DETAILED ACTION
Acknowledgement
This non-final office action is in response to claims filed on 12/22/2023.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 12/22/2023 and 03/04/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, “System and a Method of Generating a Training Set of Data for Training a Machine-Learning Algorithm”, is directed to an abstract idea, specifically Mental Processes, Mathematical Concepts, and Certain Methods of Organizing Human Activity, without significantly more. The claims as a whole do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the abstract idea because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-20 are directed to a statutory category, namely a method (claims 1-19) and a machine (claim 20).
Step 2A (1): Claims 1-16 and 19-20 are directed to an abstract idea of Mental Processes, Mathematical ,Concepts and Certain Methods of Organizing Human Activity, based on the following claim limitations:
Mental Processes:
A method of generating a training set of data…; the generating comprising generating synthetic user feedback for new digital items…; to determine the predicted indication of the user feedback for the given digital item using the feedback distribution of the indications of the user feedback over the old portion of digital items as a ground truth;… to generate a plurality of scoring training digital objects, a given one of which includes: (i) a given old digital item of the old portion of digital items; and (ii) a respective indication of the user feedback received by the given old digital item;… to determine the predicted indication of the user feedback for the given digital item using the feedback distribution of the indications of the user feedback over the old portion of digital items as a ground truth;… determine a respective indication of the synthetic user feedback for the given new digital item; generating,…, the training set of data including a plurality of training digital objects, a given training digital object including (i) the given one of the respective plurality of new digital items of the given new item provider; and (ii) the respective indication of the synthetic user feedback (claims 1 and 20); a method of generating a training set of data…; the generating comprising generating synthetic user feedback for new digital items; analyzing,…, the given new item provider and the plurality of old item providers for determining a respective reference old item provider, the determining being executed based on a similarity parameter between the given new item provider and each of the plurality of old item providers; based on the user feedback received by the respective plurality of old digital items of the respective reference old item provider, determining,…, the synthetic user feedback for the plurality of new digital items of the given new item provider, the determining including: determining, …, a feedback distribution of indications of the user feedback over the respective plurality of reference digital items associated with the respective reference old item provider; and assigning,…, indications of the synthetic user feedback over the plurality of new digital items of the given new item provider in accordance with the feedback distribution of the indications of the user feedback over the respective plurality of reference digital items of the respective reference old item provider; generating,…, the training set of data including a plurality of training digital objects, a given training digital object including (i) the given one of the plurality of new digital items of the given new item provider; and (ii) a respective indication of the synthetic user feedback (claim 19).
Mathematical Concepts:
determining a first probability value of the given new digital item of the given new digital item provider receiving an indication of positive user feedback, the determining being based on a respective similarity score of the given new item provider relative to the plurality of old item providers (claim 2).
the determining the first probability value is executed in accordance with a formula: artistProb= a∙artistScore+b, where artistScore is the respective similarity score of the given new item provider relative to the plurality of old item providers; and a and b are coefficients determined…(claim 3)
determining,…, the respective similarity score of the given new item provider relative to the plurality of old item providers. (claim 4)
wherein the determining the respective similarity score of the given new item provider comprises: acquiring, for a given old item provider of the plurality of old item providers, a respective value of a similarity parameter between the given old item provider and the given new item provider; based on respective values of the similarity parameter between each one of the plurality of old item providers and the given new item provider, generating, by the server, a directed graph structure, such that: a given vertex of the directed graph structure is representative of a respective item provider of one of the plurality of old item providers and the plurality of new item providers; a given edge connecting a pair of vertices is representative of a non-zero respective value of the similarity parameter between the given new item provider and a respective one of the plurality of old item providers; determining,…, for the given edge, a respective weight value indicative of the respective value of the similarity parameter between the given new item provider and the respective one of the plurality of old item providers; and determining, …, the respective similarity score of the given new item provider as being a maximum total weight value along a transition from a respective vertex of the directed graph structure associated with the given new item provider to an initial vertex thereof (claim 5);
wherein the determining the respective weight value comprises determining an inverse value of the respective value of the similarity parameter between the given new item provider and the respective one of the plurality of old item providers (claim 6)
wherein the similarity parameter is a rank of similarity between the given new item provider and each one of the plurality of old item providers (claim 7).
receiving the rank of similarity between the given new item provider and each one of the plurality of old item providers from a human assessor (claim 8).
…determining a second probability value of the given new digital item of the given new digital item provider receiving the indication of positive user feedback, the determining being based on a respective popularity value of the given new digital item in a given area (claim 9).
the determining the second probability value is executed in accordance with a formula: trackProb=c∙Populairty-minPopularitymaxPopularity-minPopularity+d, where Popularity is the respective popularity value of the given new digital item in the given area; maxPopularity is the respective popularity value of a most popular new digital item of the respective plurality of digital items of the given new item provider in the given area; minPopularity is the respective popularity value of a least popular new digital item of the respective plurality of digital items of the given new item provider in the given area; and c and d are coefficients determined… (claim 10)
…the respective popularity value of the given new digital item is indicative of at least one of: (i) a number of streams of the audio feed on a given media resource; (ii) a number of requests to play the audio feed on the given media resource; (iii) a position of the audio feed in a given record chart; and (iv) a number of sold albums including the audio feed. (claim 11)
in response to at least one of the first and second probability values being greater than a respective upper probability threshold, assigning,…, to the given new digital item, an indication of the positive synthetic user feedback; and in response to the at least one of the first and second probability values being lower than a respective lower probability threshold, the respective lower probability threshold being lower than the respective upper probability threshold, assigning, …, to the given new digital item, an indication of negative synthetic user feedback (claim 12)
selecting,…, the given new digital item of the respective plurality of new digital items. (claim 13)
the selecting comprises selecting the given new digital item from the respective plurality of new digital items one of (i) randomly; (ii) with a uniform sampling probability; (iii) with a respective sampling probability value proportional to the respective popularity value of the given new digital item in the given area; and (iv) with the respective sampling probability value proportional to the respective popularity value of the given new digital item in the given area (claim 14)
wherein, prior to the generating the training set of data, the selecting and the applying are executed iteratively for a predetermined number of iterations. (claim 15)
Certain Methods of Organizing Human Activity:
…generate digital item recommendations for users of an online recommendation platform; to generate the digital item recommendations (claims 1 and 20); …to generate digital item recommendations for users of an online recommendation platform…; to generate the digital item recommendations (claim 19).
