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 communication filed on 04/27/2026.
Claims 1, 3, 7, 10 and 16 have been amended.
4. Claims 1-20 are currently pending and are considered below.
Information Disclosure Statement
5. The Applicant is respectfully reminded that each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability as defined in 37 CFR 1.56.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a system, which is a statutory class, comprising at least one processor, and a non-transitory computer-readable medium storing instructions that, when executed by the at least one processor, cause the system to:
generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member;
generate a member activity embedding reflecting member activity data associated with the member,
generate a member embedding based on the member information embedding and the member activity embedding using a member embedding model comprising a wide and deep model for creating cross-features between the member information embedding and the member activity embedding:
generate a content item embedding reflecting information about a content item using a content item model for capturing semantic meaning and attributes of data associated with the content item;
determine a similarity score indicating a similarity between the content item embedding and the member embedding;
generate a target content item group based on determining that the similarity score satisfies a threshold similarity score;
determining a filtering threshold for the target content item group, wherein the filtering threshold comprises a criteria used for determining whether the member belongs to the target content item group; and
filter the member of the target content item group based on the filtering threshold.
The steps of,
generate a member information embedding reflecting information stored in a member profile, wherein the member profile comprises a set of profile attributes for a member;
generate a member activity embedding reflecting member activity data associated with the member,
generate a member embedding based on the member information embedding and the member activity embedding using a member embedding model comprising a wide and deep model for creating cross-features between the member information embedding and the member activity embedding:
generate a content item embedding reflecting information about a content item using a content item model for capturing semantic meaning and attributes of data associated with the content item;
determine a similarity score indicating a similarity between the content item embedding and the member embedding;
generate a target content item group based on determining that the similarity score satisfies a threshold similarity score;
determining a filtering threshold for the target content item group, wherein the filtering threshold comprises a criteria used for determining whether the member belongs to the target content item group; and
filter the member of the target content item group based on the filtering threshold,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a system for generating a content item group comprising members that have interest in a content item. The above identified system steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to generate a target content item group corresponding to the content item.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as commercial interaction, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of at least one processor, memory, a publisher device. The at least one processor, memory, a publisher device is recited at a high level of generality (i.e., as a generic processor performing a generic computer functions of
generate a member information embedding; generate a content item embedding; determine a similarity score ; and generate a target content item group) such that they amount to no more than mere instructions to apply the exception using generic computer components. As for the limitation using a large language model to analyze raw material and a wide and deep model to generate an output layer, this feature is considered math, and therefore is a part of the abstract idea. Because the large language model and a wide and deep model in this claim is used as a tool for improving the abstract idea, rather than improving any technical feature or function, it is not sufficient to integrate the judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
at least one processor, memory, a publisher device amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is already present when the steps are considered separately. That is, at least one processor, memory, a publisher device, performing commercial interactions including
generating a member information embedding; generate a content item embedding; determine a similarity score; and generate a target content item group, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, independent claims 1, 10 and 16 are not eligible.
As for dependent claims 2-9, 11-15 and 17-20, these claims recite limitations that further define the same abstract idea in claims 1, 10 and 16, to generate a target content item group corresponding to the content item. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
8. Applicant’s arguments, filed on 04/27/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 103(a) have been fully considered and are persuasive. The rejection of claims 1-20 under 35 U.S.C. 103(a) has been withdrawn.
9. Applicant's arguments filed on 04/27/2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
9. Applicant argued that “…Revised Guidance Step /
Step 1 of the Revised Guidance asks whether the claimed subject matter falls
within the four statutory categories of patentable subject matter identified by 35 U.S.C. §
101: process, machine, manufacture, or composition of matter. See Revised Guidance.
The representative claim recites an apparatus. Therefore, the representative claim falls
within the machine category.
