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 .
The following is a Non-Final Office Action in response to communications received August 17, 2026. Claim 2 has been canceled. Claims 1 and 18-19 have been amended. No new claims have been added. Therefore, claims 1 and 3-19 are pending and addressed below.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
Priority
Application No. 18233466 filed 08/14/2023 Claims Priority from Provisional Application 63400528 , filed 08/24/2022
Applicant Name/Assignee: Royal Bank of Canada
Inventor(s): Beltran, Nohra; AISibai, Dana; Cliff, Christopher; Nandakumar, Hariish; McIsaac, Hanna; Goncalves, Kelly; Soo, Selene; Lam, Chai
Response to Arguments/Amendments
Claim Rejections - 35 USC § 112(a)
Applicant's amendments in response to the 112(a) rejection for failing to comply with written description requirements is sufficient to overcome the rejection set forth in the previous Office Action. The examiner withdraws the 112(a) rejection of claims 1 and 3-19.
Claim Rejections - 35 USC § 101
Applicant's arguments filed 08/16/2026 have been fully considered but they are not persuasive.
In the remarks applicant points to MPEP 2106.04(II), MPEP 2106.04(d) under step 2A prong 2, arguing that the claimed limitations when considered as a whole integrates any alleged abstract idea into a practical application. Applicant argues that unlike Recentive Analytics v Fox, the claims define a particular data processing workflow including preprocessing, feature selection, vector generation, prediction generation, user profile updating, batched training and conditional deployment of predictor models. The ordered implementation for preparing transactional data, generating life event prediction and maintaining he predictor model used by the recommendation platform is not merely invoking ML at a high level to achieve a business objective. The specific ordered data pipeline in which recommendation engine’s life event predictor model is trained using transaction data, that is processed by “cleaning and formatting” transactional data, performing encoding for categorical columns, performing “factorization for numerical columns”, features selected form preprocessed data by performing feature importance using “random forest classifier” for identifying a non-linear relationship between features and a target variable, selected features are converted into vectors, model executed on vectors to generate event prediction and prediction informs identification and presentation of compatible services. Applicant argues the ordered sequence described above constrains how the event prediction is generated from transactional data. Eligibility arises from the cooperations between these operations as an ordered combination. The examiner respectfully disagrees with applicant’s argument. The operations above do not recite any ordered combination of a particular technical process or for a process to improve the operations/capability of technology or provide a solution to a problem in the technology itself or its operations. Rather the combination merely uses generic technology to collect, process data for analysis to output a recommendation based on a calculated predication based on transactional data. The rejection is maintained.
In the remarks applicant points to the previous Office action which determined that similar to Intellectual Ventures the claim limitation provide result oriented solution with insufficient detail for how a computer performs the operations. Applicant argues the amended limitations provide a specific technical means by which data is prepared and by which features are selected for input into a predictor model, rather than results of transforming data into a vector representation. Applicant argues the data processing pipeline recite updating the stored user profile based on user activity, performing batched training, selectively deploying a predictor model. The limitations define a particular manner in which predictions are generated and used by the recommendation platform, thereby integrating any alleged abstract idea into a practical application. Applicant’s argument is not persuasive. The specification nominally mentions “one-hot encoding” and “factorization for numerical columns” (para 0050) and is silent with respect to any particular technical process. For examination purposes, the examiner is interpreting the term by its ordinary meaning in the art which is “encoding data into categorical variables” into a binary matrix which is commonly applied in many machine learning algorithms that require numerical input. (see articles- “Encoding Categorical Variables: One-hot vs Dummy Encoding” by Pramoditha (2021); Data Science in 5 Minutes: What is One Hot Encoding? by Systems (2021): Introduction to One-Hot Encoding by Yanghai (2021); Ordinal and One-Hot Encodings for Categorical Data by Brownlee (2020); How to One Hot Encode Sequence Data in Python by Brownlee (2019); Non-Negative matrix factorization (NMF) on mixed data using 1-hot encoding by Doedork (2018); “How to one-hot-encode factor variables with data.table? by Stackoverflow (2016); US Pub No. 2022/0044133 A1 by Otto et al -para 0033, para 0056, para 0061; CA 3174372 A1 by Jain et al). Nominally mentioning a process commonly applied in machine learning processes of converting data into numerical values for input into machine learning for machine learning data processing does not qualify under step 2A prong 2 or 2B. With respect to the “batch training”, the specification nominally mentions “the event predictor 54a is made (using the different models 55, 57) as a combined input 300a to the service matching (implemented by the recommendation engine 54b) which is based on user preferences 101a; 5) 30 batched training of Life event predictors 300,” (para 0045) and “batch training architecture, once sufficient amount of data is collected, the model can be trained” (para 0050) without any details or disclosure of technical implementation or details on “batch training”.
The amended limitations recite:
preprocessing at least a portion of the user transactional data by cleaning and formattinq the user transactional data,
performing one-hot encoding for categorical columns, and performing factorization for numerical columns;
performing feature selection on the preprocessed user transactional data, comprising performing feature importance using a Random Forest Classifier for identifying a non-linear relationship between features and a target variable…
performing batched training of the life event predictor model with new data collected in response to the activity of the user in the application in predefined intervals: and
deploying the life event predictor model trained in the batched training for use by the recommendation engine in response to determining that the retrained life event predictor model trained in the batched training performs better than a previously deployed life event predictor model in at least one performance metric
The rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments are moot in light of the new ground of rejection that was necessitated by Applicant's amendments. Based on an updated search of the art, a new reference was used in the rejection below
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 and 3-19 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1 and 3-17:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recite method steps (1) obtaining profile data (2) comparing profile data (3) training the model using data (4) preprosessing …data by cleaning and formatting …data, performing one-hot encoding for categorical columns and factorization for numerical columns (5) performing feature selection on data comprising performing feature importance using Random Forest Classifier (6) transforming data by converting portions of data into vectors (7) converting selected features derived from ...user data into vectors to be input to …model (8) executing model using vectors to generate prediction (9) identifying one or more services compatible with life prediction based on comparing and responsive to life event prediction (10) presenting services (11) receiving a request from user for access to services (12) updating contents stored (12) performing batched training of predictor model with new data (13) deploying batched trained predictor model
When considered as a whole the claimed subject matter is directed toward receiving, analyzing life stage services and updating user life stage profile, which is a process directed toward abstract subject matter. It is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. The Specification is titled “System and Method for Applying User Data in Accessing of Institutional Products,” and discloses, in the Background section, that it can be difficult, in for users lacking financial literacy for financial institutions to have good relationship terms when in circumstances of wealth transfer events” (Spec. ¶1-2). The specification states that the focus of the invention is to provide a method for applying user data for providing services (spec ¶ 5) by comparing user profile data to different potential life stages in order to identify one or more life stage services (spec ¶6).
Such concepts can be found in the abstract category of marketing or sales activities. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include “recommendation engine being a machine learning engine”, “predictor model”, a “user interface of a user device” and “user device”.
The “obtaining …data pertaining to the user of a network system” is not tied to any technology for performing the operation. The additional element “user device” is applied to perform the “receiving a request” and “user interface” is applied at a high level lacking technical details to perform the “presenting…services” step. The additional element “user device” is applied at a high level lacking technical details to perform the operation “receiving a request’. According to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
The additional element recited in the claim beyond the abstract idea includes “a recommendation engine being a machine learning based engine…comprising …predictor model” used for “comparing the user profile data to a plurality of different …life stages” where the predictor model is used to implement “comparing life event utilizing transactional data in order to generate a life event prediction of current/upcoming life event” “utilizing transaction data” which is merely applying technology for use in analyzing data in order to predict a commercial outcome.
The claim limitations recite the step “training…the predictive model using …historical transaction data…and associated life-event information”, “preprocessing …data by cleansing and formatting …data, performing one-hot encoding”, “performing feature selection on …data…using random forest classifier”, “transforming a portion of …data …into vectors”, “executing …model using vectors”, “identifying one or more services”, “updating contents of stored user profile”, and “deploying the life event predictor model” steps are not tied to any technology and lacks technical disclosure. The wherein clause does not further limit the technical operation of the “updating” step instead merely limits the data acted upon.
The functions are is recited at a high-level of generality such that it amounts to no more than applying the exception using generic computer components. Taking the claim elements separately, the operation performed by the method at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter is so high level that any generic programming could be applied and the functions could be performed by any known means. Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-2 are directed toward collecting a data and using a recommendation engine comprising a model applied training a model and vectorizing data obtained by applying a learning machine for comparing data which amounts to no more than mere instructions to analyze data using generic technology. The combination of limitations 1-2 and 3 are directed toward training a model using transactional data without any technical details, where the model is trained for use in analyzing data directed toward a business process. The combination of limitations 4-7 is directed toward preprocessing data for input into a learning algorithm for analysis-mere data manipulation. For data, mere “manipulation” of basic mathematical constructs [i.e.,] the paradigmatic ‘abstract idea,’" has not been deemed a transformation. CyberSource v. Retail Decisions, 654 F.3d 1366, 1372 n.2, 99 USPQ2d 1690, 1695 n.2 (Fed. Cir. 2011) (quoting /n re Warmerdam, 33 F.3d 1354, 1355, 1360 (Fed. Cir. 1994). Whether the transformation is extra-solution activity or a field-of-use (/.e., the extent to which (or how) the transformation imposes meaningful limits on the execution of the claimed method steps). A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application). Mayo, 566 U.S. at 76, 101 USPQ2d at 1967. The Supreme Court disagreed, finding that this step was only a field-of-use limitation and did not provide significantly more than the judicial exception. /d. See MPEP § 2106.05(g) & (h).
