims, the independent claims and those that depend thereon are allowable over prior art if rewritten to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive.
Claim Rejection – 35 §USC 101
Applicant argues that the claims recite “performing hyperparameter tuning of an item intent model using at least a portion of item data and, based on the hyperparameter tuning, determine a value for each of a plurality of first weights of the item intent model,” which Applicant argues is “an adjustment of parameters of a machine learning model associated with tasks or workstreams,” and therefore “improves computer functionality.”
Examiner respectfully disagrees. With reference to the Memo regarding updates to MPEP 2106.05(a), the additional example includes “improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams,” not merely any form of adjustment. In the pending claims, the additional element of hyperparameter tuning is recited at a high level of generality, without any of the specificity of Desjardins as to how the adjustments are performed, their effects on the system, or any reference in the Specification to alleged technical improvements therefrom. This limitations provides only a general linking to ML technology, such that it amounts to mere instructions to apply the abstract idea in a technological environment [MPEP 2106.05(f)], and as such does not integrate the abstract idea into a practical application.
Applicant further argues, with reference to Example 36, that the claims “represent a specific, technological improvement to existing machine learning based systems that cannot be implemented via the …prior art of record,” such that the claims “represent a technological solution that can provide these and other technical advantages.”
Examiner respectfully disagrees. With reference to the rejection above, the additional elements do not provide any defined technological solution to a technological problem. At best, the additional elements provide only “the improved speed or efficiency inherent with applying the abstract idea on a computer,” which does not integrate a judicial exception into a practical application or provide an inventive concept. [MPEP 2106.05(f)]. The additional elements, rather than providing technical advantages that amount to significantly more than the abstract idea, are invoked as mere instructions to apply the abstract idea in a technological environment [MPEP 2106.05(f)].
Conclusion
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689