DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Application filed on 5/22/2024.
Claims 1-20 are pending. Claims 1, 10, and 19 are independent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10-18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites “based on the user attributes, b, and the item selection data,” in line 10. The meaning of term “b” is ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention. See MPEP 2173.05(a). Claims 11-18 depend from 10 and do not fix the issue.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 4, 10, 11, 13, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inan et al. (US2024/0242069) in view of Singh (US2024/0086734).
In regards to claim 1, Inan et al. discloses a method for automatically recommending items in a software application through machine learning, the method comprising:
retrieving a plurality of attributes of a user of the software application (Inan et al. Fig. 8 816 para[0092], user session data is obtained from a user device of a query user);
providing inputs to the machine learning model based on the plurality of attributes of the user (Inan et al. fig. 8 818 para[0092],provide user session data and inferred user embeddings to model to generate recommended items);
receiving, from the machine learning model, in response to the inputs, indications of one or more recommended items of the first item type or the second item type (Inan et al. fig. 8 818 para[0092],generate recommended items based on user session data and inferred user embeddings); and
displaying, via a user interface, an item selection recommendation based on the indications of the one or more recommended items of the first item type or the second item type (Inan et al. fig. 8 820 para[0092], transmit information about the recommended items to the user device for display to the user).
Inan does not explicitly disclose retrieving a machine learning model that has been trained through a supervised learning process based on labeled training data indicating whether a plurality of users represented by a plurality of user features historically selected, within the software application, first items of a first item type and second items of a second item type, wherein the machine learning model is configured, as a result of the supervised learning process, to recognize latent relationships between the first items of the first item type and the second items of the second item type based on distances between embeddings.
However Singh discloses retrieving a machine learning model that has been trained through a supervised learning process based on labeled training data indicating whether a plurality of users represented by a plurality of user features historically selected, within the software application, first items of a first item type and second items of a second item type, wherein the machine learning model is configured, as a result of the supervised learning process, to recognize latent relationships between the first items of the first item type and the second items of the second item type based on distances between embeddings (Singh et al. para[0039], training includes supervised learning with labeled training data, para[0093], identifies confidence in multiple categories of repair).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the prediction method of Singh in order to identify key information and perform efficient and effective decision-based processing (Singh para[0011]).
In regards to claim 4, Inan et al. as modified by Singh discloses the method of Claim 1, wherein the labeled training data set does not include identifiers of the first items of the first item type and the second items of the second type, and wherein the labeled training data set includes a respective embedding of each item of the first items of the first item type and the second items of the second type (Singh et al. para[0039], labels may be used to jointly train the feature embedding layer as well as a regression output branch).
In regards to claim 10, Inan et al. discloses a method of machine learning model training, the method comprising:
retrieving user attributes of a plurality of users of a software application (Inan et al. Fig. 8 816 para[0092], user session data is obtained from a user device of a query user);
retrieving item attributes of a first plurality of items of a first item type and a second plurality of items of a second type (Inan et al. fig. 4 para[0069], Each row of the user interaction matrix represents a user u and each column represents an item i);
generating embeddings of the first plurality of items and the second plurality of items based on the plurality of item attributes (Inan et al. para[0092], inferred user embeddings are generated by applying the trained deep learning model to the plurality of inference data batches in parallel);
retrieving item selection data indicating whether items of the first plurality of items and the second plurality of items were historically selected by the plurality of users (Inan et al. para[0091], user-item interaction data with respect to a plurality of users is obtained).
Inan does not explicitly disclose generating a labeled training data set for a machine learning model based on the user attributes, b, and the item selection data, wherein the training data set does not include identifiers of the first plurality of items and the second plurality of items; and
training the machine learning model through a supervised learning process based on the labeled training data set to output indications of one or more recommended items of the first item type or the second item type in response to input user features, wherein the machine learning model is configured, as a result of the supervised learning process, to recognize latent relationships between the first plurality of items of the first item type and the second plurality of items of the second item type based on distances between the embeddings.
However Singh discloses generating a labeled training data set for a machine learning model based on the user attributes, b, and the item selection data, wherein the training data set does not include identifiers of the first plurality of items and the second plurality of items (Singh et al. para[0039], training may include supervised learning with labeled training data (e.g. historical inference input with two layers of labels for training purposes)); and
training the machine learning model through a supervised learning process based on the labeled training data set to output indications of one or more recommended items of the first item type or the second item type in response to input user features, wherein the machine learning model is configured, as a result of the supervised learning process, to recognize latent relationships between the first plurality of items of the first item type and the second plurality of items of the second item type based on distances between the embeddings (Singh et al. para[0039], training includes supervised learning with labeled training data, para[0093], identifies confidence in multiple categories of repair).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Inan et al. with the prediction method of Singh in order to identify key information and perform efficient and effective decision-based processing (Singh para[0011]).
