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 .
Response to Amendments
Claims 1, 9, 15, and 20 have been amended.
Claims 8 and 17 have been canceled.
Claims 21-22 have been newly added.
Claims 1-7, 9-16, and 18-22 remain pending in the application.
The amendment filed 02/25/2026 is sufficient to overcome the 35 U.S.C. 101 rejections of claims 1-7, 9-16, and 18-22. The previous rejections have been withdrawn.
The amendment filed 02/25/2026 is sufficient to overcome the 35 U.S.C. 103 rejections of claims 1, 4, 6-7, 9-12, 15-16, 18, and 20 over Wang in view of Lee, claims 2-3 over Wang in view of Lee and further in view of Cashion, and claims 5, 13, 14, and 19 over Wang in view of Lee and further in view of Pillay. The previous rejections have been withdrawn.
Response to Arguments
Argument 1, regarding the 101 rejections, applicant argues that the 101 rejections should be withdrawn because the claims integrate the abstract ideas into the practical application of improving a trained prediction network based on a confidence score that has been adjusted based on a correlation score for a category in which the machine generated input and user generated input are classified. Examiner agrees and the 101 rejections have been withdrawn.
Argument 2, regarding the 103 rejections, applicant argues that the 103 rejections should be withdrawn because none of the cited art teaches “using the first score to adjust the second score to an adjusted second score for a category in which the first machine generated input and the first user generated input is classified" and "adjusting, by the computing device, a parameter of the prediction network based on the adjusted second score and the first machine generated input and the first user generated input being classified in the category”. Applicant argues that Wang and Lee do not disclose confidence scores being adjusted based on a correlation for a category in which the machine generated input and the user generated input is classified.
Examiner notes this argument is moot in view of Sun et al (US 20150286723 A1), hereafter Sun. Sun teaches using, by the computing device, the first score to adjust the second score to an adjusted second score for a category in which the machine generated input and the user generated input is classified (confidence scores may be adjusted based on a correlation between two or more entity categories, P0018).
Claim Objections
Claim 22 objected to because of the following informalities: claim 22 recites “and the first score is used to adjust the second score in first layer in the second layer”. This appears to be a mistake. Examiner recommends amending the claim to recite “and the first score is used to adjust the second score in the first layer within in the second layer”. Appropriate correction is required.
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.
Claims 1, 4, 6-7, 9-12, 15-16, 18, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (Pub. No.: US 20240346604 A1), hereafter Wang in view of Lee et al (Pub. No.: US 20230152757 A1), hereafter Lee and Sun et al (US 20150286723 A1), hereafter Sun.
Regarding claims 1, 15, and 20, Wang teaches a method, apparatus, and non-transitory computer-readable storage medium comprising instructions for controlling one or more computer processors to be operable for: (P0006, P0012) receiving, by a computing device (“FIG. 7 is a computer architecture diagram illustrating a computing device architecture for a computing device capable of implementing aspects of the techniques and technologies presented herein”, P0025), machine generated input and user generated input at a first layer of a model of a prediction network (Recommended hashtags and content categories (machine generated input) and user interaction data (user input) are used to train a graph convolution network to create a prediction model, P0043, P0005); receiving, by the computing device, a link between a type of machine generated input and a type of user generated input at a second layer of the model (The correlation model evaluates an association between a hashtag and micro-video content that a user is viewing, P0030); generating, by the second layer, a first score that represents a correlation between the type of machine generated input and the type of user generated input (“calculating similarity scores for hashtags from content from the content category and a product of the micro-video semantic features and user-specific hashtags, and determining the correlation of hashtags to the content category from the similarity scores”, P0119); analyzing, by the first layer, the machine generated input and the user generated input using the model of the prediction network to correlate the machine generated input and the user generated input to a category (A correlation is determined between at least one content category to the user interaction semantic data using a multi-layer graph convolution network. Hashtags correlated with the content category are output, P0124)… and adjusting, by the computing device, a parameter of the prediction network based on… the machine generated input and the user generated input being classified in the category (graph convolution network may be adjusted based on recommended hashtags and content categories (machine generated input) and user interaction data (user input), P0005, P0043).