The combination of limitations 1-7 and 8-10 is directed toward analyzing the data received and preprocessed in limitations 1-7 using a generic algorithm to identify services compatible with a life event prediction and outputting the results for use in a business practice.
The combination of limitations 11-14 are directed toward receiving a request from a user to access services, updating contents of user profile to include user activity with services and performing batched training of the predictor model with new data. The specification para 0051 discloses batch manner as “once sufficient amount of new data collected the model can be trained” and discloses a “batch training architecture”. The common meaning of “batched training learning architecture” refers to machine learning training where the model is trained on fixed complete preprocessed datasets in batches rather than continuously updated with new data. The claim limitations merely apply known technology to apply data for “training” learning models for use in analyzing business services. The combination of limitations 1-14 and 15 are directed toward deploying models for use by the recommendation engine for analyzing data for predicting services for life events- applying technology for a business practice. The wherein clause merely limits the content used in updating content as data associated with other users of different user types which does not further limit the technical process of “updating”.
Accordingly the combination of limitations 1-10 is to apply technology to receive data, analyzing data and output results for use in analyzing a commercial activity. The combination of limitations 1-10 and 11-15 is directed toward receiving a request to access more services, update provide and user activity content for analysis that is applied in training the predictor model and then deploying the predictor model for use in response to determining trained model performs better than previous model in determining predicted life events – which is directed toward applying at a high level a predictive model to analyzed updated user profile data based on user interaction with respect to the request to access services, which is directed toward applying technology to analyze new data received for use in training the model in a batched manner which is merely adjusting the model to reflect new data received. The deploying step in combination with limitations 1-14 is directed toward deploying the trained model in response to determining the trained model performs better than the previous model, in the analysis of data- which is directed toward applying technology at a high level to receive and respond to new data analyzed for use in a commercial activity where a model is trained on new data and replaces the model that was generated with the older data - . applying technology to analyze updated data for a commercial activity. The combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. This is because the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The functions recited in the claims recite the concept of retrieving data, analyzing/identifying services and user profile and updating user profile data based on user activity a sales/marketing activity which is a process directed toward a business practice. The claim provides no technical details regarding how the “embedding” operation is performed. Instead, similar to the claims at issue in Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332 (Fed. Cir. 2017), “the claim language . . . provides only a result-oriented solution with insufficient detail for how a computer accomplishes it. Our law demands more.” Intellectual Ventures, 850 F.3d at 1342 (citing Elec. Power Grp. LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016)).
The claim limitations do not claim the machine learning model technique itself but instead the application of the machine learning technique to a specific context of life event predictions. Because the machine learning limitations are no more than “broad, functionally described techniques with an expected outcome for analyzing data for predicting life events, the claim limitations are not directed toward the learning technology for improvement or any other indications of patent eligibility, doing no more than apply established methods of machine learning to new data environment. Accordingly the claim limitations do not focus on specific improvements in computer capabilities, but instead focus on a process that qualifies as an abstract idea for which computers are invoked as a tool.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using an application. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments apply what generic computer functionality in the related arts. The steps are still a combination made to update user profile based on user activity when identifying and accessing life stage services and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an 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 because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include “recommendation engine being a machine learning engine”, “predictor model”, a “user interface of a user device” and “user device”.
The additional element recited in the claim is user interface applied to present data and user device applied to receive request perform well understood routine computer processes. The claimed recommendation engine comprising ML model used to compare data and output result merely applies technology for data analysis without significantly more. The specification discloses the user interface provides access to one or more services for presentation to the user (para 0026) or applied for linking services to educate users alongside the definition provided in the user interface, which are ordinary functions of user interface technology.
The specification discloses vectorizing data for model training using off-the-shelf data transformers and that the training of the model “involves” known generic mathematical techniques “K-means algorithm” combined with services which can perform “ELBOW” and Silhouette analysis (para 0053). The specification further list other off-the-shelf known learning algorithms and features without any attempt to describe specific improvements in training or learning capabilities/technology or solutions to problems rooted in learning technology (see para 0043 “algorithm can be implemented using technology such as but not limited to Public Python Libraries (e.g. Scikit Learn, Pandas, Numpy, NLTK, Collections, Matplotlib, Seaborn, radar, boto3, imblearn, 20 XGBoost, tqdm, botocore, pyyaml) and machine learning algorithms such as but not limited to: XGBoost Machine Learning Model with SMOTE to handle the class imbalance and feature importance using Random Forest algorithm to select the most important features and feed to the model 59 to predict a user's life event 300 by the predictor 54a; K-Means Clustering algorithm with NLTK data descriptive analysis along with Google Gensim word2vec model to cluster the institution in-house and third party partnered services 90a,b,c,d into k clusters for content-to content recommendation via the model 57a in order to predict 302 a selected set (e.g. top 5) relevant services 90a,b,c,d. K that can be chosen with Elbow analysis followed by Silhoutte analysis; and Cosine similarity scoring function with NLTK data descriptive analysis along with gensim word2vec model for an optimized financial glossary search engine for the get smart guide 54e.”. The claim limitations recite generic “training” function using “historical data” and “life event information” which is merely limiting the data acted upon in the analysis applied which applying the “training”. This is equally true with respect to the “transforming…data….by converting …features derived …into vectors”. The specification describes applying off the shelf technology for performing the vectoring of the data and the claims recite high level functions with an expected outcome.
Furthermore, in machine learning technology is it a common process to preprocess raw data into vectors for input into machine learning models. The specification is silent with respect to a batch training techniques, nominally mentioning batched training (see para 0045, para 0050) which merely discloses the use of such model production for a business application. With respect to the “deploying” limitation, the specification nominally mentions comparing new model to existing model pushing to production only if it performs better (para 0053) lacking any technical disclosure.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. The analysis concludes that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The claimed subject matter is analogous to Trading Technologies International, Inc, v IBG LLC Interactive Brokers LLC, where the court determined the claims did not recite a technological feature that is novel and unobvious over the prior art because the patent indicates that the claimed technological features are known technologies. The current application similarly does not recite a technical solution to a technical problem because the problem disclosed in the is the need to predict and provide recommendations related to predicted life services based on the analysis of user transactional data, which is business problem, not a technical one. With respect to the “training” of the model as claimed, the claimed subject matter similar to Recentive Analytics, Inc v Fox Corp, simply uses machine learning to perform a task. The court found that machine learning is now viewed as a common tool rather than a technological breakthrough. The current specification fails to clearly describe a technical problem or explain how the claimed invention improves the “functioning” of the technology, rather than the outcome of applying the technology for analysis. Furthermore, similar to Recentive, the “training” steps i) receiving input data ii) generate a predictive model iii) collecting data new data applied to training, where the specification teaches the learning model trained using new data, makes clear that any suitable machine learning technique can be applied which allows data to be inputted and changed based on changes in data. According to Recentive “The requirements that the machine learning model be “trained” in the Machine Learning Training patents do not represent a technological improvement… Using updated data for user in training are incident to the very nature of machine learning. See, e.g., Opposition Br. 9 (“[U]sing a machine learning technique[] . . . necessarily includes [an] iterative[] training step . . . .” (internal quotation marks and citation omitted)); Transcript at 26:21–24 (“[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input”).”. Accordingly, the examiner maintains that similar to Recentive the claimed ML limitations do not transform the claimed subject matter into patent eligibility.
With respect to the “preprocessing” of data for use in machine learning by “cleaning and formatting the user transactional data”, is one of the most basic elements of machine learning application. And where the formatting is performing “one-hot encoding for categorical columns, and performing factorization for numerical columns”, which is commonly applied in many machine learning algorithms that require numerical input. The specification discloses high level use of the recommendation engine lacking technical disclosure. Although the specification provides a laundry list of different learning algorithms, the specification makes clear that these algorithms are merely options that can be applied for performing the abstract idea:
With respect to “batch training” the specification fails to provide any technical details as to the batch training process and nominally mentions the process.
[0050]…In terms of model monitoring and comparison, according to the batch training architecture, once sufficient amount of new data is collected, the model can be trained… Finally, the model 55, 57 is evaluated by considering the recall value in the confusion matrix as False negatives are considered to be more important in this scenario. In terms of model monitoring and comparison, according to the batch training architecture, once sufficient amount of new data is collected, the model can be trained.
See also WO 2022/261766 A1 “Machine learning related domains; Image segmentation, object detection, regression, clustering, anomaly detection, ranking, recommendation, forecasting, dimensionality reduction, reinforcement learning, semi-supervised learning, unsupervised learning, active batch selection methods for faster machine learning model training, adversarial learning, dual learning, distributed machine learning, transfer learning or any other machine learning related task”; US Pub No. 2021/0397610 A1 by Singh et al.-para 0088, US Pub No. 2024/0320267 A1 by Ram – para 0846, para 1004, para 1007; US Pub No. 2022/0269978 A1 by Edwards et al.-para 0030, para 0033, para 0036, para 0051 wherein the prior art teaches “the batch historical data may be utilized to train or re-fit the machine learning model”; US Pub No. 2021/0224684 A1 by Sarkar -para 0084, para 0122)
The specification nominally mentions “one hot encoding” and “data preprocessing” and is silent with respect to any indications of patent eligibility when considered under Desjardins requirements.
