Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims recite a method, non-transitory computer-readable
storage medium, and apparatus, each of which are one of the four categories of eligible subject matter.
Claims 1, 15, and 20
Step 2A Prong 1: The claims recite the following limitations:
generating, by the computing device, a first score that represents a correlation between the type of machine generated input and the type of user generated input (Mental Process and Mathematical Concept); analyzing, by the computing device, 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, wherein a second score associated with a confidence that the machine generated input or the user generated input belongs to the category is output (Mental Process and Mathematical Concept); and adjusting, by the computing device, a parameter of the prediction network based on the first score and the second score (Mental Process).
Generating a score that represents a correlation between machine generated input and user generated input is a mental process and mathematical concept because a human mind can practically calculate a correlation value between two inputs with the aid of a pencil, paper, and data. Analyzing a machine generated input and a user generated input to generate a confidence score is a mental process and mathematical concept because a human mind can practically calculate a confidence score with the aid of a pencil, paper, and data. Adjusting a parameter is a mental process under the broadest reasonable interpretation of the claim language. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application and the claim recites the following additional elements:
receiving, by a computing device, machine generated input and user generated input for training a model of a prediction network; receiving, by the computing device, a link between a type of machine generated input and a type of user generated input.
The non-transitory computer-readable storage medium, processors, and computing device are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Receiving machine generated input and a link between a type of machine generated input and a type of user generated input is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are directed towards an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The non-transitory computer-readable storage medium, processors, and computing device are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Receiving machine generated input and a link between a type of machine generated input and a type of user generated input is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are not patent eligible.
Dependent Claims:
Claims 2, 3, 4, 6, 7, 8, 9, 12, 16, and 17: These claims recite further abstract ideas (mental processes and mathematical concepts) and thus are ineligible.
Claims 5, 10, 11, 13, 14, 18, and 19: These claims recite further mere data gathering and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
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, 6-12, 15-18, 20 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.
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 for training 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 (The correlation model evaluates an association between a hashtag and micro-video content that a user is viewing, P0030); generating, by the computing device, 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 computing device, 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).
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 first score and the 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 first score and 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).
Regarding claim 4, Wang in view of Lee 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 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 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 claims 8 and 17, Wang in view of Lee teaches the limitations of claims 1 and 15 as outlined above. Lee further teaches wherein adjusting the parameter of the prediction network comprises: adjusting the second score using the first score to generate an adjusted score, and adjusting the parameter based on the adjusted score (False positive and false negative prediction models may produce an output based on a comparison between confidence scores and a threshold value. The results of the comparisons are used to adjust parameters of the prediction models, P0251-P0253).
Regarding claim 9, Wang in view of Lee teaches the limitations of claim 8 as outlined above. Lee further teaches wherein the parameter is adjusted based on a difference between the adjusted 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 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 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 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).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lee and further in view of Cashion et al (Pub. No.: US 20230195734 A1), hereafter Cashion.
Regarding claim 2, Wang in view of Lee teaches the limitations of claim 1 as outlined above. Wang in view of Lee 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, 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 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 further in view of Pillay et al (Pub. No.: US 9710122 B1), hereafter Pillay.
Regarding claim 5, Wang in view of Lee teaches the limitations of claim 1 as outlined above. Wang in view of Lee 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, 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 teaches the limitations of claims 1 and 18 as outlined above. Wang in view of Lee 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, 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 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 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, 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20200226477 A1 (Jayaraman et al) teaches a machine learning classifier with confidence thresholds.
US 11694059 B2 (Li et al) teaches a method for predicting user attributes including determining correlations between multiple feature types.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISHAN MOUNDI whose telephone number is (703)756-1547. The examiner can normally be reached 8:30 A.M. - 5 P.M..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/I.M./ Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
Prosecution Insights