DETAILED ACTION
The following is a non-final, first office action in response to the amendment filed May 13, 2026. Claims 1-6 and 10 have been elected. Claims 7-9 have been withdrawn. Claims 1-6 and 10 are currently pending and have been examined.
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 .
Response to Arguments
Claims 7-9 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on April 28, 2022.
Applicant's election with traverse has been fully considered but is not persuasive. Claim 7 has separate utility such as processing the first information by using a first to-be-trained model to obtain a first processing result, wherein the first processing result is used to determines a probability of tapping the item by the user, the probability of tapping the item by the user is used to determine the item recommended to the user, obtaining a target loss based on the probability of tapping the item by the user and a real probability of tapping the item by the user, wherein the target loss indicates a difference between the probability of tapping the item by the user and the real probability of tapping the item by the user; and updating a parameter of the first to-be-trained model based on the target loss until a model training condition is met, to obtain a first model. While claim 1 requires a processing the first information by using a first model to obtain a first processing result, wherein the first processing result is used to determines the item recommended to the user not required by claims 7. Independent claims 1 and 7 may overlap but require different searches, therefore the restriction is proper.
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 1-6 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Step 1: Statutory Category
(MPEP § 2106)
The claims are directed to a method and an apparatus.
Conclusion: The claims are directed to a statutory category: a process and a machine as defined under 35 U.S.C. § 101.
Regarding Claim 1:
Step 2A, Prong One: Judicial Exception – Abstract Idea (MPEP § 2106.04)
The claim recites an abstract idea.
More specifically, the claim recites:
collecting information regarding a user and items,
analyzing that information using mathematical operations (linear and nonlinear operations), and
determining an item recommendation based on the analysis.
The limitations directed to performing linear operations, nonlinear operations, and generating a recommendation constitute mathematical concepts because they involve mathematical calculations and relationships. The recommendation itself represents an evaluation or judgment derived from the mathematical analysis.
Accordingly, the claim recites the judicial exception of mathematical concepts and alternatively certain methods of organizing human activity, namely recommending products/items to users in a commercial environment.
Step 2A, Prong Two: Integration into a Practical Application (MPEP § 2106.04(d))
The claim does not integrate the exception into a practical application.
The additional elements merely:
obtain user and item information,
process the information using a generic "model," and
output a recommendation.
The claim does not:
improve computer functionality,
improve model training,
improve storage, networking, memory utilization, processor efficiency, or any other computer technology,
control a machine,
transform an article, or
apply the recommendation in a meaningful way.
The claimed model is used merely as a tool for performing the mathematical analysis that produces the recommendation.
The result is simply information identifying a recommended item. The claim therefore amounts to using a computer to perform mathematical calculations and present a recommendation.
Accordingly, the claim is directed to the abstract idea itself and does not integrate the exception into a practical application.
Step 2B: Inventive Concept (MPEP § 2106.05)
The claim does not recite significantly more than the abstract idea.
The additional elements include:
obtaining information,
using a model,
determining a recommendation.
These are generic computer functions performed in their ordinary capacities.
Although the claim recites a particular sequence of linear and nonlinear operations, merely limiting an abstract idea to particular mathematical techniques does not amount to significantly more. The operations themselves are part of the abstract mathematical concept rather than an inventive technological application of that concept.
The claim does not recite:
a particular technological improvement,
unconventional computer architecture,
specialized hardware,
an improvement to machine learning training,
reduced computational complexity,
improved memory usage,
or any other technological solution to a technological problem.
Therefore, the claim is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding Claim 10
Independent Claim 10 is parallel in scope to claim 1 and ineligible for similar reasons.
Regarding Claims 2-6
Dependent claims 2-6 merely set forth further embellishments to the abstract idea, and therefore do not confer eligibility on the claimed invention and are ineligible for similar reasons to claim 1.
Claim Rejections - 35 USC § 102
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.
Claims 1-6 and 10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by PTO-892 Reference U.
Regarding claims 1 and 6, Reference U discloses an item recommendation method, wherein the method comprises:
obtaining first information, wherein the first information comprises attribute information of a user and attribute information of an item; and processing the first information by using a first model to obtain a first processing result, wherein the first processing result is used to determines the item recommended to the user, and the first model is configured to: (Reference U: page 1 - A recommender system can be viewed as a search ranking system, where the input query is a set of user and contextual
information, and the output is a ranked list of items.);
perform a linear operation on the first information to obtain second information; (Reference U: 3.1 - the wide component);
perform a nonlinear operation on the second information to obtain third information; and (Reference U: 3.2 - the deep component);
obtain the first processing result based on the third information (Reference U: 3.3 - The wide component and deep component are combined using a weighted sum of their output log odds as the pre- diction, which is then fed to one common logistic loss function for joint training.).