Revised Guidance Step 2A, Prong 1
Under Step 2A, Prong 1 of the Revised Guidance, the courts determine whether
the claims recite any judicial exceptions, including certain groupings of abstract ideas
(i.e., mathematical concepts, certain methods of organizing human activity such as a
fundamental economic practice, or mental processes). MPEP § 2106.04(a). The Office
Action asserts that the claims fall under the certain methods of organizing human activity grouping of abstract ideas. Office Action 4. Applicant respectfully traverses this
assertion…” Remarks page 13
11. Examiner notes that the claim recites a system (machine) and a computer-implemented method (process) that are statutory categories. Furthermore, Examiner notes that for abstract idea directed to "Certain Methods of organizing Human Activity" and specifically abstract idea that fall within the subgrouping of commercial and legal activities namely advertising, marketing and sales activities, the courts have determined that steps directed to gathering data, analyzing data, determining results generating tailored content, and transmitting the tailored content are all part of the abstract idea itself. In the instant case, the argued limitation are all directed to analyzing data and determining results in the process of performing advertising, marketing of sales activities. As such, the argued limitations are clearly part of the identified abstract idea and fall squarely within the "Certain Method of Organizing Human Activity." Thus, the rejection has been maintained."
12. Examiner notes that aside from the " one processor, non-transitory computer readable medium, memory, a publisher device" which are "additional elements', the remainder of the claims have been identified as part of the abstract idea itself which is merely applied using a general-purpose computer (i.e., processing device coupled to a data storage device executing software). In order to overcome a 35 USC 101 rejection under Step 2a, Prong 2 the purported improvement must be rooted in the "additional elements'. Additional elements are defined as those elements outside of the identified abstract idea itself. Thus, the "additional elements" as a whole are just a processing device coupled to a data storage device executing software upon which an abstract idea is merely being applied which is insufficient to transform the abstract idea into a practical application. Any purported improvement obtained by practicing the claimed invention is an improvement to the abstract idea which is an improvement in ineligible subject matter. Thus, the rejection has been maintained.
13. Applicant argued “…The representative claim is similar to Example 39 of the Revised Guidance, which is directed to facial expression recognition based on McRO, Inc. dba Planet Blue V. Bandai Namco Games America Inc., 120 USPQ2d 1091 (Fed. Cir. 2016). As stated in page 2 of the Bahr Memo, in a discussion of the teachings of McRO, "[a]n "improvement in computer-related technology' is not limited to improvements in the operation of a computer or a computer network per se, but may also be claimed as a set of 'rules' (basically mathematical relationships) that improve computer-related technology by allowing computer performance of a function not previously performable by a computer." The Bahr Memo further states "[a]n indication that a claim is directed to an improvement in computer-related technology may include-(1) a teaching in the specification about how the claimed invention improves a computer or other technology."
Similar to McRO, the representative claim is directed to a set of rules that improve computer-related technology by allowing computer performance of a function not previously performable by a computer…” Remarks page 17
14. Examiner notes that Example 39 uses training data of digital facial images that are transformed various ways to create a first training set used to train a neural network. Then, a second training set is created from the first training set and non-facial images that were incorrectly detected as facial images during the first stage. 2019 Subject Matter Eligibility Examples: Here, the training data is not transformed or curated in any particular way in order to train the machine learning model. Nor is the model trained in stages to improve its accuracy as in Example 39.
15. Applicant argued “…Claim 3 of Example 47 recites a method of using an artificial neural network (ANN) to detect malicious network packets. The method includes training the ANN based on input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm. Then, one or more anomalies are detected in network traffic using the trained ANN. Next, at least one detected anomaly is determined to be associated with one or more malicious network packets. The source address of the malicious network packets is determined, one or more malicious network packets are dropped, and then future traffic from the source address is blocked. Though Claim 3 of Example 47 is determined to recite the abstract idea of mathematical concepts and mental processes, the claim is nonetheless deemed to be eligible under 35 U.S.C. § 101 because the claim as a whole integrates the alleged judicial exception into a practical application…” Remarks page 20
16. Examiner notes that the claims in claim 3 of Example 47 is directed to detecting anomalies in network traffic, detecting anomaly with network packets, then detecting a source address, then dropping the malicious network packets in real time and blocking future traffic from the source addresses therefore enhancing security by acting in real time to proactively prevent network intrusions. The instant claim predicts, based on data of the patient, an optimal value for at least one of the first time window, the lower evaluation window, and the lower threshold for determining patient discharge readiness.