In regards to claim 11, Inan et al. as modified by Singh discloses the method of Claim 10, wherein the machine learning model is trained to process the input user features along with each embedding of the embeddings (Inan et al. para[0092] recommended items are generated at operation 818 based on the user session data and the inferred user embeddings).
In regards to claim 13, Inan et al. as modified by Singh discloses the method of Claim 10, further comprising:
generating updated labeled training data based on user feedback with respect to an item selection recommendation output by the trained machine learning model (Singh et al. para[0045], a new batch of training data may be fed through the model, and the weights adjusted based on the gradients calculated from the loss), wherein the user feedback comprises a selection of one or more items within the software application (Inan et al. para[0068], user-item interaction data may include some implicit user feedback data like browsing history, purchase history, etc.); and
re-training the machine learning model based on the user feedback, wherein the re-trained machine learning model is used to determine a subsequent item selection recommendation (Singh et al. para[0052], the entire or partial process may be adjusted or updated where the ML model is retrained to incorporate newer data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Inan et al. with the prediction method of Singh in order to identify key information and perform efficient and effective decision-based processing (Singh para[0011]).
In regards to claim 18, Inan et al. as modified by Singh discloses the method of Claim 10, further comprising: generating a unified data set by merging the user attributes and the item attributes based on the item selection data using the identifiers of the first plurality of items and the second plurality of items and user identifiers of the plurality of users (Inan et al. para[0057], the item engagement data may include one or more of a session ID, item clicks identifying items which a user clicked, items added-to-cart identifying items added to user’s online shopping cart, advertisements viewed identifying advertisements the user viewed during the browsing session, advertisements clicked and user ID); and
converting textual features in the unified data set into numerical features to produce a converted unified data set, wherein the labeled training data set is generated based on the converted unified data set (Inan et al. para[0072], data parsing logic that is configured to obtain the user session data and generate some training data for training a scalable deep learning architecture).
Claim 19 recites substantially similar limitations to claim 1. Thus claim 19 is rejected along the same rationale as claim 1.
Claim(s) 2, 5, 9, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inan et al. in view of Singh and Suag (US12,547,935).
In regards to claim 2, Inan et al. as modified by Singh discloses the method of Claim 1. Inan et al. does not explicitly disclose wherein the machine learning model processes the inputs along with each embedding of a plurality of embeddings generated based on the first items of the first item type and the second items of the second item type.
However Suag et al. discloses wherein the machine learning model processes the inputs along with each embedding of a plurality of embeddings generated based on the first items of the first item type and the second items of the second item type (Suag et al. col9 ln11-19 ln51-54, generates feature embeddings for different products).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Suag et al. in order to identify features for candidate ranking and recommendation algorithms (Suag et al. col1 ln31-40).
In regards to claim 5, Inan et al. as modified by Singh discloses the method of Claim 1. Inan et al. does not explicitly disclose further comprising: receiving, in response to the item selection recommendation, user feedback comprising a selection of one or more items within the software application, wherein the machine learning model is re-trained based on the user feedback; and
using the re-trained machine learning model to determine a subsequent item selection recommendation.
However Suag et al. discloses further comprising: receiving, in response to the item selection recommendation, user feedback comprising a selection of one or more items within the software application, wherein the machine learning model is re-trained based on the user feedback (Suag et al. col6 ln2-5, A candidate ranking machine learning algorithm is modeled using a unique set of domain features relevant to the. Marketing and that optimizes itself further on every new interaction of the user through the feedback loop); and
using the re-trained machine learning model to determine a subsequent item selection recommendation (Suag et al. col 6 ln6-12, the candidate ranking process uses a LambdaMART model to train the candidate ranking algorithm).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Suag et al. in order to identify features for candidate ranking and recommendation algorithms (Suag et al. col1 ln31-40).
In regards to claim 9, Inan et al. as modified by Singh discloses the method of Claim 1.
Inan et al. does not explicitly disclose wherein the machine learning model comprises a gradient boosted tree model.
However Suag et al. discloses wherein the machine learning model comprises a gradient boosted tree model (Suag et al. col6 ln6-12, other models, such as Gradient Boosted Trees and Deep Neural Networks may also be used).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Suag et al. in order to identify features for candidate ranking and recommendation algorithms (Suag et al. col1 ln31-40).
In regards to claim 17, Inan et al. as modified by Singh discloses the method of Claim 10.
Inan et al. does not explicitly disclose wherein the machine learning model comprises a gradient boosted tree model.
However Suag et al. discloses wherein the machine learning model comprises a gradient boosted tree model (Suag et al. col6 ln6-12, other models, such as Gradient Boosted Trees and Deep Neural Networks may also be used).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Suag et al. in order to identify features for candidate ranking and recommendation algorithms (Suag et al. col1 ln31-40).
Claim 20 recites substantially similar limitations to claim 2. Thus claim 20 is rejected along the same rationale as claim 2.