Wang does not appear to explicitly teach “wherein a second score associated with a confidence that the machine generated input or the user generated input belongs to the category is output… and adjusting, by the computing device, a parameter of the prediction network based on the adjusted second score”.
Lee teaches wherein a second score associated with a confidence that the machine generated input or the user generated input belongs to the category is output (Confidence scores are produced that indicate whether or not a prediction model outputs a false positive or false negative. These scores are also based on a user’s input for accuracy of predictions, P0251-P0253); …and adjusting, by the computing device, a parameter of the prediction network based on the… second score (Confidence scores may be used to adjust weights of prediction models, P00251-P00252).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang and Lee before them, to include Lee’s specific teaching of confidence scores based on a prediction model’s outputs being used to adjust parameters of the model in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of confidence scores based on a prediction model’s outputs being used to adjust parameters of the model (see Lee P0251-P0253), and using calculated similarity scores to determine correlation of content categories using a graph convolution model (see Wang P0119) to improve the accuracy and speed with which machine learning models are trained to be employed at individual building management systems (see Lee P0255).
Wang in view of Lee does not appear to explicitly teach “using, by the computing device, the first score to adjust the second score to an adjusted second score for a category in which the machine generated input and the user generated input is classified”.
Sun teaches using, by the computing device, the first score to adjust the second score to an adjusted second score for a category in which the machine generated input and the user generated input is classified (confidence scores may be adjusted based on a correlation between two or more entity categories, P0018).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang, Lee, and Sun before them, to include Sun’s specific teaching of confidence scores being adjusted based on a correlation between two or more entity categories in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of confidence scores being adjusted based on a correlation between two or more entity categories (see Sun P0018), and using calculated similarity scores to determine correlation of content categories using a graph convolution model (see Wang P0119) to improve the accuracy of a search engines determination of categories for queried entities (see Sun P0002).
Regarding claim 4, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang further teaches generating a first set of embeddings from the machine generated input; and generating a second set of embeddings from the user generated input (User specific semantic data and hashtag specific semantic data may be embedded. The semantic data information can be graphed to produce a model of the relationships between user and hashtag data, P0054), wherein the first set of embeddings and the second set of embeddings are analyzed by the prediction network (User specific semantic data and hashtag data may be analyzed using a multi-layer graph convolution network, P0124).
Regarding claims 6 and 16, Wang in view of Lee and Sun teaches the limitations of claims 1 and 15 as outlined above. Wang further teaches wherein generating the first score comprises: generating the first score that represents a similarity between the type of machine generated input and the type of user generated input (Similarity scores are generated based on a correlation between content categories, hashtags, and user interaction semantic data, P0119).
Regarding claim 7, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Lee further teaches wherein the first score is a vector (Vectors may be generated upon determining a value’s association to another is above a threshold. Association here is interpreted to mean correlation based on the association being above a threshold value. P0241).
Regarding claim 9, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Lee further teaches wherein the parameter is adjusted based on a difference between the first score and the second score (A loss function calculating the difference between outputs of false positive and false negative prediction models and expected outputs is used to modify parameters of the models, P0251-P0253).
Regarding claims 10 and 18, Wang in view of Lee and Sun teaches the limitations of claims 1 and 15 as outlined above. Wang further teaches outputting a plurality of categories in which to categorize the machine generated input (Graph convolution network provides recommended hashtags for content categories based on user interaction semantic data, P0043, P0005).
Regarding claim 11, Wang in view of Lee and Sun teaches the limitations of claim 10 as outlined above. Wang further teaches wherein the plurality of categories is extracted from the machine generated input or the user generated input (Content categories are obtained from hashtags and user interaction semantic data, P0005).
Regarding claim 12, Wang in view of Lee and Sun teaches the limitations of claim 10 as outlined above. Wang further teaches determining a priority of a category in the plurality of categories based on a number of instances of machine generated input or user generated input that is classified in the category (Hashtags are ranked based on popularity levels and relevance and the operation of providing the hashtags correlated with the content category to the content service comprises providing the ranking of the hashtags correlated with the content category to the content service, P0007).
Regarding claim 21, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang further teaches wherein the first score is used to adjust the second score in a third layer (operator 340 may be used to calculate scores based on data input from previous layers of the multi-layer graph convolution network, figure 3, P0051-P0053, P0055).