[0050]… The ML pipeline can consists of components 25 namely Data preprocessing and transformation, Feature selection, Model Training & Evaluation and Model monitoring and comparison. For data preprocessing and transformation, the clients' data 101 is imported from S3 using credentials stored as part of Vault. The incoming data 101 is cleaned, formatted, dropped similar data points, performed one hot encoding for categorical columns and factorization for numerical columns.…”
(see related evidence and articles with respect to “one hot encoding”- “Encoding Categorical Variables: One-hot vs Dummy Encoding” by Pramoditha (2021); Data Science in 5 Minutes: What is One Hot Encoding? by Systems (2021): Introduction to One-Hot Encoding by Yanghai6 (2021); Ordinal and One-Hot Encodings for Categorical Data by Brownlee (2020); How to One Hot Encode Sequence Data in Python by Brownlee (2019); Non-Negative matrix factorization (NMF) on mixed data using 1-hot encoding by Doedork (2018); “How to one-hot-encode factor variables with data.table? by Stackoverflow (2016); US Pub No. 2022/0044133 A1 by Otto et al -para 0033, para 0056, para 0061; CA 3174372 A1 by Jain et al).
(see related evidence with respect to preprocessing data for training models-US Pub No. 2020/0089650 A1 by Sharma et al-Fig. 2 wherein the prior art illustrates conventional data preprocessing; para 0003-0004, para 0007, para 0013, para 0019, para 0039, para 0041, para 0044, para 0049; US Patent No. 7,499,897 B2 by Pinto et al. -Col 15 lines 15-47, Col 16 lines 16-24, Col 21 lines 15-29; US Pub No. 2013/0226842 by Chu et al-para 0009, para 0047, para 0049)
[0032]… For premium existing institution clients (an embodiment of user type), transactions data 101b can be leveraged by the system 99 to build an immersive user experience recommendation by combining with the state-of-the-art machine learning algorithms of the recommendation engine 54b and the life event predictor 54a. Further,
15 based on the extensive user research, it is understood that there is a vacuum when it comes to financial jargons and the need for financial education. Hence, the smart guide 54e is used by the user for providing financial education and recommending the relevant services 90a,b,c,d by the recommendation engine 54b based on the searches performed by the user when using the smart guide 54e.
[0033] In view of the above, there can be multiple categories / types of users and recommendations, such as but not limited to: 1) Cold Start - Visitors which involves no to little personalization but can browse and search through the home page to find the most relevant services; 2) Semi Cold Start - Visitors which involves those who have signed up and can leverage questionnaire data 303 to understand the life event 300 and provide matching services by the recommendation engine 54b along with content-to-content 57a similarity (and/ or collaborative 57b similarity) based on user behavior 101a and preferences 101a collected within the application 91 based on user interaction with the consent 103 served on the user interface 212 by the microservices 54 in conjunction with the services 90a,b,c,d selected (by the user and/or by the system 99) during operation of the application 91; 3) Warm Start - institution clients which involves personalized service recommendation 302 based on the life event 300 derived from exclusive institutional data 101b, 101c. Like semi-cold start, the application 91 can recommend content-to-content 57a similarity (and / or collaborative 57b similarity) for the prediction 302, based on user behavior 101a for enhanced user experience. It is recognised that the prediction 302 based on real-time user activity 101a can be performed subsequent to the initial prediction 302 provided in response to use of data 101 other than the data 101a.
[0036]…In conjunction with manipulation of the application 91 by the user, the system 99 can apply a machine learning recommendation engine 54b (see Figure 3) to generate a recommendation I prediction 302 of the Right Services (e.g. 90a,b,c,d) at the right time from a curated platform of services 90 to meet the identified needs
301. The Platform of Services 90 can be curated from best in class institution (holding the user profile 101 and application interactions 101a of the user) and third party partner services 90a,b,c,d to address the identified needs 301 of each identified life event and life state 300. In addition, the application 91 can be used to provide just in time financial education by having a searchable smart guide 54e that can explains complex financial terms in simple language and link the financial terms to associated services 90a,b,c,d in the platform of services 90, as further described below. It is recommended that user interactions 101a (e.g. in selecting offered services 90a,b,c,d as well as/ or selecting smart guide 54e content) can be generated (and identified by the application 91) and then used by the system 99 to generate further recommendation(s) /prediction(s) 302 of the services 90a,b,c,d available, for example, in the platform 90.
[0037]… Further, the application 91 toolbox can contain trusted in-house and third-party services 90a,b,c,d that users can utilize with confidence, as provided by the recommendation engine 54b. These services 90a,b,c,d can extend from "traditional" banking and offer a variety of useful applications for financial services, expense management, health and wellness, care giving, travel, and retail shopping, as examples only…. see Figure 2) through one or more Service Matching Model (SMM) of the recommendation engine 54b, also further discussed below by example
10 operation, which can inhibit the task of navigating the general internet 102 for the correct services 90a,b,c,d by the user.
[0040] The service recommendation engine 54b can provide recommended services 302 based on stored user information 101, 101a through its connection with the data science models 59 that are hosted with flask and S3, for example. Through the use of this model 59, the frontend 50 can continue to provide data (e.g. user interactions 101a) to the models 59 in order to make the recommendations 302 more accurate, and request new recommendations 302 both for individual users, and for a content-based matching algorithm 57a and/or a collaborative-based matching algorithm 57b and used to display similar services 90a,b,c,d when in the service details page of the application 91 on the user interface 202, as further described below.
[0041] The service information 54c microservice is used to retrieve specific details about the services 90a,b,c,d available with application 91, as predicted I recommended 302 by the recommendation engine 5b. This microservice 54c can be decoupled from the service recommendation 54b microservice in order to facilitate scalability and provide for the case where service information can be obtained without having to involve the data models 59 or any sort of
10 recommendations 302. This microservice 54c can be heavily integrated with the stored service information of the services 90a,b,c,d in MongoDB (e.g. implementing the service platform 90), and can act as a bridge to serve specific tailored information from the database based on frontend 50 requests via the gateway 54.
[0043] Referring to Figure 4, the recommendation engine 54b algorithm can be implemented using technology such as but not limited to Public Python Libraries (e.g. Scikit Learn, Pandas, Numpy, NLTK, Collections, Matplotlib, Seaborn, radar, boto3, imblearn, XGBoost, tqdm, botocore, pyyaml) and machine learning algorithms such as but not limited to: XGBoost Machine Learning Model with SMOTE to handle the class imbalance and feature importance using Random Forest algorithm to select the most important features and feed to the model 59 to predict a user's life event 300 by the predictor 54a; K-Means Clustering algorithm with NLTK data descriptive analysis along with Google Gensim word2vec model to cluster the institution in-house and third party partnered services 90a,b,c,d into k clusters for content-to content recommendation via the model 57a in order to predict 302 a selected set (e.g. top 5)
relevant services 90a,b,c,d. K that can be chosen with Elbow analysis followed by Silhoutte analysis; and Cosine similarity scoring function with NLTK data descriptive analysis along with gensim word2vec model for an optimized financial glossary search engine for the get smart guide 54e.
[0045] Further considerations for generation of the event stage prediction 300 can 15 include: 1) a cold start case for the recommendation system 54b where there is limited to no information (e.g. data 101) about the user. This cold start case is solved by using life events defined through a gamified questionnaire (e.g. set of queries 303 posed to the user on the user interface 212 via the predictor 54a) to understand the client's current life stage; 2) Clustering of
the institution's partnered and third party services 90a,b,c,d using K-Means Clustering with 20 ELBOW and silhouette analysis to identify the right K; a combination of life event predictor model 54a along with the content-to-content similarity and service matching model 57a based on life event labels that can return contents 103 from various sectors based on user preferences (e.g. leveraging application interactions 101a); 3) combining user preferences 101a with user behavior 101a within the application 91 (e.g. services 90a,b,c,d chosen) for manipulation by the content-to-content filtering algorithm 57b; 4) combining the architecture of mixed hybrid recommendation system 500 (see Figure 5) with a cascade recommendation system in which the output of a plurality (e.g. a pair) of life event predictors 300 by the event predictor 54a is made (using the different models 55, 57) as a combined input 300a to the service matching (implemented by the recommendation engine 54b) which is based on user preferences 101a; 5)
30 batched training of Life event predictors 300, Content based filtering 57a, collaborative filtering 57b and/or Smart guide 54e with new data 101a collected in response to user activity in the application 91 in pre-defined intervals; and 6) diversity in content 102 displayed to users to inhibit repetition by adapting user behaviors of selecting services of interest ( e.g. via monitoring of the application activity 101a). IT is also recognised that the recommendation engine 54b and the event predictor 54a have access to datasets such as but not limited to: institution in-house and
third-party partners services dataset (e.g. profile data 101); Life Event Predictor Model's 55, 57 dataset (e.g. Retirement model dataset, having a baby model dataset, etc.); and/or a Glossary for Financial terms dataset incorporated in the get smart guide 54e.
