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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
1. 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.
Claims 1-20 are directed to recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts, which is considered a commercial interaction. Commercial interactions fall within a subject matter grouping of abstract ideas which the Courts have considered ineligible (Certain methods of organizing human activity). The claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claim 1-10 recite a method and at least one step. Claims 11-19 recite a non-transitory computer readable storage medium. Claim 20 recites a system comprising a processor and a non-transitory computer readable storage medium. Therefore, the claims are each directed to one of the four statutory categories of invention (process, apparatus, manufacture).
Under step 2A of the Alice/Mayo framework, it must be considered whether the claims are “directed to” an abstract idea. That is, whether the claims recite an abstract idea and fail to integrate the abstract idea into a practical application.
Regarding independent claim 1, the claim sets forth a process in which acquisition of an item the customer was previously interested in acquiring is recommended following failed attempts, in the following limitations:
during a first session, receiving a request to fulfill an order;
receiving a message indicating that an item from the order was not fulfilled;
logging the item in connection with a profile of the user
during a second session, the second session subsequent to the first session,
receiving a message indicating that the logged item is available for fulfillment;
applying an intent prediction model to output an intent score indicative of an intent of a user of the user device to acquire the logged item,
wherein the intent prediction model was trained by:
accessing a set of training examples including shopping intent training data, user training data, item training data, and order training data,
applying the intent prediction model to the set of training examples to generate a training output corresponding to a predicted training set of logged items and associated training intent scores,
back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the intent prediction model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted training set of logged items and associated training intent scores, and
stopping the back-propagation after the one or more loss functions satisfy one or more criteria;
ranking the logged item for the user based on the intent score;
generating, based on the ranking, a recommendation to acquire the logged item;
.
The above-recited limitations establish a commercial interaction with a consumer to make a recommendation to the consumer to acquire an item the customer was previously interested in acquiring after failed attempts. This arrangement amounts to both a sales activity or behavior; and business relations. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts (See MPEP 2106.04(a)).
Claim 1 does recite additional elements:
between the online system and a user device
from the user device,
at the online system,
stored in a database maintained by the online system;
between the online system and the user device
wherein the intent prediction model was trained by:
accessing a set of training examples including shopping intent training data, user training data, item training data, and order training data,
applying the intent prediction model to the set of training examples to generate a training output corresponding to a predicted training set of logged items and associated training intent scores,
back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the intent prediction model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted training set of logged items and associated training intent scores, and
stopping the back-propagation after the one or more loss functions satisfy one or more criteria;
a user interface including and
causing the user device to display the generated user interface
These additional elements merely amount to the general application of the abstract idea to a technological environment (e.g., “between the online system and a user device”) and insignificant post-solution activity (causing to display). The Examiner notes that while the steps of the training process are abstract in their recitation, as indicated above, the process set forth (applying a set of training examples to a model, back-propagating error terms from loss functions, and stopping the back-propagation after the loss functions satisfy criteria) describe a generic machine learning-based training process. Put another way, as noted in Recentive, “Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning” (Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)). Accordingly, the recited iterative training represents the mere computerization of the intent prediction model (i.e., application to machine learning of an intent prediction model). Additionally, the specification makes clear the general-purpose nature of the technological environment. Paragraphs [0117]—0120] indicate that while exemplary general-purpose systems may be specific for descriptive purposes, any elements or combinations of elements capable of implementing the claimed invention are acceptable. That is, the technology used to implement the invention is not specific or integral to the claim.
Therefore, considered both individually and as an ordered combination, the additional elements do no more than generally link the use of the abstract idea to a particular technological environment or field of use. That is, given the generality with which the additional limitations are recited, the limitations do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim. Additionally, the claims do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing; and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea. Accordingly, the Examiner concludes that the claim fails to integrate the abstract idea into a practical application, and is therefore “directed to” the abstract idea.
Under step 2B of the Alice/Mayo framework, it must finally be considered whether the claim includes any additional element or combination of elements that provide an inventive concept (i.e., whether the additional element or elements are sufficient to amount to significantly more than the abstract idea). As indicated above, considered both individually and as an ordered combination, the additional elements do not implement the abstract idea with, or use the abstract idea in conjunction with, a particular machine or manufacture that is integral to the claim, do not reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition, do not effect a transformation or reduction of a particular article to a different state or thing, and do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the abstract idea
Further, the additional elements (recited above) simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Receiving or transmitting data over a network has been repeatedly considered well-understood, routine, and conventional activity by the Courts (See MPEP 2106.05(d)). Accordingly, the Examiner asserts that the additional elements, considered both individually, and as an ordered combination, do not provide an inventive concept, and the claim is ineligible for patent.
