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
The following is a non-final office action.
Claims [1-20] are currently pending and have been examined.
Claims 1, 11, and 20 have been amended see REMARKS September 18, 2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 September 18, 2025 has been entered.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: claims 1-10 recite a method (i.e. a series of steps), claims 11-19 recite a system, and claim 20 recite a non-transitory computer readable medium, and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1, 11, and 20 recite: A method comprising: identifying an influencer based on a set of criteria; determining a first attribute of the influencer based on context data associated with the influencer; identifying a second attribute of an item; generating a first vector that represents the first attribute of the influencer and a second vector that represents the second attribute of the item, the generating of the first vector comprises: generating a pooled interest graph that reserves a plurality of core attributes associated with the influencer, and generating a reduced attribute sequence based on the pooled interest graph, the generating of the reduced attribute sequence including flattening the pool interest graph to identify one or more core attributes representative of preferences of the influencer; generating a similarity score that represents a degree of similarity between the influencer and the item based on the first vector representing the first attributes as a core attribute representative of the preferences of the influencer and the second vector representative of the second attribute of the item; and causing display of the similarity score.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims recite a series of steps for identifying an influencer to be associated with an item based on a series of criteria. Therefore, the claims recite an abstract idea as they are a series of steps identifying an influencer based on a set of criteria, determining an attributed of an item and generating a similarity score that represents a degree of similarity between the influencer and an item. Merely performing a series of steps to generate a list of influencers to associated with an item by determining the similarity of their attributes is an abstract idea.
Alternatively, the claims recite a mental process. The claims recite a method of identifying an influencer based on a set of criteria, identifying an item, and generating a similarity score that represents a degree of similarity between the influencer and the item. The claims therefore, recite a mental process as a person is capable of performing a series of steps of determining an influencer to potentially associate with an item based on performing a series of comparisons or steps to generate a similarity score between the influencer and the item in their mind or by using simple tools such as pen and paper. As these steps are capable of being performed by someone such as a marketing agent looking for an influencer to collaborate with to help improve the reach of a product. Additionally, the claims recite steps and procedures that are similar to concepts the courts have identified as a mental process such as observations, evaluations, judgements, and opinions. Therefore, the claims recite an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: using a first machine learning model, using a second machine learning model, retraining, using a feedback loop mechanism, the third machine learning model based on the generated first vector and feedback provided by a system administrator, the retraining comprising adjusting parameters of the third machine learning model to improve accuracy of model outputs; using the retrained third machine learning model, a user interface of a device.
Claim 11: A system comprising: a memory storing instructions; and one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations using a first machine learning model, using a second machine learning model, retraining, using a feedback loop mechanism, the third machine learning model based on the generated first vector and feedback provided by a system administrator, the retraining comprising adjusting parameters of the third machine learning model to improve accuracy of model outputs; using the retrained third machine learning model, a user interface of a device.
Claim 20: A non-transitory computer-readable medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations, using a first machine learning model, using a second machine learning model, retraining, using a feedback loop mechanism, the third machine learning model based on the generated first vector and feedback provided by a system administrator, the retraining comprising adjusting parameters of the third machine learning model to improve accuracy of model outputs; using the retrained third machine learning model, a user interface of a device.
The additional element of generic computer elements to receive and transmit information as well as perform the abstract idea of generating a similarity score and displaying a result based on information such as influencer and item attributes are directed to mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer platform to transmit, display, and manipulate information is not an improvement to a technology or technical field. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the 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.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements do not recite an improvement to a technology or technical field but merely utilize the generic computer elements such a machine learning models and a user device to perform the abstract idea of obtaining information such as influencer context data and item attribute data and determining a similarity score. Therefore, the additional elements do not direct the claims to significantly more.
The dependent claims 2-10 and 12-19 further narrow the abstract idea of determining of determining a list of influencers based on a similarity score between the influencers and a plurality of items, recited in the independent claims 1, 11, and 20 and are therefore directed towards the same abstract idea.
The dependent claims recite the following additional elements:
Claims 5 and 15: a Graph convolutional network machine learning model associated with a multimodal machine learning framework.
Claims 6 and 16: A language model associated with Bidirectional encoder representations from transformers technique.
Claims 7 and 17: a personal-object similarity calculation machine learning model.
Claim 8: A supervised machine learning model associated with a Naïve Bayes Classification algorithm.
However, the additional elements are directed to merely “apply it” or being applied to perform the abstract idea.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101.
Response to arguments:
Applicant’s arguments, see REMARKS September 18, 2025, and with respect to the rejections of claims [1-20] under U.S.C. 101 have been fully considered and are not persuasive.