[0046] Referring to Figures 3, 4, 5, the mixed hybrid recommendation system 500, as an embodiment of the system 99, can be implemented as follows. The first stage of the recommendation engine 54b is to identify the life stage 300 of a person/user using the profile data 101, as well as for example any life event/ stages 300 of associated/linked user types (e.g. of the first user such as an identified benefactor - beneficiary relationship between the first user and the second user as reflected in the user profile data 101). Based on the life event 300, a set of relevant services 90a,b,c,d can be predicted with using the profile data 1010 and /or user preferences 101a (also referred to as historical interactions, application interaction, etc.). The system 99 can use client's demographic 101c, historical interactions 101a, last three-month transaction history (e.g. transaction data 101b) optionally coupled with unique retirement plan and time to achieve retirement plan as features. For non-institutional clients, the application 91 can use the questionnaire results 303 to identify the life stage 300. Example life events / stages 300 can be as
follows (which have matching services 90a,b,c,d as mapped via the recommendation engine 54b): First Job - Entertainment, Electronics & Digital, etc.; Job Loss - Job Aggregators, Skill development platform, etc.; Getting Married - Apparels, Aggregated Retails, etc.; Having a baby - Baby Accessories, Baby Gear, Finance, etc.; Retirement - Health & Wellness, Finance, Travel, 25 Executor services, etc.; and Caregiver - Health & Wellness, Aggregated Retail, etc.,
[0047]… Based on the specific life phase of the client's life 13 stage 300 and historical client preferences 101a, an intuitive way of service matching is performed by the recommendation engine 54b to offer the clients with best-in-class services at the right time.
[0050] Referring to Figure 7, life event predictor 54a is one of the modules associated with 20 of the application's 91 recommendation system 54b (e.g. operated as a hybrid system - see Figure 5). As discussed, the application 91 can have a plurality (e.g. one or more pairs) of life event predictor models 55, 57, one for predicting retirement 55 and other one for predicting whether a user is going to have a baby or not 57. A machine learning (ML) pipeline can be used for generating the life event predictor results 300 is as follows. The ML pipeline can consists of components
25 namely Data preprocessing and transformation, Feature selection, Model Training & Evaluation and Model monitoring and comparison. For data preprocessing and transformation, the clients' data 101 is imported from S3 using credentials stored as part of Vault. The incoming data 101 is cleaned, formatted, dropped similar data points, performed one hot encoding for categorical columns and factorization for numerical columns. For feature selection, in general, since the number of people retiring or going to have a baby (examples of stages 300) are more or less in
number compared to the whole population distribution, there can exist a class imbalance issue. Hence, the application 91 can use SMOTE analysis to balance classes by performing oversampling yet maintaining a similar distribution, so that it represents the actual ground truth. Feature 5 importance can be performed using Random Forest Classifier as it can help in identifying non -linear relationship between features and target variable. Then, it can be plotted as a bar graph using matplotlib with descending values, which is used to perform visual analysis and set a
threshold where the values drop massively. In addition to this, dimensionality reduction is can be performed using correlation matrix to identify the topmost important features. For model 10 selection, training and evaluation, the application 91 can use multiple models like Logistic Regression, Decision Tree and XGBoost, compared their metrics and finally chose XGBoost as the ML model. XGBoost can add trees to the algorithm until there is an improvement in the metrics and stops when there is no betterment. Hyper parameter tuning can be performed using grid search
CV and XGB Classifier with 'recall' as scoring to identify the right weights to be used as this is an imbalanced class, thus not giving importance to specific features which eventually will affect the prediction. Finally, the model 55, 57 is evaluated by considering the recall value in the confusion matrix as False negatives are considered to be more important in this scenario. In terms of model monitoring and comparison, according to the batch training architecture, once sufficient amount of new data is collected, the model can be trained. In order to have the best model in production, we can do monitoring for model 55, 57 performance with sample data and compare the existing
model 55, 57 with new model metrics and is pushed into production only if it performs better than the previous model. The generated data and models can be stored in an S3 bucket. The S3 bucket's secrets are stored in Vault and imported as OS variables when accessing it.
[0054]… It is also recognised that in view of available users and user interactions data, the recommendation engine 54b can offer collaborative filtering 57b and recommend users with services 90a,b,c,d which are used by users with similar interests to the user employing the application 91.
[0056] In view of the above, the models 55, 57, 57a,b, 59 used by the system 99 (e.g. which can be referred to as a personalization engine) can be employed by the application 91 to offer the 30 best-in-class personalized recommendations 302 of the services 90a,b,c,d based on the profile data 17 101 known about the users and user behavior/preferences data 101a marked by viewed, searched and selected content 103 within the system 99. This can be provided by machine learning (ML) algorithms like XGBoost, Natural Language Processing (NLP) algorithms like Gensim coupled with cosine similarity cost function. Accordingly, the application 91 can recommend 302 a variety of services 90a,b,c,d based on the previous clients' preferences 101a within the application 91. In terms of diversity, it is facilitated that a range of services 90a,b,c,d can be recommended without tampering the content similarity, as discussed by example above.
0059] In having access and manipulation of the data 101, 101a, the recommendation engine evaluation 54b can be performed in a variety of ways, as desired by the configuration of the system 99, in particular in view of the classification(s) (e.g. life event 300) of the user. In this regard, it is recognised that model evaluation can be an integral part of the model development process. The evaluation criteria can be focused on the content relevance and variety during content based filtering, on-point definition and displaying relevant services for smart guide search and
predicting the right life event of the clients. In the case of life event prediction, which is a classification problem, predicting the retirement and having a baby life stage event of clients who are actually falling in the respective category is relevant. Hence, false negatives are crucial. A great approach to assess this scenario is through considering the recall value of the confusion matrix. The life event predictor model 54a employed by the application 91 has a recall value of 94% for the data set 101, 101a used. The application 91 employs the K-Means clustering model combined with the cosine similarity cost function for the accurate and better prediction top N recommended
services 90a,b,c,d that is similar to the one the user has already chosen (e.g. as represented by the user data 101a identified by the system 99 during use of the application 91 by the user). For evaluating K-Means clustering, the application is configured by performing: calculation of spatial distance between centroids; and Lowes distance ratio.
The ML technology recited in the claim as the specification demonstrates is conventional and can be applied using any known ML techniques. The application employs only generic system components and focuses on the data analyzed in order to predict a recommendation for services.
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 3-17 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 3 is directed toward utilizing similarity content-content filter and collaborative similarity filter for comparing analyzing user profile data and collaborative similarity filter analyzes other users having similar interest- a business process. Dependent claim 4 is directed toward obtaining data used to generate user demographic data- insignificant extra solution activity of outputting data and a business practice. Dependent claim 5 is directed toward data content displayed-non-functional descriptive subject matter for a business practice. Dependent claim 6-7 are directed toward obtaining data-insignificant extra solution activity of data gathering. Dependent claim 8 is directed toward data updated reflecting financial changes – business practice. Dependent claim 9 is directed toward additional content used to identify services- a business practice. Dependent claims 10-11 and 14 are directed toward different user types – business practice. Dependent claim 12 is directed toward determining user associated with other users- a business practice. Dependent claim 13 is directed toward utilizing predictor to implement comparing services compatible - business practice and well understood technology. Dependent claim 15 is directed toward inviting other users to join by navigating pages to provide selections for inviting users or supply data- applying technology in a conventional manner for a business process. Dependent claim 16 is directed toward sending user a recommended service – a business practice. Dependent claim 17 is directed toward platform services is provided by financial institution for hosting banking services- well understood application of technology.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2-17 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claim 18:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a system, as in independent Claim 18. Such systems fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The functions of system claim 18 corresponds to steps of method claim 1. Therefore, claim 18 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The functions of system claim 18 corresponds to steps of method claim 1. Therefore, claim 18 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include a “system” comprising “set of stored instructions for execution by one or more processors” “recommendation engine being a machine learning engine”, “predictor model”, a “user interface” and “user device”. The claimed recommendation engine comprising ML model used to compare data and output result merely applies technology for data analysis without significantly more. The specification discloses the user interface provides access to one or more services for presentation to the user (para 0026) or applied for linking services to educate users alongside the definition provided in the user interface, which are ordinary functions of user interface technology.
The specification describes the system elements as:
[0025] Referring now to FIG. 1, there is shown a computer network 100 that comprises an example embodiment of a system 99 for manipulating and maintaining the user profile 101. More particularly, the computer network 100 comprises a wide area network 102 such as the Internet to which various user devices 104 (for example a mobile device), an ATM 110, and data center 106 are communicatively coupled. The data center 106 comprises a number of servers 108 networked together to collectively perform various computing functions. For example, in the context of a financial institution such as a bank (one example of an institution), the data center 106 may host online banking services that facilitates users to log in to those servers 108 using user accounts that give them access to various computer-implemented banking services, such as online fund transfers. For example, in the context of a financial institution such as a bank, the data center 106 can host an online service application 91 that facilitates users to log in to those servers 108 using user accounts, for example, that give the user access to various computer implemented user profile functionality, such maintaining of user profiles IO I and user application interactions 101a, as well as access to served content 103 (e.g. supplied by a service platform 90 for services 90a,b,c,d). For example, the user service platform 90 can be accessed via the network 102 using a client - server model, e.g. the service application 91 executed on the user device 104 (or otherwise hosted on the system 99) that communicates with the service platform 90 hosted on one or more of the servers 108.