Regarding claim 22, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang further teaches wherein the first layer is in the second layer, and the first score is used to adjust the second score in first layer in the second layer (concatenation layer 320 inputs data to fully connected layer 322, P0052).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lee and Sun and further in view of Cashion et al (Pub. No.: US 20230195734 A1), hereafter Cashion.
Regarding claim 2, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang in view of Lee and Sun does not appear to explicitly teach “analyzing the machine generated input to generate a first set of tokens that represent the machine generated input; and analyzing the user generated input to generate a second set of tokens that represent the user generated input”.
Cashion teaches analyzing the machine generated input to generate a first set of tokens that represent the machine generated input; and analyzing the user generated input to generate a second set of tokens that represent the user generated input (Tokenization is performed on first and second identifier sets to obtain a first set of tokens and a second set of tokens, P0016).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang, Lee, Sun, and Cashion before them, to include Cashion’s specific teaching of tokenizing sets of data in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of tokenizing sets of data (see Cashion P0016), and using various software components to secure data (see Wang P0097-P0098).
Regarding claim 3, Wang in view of Lee and Sun and further in view of Cashion teaches the limitations of claim 2 as outlined above. Cashion further teaches generating a first set of embeddings from the first set of tokens; and generating a second set of embeddings from the second set of tokens, wherein the first set of embeddings represents the machine generated input in a space and the second set of embeddings represents the user generated input in the space (Embeddings are generated for each token in the plurality of tokens in the first set of tokens and the second set of tokens, P0024, P0035).
Claims 5, 13, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lee and Sun and further in view of Pillay et al (Pub. No.: US 9710122 B1), hereafter Pillay.
Regarding claim 5, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang in view of Lee and Sun does not appear to explicitly teach wherein the link is based on a previous issue ticket and a previous error report being correlated together.
Pillay teaches wherein the link is based on a previous issue ticket and a previous error report being correlated together (Correlation and association may be determined between an error report and known errors. In this context known errors are interpreted as issue tickets because known errors are identified as errors, C4:L1-19).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang, Lee, Sun, and Pillay before them, to include Pillay’s specific teaching of determining correlation and association with error reports and known errors in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of determining correlation and association using error reports and known errors (see Pillay C4:L1-19), and using a graph neural network to determine the correlation of content categories and user-specific content (see Wang P0005).
Regarding claims 13 and 19, Wang in view of Lee and Sun teaches the limitations of claims 1 and 18 as outlined above. Wang in view of Lee and Sun does not appear to explicitly teach “wherein the category is associated with an issue ticket and a resolution for the issue ticket”.
Pillay teaches wherein the category is associated with an issue ticket and a resolution for the issue ticket (Metadata may include symptoms of known errors as well as steps for resolving errors. Symptoms of the errors are interpreted as a category the error belongs to, C5:L24-28, C6:L24-29).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang, Lee, Sun, and Pillay before them, to include Pillay’s specific teaching of known errors being categorized based on their symptoms in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of categorizing known errors based on their symptoms (see Pillay C5:L24-28, C6:L24-29), and using a graph neural network to determine the correlation of content categories and user-specific content (see Wang P0005).
Regarding claim 14, Wang in view of Lee and Sun teaches the limitations of claim 1 as outlined above. Wang further teaches the user generated input is generated by a user based on the user using the application (User interaction data is collected based on what content a user is viewing/interacting with, P0030-P0031).
Wang in view of Lee and Sun does not appear to explicitly teach “the machine generated input comprises an error report that is automatically generated by an application”.
Pillay teaches the machine generated input comprises an error report that is automatically generated by an application (Machine learning algorithm may be used to generate data for an error report, C7:L49-57).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Wang, Lee, Sun, and Pillay before them, to include Pillay’s specific teaching of using a machine learning algorithm to generate data for an error report in Wang’s method of mapping micro-video hashtags to content categories. One would have been motivated to make such a combination of using a machine learning algorithm to generate data for an error report (see Pillay C7:L49-57), and using a graph neural network to determine the correlation of content categories and user-specific content (see Wang P0005).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/I.M./Examiner, Art Unit 2141
/ANDREW L TANK/Primary Examiner, Art Unit 2141
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