These claims describe a process of generating training data of old and new items offered by various providers and associated user feedback to train a scoring model to predict/determine user feedback and generate item recommendations, which can practically be performed in the human mind with pen and paper using mathematical concepts, observation, evaluation, and judgement. Training is a computational process to generate a model by discovering patterns in the training data. Training a scoring model to predict an output can be performed practically in the mind with pen and paper. For example, a regression type scoring model can be trained with various sets of data. This training could involve the adjustment of coefficients or weights in the algorithm/formula to provide a specific output. Dependent claims 2-16 further describes the application of the scoring model using mathematical formulas and processes to determine probability values, respective similarity scores/parameters, weights/coefficients, and the selection of new digital items, which can be performed mentally with pen and paper. Recommending items to a user on an online platform based on user feedback is considered acts of managing personal behavior and facilitating commercial interactions. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions, Mathematical Concepts Mathematical Concepts which encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, and Certain Methods of Organizing Human Activity which encompasses fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior, relationships or interactions between people. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping) (MPEP 2106.04(a)(2)). Therefore, claims 1-20 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1-5, 9-14, 16-20 recite additional elements of “training a Machine-Learning Algorithm (MLA)…; the online recommendation platform hosting a plurality of digital items, the plurality of digital items including: (i) an old portion of digital items,…. and (ii) a new portion of digital items…; the method being executable by a server hosting the online recommendation platform; during a first stage, training, by the server, a scoring machine-learning model…; the training comprising: acquiring, by the server, indications of the old portion of digital items…; and feeding, by the server, the plurality of scoring digital objects to the scoring machine-learning model, thereby training the scoring machine-learning model…;…executing: acquiring, by the server, an indication of a given new item provider of the plurality of new item providers;…; applying, by the server, the scoring machine-learning model to a given new digital item of the respective plurality of new digital items of the given new item provider…; generating, by the server…; and feeding, by the server, the plurality of training digital objects to the MLA to train the MLA…(claims 1 and 20); … training a Machine-Learning Algorithm (MLA); the online recommendation platform hosting a plurality of digital items; the method being executable by a server hosting the online recommendation platform; acquiring, by the server, an indication of a given new item provider of the new item providers,…; acquiring, by the server, an indication of a plurality of old item providers having uploaded a respective plurality of old digital items,…; analyzing, by the server…; determining, by the server,…; assigning, by the server,...; generating, by the server…; and feeding, by the server, the plurality of training digital objects to the MLA to train the MLA (claim 19); A server for generating a training set of data for training a Machine-Learning Algorithm (MLA)…(claim 20); applying the scoring machine-learning model (claims 2, 9, and 13); by the server (claims 4, 5, 12, 13, and 16); via the training the scoring-machine-learning model (claims 3 and 10); the new digital item is an audio feed (claim 11); wherein the scoring machine-learning model is a matrix factorization machine-learning model (claim 17); , wherein the MLA is one of a decision tree-based MLA, Transformer-based MLA, and a Deep Semantic Similarity MLA (claim 18)”. The Examiner evaluated the claims in light of the Applicant’s specification and determined that the additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computer components that are used to perform the abstract processes identified in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). The use of machine learning and trained models/algorithms are considered instructions to apply or implement a model on a computer. Limitations that amount to merely indicating a field of use or technological environment (e.g. machine learning) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1-20 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1-5, 9-14, 16-20 recite additional elements stated above in Step 2A(2). These additional elements are viewed as mere instructions to implement an abstract idea on a computer and merely indicates a field of use or technological environment in which to apply a judicial exception. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-20 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Conclusion
The closest prior art(s) to the claimed invention include US: Kim et al. (US 2023/0143721 A1) “Teaching a Machine Classifier to Recognize a New Class”, Luo et al. (US 2021/0034975 A1) “AI Job Recommendation Neural Network ML Training…”, Cetintas et al. (US 2021/0398193 A1) “Neural Contextual Bandit Based Computational Recommendation Method and Apparatus”, Xie et al. (US 2018/0218428 A1) “Systems & Methods for Recommending Cold-Start Items on a Website of a Retailer”, FOR: ZHU, Yong-chun (CN-112102015-A) "Article Recommending Method, Meta-network Processing Method, Device, Storage Medium And Device" and NPL: ZHU, Yong-chun (CN-112102015-A) "Article Recommending Method, Meta-network Processing Method, Device, Storage Medium And Device". However, none of the prior art(s) alone or in combination teach the claimed invention as detailed in independent claims 1, 19, and 20.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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