[0035] Referring now to FIG. 2, there is depicted an example embodiment of one of the servers 108 that comprises the data center 106. The server comprises a processor 202 that controls the server's 108 overall operation. The processor 202 is communicatively coupled to and controls several subsystems. These subsystems comprise user input devices 204, which may comprise, for example, any one or more of a keyboard, mouse, touch screen, voice control; random access memory ("RAM") 206, which stores computer program code (e.g. service platform 90, microservices 54, user interface 212 embodied as the application 91, etc.) for execution at runtime by the processor 202; non-volatile storage 208, which stores the computer program code executed by the RAM 206 at runtime; a display controller 210, which is communicatively coupled to and controls the display 212; and a network interface 214, which facilitates network communications with the wide area network 104 and the other servers 108 in the data center 106. The non-volatile storage 208 has stored on it computer program code that is loaded into the RAM 206 at runtime and that is executable by the processor 202. When the computer program code is executed by the processor 202, the processor 202 causes the server 108 to implement a method for manipulating the user profile 101 and application interactions 101a, such as is described in more detail below. Additionally or alternatively, the servers 108 may collectively perform that method using distributed computing. While the system depicted in FIG. 2 is described specifically in respect of one of the servers 108, analogous versions of the system 99 can also be used for the user devices 104.
[0063] For example, applying the user profile data pertaining to a user of a network system 99 of the institution for providing services 90a,b,c,d to a user from a platform of services 90. For example: obtaining 702 user profile data lOl pertaining to the user of a network system 99 of an institution; comparing 704 the user profile data 101 to a plurality of different potential life stages 300 in order to determine a selected life stage 300, 300a; identifying 706 one or more services 90a,b,c,d from the platform of services 90 based on the selected life stage 300, 300a; identifying 5 708 the one or more services 90a,b,c,d to the user via a user interface 212 of a user device 104; receiving 710 a request 103a from the user through the user device 104 for access to the one or more services 90a,b,c,d; and updating 712 contents of the user profile 10 to include or otherwise be associated with additional profile content 101a related to activity 103a of the user with the one or more services 90a,b,c,d.
[0069] The processor used in the foregoing embodiments may comprise, for example, a processing unit (such as a processor, microprocessor, or programmable logic controller) or a microcontroller (which comprises both a processing unit and a non-transitory computer readable medium). Examples of computer readable media that are non-transitory include disc-based media such as CD-ROMs and DVDs, magnetic media such as hard drives and other forms of magnetic disk storage, semiconductor based media such as flash media, random access memory (including DRAM and SRAM), and read only memory. As an alternative to an implementation that relies on processor-executed computer program code, a hardware-based implementation may be used. For example, an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), system-on-a-chip (SoC), or other suitable type of hardware implementation may be used as an alternative to or to supplement an implementation that relies primarily on a processor executing computer program code stored on a computer medium.
The operations of system claim 18 corresponds to steps of method claim 1. Therefore, claim 18 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
Therefore, Claim 18 has been analyzed, evidence provided and rejected as previously discussed with respect to claim 1.
In reference to Claim 19:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a computer readable medium, as in independent Claim 19. Such mediums fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of medium claim 19 corresponds to steps of method claim 1. Therefore, claim 19 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and Methods of Organizing Human Activity previously discussed with respect to claim 1.
STEP 2A Prong 2: The instructions of medium claim 19 corresponds to steps of method claim 1. Therefore, claim 19 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a computer readable medium comprising a set of stored instructions for execution by one or more computer processors, a recommendation engine being a machine learning based engine comprising a predictor model, a user interface and user device–is purely functional and generic. Nearly every computer readable medium for implementing a process will include a “set of stored instructions for execution by one or more computer processors” capable of performing the basic computer functions -of “obtaining”, “comparing”, “identifying”, “identifying via a user interface”, “receiving”, “updating” processes recited in the claim limitations. As a result, none of the hardware recited by the medium claims offers a meaningful limitation beyond generally linking the use of the business method to a particular technological environment, that is, implementation via computers.
The functions of system claim 18 corresponds to steps of method claim 1. Therefore, claim 18 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
The specification discloses the system as conventional technology applied to implement the abstract idea:
[0069] The processor used in the foregoing embodiments may comprise, for example, a processing unit (such as a processor, microprocessor, or programmable logic controller) or a microcontroller (which comprises both a processing unit and a non-transitory computer readable medium). Examples of computer readable media that are non-transitory include disc-based media such as CD-ROMs and DVDs, magnetic media such as hard drives and other forms of magnetic disk storage, semiconductor based media such as flash media, random access memory (including DRAM and SRAM), and read only memory. As an alternative to an implementation that relies on processor-executed computer program code, a hardware-based implementation may be used. For example, an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), system-on-a-chip (SoC), or other suitable type of hardware implementation may be used as an alternative to or to supplement an implementation that relies primarily on a processor executing computer program code stored on a computer medium.
The instructions of medium Claim 19 corresponds to the steps of method Claim 1. Therefore, Claim 19 has been analyzed and rejected as previously discussed with respect to claim.
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.
Claim(s) 1, 4-14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,467,663 B1 by Ocampo et al (Ocampo), in view of US Pub No. 2020/0089650 A1 by Sharma et al. (Sharma) and WO 2022/256802 A1 by Park et al. (Park) and further in view of JP 7440017 B2 by 山本 哲也 wherein the translation has been annotated by the examiner for mapping (JP 017)
In reference to Claim 1:
Ocampo teaches:
(Currently Amended) A method on applying user data for providing services to a user from a platform of services ((Ocampo) in at least Col 6 lines 22-58), the method comprising the steps of:
obtaining stored user profile data pertaining to the user of a network system of an institution ((Ocampo) in at least Col 5 lines 37-63 wherein the prior art teaches when receiving request from user device retrieves recipient profile data from database);
using a recommendation engine for comparing the user profile data to a plurality of different potential life stages in order to determine a selected life stage based on at least the user profile data ((Ocampo) in at least FIG. 4-5, FIG. 7; abstract wherein the prior art teaches using analytics engine for recommending products best aligned with recipient goals; Col 3 lines 21-32, lines 49-Col 4 lines 1-33, lines 49-64 wherein the prior art teaches analytics engine compares user profile and goals against allocation of funds resources to recommend one or more products when analyzing user profiles, Col 11 lines 55-67, Col 12 lines 38-Col 13 lines 1-9, Col 14 lines 57-Col 15 lines 1-5, Col 19 lines 7-36, Col 21 lines 4-59, Col 24 lines 20-42 wherein the prior art teaches engine analyzes user profiles to determine user goals used for comparison of available product available), the recommendation engine being a … engine comprising:
the life event predictor model utilizing user transactional data contained in the user profile data to generate a life event prediction of a current or upcoming life event of the user ((Ocampo) in at least Abstract; Col 2 lines 3-54, Col 3 lines 25-47, Col 4 lines 9-65, Col 11 lines 55-67, Col 26 lines 4-17);
…the life event predictor model usinq historical transactional data of a plurality of past users and associated life-event information to determine associations between the historical transactional data and respective life events ((Ocampo) in at least Col 2 lines 12-38, Col 3 lines 12-32, Col 4 lines 1-37, Col 6 lines 22-38, Col 25 lines 12-24);
executing the life event predictor model using the vectors to generate the life event prediction ((Ocampo) in at least FIG. 6; Col 9 lines 32-44, Col 15 lines 5-30, Col 23 lines 55-Col 24 lines 1-3 wherein the prior art teaches recently married, buying a home; Col 24 lines 20-64);
based on the comparing, and responsive to the live event prediction, identifying one or more services compatible with the life event prediction, from the platform of services based on the selected life stage ((Ocampo) in at least FIG. 6; Col 9 lines 32-44, Col 15 lines 5-30, Col 23 lines 55-Col 24 lines 1-3 wherein the prior art teaches recently married, buying a home; Col 24 lines 20-64 wherein the prior art teaches comparison of insurance products associated with user profile goals wherein the analysis engine selects one or more product offering to complement goals);
presenting the one or more services to the user via a user interface of a user device ((Ocampo) in at least FIG. 6; Col 10 lines 7-15, Col 14 lines 25-42, Col 24 lines 30-Col 25 lines 1-24 wherein the prior art teaches recommend engine present multiple products to fulfil goals);
receiving a request from the user through the user device for access to the one or more services ((Ocampo) in at least Col 13 lines 30-49, col 14 lines 25-42, Col 24 lines 65-Col 25 lines 1-24 wherein the prior art teaches user review products the recipients select one or more products and Col 24 lines 57-Col 26 lines 1-3, ); and