Independent Claims 11 and 20 are parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-10
Claims 2-10 merely embellish the abstract idea of recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts. While the claims do set forth additional limitations, the recitations are similar to the additional limitations in claim 1, as they do no more than generally link the use of the abstract idea to a particular technological environment. As such, they do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 1.
Regarding Claims 12-19
Claims 12-19 merely embellish the abstract idea of recommending to a consumer to acquire an item the customer was previously interested in acquiring after failed attempts. While the claims do set forth additional limitations, the recitations are similar to the additional limitations in claim 11, as they do no more than generally link the use of the abstract idea to a particular technological environment. As such, they do not integrate the abstract idea into a practical application, and do not provide an inventive concept. Accordingly, the claims do not confer eligibility on the claimed invention and is ineligible for similar reasons to claim 11.
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.
2. Claims 1, 11, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 6470323 B1, hereinafter Suzuki) in view of Di Capua et al. (US 20240353228 A1, hereinafter Di Capua).
Regarding Claim 1
Suzuki discloses a method, performed at an online system comprising a processor and a non-transitory computer readable medium, comprising:
during a first session between the online system and a user device, receiving, from the user device, a request to fulfill an order; (Suzuki: see at least col. 1, line 39 – col. 2, line 4; col. 2: 52-67: user transmits order request)
receiving, at the online system, a message indicating that an item from the order was not fulfilled; (Suzuki: see at least col. 4: 48 – col. 5: 62: message sent when requested product out-of-stock)
logging the item in connection with a profile of the user stored in a database maintained by the online system (Suzuki: see at least col. 5: 7-33: unsatisfied portion of order is stored in database)
during a second session between the online system and the user device, the second session subsequent to the first session, receiving a message indicating that the logged item is available for fulfillment (Suzuki: see at least col. 5: 34-62: message sent when previously unsatisfied portion of order is in stock)
Suzuki does not explicitly disclose, but Di Capua teaches in a similar environment:
applying an intent prediction model to output an intent score indicative of an intent of a user of the user device to acquire the logged item (Di Capua: see at least ¶74-75: model trained to determine priority of items based on user preferences)
wherein the intent prediction model was trained by:
accessing a set of training examples including shopping intent training data, user training data, item training data, and order training data (Di Capua: see at least ¶156-157, 243-244)
applying the intent prediction model to the set of training examples to generate a training output corresponding to a predicted training set of logged items and associated training intent scores (Di Capua: see at least ¶238-242),
back-propagating one or more error terms obtained from one or more loss functions to update a set of parameters of the intent prediction model, and one or more of the error terms are based on a difference between a label applied to a test interaction of the set of training examples and the predicted training set of logged items and associated training intent scores, (Di Capua: see at least ¶238-242)
stopping the back-propagation after the one or more loss functions satisfy one or more criteria; (Di Capua: see at least ¶238-242)
ranking the logged item for the user based on the intent score (Di Capua: see at least ¶75)
generating, based on the ranking, a user interface including a recommendation to acquire the logged item (Di Capua: see at least ¶68-69, 76-77)
causing the user device to display the generated user interface (Di Capua: see at least ¶68-69, 76-77)
It would have been obvious to one of ordinary skill in the art at the time of filing to have modified the invention of Suzuki, to have included the features of Di Capua, since such a modification would have provided increased efficiency in a shopping process by prioritizing item pickup based on user item priority and preferences (see at least ¶75 of Di Capua).
Regarding Claims 11, 20
Claims 11 and 20 are parallel in scope to claim 1 and are rejected on similar grounds.
Allowable Subject Matter
Claims 2-10, 12-19 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and amended to overcome the 35 USC 101 rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alagappan et al. (US 12566976 B2) discloses a machine learning system for forecasting customer demand, including training a purchase intent model with training examples including customer actions.
Trandal et al. (US 20100306080 A1) discloses methods and systems for receipt management and price comparison, including transmitting messages regarding previously out-of-stock items becoming available for purchase.
On the platform but will they buy? Predicting customers' purchase behavior using deep learning (PTO-892 Reference U) discloses using machine learning to train models to predict purchasing intent of online shoppers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL A MISIASZEK whose telephone number is (571)272-6961. The examiner can normally be reached Monday-Thursday. 8:00 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL MISIASZEK/Primary Examiner, Art Unit 3688
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