The representative argues that the independent claims 1, 11, and 20 are directed to a practical application as they recite an improvement to the technology of a machine learning system to provide comprehensive content. The applicant argues that the system reflects an improvement to a machine learning model to provide comprehensive content understanding. The system employs an attribute-based matching system using machine learning to generate attribute vectors for influencers and items enabling accurate similarity determination. And that the system improves the accuracy and efficiently of identifying relevant influencers beyond simple metrics. The applicant further argues that the claims recite an improvement including an adaptive feedback loop mechanism that continuously improves the machine learning model performance over time to help improve the accuracy and efficiency of identifying relevant influencers. The model is improved based on generated vectors and administrative feedback comprising adjusting parameters to improve model output and accuracy. However, the examiner respectfully disagrees as the claims recite the abstract idea of a method comprising identifying an influencer based on a set of criteria; determining a first attribute of the influencer; identify a second attribute of an item; generating a first vector representing the first attribute of the influencer and a second vector that represents the second attribute of the item by generating a pool interest graph and generating a reduced attributed sequence based on the pooled interest graph, the generating of the reduced attribute sequence including flattening the pool interest graph to identify one or more core attributes representative of preferences of the influencer; generating a similarity score that represents a degree of similarity between the influencer and the item; and presenting the similarity score. The claims recite a mental process as an individual such as a campaign manager seeking an influencer or celebrity to cooperate with to market a product would be capable of mentally identifying an influencer based on a set of criteria, determine their attributes based on context data associated with the influencer, and determine a similarity score between the user and a product using a specific algorithm. Additionally, merely performing calculations or generic procedures to generate a pooled interest graph that reserves a plurality of core attributes associated with the influencer and generating a reduced attribute sequence based on the pool interest graph including one or more core attributes representative of preferences of the influencer are steps that can be further performed in the human mind or with simple tools such as pen and paper. As the amended claims merely recite additional steps to analyze the attribute information of an influencer by creating a graph, generated an attribute sequence representing core attributes, and generating similarity scores that represent a degree of similarity between an influencer and an item. Therefore, claims recite an abstract idea. Furthermore, the additional elements of a plurality of machine learning models being used to perform the steps of determining attributes, generating vector representations of the attributes, and generating a similarity score between the attributes of an influencer and an item, are directed to merely “apply it.” As the additional elements merely recite using generic computer elements and machine learning elements to perform generic processes of displaying information and performing a series of calculations to analyze information such as determining a similarity score between vectors. The examiner finds that merely adjusting parameters to train and retrain a machine learning model is not an improvement to the technical field of a machine learning model. But merely recites performing a standard and generic step for utilizing machine learning models trained to perform a function such as receiving input information, normalizing the information by generating a vector representation, and determining an output such as a similarity score. Therefore, the claims do not recite an improvement to the functioning of a computer or an improvement to a technology or technical field but merely utilize generic computer and machine learning elements to perform the abstract idea of matching influencers with items. Therefore, the additional elements do not recite an improvement to a technology or technical field and do not direct the claims to a practical application.
The representative further argues that the claims are similar to examples 39 of the 2019 PEG examples and example 47. However, the examiner respectfully disagrees as the steps of example 39 recite a method for training a machine learning model to detect faces in distorted images which are not equivalent to the recited claim limitations of identifying an influencer with a set of criteria that matches the attributes of an item. While claim 1 of example 47 is patent eligible as it does not recite an abstract idea it is not similar in any way to the current claims. Claim 2 of example 47 is more similar in structure as it recites receiving training data, training a machine learning model, and then using a trained model to analyze data and output a result. Claim 2 of example 47 is found to be ineligible as merely using a machine learning model to perform the abstract idea of analyzing data and outputting a result is not a practical application. Additionally, the example finds that merely performing generic steps of training a machine learning model to perform an abstract idea is also insufficient to direct the claims to a practical application.
Therefore, the examiner maintains the current 101 rejection.
The applicant argues that the dependent claims 2-10 and 12-19 are allowable as being dependent on claims 1, 11, and 20 and therefore are rejected under the same 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Landry (US 2021/0118010) System and method for contact matching for marketing campaigns.
Whitman (US 2014/0195544) Demographic and media preference predictions using media content data analysis.
Paul (US 2009/0063254) Method and apparatus to identify influencers.
Arini (US 2014/0278796) Identifying target audience for a product or service.
Silberman (US 2021/0035152) Predicting the effectiveness of a marketing campaign prior to deployment.
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/COREY RUSS/Examiner, Art Unit 3629