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 .
Status of Claims
This office action is in response to the RCE filed on 2/12/2026.
Claims 1, 3, 4, 6, 8, 9, 13, 15, 16, 18, and 20 have been amended.
Claims 1-4, 6, 8-11, 13-16, 18, and 20 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/12/2026 has been entered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202210662836.2, filed on 6/13/2022.
Claim Interpretation
The limitations of “wherein in response to determining, based on a conversion identifier ,that the new user has converted information in a to-be-recommended information library, the method further comprises:
determining target first-order information of the new user based on feature information of the converted information,
determining a second nonlinear mapping of the target first-order information, and
combining the second nonlinear mapping with the first nonlinear mapping to determine the new user feature information;” has little to no patentable weight because it is optional language in a method claim. The limitation is not recited such that it has to occur. (See MPEP 2111.04 | “Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure.”) The “in response to” condition has not been positively recited as occurring. The examiner recommends adding a step similar to the following: “determining, based on a conversion identifier that the new user has converted information in a to-be-recommended information library.” This would make clear that the determination has been made, and thus the “in response” portion would then occur. The examiner has treated the limitation as if it is necessarily occurring in anticipation of applicant fixing the issue to promote compact prosecution. This is not an issue in claims 13 and 20 because claim 13 is an apparatus claim and claim 20 is non-transitory computer readable medium claim. (See MPEP 2111.04 Il... The broadest reasonable interpretation of a system (or apparatus or product) claim having structure that performs a function, which only needs to occur if a condition precedent is met, requires structure for performing the function should the condition occur. The system claim interpretation differs from a method claim interpretation because the claimed structure must be present in the system regardless of whether the condition is met and the function is actually performed.
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 1-4, 6, 8-11, 13-16, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-4, 6, 8, and 9-11 are directed to a method. Claims 13-16 and 18 are directed to an apparatus. Claim 20 is directed to a non-transitory computer readable storage medium. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
Claims 1, 13, and 20 recite virtually identical limitations. Claim 1 will be used as representative. Each claims additional elements will be addressed individually. The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claims 1, 13, 20:
retrieving interaction records and conversion records from a data storage;
constructing a heterogeneous graph structure based on the interaction records and the conversion records, the heterogeneous graph structure including user vertices connected based on the interaction records and information vertices connected to user vertices based on the conversion records;
determining target second-order information by aggregating feature information of the user vertices connected to a new user and feature information of the information vertices connected to the user vertices connected to the new user within the heterogeneous graph structure, the target second-order information being based on current user feature information of at least one current user associated with the user vertices connected to the new user and first feature information of recommended information previously recommended to the at least one current user;
determining a first nonlinear mapping of the target second-order information based on a spatial distance between the target second-order information and center information of second- order information including the target second-order information, the first nonlinear mapping being configured to generate a feature representation for the new user within the heterogeneous graph structure;
determining new user feature information of the new user based on the first nonlinear mapping of the target second-order information, wherein in response to determining, based on a conversion identifier, that the new user has converted information in a to-be-recommended information library, the method further comprises:
determining target first-order information of the new user by aggregating feature information of the converted information,
determining a second nonlinear mapping of the target first-order information, and
combining the second nonlinear mapping with the first nonlinear mapping to determine the new user feature information;
determining conversion rates (CVRs) for a plurality of to-be-recommended information items based on the new user feature information;
selecting target to-be-recommended information from the plurality of to-be- recommended information items based on the CVRs; and
outputting for display the selected target to-be-recommended information to the new user.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
Claim 2, 14:
wherein the previously recommended information includes advertisements converted by the at least one current user.
Claim 3, 15:
wherein the first nonlinear mapping is based on a plurality of mapping parameters, each of the plurality of mapping parameters representing a mapping space range, wherein
Claims 4, 16:
further comprising: determining an interaction weight between the new user and a current user of the at least one current user, the interaction weight representing an interaction degree between the new user and the current user;
determining a conversion weight between the current user and the previously recommended information, the conversion weight representing a conversion degree between the current user and the previously recommended information;
determining a first combination of the current user feature information and the new user feature information based on the interaction weight;
determining a second combination of the first feature information based on the conversion weight; and
determining target second-order information corresponding to the new user based on the first combination and the second combination.
