DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 5/30/2023 for application number 18/203,210.
Claims 1-20 are pending.
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 .
Claim Objections
Claim 8 is objected to because of the following informalities: The term “the one or more events” lacks antecedent basis. It appears this claim should depend on claim 7 and not claim 4. Appropriate correction is required.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 (representative of independent claims 13 and 20) recites:
A computing platform for enhanced generation of a recommendation for a user account, using machine learning (ML) to process unstructured media data from a media platform and account information associated with the user account to determine the recommendation, comprising: at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive the account information, wherein the account information comprises historical account data and one or more user-defined account rules; input, into a user ML model, the account information; process, by the user ML model, the historical account data and the one or more user-defined account rules to determine a plurality of account features; output, by the user ML model, the plurality of account features; receive, from the media platform, unstructured media data, wherein the unstructured media data comprises objects and media parameters; input, into a media ML model, the unstructured media data, wherein the media ML model is a convolutional neural network; classify, by the media ML model, the objects in the unstructured media data as keywords associated with the media parameters to determine a plurality of media features; output, by the media ML model, the plurality of media features; input, into a recommendation ML model, the plurality of account features and the plurality of media features; generate, by the recommendation ML model, tokens representing each of the plurality of media features and each of the plurality of account features, wherein the tokens are connected together in a fully connected graph structure; delete, by the recommendation ML model, each of the tokens representing media features not matching with any of the tokens representing the plurality of account features; process, by the recommendation ML model, a recommendation score based on the tokens in the fully connected graph structure representing the plurality of account features and the plurality of media features; output, by the recommendation ML model, the recommendation score; generate the recommendation for the user account based on the recommendation score; and send, to a user computing device associated with the user account, the recommendation execute, by the user computing device, an action on the user account based on the recommendation.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. A human can (1) make a mental judgement about account features based on historical data and rules, (2) mentally judge keyword features from media, (3) draw a fully-connected graph of the features, remove non-matching nodes, make a mental judgment about a recommendation score and recommendation for the account.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims contain the additional limitations of (a) generic computer components like a processor, (b) data receiving and outputting steps for: account data and features, media data and features, account recommendation scores and actions; (c) processing steps for user, media, and recommendation ML models and executing actions. Additional element (a) is a mere instruction to apply the exception because it only adds generic computer components after the fact to the mental process. Element (b) is insignificant extra-solution activity because it is mere necessary data gathering and outputting for the mental process. Element (c) is also a mere instruction to apply the exception because these ML model limitations only recite the idea of a solution (that the ML models determine features and output scores and an action is executed on the account) and does not recite any details of how the solution is accomplished (there is no claimed detail on how these ML models function, or what the action is supposed to be or how it is executed). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements merely add insignificant extra-solution activity and instructions to apply the exception to the mental process.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (a) and (c) are mere instructions to apply, as explained above. Element (b) is well-understood, routine, and conventional, analogous to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d) citing Intellectual Ventures v. Symantec, 838 F.3d 1307, 1321; 120 USPQ2d 1353, 1362 (Fed. Cir. 2016) and presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional and instructions to apply the exception to the mental process. The claim as a whole is directed to a mental process of recommending actions to a user based on past account information, user rules, and media keywords, and the additional elements do not amount to significantly more than the abstract idea itself because they are merely gathering the data from the mental process and instructions to execute the mental process on a computer.
Dependent claims 2-4 and 14-16 recite the additional element of training the user / media / recommendation ML model. (2A, prong 2) This additional element does not integrate the judicial exception into a practical application because it is a mere instruction to apply the exception: this element merely states the solution (that the ML models are trained to make predictions) and not how the solution is accomplished (how specifically the ML models are trained). (2B) This additional element does not amount to significantly more than the abstract idea itself because it is a mere instruction to apply, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional and instructions to apply the exception to the mental process.
Dependent claims 5 recites the additional element of sending the recommendation as a text, email, or push notification message. (2A, prong 2) This additional element does not integrate the judicial exception into a practical application because insignificant extra-solution activity of mere necessary data output of outputting a recommendation to a user. (2B) This additional element does not amount to significantly more than the abstract idea itself because it is well-understood, routine, and conventional, analogous to presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional and instructions to apply the exception to the mental process.