Claim(s) 3, 6-8, 12, and 14-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Inan et al. in view of Singh and Furbish (US2024/0257267).
In regards to claim 3, Inan et al. as modified by Singh discloses the method of Claim 1.
Inan et al. does not explicitly disclose wherein the indications output by the machine learning model in response to the inputs comprise confidence scores for each of the first items of the first item type and for each of the second items of the second item type.
However Furbish discloses wherein the indications output by the machine learning model in response to the inputs comprise confidence scores for each of the first items of the first item type and for each of the second items of the second item type (Furbish para[0107], recommendation item may include meta data, a recommendation excerpt and a confidence score).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of tax payer (Furbish para[0006]).
In regards to claim 6, Inan et al. as modified by Singh discloses the method of Claim 1.
Inan et al. does not explicitly disclose wherein the first item type is tax deductions, wherein the first items comprise a plurality of different tax deductions, wherein the second item type is tax credits, and wherein the second items comprise a plurality of different tax credits.
However Furbish discloses wherein the first item type is tax deductions, wherein the first items comprise a plurality of different tax deductions, wherein the second item type is tax credits, and wherein the second items comprise a plurality of different tax credits (Furbish para[0044], a sub selection of topical completeness graphs and tax calculation graphs can be used for intermediate tax results such as Adjusted Gross Income or Taxable Income, itemized deductions, tax credits, and the like).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayers (Furbish para[0006]).
In regards to claim 7, Inan et al. as modified by Singh and Furbish discloses the method of Claim 6, wherein the embeddings are generated based on features of the plurality of different tax deductions and the plurality of different tax credits, and wherein the features comprise names and categories (Furbish para[0071], Tax related information may also include information that pertains to tax deductions or tax credit.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayers (Furbish para[0006]).
In regards to claim 8, Inan et al. as modified by Singh and Furbish discloses the method of Claim 6, wherein the plurality of attributes of the user comprise one or more of:
tax information for the user for a current year (Furbish para[0051], statistical data may be mined for existing or current year tac filings); and
tax information for the user for a prior year (Furbish para[0072], a user may select prior year tax returns to be searched.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayer (Furbish para[0006]).
In regards to claim 12, Inan et al. as modified by Singh discloses the method of Claim 11.
Inan et al. does not explicitly disclose wherein the indications comprise confidence scores for each of the first plurality of items and for each of the second plurality of items.
However Furbish discloses wherein the indications comprise confidence scores for each of the first plurality of items and for each of the second plurality of items (Furbish para[0107], recommendation item may include meta data, a recommendation excerpt and a confidence score).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayers (Furbish para[0006]).
In regards to claim 14, Inan et al. as modified by Singh discloses the method of Claim 10.
Inan et al. does not explicitly disclose wherein the first item type is tax deductions, wherein the first plurality of items comprise a plurality of different tax deductions, wherein the second item type is tax credits, and wherein the second plurality of items comprise a plurality of different tax credits (Furbish para[0044], a sub selection of topical completeness graphs and tax calculation graphs can be used for intermediate tax results such as Adjusted Gross Income or Taxable Income, itemized deductions, tax credits, and the like).
However Furbish discloses wherein the first item type is tax deductions, wherein the first plurality of items comprise a plurality of different tax deductions, wherein the second item type is tax credits, and wherein the second plurality of items comprise a plurality of different tax credits (Furbish para[0044], a sub selection of topical completeness graphs and tax calculation graphs can be used for intermediate tax results such as Adjusted Gross Income or Taxable Income, itemized deductions, tax credits, and the like).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayer (Furbish para[0006]).
In regards to claim 15, Inan et al. as modified by Singh discloses the method of Claim 10.
Inan et al. does not explicitly disclose wherein the item features comprise names and categories.
However Furbish discloses wherein the item features comprise names and categories (Furbish para[0071], Tax related information may also include information that pertains to tax deductions or tax credit.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayer (Furbish para[0006]).
In regards to claim 16, Inan et al. as modified by Singh and Furbish discloses the method of Claim 15, wherein the user attributes comprise one or more of:
tax information for the plurality of users for a current year (Furbish para[0051], statistical data may be mined for existing or current year tac filings); and
tax information for the plurality of users for a prior year (Furbish para[0072], a user may select prior year tax returns to be searched.).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the recommendation system of Inan et al. with the optimization method of Furbish in order to identify tax variables which can affect the tax result of taxpayers (Furbish para[0006]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sankararaman et al. (US11,948,207) teaches using supervised learning to recommend different categories of tax deductible expenses.
Zatorski et al. (US 2023/0100788) teaches recommencing content and obtaining a feedback vector.
Houseworth et al. (US2013/0198047) teaches a method to predicting impact of a tax event and recommending actions to maximize credits and deductions.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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