17. Applicant argued that “…Revised Guidance Step 2B
Under Step 2B of the Revised Guidance, the courts next determine whether the
claims recite an "inventive concept" that "must be significantly more than the abstract
idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer." BASCOM Glob. Internet Servs., Inc. V. AT& Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016). There must be more than "computer functions [that] are 'well-understood, routine, conventional activities]' previously known to the industry." Alice, 573 U.S. at 225 (second alteration in original) (quoting Mayo, 566 U.S. at 73).
Under Step 2B of the Revised Guidance, the representative claim recites an
inventive concept that is significantly more than the alleged abstract idea itself. The
Office Action does not explain why the additional elements do not amount to
significantly more than the identified judicial exception. Further, the Office Action does
not consider the extra-solution activity identified in Step 2A and whether those elements
recite significantly more than the alleged abstract idea. Additionally, the Office Action
does not adequately support a finding that the recited steps are well-understood, routine, and conventional activity. Remarks pages 23-24
18. Examiner notes that in order to overcome a 35 USC 101 rejection under Step 2b
it is the "additional elements" that must be considered "significantly more". Additional
elements are defined as those elements outside of the identified abstract idea itself. In
the instant case, the only "additional elements" found in the claim are one processor, memory, a publisher device, which is merely a general-purpose computer upon which the abstract idea is being applied. Thus, the additional elements cannot be considered significantly more than the abstract idea. The purported improvement of the technology in the machine learning technology is by enhancing the accuracy and performance of the machine learning model for predicting a higher member interest in the content item in the manner claimed is part of the abstract idea and, as such, cannot be considered “significantly more” than the abstract idea under Step 2B. Thus, the rejection has been maintained.
Conclusion
19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20. O’Malley (U.S. Patent No. 12,094018) discloses a perceptron method (see at least Column 12 line 66 through column 13 line 15).
21. Smith et al. (U.S. Patent No. 7,818,206) discloses tracking user activity at a terminal on a communication network and, more particularly, to methods and systems for generating user profiles based on user activity a communication terminal (see at least paragraph 2).
22. Updated Search found:
23. Ulanov et al. (U.S. Pub. No. 2024/0020345) discloses a system uses semantic analysis of text associated with content items to recommend content for display to a user. A subset of representative words from a content description are determined and a content embedding that models the content is generated using a combination of word embeddings associated with each of the representative words. User embeddings are generated using a combination of content embeddings for content that a user has had particular interactions with in a set period of time. Separate user embeddings may be generated to represent user interactions with different categories of content (e.g., travel, photography, apparel, comedy, etc.). The system uses the content embeddings and user embeddings as input to predictive functions which determine a candidate content item that a user is likely to interact with if the candidate content is displayed to the user (see at least the Abstract).
24. Lamba et al. (U.S. Pub. No. 2022/0366295) discloses providing techniques for training a machine learning model. Embodiments include providing features of a plurality of content items as inputs to an embedding model and receiving embeddings of the plurality of content items as outputs from the embedding model. Embodiments include receiving a data set comprising features of a plurality of users associated with content items of the plurality of content items that correspond to the plurality of users. Embodiments include generating a training data set for a machine learning model, wherein the training data set comprises the features of the plurality of users associated with respective labels indicating which respective embeddings of the embeddings correspond to each respective user of the plurality of users. Embodiments include training the machine learning model, using the training data set, to output corresponding embeddings of relevant content items for users based on features of the users (see at least the Abstract).
25. The references alone or in combination fail to teach or suggest the following amended limitations 1, 10 and 16 “generate a member embedding based on the member information embedding and the member activity embedding using a member embedding model comprising a wide and deep model for creating cross-features between the member information embedding and the member activity embedding; generate a content item embedding reflecting information about a content item using a content item model for capturing semantic meaning and attributes of data associated with the content item; and determine a filtering threshold for the target content item group, wherein the filtering threshold comprises a criterion used for determining whether the member belongs to the target content item group”.
26. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, llana Spar can be reached at 571)270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 06/27/2026
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