Claims 6, 18:
further comprising: determining initial aggregation information based on the second nonlinear mapping and the first nonlinear mapping;
determining a first combination weight negatively correlated with the initial aggregation information and positively correlated with the second nonlinear mapping;
determining a second combination weight based on the first combination weight;
determining a third combination result based on the first combination weight and the second nonlinear mapping;
determining a fourth combination result based on the second combination weight and the first nonlinear mapping; and
determining the new user feature information based on the third combination result and the fourth combination result.
Claim 8:
further comprising: generating a to-be-updated heterogeneous graph based on the heterogeneous graph structure according to a common user between first users in the interaction records and second users in the conversion records;
iteratively updating each user vertex in the to-be-updated heterogeneous graph based on the first nonlinear mapping corresponding to second-order information of the respective user vertex in the to-be-updated heterogeneous graph; and
determining the new user feature information and a to-be-recommended information feature based on the to-be-updated heterogeneous graph.
Claim 9:
further comprising: updating the each user vertex in the to-be-updated heterogeneous graph based on the first nonlinear mapping of the second-order information of the respective user vertex;
performing attention updating on an edge weight in the updated to-be-updated heterogeneous graph to determine a to-be-updated edge weight;
obtaining a target edge weight based on the to-be-updated edge weight;
determining the updated heterogeneous graph as a current heterogeneous graph;
determining second-order information of each current user vertex in the current heterogeneous graph through aggregation based on the target edge weight; and
iteratively updating the each current user vertex in the current heterogeneous graph based on the first nonlinear mapping corresponding to the second-order information of the respective current user vertex.
Claim 10:
further comprising: determining at least one adjacent user vertex corresponding to the each current user vertex;
determining an attention interaction weight between the each current user vertex and the at least one adjacent user vertex of the respective current user vertex;
determining at least one adjacent information vertex corresponding to the each current user vertex; and
determining, for each current user vertex, an attention conversion weight between the respective current user vertex and each of the at least one adjacent information vertex corresponding to the respective current user vertex based on the at least one adjacent information vertex, the to-be-updated edge weight being the attention interaction weight or the attention conversion weight.
Claim 11:
further comprising: determining at least one to-be-updated edge weight, the at least one to-be-updated edge weight is adjacent to a target to-be-updated edge weight and different from the target to-be-updated edge weight; and
determining the target edge weight based on the target to-be-updated edge weight and the at least one to-be-updated edge weight.
The claims provide a manner of trying to account for the cold start problem to determine predicted conversion rates for targeted advertisements. The process includes determining second order information of a current user, determining a first nonlinear mapping within a heterogenous graph, determining new user feature information of a new user based on the first nonlinear mapping, indicating a new user has converted, determine target first order information for the new user, determine a second nonlinear mapping, combine the second nonlinear mapping with the first nonlinear mapping, determine conversion rates for to be recommended items based on new user feature information, select to be recommended information based on the conversion rates, and output the selected target to be recommended information. Thus, when considered individually and as an ordered combination, the claims embody certain methods of organizing human activity. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors).
Further, but for the inclusion of generic computing devices (processing circuitry, etc.), the steps of the claims can be performed either mentally or with pen and paper. A human analog would be able to determine second order information of a current user, determine a first nonlinear mapping within a heterogenous graph, determine new user feature information of a new user based on the first nonlinear mapping, indicate a new user has converted, determine target first order information for the new user, determine a second nonlinear mapping, combine the second nonlinear mapping with the first nonlinear mapping, determine conversion rates for to be recommended items based on new user feature information, select to be recommended information based on the conversion rates, and output the selected target to be recommended information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: processing circuitry (claim 1, 13); database (claims 1, 13, 20); non-transitory computer readable storage medium storing instructions executable by a processor (claim 20);
The processing circuitry, database, and non-transitory computer readable storage medium storing instructions executable by a processor are recited at a high level of generality and amount to mere instructions to “apply it” (the abstract idea) using generic computing devices. The computing devices merely process data (determining, generating, combining, constructing, updating, performing, obtaining, selecting). Nothing in the claims improves upon computer themselves, technology, or a technical field (See MPEP 2106.05(f)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea.