Dependent claims 6, 9-12, and 17-19 recite the additional elements the media platform being social media or an online marketplace; the media being text, image, audio or video; and the rules and actions being for banking. (2A, prong 2) These additional elements are field of use and technological environment limitations. The content being from social media or an online marketplace generally links the mental process to the technological environment of online social media and shopping. The media itself being text, image, audio or video generally links the mental process to the technological environment computer media. The rules and actions being for banking generally links the mental process to the field of use of banking. Confining the use of the mental process to different technological environments and fields does integrate the abstract idea into a practical application. (2B) These additional elements do not amount to significantly more than the abstract idea itself because they are field of use and technological environment limitations, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional, instructions to apply the exception to the mental process, and field of use limitations to the abstract idea.
Dependent claims 7-8 recite (2A, prong 1) modifying a recommendation based on event comprising weather, employment, geopolitical, or civic unrest. This is a further mental process step: a human can make a mental judgment about modifying a recommendation based on these events. (2A, prong 2) The claims contain the additional element of retrieving events from an external data source. This limitation does not integrate the abstract idea into a practical application because it is insignificant extra-solution activity that is mere data gathering for the mental process. (2B) The additional element does not amount to significantly more than the abstract idea itself because it is well-understood, routine, and conventional, analogous to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d) citing Intellectual Ventures v. Symantec, 838 F.3d 1307, 1321; 120 USPQ2d 1353, 1362 (Fed. Cir. 2016). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional, instructions to apply the exception to the mental process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 6, 9, 13-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph (see attached NPL) in view of Ghoshal et al. (US 2022/0012268 A1) and Guo et al. Attention Guided Graph Convolutional Networks for Relation Extraction (see attached NPL).
In reference to claim 1, Liu teaches a computing platform for enhanced generation of a recommendation for a user account (recommending system for user, Introduction pages 181-82), using machine learning (ML) to process unstructured media data from a media platform and account information associated with the user account to determine the recommendation, comprising: at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive the account information, wherein the account information comprises historical account data (users historical interactions are obtained, 3. Problem Formulation and 4. The Proposed Recommendation Model, pages 184-85); input, into a user ML model, the account information; process, by the user ML model, the historical account data … to determine a plurality of account features; output, by the user ML model, the plurality of account features (embedding of user’s historical interactions is determined, 3. Problem Formulation and 4. The Proposed Recommendation Model, pages 184-85); receive, from the media platform, unstructured media data, wherein the unstructured media data comprises objects and media parameters (item of interest is input, 3. Problem Formulation and 4. The Proposed Recommendation Model, pages 184-85; items can comprise text and parameters including LastFM music selections, move ratings, etc., 5.1.1. Datasets, page 187); input, into a media ML model, the unstructured media data … ; classify, by the media ML model, the objects in the unstructured media data as keywords associated with the media parameters to determine a plurality of media features (entities, which are keywords, can be associated with input items, 3. Problem Formulation and 4. The Proposed Recommendation Model, pages 184-87); output, by the media ML model, the plurality of media features; input, into a recommendation ML model, the plurality of account features and the plurality of media features; generate, by the recommendation ML model, tokens representing each of the plurality of media features and each of the plurality of account features, wherein the tokens are connected together in a … graph structure (recommendation model creates local and non-local graph structures connecting features of the input user’s history and input item, 3. Problem Formulation and 4. The Proposed Recommendation Model, pages 184-87); delete, by the recommendation ML model, each of the tokens representing media features not matching with any of the tokens representing the plurality of account features (“noise” entities that are not relevant, i.e. do not match, can be filtered out, paragraph between equations (10) and (11) on page 186); process, by the recommendation ML model, a recommendation score based on the tokens in the fully connected graph structure representing the plurality of account features and the plurality of media features; output, by the recommendation ML model, the recommendation score (score for item of interest is output, pages 186-88); generate the recommendation for the user account based on the recommendation score; and send, to a user computing device associated with the user account, the recommendation execute, by the user computing device, an action on the user account based on the recommendation (suggested item can be displayed to user as a list of items, 1. Introduction, page 181; it would be obvious that the user could view the item, which is executing an action).