updating contents of the stored user profile to include additional profile content related to activity of the user with the one or more services ((Ocampo) in at least Col 3 lines 15-20, Col 4 lines 50-64, Col 9 lines 26-32, Col 12 lines 28-35, Col 13 lines 30-49, Col 16 lines 60-63, Col 20 lines 25-38, Col 23 lines 55-Col 24 lines 1-2, Col 25 lines 15-24); …
wherein said updating of the contents of the stored user profile includes both user content associated with the user of a first user type as well as other content associated with one or more other users of a second user type, such that the other content includes contact information of the one or more other users and at least one of financial information or life event status information of the one or more other users..((Ocampo) in at least FIG. 5, FIG. 7; Col 12 lines 1-38, Col 13 lines 22-56, Col 14 lines 4-21, lines 57-Col 15 lines 1-30; Col 18 lines 1-13, Col 23 lines 20-Col 24 lines 1-2)
Ocampo does not explicitly teach:
the recommendation engine being a machine learning-based engine comprising: the life event predictor model…
training the life event predictor model using historical transactional data of a plurality of past users and associated life-event information to determine associations between the historical transactional data and respective life events;
preprocessing at least a portion of the user transactional data by cleaning and formatting the user transactional data,
performing one-hot encoding for categorical columns, and performing factorization for numerical columns;
performing feature selection on the preprocessed user transactional data, comprising performing feature importance using a Random Forest Classifier for identifying a non-linear relationship between features and a target variable;
converting one or more selected features derived from the user transactional data into vectors to be input to the life event predictor model;
deploying the trained life event predictor model trained in the batched training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the batched training performs better than a previously deployed life event predictor model in at least one performance metric;
Sharma teaches:
training the life event predictor model using historical transactional data of a plurality of past users and associated life-event information to determine associations between the historical transactional data and respective life events ((Sharma) in at least Abstract; Fig. 4; para 0007, para 0016, para 0018-0020, para 0023-0025, para 0040-0042, para 0044, para 0049-0052, para 0054-0055)
preprocessing at least a portion of the user transactional data by cleaning and formatting the user transactional data, performing one-hot encoding for categorical columns, and performing factorization for numerical columns ((Sharma) in at least FIG. 2-3; para 0007-0008, para 0013, para 0019, para 0041-0042, para 0044, para 0049, para 0055);
performing feature selection on the preprocessed user transactional data, comprising performing feature importance using a Random Forest Classifier [XGBoost] for identifying a non-linear relationship between features and a target variable ((Sharma) in at least para 0005, para 0011-0013, para 0019, para 0044, para 0049);
converting one or more selected features derived from the user transactional data into vectors [numerical representation] to be input to the life event predictor model ((Sharma) in at least para 0004, para 0011, para 0022, para 0041, para 0046, para 0054; Table 2);
deploying the trained life event predictor model trained in the … training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the …training performs better than a previously deployed life event predictor model in at least one performance metric ((Sharma) in at least FIG. 1; para 0006);
Both Ocampo and Sharma are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Sharma teaches the motivation of preprocessing data and applying one hot encoding for use in machine learning analysis as machine learning models typically begins with data collection where data is preprocessed and transformation is applied to data before being fed into a model. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analytics engine process and use of Ocampo to include data preparation of data sets for input when training a model as taught by Sharma since Sharma teaches the motivation of preprocessing data and applying one hot encoding for use in machine learning analysis as machine learning models typically begins with data collection where data is preprocessed and transformation is applied to data before being fed into a model.
Park teaches:
Random Forest Classifier …((Park) in at least para 0097)
converting one or more selected features derived from the user transactional data into vectors [numerical representation] to be input to the life event predictor model ((Park) in at least para 0026, para 0031, para 0050-0051, para 0071-0072, para 0078-0079, para 0081)
deploying the trained life event predictor model trained in the batched training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the batched training performs better than a previously deployed life event predictor model in at least one performance metric ((Park) in at least para 0023, para 0029, para 0067-0069, para 0071, para 0077 wherein the prior art teaches batch of data used to train/update at a regularization rate defied to scale influence, para 0078-0081)
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of converting data into vectors for use in machine learning analysis so the algorithm can process, analyze and learn from the data where the vector represents a corresponding feature of a data point. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify data processing inputted into a machine learning algorithm of Ocampo to convert the data into vectors as taught by Park since Park teaches the motivation of converting data into vectors for use in machine learning analysis so the algorithm can process, analyze and learn form the data where the vector represents a corresponding feature of a data point.
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of narrowing the learning algorithm to a random forest in order to use the learning technique to analyze and perform learning task. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the generic learning algorithm of Ocampo to include random forest learning techniques of Park since Park teaches the motivation of narrowing the learning algorithm to a random forest in order to use the learning technique to analyze and perform learning task.
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of performing batch data to train or update models in order to scale the influence of negative loss component in the mathematical prediction calculations. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data inputted into the learning model of Ocampo to include batch training of Park since Park teaches the motivation of performing batch data to train or update models in order to scale the influence of negative loss component in the mathematical prediction calculations.
JP 017 teaches:
the recommendation engine being a machine learning-based engine comprising: the life event predictor model…((JP 017) in at least para 0012, para 0014-0017, para 0020, para 0023, para 0040, para 0043-0044, para 0063-0064, para 0070-0071, para 0077)
Both Ocampo and JP 017 are directed toward using analytics engines for predicting future financial events. JP 017 teaches the motivation of using machine learning models to perform the analysis for recognizing and determining financial actions recommendation. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analytics engine of Ocampo to include the ML engine of JP 017 since JP 017 teaches the motivation of using machine learning models to perform the analysis for recognizing and determining financial actions recommendation.
In reference to Claim 4:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 4
(Previously Presented) The method of claim 1 (see rejection of claim 1 above), wherein said obtaining of the additional profile content is by way of a user generated communication directed to the network system over a communications network the user generated communication includes demographic data supplied by the user ((Ocampo) in at least FIG. 7; Col 12 lines 1-37, Col 14 lines 57-Col 15 lines 1-30, Col 26 lines 4-27).
In reference to Claim 5:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of dependent claim 4. Ocampo further discloses the limitations of dependent claim 5
(Previously Presented) The method of claim 4 (see rejection of claim 4 above), wherein the demographic data includes
beneficiary data describing the second user type of a beneficiary, such that the user is of the first user type as a benefactor, such that the one or more services are associated with the second user type based on the beneficiary data ((Ocampo) in at least Abstract; FIG. 5; Col 2 lines 55-Col 3 lines 1-32, lines 38-62, Col 4 lines 49-Col 5 lines 1-37, lines 38-63, Col 16 lines 60-Col 17 lines 1-18; wherein the prior art teaches donor recipients in the plurality).
In reference to Claim 6:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 6
(Previously Presented) The method of claim 1 (see rejection of claim 1 above),
wherein said obtaining of the additional profile content is by way of accessing the user transactional data associated with the user in order to update the contents of the user profile. ((Ocampo) in at least Col 3 lines 15-20, Col 4 lines 50-64, Col 9 lines 26-32, Col 12 lines 28-35, Col 13 lines 30-49, Col 16 lines 60-63, Col 20 lines 25-38, Col 23 lines 55-Col 24 lines 1-2, Col 25 lines 15-24)
In reference to Claim 7:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 7
(Original) The method of claim 1 (see rejection of claim 1 above),
wherein said obtaining of the user profile data is by way of a network system generated communication previously directed to the user over a communications network. ((Ocampo) in at least Col 5 lines 37-63, Col 8 lines 54-65, Col 9 lines 5-18, Col 10 lines 55-61, Col 14 lines 11-21)
In reference to Claim 8:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 8
(Previously Presented) The method of claim 1 (see rejection of claim 1 above),
wherein the contents of the stored user profile are updated to reflect changes to a financial service of the one or more services ((Ocampo) in at least Col 3 lines 15-20, Col 4 lines 50-64, Col 9 lines 26-32, Col 12 lines 28-35, Col 13 lines 30-49, Col 16 lines 60-63, Col 20 lines 25-38, Col 23 lines 55-Col 24 lines 1-2, Col 25 lines 15-24), the financial service selected from the group consisting of:
a financial transaction; insurance; and a mortgage ((Ocampo) in at least Abstract; Col 3 lines 49-61, Col 9 lines 33-36, Col 11 lines 3-22, Col 24 lines 54-64).