As a result, the claims are not patent eligible.
Allowable Subject Matter
Claims 1-4, 6, 8-11, 13-16, 18, and 20 are allowed over the prior art however remain rejected under 35 USC 101. The examiner was unable to find a reasonable combination of references to teach the following in the context of the claimed invention:
determining, by the processing circuitry, new user feature information of the new user based on the first nonlinear mapping of the target second-order information, wherein in response to determining, based on a conversion identifier, that the new user has converted information in a to-be-recommended information library, the method further comprises:
determining target first-order information of the new user by aggregating feature information of the converted information,
determining a second nonlinear mapping of the target first-order information, and
combining the second nonlinear mapping with the first nonlinear mapping to determine the new user feature information;
determining conversion rates (CVRs) for a plurality of to-be-recommended information items based on the new user feature information;
selecting target to-be-recommended information from the plurality of to-be- recommended information items based on the CVRs; and
outputting for display the selected target to-be-recommended information to the new user.
The closest prior art is Hong et al (US 10,970,750). Hong generally teaches an action log that may record a user's interactions with advertisements on an online system. In some embodiments, data from the action log is used to infer interests or preferences of a user, augmenting the interests included in the user's user profile and allowing a more complete understanding of user preferences. Initially, a group of seed users is identified for a sponsored content. These seed users are users that are expected to have the highest value for an entity's sponsored content. After identifying seed users, the tiered user identification module identifies characteristics for these seed users. In one embodiment, the tiered user identification module places the seed users in a central tier for a tiered set of users for the sponsored content, and places the additional users in separate tiers that move outward from the central tier. Each tier that is further away from the central tier have users with progressively lower similarity scores, with the outermost tier including the users who are at the border of a threshold measure of similarity with the seed users.
Gerace (US 5,991,735) teaches to ensure that sponsors achieve the optimal result from the ads they place, regression analysis with a weighting technique to achieve real-time, automatic optimization is used. Under this auto-targeting system, an ad package is shown to general users. After a large number (e.g., 10,000) hits, program runs a regression on a subject Ad Package Object to see what characteristics are important, and who (type of user profile) the ad appeals to most. Program then automatically enters weighting information based on that regression to create a targeted system and runs the advertisement (Ad Package Object) again in front of this new targeted group.
Rice et al (US 2022/0269927) teaches in particular embodiments, social-networking system may store one or more social graphs in one or more data stores. In particular embodiments, a social graph may include multiple nodes—which may include multiple user nodes (each corresponding to a particular user) or multiple concept nodes (each corresponding to a particular concept)—and multiple edges connecting the nodes. For example, the feature knowledge graph system may determine one or more features for an Ads recommendation model using the knowledge graph and deploy the Ads recommendation model with these features to the application. The application may measure and track the inference values (e.g., click-through rates, exposure rates, conversion rates, revenuer per impress, conversion per impression) of the Ads recommendation model and send the inference values back to the feature knowledge graph system. The feature knowledge graph system may use these inference values to determine or update the weight values in the knowledge graph. In particular embodiments, the system may provide an effective solution of smart data warehouse powered by the nested heterogeneous graphs along with the graph learning models.
Roychowdhury et al (US 2017/0206457) teaches the prediction result is used by a service control module to control which advertisements are included as part of webpages and even what webpages are made available based on the prediction result. The prediction result, for instance, may indicate that particular advertisements have an increased likelihood in comparison with other advertisements in resulting in a selection or conversion.
Response to Arguments
With regard to arguments under 35 USC 101, no arguments have been presented. The claims are rejected as outlined above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraff can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/Primary Examiner, Art Unit 3621