However, Liu does not explicitly teach one or more user-defined account rules; wherein the media ML model is a convolutional neural network.
Ghoshal teaches one or more user-defined account rules (user preferences can be used for recommending different content, para. 0135); wherein the media ML model is a convolutional neural network (CNN can be used to process image data, para. 0123).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu and Ghoshal before the earliest effective filing date, to modify the ML system of Liu to include the user rules and CNN of Ghoshal.
One of ordinary skill in the art would have been motivated to modify the ML system of Liu to include the user rules and CNN of Ghoshal because it would allow the system to account for user preferences in content type (Ghoshal, para. 0135) and process more types of items, like image data (Ghoshal, para. 0123).
However, Liu and Ghoshal do not explicitly teach a fully connected graph structure.
Guo teaches a fully connected graph structure (see 2.2 Attention Guided Layer, pages 243-44).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, and Guo before the earliest effective filing date, to modify the graph of Liu to include the fully connected graph of Guo.
One of ordinary skill in the art would have been motivated to modify the graph of Liu to include the fully connected graph of Guo because it can help make more accurate predictions (Guo, pages 241-43).
In reference to claim 2, Liu teaches the computing platform of claim 1, wherein the memory stores additional computer- readable instructions that, when executed by the at least one processor, cause the computing platform to: train the user ML model based on the historical account data and one or more user- defined account rules to determine the plurality of account features (model are trained, 4.3 Model Training, page 187).
In reference to claim 3, Liu teaches the computing platform of claim 1, wherein the memory stores comprise additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train the media ML model based on a plurality of unstructured media data to determine the plurality of media features (model are trained, 4.3 Model Training, page 187).
In reference to claim 4, Liu teaches the computing platform of claim 1, wherein the memory stores additional computer- readable instructions that, when executed by the at least one processor, cause the computing platform to: train the recommendation ML model based on the plurality of account features and the plurality of media features to determine the recommendation score (model are trained, 4.3 Model Training, page 187).
In reference to claim 6, Liu teaches the computing platform of claim 1, wherein the media platform comprises at least one of a social media platform or an online marketplace, or a combination thereof (social media, 1. Introduction, page 181).
In reference to claim 9, Liu teaches the computing platform of claim 1, wherein the unstructured media data comprises at least one of textual data, image data, audio data, or video data, or a combination thereof (text data, 5.1.1. Datasets, page 187).
In reference to claim 13, this claim is directed to a method associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a method associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a method associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a method associated with the system claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a non-transitory computer-readable storage medium associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
Claim(s) 5, 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph (see attached NPL) in view of Ghoshal et al. (US 2022/0012268 A1) and Guo et al. Attention Guided Graph Convolutional Networks for Relation Extraction (see attached NPL) as applied to claim 1 above, and in further view of Estes et al. (US 2023/0260046 A1).
In reference to claim 5, Liu, Ghoshal, and Guo do not explicitly teach the computing platform of claim 1, wherein the recommendation is sent to the user computing device in a text message, e-mail message, or a push notification message.
Estes teaches the computing platform of claim 1, wherein the recommendation is sent to the user computing device in a text message, e-mail message, or a push notification message (push notification, para. 0227).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, Guo, and Estes before the earliest effective filing date, to modify the recommendation of Liu to include the push notification of Estes.
One of ordinary skill in the art would have been motivated to modify the recommendation of Liu to include the push notification of Estes because it would provide more ways of presenting the user with recommendations.
In reference to claim 7, Liu, Ghoshal, and Guo do not explicitly teach the computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: retrieve, from one or more external sources of data, one or more events that may impact the recommendation and location information associated with the user account; and modify the recommendation for the user account based on the one or more events that may impact the recommendation and the location information.
Estes teaches the computing platform of claim 1, wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: retrieve, from one or more external sources of data, one or more events that may impact the recommendation and location information associated with the user account; and modify the recommendation for the user account based on the one or more events that may impact the recommendation and the location information (external events that may affect user’s recommendations are retrieved and used to modify recommendation, para. 0239).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, Guo, and Estes before the earliest effective filing date, to modify the recommendation of Liu to include the events of Estes.