In reference to Claim 9:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 9:
(Original) The method of claim 1 (see rejection of claim 1 above),
wherein the additional profile content is used by the system to identify one or more further services from the platform of services and identifying the further one or more services to the user via the user interface. ((Ocampo) in at least Col 9 lines 26-40, Col 10 lines 7-14, Col 13 lines 57-Col 14 lines 1-42)
In reference to Claim 10:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 10:
(Previously Presented) The method of claim 1 (see rejection of claim 1 above),
wherein the user is a benefactor type and said updating contents includes user data of the second user type of as a beneficiary. ((Ocampo) in at least FIG. 5; Col 13 lines 10-56, Col 15 lines 5-30, Col 19 lines 7-36)
In reference to Claim 11:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 11
(Previously Presented) The method of claim 1 (see rejection of claim 1 above),
wherein the user is both of a first user type and the second user type. ((Ocampo) in at least FIG. 5, FIG. 7; Col 13 lines 10-56, Col 15 lines 5-30, Col 19 lines 7-36, Col 23 lines 39-47)
In reference to Claim 12:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of dependent claim 11. Ocampo further discloses the limitations of dependent claim 12
(Previously Presented) The method of claim 11 (see rejection of claim 11 above) further comprising
using the other content for determining the user is associated with one the one or more other users, the one or more other users being designated as the second user type.. ((Ocampo) in at least FIG. 5; Col 12 lines 5-16, Col 13 lines 10-56, Col 15 lines 5-30, Col 19 lines 7-36, col 23 lines 39-47)
In reference to Claim 16:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 16:
(Original) The method of claim 1 (see rejection of claim 1 above) further comprising
sending to the user a recommended service from the platform of services by using the user profile data to identify life needs of a secondary user associated with the user.((Ocampo) in at least FIG. 4-5, FIG. 7; Col 11 lines 37-45, Col 12 lines 1-38, Col 13 lines 31-56, Col 14 lines 11-22)
Claim(s) 3 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,467,663 B1 by Ocampo et al (Ocampo), in view of US Pub No. 2020/0089650 A1 by Sharma et al. (Sharma) and WO 2022/256802 A1 by Park et al. (Park) in view of JP 7440017 B2 by 山本 哲也 wherein the translation has been annotated by the examiner for mapping (JP 017) as applied to claim 1 above, and further in view of US Pub No. 2020/0349649 A1 by Willis (Willis)
In reference to Claim 3:
The combination of Ocampo, Sharma, Park and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 3
(Previously Presented) The method of claim 1 (see rejection of claim 1 above),
Ocampo does not explicitly teach:
further comprising the recommendation engine utilizing both a content-to-content similarity filter and a collaborative similarity filter used to implement said comparing, such that the content-to- content similarity filter analyzes the user profile data of the user and the collaborative similarity filter analyzes other users having similar interests to said user based on user profiles of the other users
Willis teaches:
further comprising the recommendation engine utilizing both a content-to-content similarity filter and a collaborative similarity filter used to implement said comparing, such that the content-to- content similarity filter analyzes the user profile data of the user and the collaborative similarity filter analyzes other users having similar interests to said user based on user profiles of the other users ((Willis) in at least FIG. 13B, FIG. 14; para 0073, para 0080, para 0082, para 0123, para 0126, para 0130-0132).
Both Ocampo and Willis are directed toward analyzing user inputted goals for financial planning. Willis teaches the motivation that to address users not filling out lengthy forms of extensive information using a filtering process that allows administer for presenting services which can be applied to identify a group of users according to attributes (i.e. similar interest) so that a list of users can be presented according to filter criteria so that service providers can gain knowledge of a group of users and present product recommendations. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the management of data collected for analysis in determining different users financial status for offering products of Ocampo to include filtering users by similar interest as taught by Willis since Willis teaches the motivation that to address users not filling out lengthy forms of extensive information using a filtering process that allows administer for presenting services which can be applied to identify a group of users according to attributes (i.e. similar interest) so that a list of users can be presented according to filter criteria so that service providers can gain knowledge of a group of users and present product recommendations.
In reference to Claim 13:
The combination of Ocampo, Sharma, Park, JP 017 and Willis discloses the limitations of dependent claim 3. Ocampo further discloses the limitations of dependent claim 13.
(Previously Presented) The method of claim 3 (see rejection of claim 3 above) further comprising
the recommendation engine operating as a hybrid recommendation engine, the recommendation engine utilizing a life event predictor model used to implement said comparing, such that the one or more services are compatible with a life event prediction generated by the life event predictor model ((Ocampo) in at least FIG. 4-5, FIG. 7; abstract wherein the prior art teaches using analytics engine for recommending products best aligned with recipient goals; Col 3 lines 21-32, lines 49-Col 4 lines 1-33, lines 49-64 wherein the prior art teaches analytics engine compares user profile and goals against allocation of funds resources to recommend one or more products when analyzing user profiles, Col 11 lines 55-67, Col 12 lines 38-Col 13 lines 1-9, Col 14 lines 57-Col 15 lines 1-5, Col 19 lines 7-36, Col 21 lines 4-59, Col 24 lines 20-42 wherein the prior art teaches engine analyzes user profiles to determine user goals used for comparison of available product available)
In reference to Claim 14:
The combination of Ocampo, Sharma, Park, JP 017 and Willis discloses the limitations of dependent claim 13. Ocampo further discloses the limitations of dependent claim 14.
(Original) The method of claim 13 (see rejection of claim 13 above),
wherein the first user type is a benefactor and the second user type is a beneficiary, such that the selected life stage is a life event of wealth transfer between the benefactor and the beneficiary.((Ocampo) in at least FIG. 5; Col 4 lines 55-64, Col 12 lines 60-Col 13 lines 1-9, lines 35-49, Col 19 lines 7-36, Col 21 lines 60-Col 22 lines 1-4, lines 55-59)
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,467,663 B1 by Ocampo et al (Ocampo) in view of US Pub No. 2020/0089650 A1 by Sharma et al. (Sharma) and WO 2022/256802 A1 by Park et al. (Park) in view of JP 7440017 B2 by 山本 哲也 wherein the translation has been annotated by the examiner for mapping (JP 017) as applied to claim 1 above, and further in view of US Pub No. 2014/0114877 A1 by Montano (Montano)
In reference to Claim 15:
The combination of Ocampo, Sharma, Park, JP 017 and Willis discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 15 (Original) The method of claim 1 (see rejection of claim 1 above) further comprising
Ocampo does not explicitly teach:
inviting by the user for an additional user to join by navigating pages of the user interface to provide selections for inviting already identified second users or to supply contact information to invite the additional user.
Montano teaches:
inviting by the user for an additional user to join by navigating pages of the user interface to provide selections for inviting already identified second users or to supply contact information to invite the additional user. ((Montano) in at least FIG. 2-3; para 0033)
Both Ocampo and Montano are directed toward processes which provide services to uses based on user desire and need. Montano teaches the motivation of determining credibility of services by asking a customer to provide reviews regarding customer service which includes asking a customer for a referral to broadcast their reviews to specific contacts as part of a marketing process. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the information related to products offered and their services for user life stages to include a review of services/product process which includes referrals as taught by Montano since Montano teaches the motivation of determining credibility of services by asking a customer to provide reviews regarding customer service which includes asking a customer for a referral to broadcast their reviews to specific contacts as part of a marketing process.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,467,663 B1 by Ocampo et al (Ocampo) in view of US Pub No. 2020/0089650 A1 by Sharma et al. (Sharma) and WO 2022/256802 A1 by Park et al. (Park), in view of JP 7440017 B2 by 山本 哲也 wherein the translation has been annotated by the examiner for mapping (JP 017) as applied to claim 1 above, and further in view of US Pub. No. 2003/0187768 A1 by Ryan et al (Ryan)
In reference to Claim 17:
The combination of Ocampo, Sharma, Park, and JP 017 discloses the limitations of independent claim 1. Ocampo further discloses the limitations of dependent claim 17
(Original) The method of claim 1 (see rejection of claim 1 above),
Ocampo does not explicitly teach:
wherein the platform of services is provided by a financial institution, the platform of services managed by data center for hosting online banking services facilitating a plurality of the users to log in using respective user accounts providing access to various computer-implemented instances of the online banking services
Ryan teaches:
wherein the platform of services is provided by a financial institution, the platform of services managed by data center for hosting online banking services facilitating a plurality of the users to log in using respective user accounts providing access to various computer-implemented instances of the online banking services.((Ryan) in at least para 0635-0638),
Both Ocampo and Ryan teach registering users with passwords using platform services of providers that analyzes life profile data and provides recommendations to the user for product and for product purchases. Ryan teaches the motivation of for customer login it is need to register and input user name/password, in order to for user to begin accessing the services of the provider, where the user can then input profile data and then proceed to the different products offered. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to expand the details of user registering with password and userID of Ocampo to include applying the password to a login process as taught by Ryan since Ryan teaches the motivation of for customer login it is need to register and input user name/password, in order to for user to begin accessing the services of the provider, where the user can then input profile data and then proceed to the different products offered.