Regarding claim 2, Reference U discloses all of the limitations as noted above in claim 1. Reference U further discloses:
wherein the first model is configured to: perform the linear operation on the first information to obtain the second information; (Reference U: 3.1 - the wide component);
perform the nonlinear operation on the first information and the second information to obtain the third information; (Reference U: 3.2 - the deep component);
fuse the second information and the third information to obtain fourth information; and (Reference U: 3.3 - The wide component and deep component are combined using a weighted sum of their output log odds as the pre- diction, which is then fed to one common logistic loss function for joint training.).
Regarding claim 3, Reference U discloses all of the limitations as noted above in claim 1. Reference U further discloses:
processing the first information by using a second model to obtain a second processing result, wherein the second model is at least one of the following: a multilayer perceptron, a convolutional network, an attention network, a Squeeze-and-Excitation network, or a model that is the same as the first model; and (Reference U: references - J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler. Joint training of a convolutional network and a graphical model for human pose estimation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, NIPS, pages 1799{1807. 2014.);
fusing the first processing result and the second processing result by using a third model, wherein a result obtained through fusion is used to determine the item recommended to the user (Reference U: 3.3 - The wide component and deep component are combined using a weighted sum of their output log odds as the pre- diction, which is then fed to one common logistic loss function for joint training.).
Regarding claim 4, Reference U discloses all of the limitations as noted above in claim 2. Reference U further discloses:
wherein the first model comprises N interaction units, an input of an ith interaction unit is an output of an (i-1)th interaction unit, N>1, and i=1, ..., or N; and the processing the first information by using the first model to obtain the first processing result comprises: performing a linear operation on the input of the ith interaction unit by using the ith interaction unit, to obtain a linear operation result of the ith interaction unit; (Reference U: 3.1 - the wide component);
performing a nonlinear operation on the input of the ith interaction unit and the linear operation result of the ith interaction unit by using the ith interaction unit, to obtain a nonlinear operation result of the ith interaction unit; and (Reference U: 3.2 - the deep component);
fusing the linear operation result of the ith interaction unit and the nonlinear operation result of the ith interaction unit by using the ith interaction unit, to obtain an output of the ith interaction unit, wherein an input of a first interaction unit is the first information, a linear operation result of a first interaction model is the second information, a nonlinear operation result of the first interaction model is the third information, an output of the first interaction model is the fourth information, and an output of an Nth interaction model is the first processing result (Reference U: 3.3 - The wide component and deep component are combined using a weighted sum of their output log odds as the pre- diction, which is then fed to one common logistic loss function for joint training.).
Regarding the limitation that the first model comprises N interaction units, wherein the input of an ith interaction unit is the output of an (i−1)th interaction unit, Jones expressly teaches such sequentially connected interaction units. Furthermore, selecting a particular number N of interaction units would have been an obvious matter of routine optimization and design choice, as Reference U teaches that additional interaction units may be stacked to increase model depth and representation capability. Repeating a known interaction unit multiple times to achieve its expected benefit would have yielded no more than predictable results.
Regarding claim 5, Reference U discloses all of the limitations as noted above in claim 4. Reference U further discloses:
wherein the processing the first information by using a first model to obtain a first processing result further comprises: performing a nonlinear operation on the input of the ith interaction unit and the nonlinear operation result of the ith interaction unit by using the ith interaction unit, toobtain a new nonlinear operation result of the ith interaction unit; and the fusing the linear operation result of the ith interaction unit and the nonlinear operation result of the ith interaction unit by using the ith interaction unit, to obtain the output of the ith interaction unit comprises: fusing the linear operation result of the ith interaction unit, the nonlinear operation result ofthe ith interaction unit, and the new nonlinear operation result of the ith interaction unit by using the ith interaction unit, to obtain the output of the ith interaction unit (Reference U: 3.3 - The wide component and deep component are combined using a weighted sum of their output log odds as the pre- diction, which is then fed to one common logistic loss function for joint training.).
Regarding the limitation that the first model comprises N interaction units, wherein the input of an ith interaction unit is the output of an (i−1)th interaction unit, Jones expressly teaches such sequentially connected interaction units. Furthermore, selecting a particular number N of interaction units would have been an obvious matter of routine optimization and design choice, as Reference U teaches that additional interaction units may be stacked to increase model depth and representation capability. Repeating a known interaction unit multiple times to achieve its expected benefit would have yielded no more than predictable results.
Regarding claim 6, Reference U discloses all of the limitations as noted above in claim 1. Reference U further discloses:
wherein the first information further comprises information about an operation performed by the user on an application and attribute information of the application, and the application is used to provide the item for the user (Reference U: abstract - We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11,853,901 B2, Ko et discloses Learning method of AI model and electronic apparatus.
US 11,593,819 B2, Chen et discloses Training a model to predict likelihoods of users performing an action after being presented with a content item.
US 2020/0242450 A1, Tang et discloses USER BEHAVIOR PREDICTION METHOD AND APPARATUS, AND BEHAVIOR PREDICTION MODEL TRAINING METHOD AND APPARATUS.
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KATHLEEN GAGE PALAVECINO
Primary Examiner
Art Unit 3625
/KATHLEEN PALAVECINO/
Primary Examiner, Art Unit 3625
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