One of ordinary skill in the art would have been motivated to modify the recommendation of Liu to include the events of Estes because it can help adapt predictions to local external conditions (Estes, para. 0239).
In reference to claim 8, Estes further teaches the computing platform of claim [7] (see objection above), wherein the one or more events comprise at least one of a weather-related event, an employment related event, a geopolitical event, or a civic unrest event, or a combination thereof (weather and employment, para. 0239).
Claim(s) 10-12 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph (see attached NPL) in view of Ghoshal et al. (US 2022/0012268 A1) and Guo et al. Attention Guided Graph Convolutional Networks for Relation Extraction (see attached NPL) as applied to claim 1 above, and in further view of Ruff et al. (US 2015/0379488 A1).
In reference to claim 10, Liu, Ghoshal, and Guo do not explicitly teach the computing platform of claim 1, wherein the one or more user-defined account rules comprise at least one or more rules associated with an automatic loan amount, a secondary funding source, automatic payment options, a budget for a specific period of time, a designated alternate decision making authority, or preferred communication channels, or a combination thereof.
Ruff teaches the computing platform of claim 1, wherein the one or more user-defined account rules comprise at least one or more rules associated with an automatic loan amount, a secondary funding source, automatic payment options, a budget for a specific period of time, a designated alternate decision making authority, or preferred communication channels, or a combination thereof (user can input budget information, funding sources, etc. para. 0055-57, 0059-63).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, Guo, and Ruff before the earliest effective filing date, to modify the rules of Ghoshal to include the financial rules of Ruff.
One of ordinary skill in the art would have been motivated to modify the rules of Ghoshal to include the financial rules of Ruff because it would allow the improved predictions and suggestions of Liu and Ghosshal to be used for suggestions in different situations, like the financial suggestions of Ruff.
In reference to claim 11, Liu, Ghoshal, and Guo do not explicitly teach the computing platform of claim 1, wherein the action on the user account comprises at least one of open a small business account, open a checking account, open a savings account, apply for a credit card, or open a line of credit, or a combination thereof.
Ruff teaches the computing platform of claim 1, wherein the action on the user account comprises at least one of open a small business account, open a checking account, open a savings account, apply for a credit card, or open a line of credit, or a combination thereof (open savings account, para. 0091).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, Guo, and Ruff before the earliest effective filing date, to modify the suggestions of Ghoshal to include the financial suggestions of Ruff.
One of ordinary skill in the art would have been motivated to modify the rules of Ghoshal to include the financial suggestions of Ruff because it would allow the improved predictions and suggestions of Liu and Ghosshal to be used for suggestions in different situations, like the financial suggestions of Ruff.
In reference to claim 12, Liu, Ghoshal, and Guo do not explicitly teach the computing platform of claim 1, wherein the action on the user account comprises at least one of decrease spending on a transaction, increase spending on a transaction, or modify a budget for a specific period of time, or a combination thereof.
Ruff teaches the computing platform of claim 1, wherein the action on the user account comprises at least one of decrease spending on a transaction, increase spending on a transaction, or modify a budget for a specific period of time, or a combination thereof (budget can me modified for time period, para. 0130-35, particularly 0133).
It would have been obvious to one of ordinary skill in art, having the teachings of Liu, Ghoshal, Guo, and Ruff before the earliest effective filing date, to modify the suggestions of Ghoshal to include the financial suggestions of Ruff.
One of ordinary skill in the art would have been motivated to modify the rules of Ghoshal to include the financial suggestions of Ruff because it would allow the improved predictions and suggestions of Liu and Ghosshal to be used for suggestions in different situations, like the financial suggestions of Ruff.
In reference to claim 17, this claim is directed to a method associated with the system claimed in claim 10 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a method associated with the system claimed in claim 11 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a method associated with the system claimed in claim 12 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References Cited: [A], [D], [E], [G], [W], [X], and [U pg 2] are all generally relevant for using machine learning / transformer models for making personalized recommendations to a user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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