Claim 18 and Claim 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 10,467,663 B1 by Ocampo et al (Ocampo), in view of US Pub No. 2020/0089650 A1 by Sharma et al. (Sharma) and WO 2022/256802 A1 by Park et al. (Park), in view of JP 7440017 B2 by 山本 哲也 wherein the translation has been annotated by the examiner for mapping (JP 017) and further in view of US Pub No. 2004/0215547 A1 by Nazari et al. (Nazari)
In reference to Claim 18:
Ocampo teaches:
(Currently Amended) A computer system for manipulating and maintaining a user profile including applying user data for providing services to a user from a platform of services ((Ocampo) in at least Abstract), the system comprising:
a set of stored instructions for execution by one or more computer processors ((Ocampo) in at least FIG. 2; Col 15 lines 42-67) for:
obtaining stored user profile data pertaining to the user of a network system of an institution ((Ocampo) in at least Col 5 lines 37-63 wherein the prior art teaches when receiving request from user device retrieves recipient profile data from database);;
using a recommendation engine for comparing the user profile data to a plurality of different potential life stages in order to determine a selected life stage based on at least the user profile data, the recommendation engine…comprising a life event predictor model used to implement said comparing, the life event predictor model utilizing user transactional data contained in the user profile data to generate a life event predication of a current or upcoming life event of the user ((Ocampo) in at least FIG. 4-5, FIG. 7; Col 2 lines 3-38, Col 3 lines 12-32, lines 49-Col 4 lines 1-33, lines 49-64, Col 11 lines 55-67, Col 12 lines 38-Col 13 lines 1-9, Col 14 lines 57-Col 15 lines 1-5, Col 19 lines 7-36, Col 21 lines 4-59);
…the life event predictor model using historical transactional data of a plurality of past users and associated life-event information to determine associations between the historical transactional data and respective life events ((Ocampo) in at least Col 2 lines 12-38, Col 3 lines 12-32, Col 4 lines 1-37, Col 6 lines 22-38, Col 25 lines 12-24);…
executing the life event predictor model using the vectors to generate the life event prediction ((Ocampo) in at least FIG. 6; Col 9 lines 32-44, Col 15 lines 5-30, Col 23 lines 55-Col 24 lines 1-3 wherein the prior art teaches recently married, buying a home; Col 24 lines 20-64);
based on said comparing, and responsive to the life event prediction, identifying one or more services compatible with the life event prediction, from the platform of services based on the selected life stage ((Ocampo) in at least FIG. 4; FIG. 6; Col 9 lines 32-44, Col 15 lines 5-30, Col 18 lines 30-50, Col 24 lines 44-64);
presenting the one or more services to the user via a user interface of a user device ((Ocampo) in at least FIG. 6; Col 9 lines 50-Col 10 lines 1-17, Col 12 lines 38-60, Col 13 lines 1-9, Col 13 lines 57-Col 14 lines 1-42, col 18 lines 15-29, Col 24 lines 65-Col 25 lines 1-24);
receiving a request from the user through the user device for access to the one or more services ((Ocampo) in at least Col 13 lines 30-49, col 14 lines 25-42, Col 24 lines 65-Col 25 lines 1-24, lines 57-Col 26 lines 1-3); and
updating contents of the stored user profile to include additional profile content related to activity of the user with the one or more services through an application. ((Ocampo) in at least Col 3 lines 15-20, Col 4 lines 50-64, Col 5 lines 40-63, Col 6 lines 5-26, Col 9 lines 26-32, Col 12 lines 28-35, Col 13 lines 30-67, Col 16 lines 60-63, Col 18 lines 15-29, Col 20 lines 25-38, Col 23 lines 55-Col 24 lines 1-2, Col 25 lines 15-24).
wherein said updating of the contents of the stored user profile includes both user content associated with the user of a first user type as well as other content associated with one or more other users of a second user type, such that the other content includes contact information of the one or more other users and at least one of financial information or life event status information of the one or more other users..((Ocampo) in at least FIG. 5, FIG. 7; Col 12 lines 1-38, Col 13 lines 22-56, Col 14 lines 4-21, lines 57-Col 15 lines 1-30; Col 18 lines 1-13, Col 23 lines 20-Col 24 lines 1-2)
Ocampo does not explicitly teach:
the recommendation engine being a machine learning-based engine comprising: the life event predictor model…
training the life event predictor model …;
preprocessing at least a portion of the user transactional data by cleaning and formatting the user transactional data, performing one-hot encoding for categorical columns, and performing factorization for numerical columns;
performing feature selection on the preprocessed user transactional data, comprising performing feature importance using a Random Forest Classifier for identifying a non-linear relationship between features and a target variable
converting one or more selected features derived from the user transactional data into vectors to be input to the life event predictor model;
performing batched training of the life event predictor model with new data collected in response to the activity of the user in the application in predefined intervals
deploying the retrained life event predictor model trained in the batched training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the batch training performs better than a previously deployed life event predictor model in at least one performance metric
Sharma teaches:
training the life event predictor model using historical transactional data of a plurality of past users and associated life-event information to determine associations between the historical transactional data and respective life events ((Sharma) in at least Abstract; Fig. 4; para 0007, para 0016, para 0018-0020, para 0023-0025, para 0040-0042, para 0044, para 0049-0052, para 0054-0055)
preprocessing at least a portion of the user transactional data by cleaning and formatting the user transactional data, performing one-hot encoding for categorical columns, and performing factorization for numerical columns ((Sharma) in at least FIG. 2-3; para 0007-0008, para 0013, para 0019, para 0041-0042, para 0044, para 0049, para 0055);
performing feature selection on the preprocessed user transactional data, comprising performing feature importance using a Random Forest Classifier [XGBoost] for identifying a non-linear relationship between features and a target variable ((Sharma) in at least para 0005, para 0011-0013, para 0019, para 0044, para 0049);
converting one or more selected features derived from the user transactional data into vectors [numerical representation] to be input to the life event predictor model ((Sharma) in at least para 0004, para 0011, para 0022, para 0041, para 0046, para 0054; Table 2);
deploying the trained life event predictor model trained in the … training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the …training performs better than a previously deployed life event predictor model in at least one performance metric ((Sharma) in at least FIG. 1; para 0006);
Both Ocampo and Sharma are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Sharma teaches the motivation of preprocessing data and applying one hot encoding for use in machine learning analysis as machine learning models typically begins with data collection where data is preprocessed and transformation is applied to data before being fed into a model. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analytics engine process and use of Ocampo to include data preparation of data sets for input when training a model as taught by Sharma since Sharma teaches the motivation of preprocessing data and applying one hot encoding for use in machine learning analysis as machine learning models typically begins with data collection where data is preprocessed and transformation is applied to data before being fed into a model.
Park teaches:
Random Forest Classifier …((Park) in at least para 0097)
converting one or more selected features derived from the user transactional data into vectors [numerical representation] to be input to the life event predictor model ((Park) in at least para 0026, para 0031, para 0050-0051, para 0071-0072, para 0078-0079, para 0081)
deploying the trained life event predictor model trained in the batched training for use by the recommendation engine in response to determining that the trained life event predictor model trained in the batched training performs better than a previously deployed life event predictor model in at least one performance metric ((Park) in at least para 0023, para 0029, para 0067-0069, para 0071, para 0077 wherein the prior art teaches batch of data used to train/update at a regularization rate defied to scale influence, para 0078-0081)
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of converting data into vectors for use in machine learning analysis so the algorithm can process, analyze and learn form the data where the vector represents a corresponding feature of a data point. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify data processing inputted into a machine learning algorithm of Ocampo to convert the data into vectors as taught by Park since Park teaches the motivation of converting data into vectors for use in machine learning analysis so the algorithm can process, analyze and learn form the data where the vector represents a corresponding feature of a data point.
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of narrowing the learning algorithm to a random forest in order to use the learning technique to analyze and perform learning task. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the generic learning algorithm of Ocampo to include random forest learning techniques of Park since Park teaches the motivation of narrowing the learning algorithm to a random forest in order to use the learning technique to analyze and perform learning task.
Both Ocampo and Park are directed toward applying machine learning models for use in analyzing data for use in predictive modelling. Park teaches the motivation of performing batch data to train or update models in order to scale the influence of negative loss component in the mathematical prediction calculations. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the data inputted into the learning model of Ocampo to include batch training of Park since Park teaches the motivation of performing batch data to train or update models in order to scale the influence of negative loss component in the mathematical prediction calculations.
JP 017 teaches:
the recommendation engine being a machine learning-based engine comprising: the life event predictor model…((JP 017) in at least para 0012, para 0014-0017, para 0020, para 0023, para 0040, para 0043-0044, para 0063-0064, para 0070-0071, para 0077)
Both Ocampo and JP 017 are directed toward using analytics engines for predicting future financial events. JP 017 teaches the motivation of using machine learning models to perform the analysis for recognizing and determining financial actions recommendation. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the analytics engine of Ocampo to include the ML engine of JP 017 since JP 017 teaches the motivation of using machine learning models to perform the analysis for recognizing and determining financial actions recommendation.
Nazari teaches:
such that the other content includes contact information of the one or more other users ((Nazari) in at least para 0019, para 0064)
According to KSR, simple substitution for one known element for another to obtain predictable results is common sense rationale. The prior art contained profile data which differed from the claimed profile data by the substitution of one profile data for another. The prior art Nazari provides evidence that the profile information which differed in the prior art and their functions were known in the art. One of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable.
Both Ocampo and Nazari are directed toward analyzing financial information for financial advice where user information is provided for the financial analysis. Nazari teaches the motivation of entering into a financial analysis system client information which includes financial data, preferences, income, liabilities and contact information. It would have been obvious to one having ordinary skill before the effective filing date of the claimed invention to modify the profile information of Ocampo to include contact information as taught by Nazari since Nazari teaches the motivation of entering into a financial analysis system client information which includes financial data, preferences, income, liabilities and contact information.
In reference to Claim 19:
The instructions of computer readable medium of claim 19 correspond to the functions of system claim 18. The additional limitations recited in claim 19 that go beyond the limitations of claim 18 include the computer readable medium ((Ocampo) in at least Col 15 lines 60-Col 16 lines 1-2) to perform the operation that correspond to claim 18 include the structure comprising:
a set of stored instructions for execution for execution by one or more computer processors ((Ocampo) in at least Col 15 lines 60-Col 16 lines 1-2)2, lines 14-24) performing operations corresponding to claim 18. The set of instructions corresponding to the functions of system claim 18
Therefore, claim 19 has been analyzed and rejected as previously discussed with respect to claim 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.“Tree-Based Machine Learning Algorithms Explained” by Ravindran; US Patent No. 12,566,987 B2 by You et al.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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/MARY M GREGG/Examiner, Art Unit 3695
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