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 § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 6-8, 11-14, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adams (US11599592B1) in view of Bax (US20210182700A1).
Regarding claim 1, Adams teaches an apparatus for actualizing future process outputs using artificial intelligence, the apparatus comprising:
at least a processor (Figure 1: 104); and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions (Col. 2, “ The apparatus may include at least a processor and a memory communicatively connected to the at least a processor. The memory may instruct the processor to receive a goal datum related to a user, wherein the goal datum comprises behavioral parameters of the user. The memory may additionally instruct the processor to classify the goal datum to a user goal. The classification may comprise training a goal classifier using a goal training data, wherein the goal training data contains a plurality of data entries correlating examples of goal datum to examples of goals. The classification may also comprise classifying the goal datum to the goal using the goal classifier. The classifier may assign the goal as a function of the classification. The memory may instruct the processor to generate a goal path as a function of the classification of the goal datum to a goal, wherein the goal path is divided into waypoints Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.”) configuring the at least a processor to:
receive input data associated with a user (Data used to generate goals, - “A user may be assigned a plurality of a user goals 108 as a function of a user input.”);
identify at least one future process output as a function of the input data (The goal which was generated is “at least one future process output” - “A user may be assigned a plurality of a user goals 108 as a function of a user input.”);
classify the input data into one or more objective groups using a classifier that has been trained with training data comprising correlations of exemplary input data and exemplary output data (Col. 6, lines 31-67, “With continued reference to FIG. 1, Processor 104 may be configured to assign the user a user goal 108 as a function of the classification of a user goal 108 to a goal datum 120. As used in the current disclosure, a “goal classifier” is a machine-learning model that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. Goal Classifier may be consistent with the classifier described below in FIG. 2. Inputs to the to the goal classifier 128 may include a plurality of user goals 108, Goal datum 120, survey data 136, behavioral parameters 124, action parameters, and the like. The output to the classifier 128 may be a goal 180 that is specific to the given user. Goal training data is a plurality of data entries containing a plurality of inputs that are correlated to a plurality of outputs for training a processor by a machine-learning process to align and classify a user's goal datum 108 to a user goal 108. Goal training data may be received from a database. Goal training data may contain information about plurality of user goals 108, Goal datum 120, survey data 136, behavioral parameters 124, action parameters, and the like. Goal training data may be generated from any past user goals 108, goal datum 120, survey data 136, behavioral parameters 124, action parameters, and the like. Goal training data may correlate an example of a user goal 108 to an example of a goal datum 120. The “example of a goal” and the “example of goal datum” may be prior a prior user goals 108 and a prior goal datum 120, respectively. In other embodiments, goal training data may be configured to correlate a user goal 108 to a goal datum 120. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers.”), wherein classifying comprises:
representing the input data and the one or more objective groups as a first vector output and one or more second vector outputs respectively (“Inputs to the to the goal classifier 128 may include a plurality of user goals 108, Goal datum 120, survey data 136, behavioral parameters 124, action parameters, and the like. The output to the classifier 128 may be a goal 180 that is specific to the given user.”, “With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like.”);
determining a distance between the first vector output and the one or more second vector outputs (“With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like.”); and
determining a similarity based on the distance between the first vector output and the one or more second vector outputs (“With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like.”);
determine at least one actualization item as a function of the one or more objective groups and the at least one future process output (Figure 1: 112), wherein determining the at least an actualization item using an actualization item machine learning model (Figure 1: 132, “With continued reference to FIG. 1, Processor 104 may be configured to generate a plurality of user goals 108 using a goal machine learning model 132. As used in the current disclosure, a “goal machine learning model” is a machine learning model, such as a mathematical and/or algorithmic representation of a relationship between inputs and outputs. A goal machine learning model 132 may be consistent with the machine learning model described herein below in FIG. 2. Inputs to the machine learning model may include an example of user goals 108, goal datum 120, survey data 136, behavioral parameters 124, action parameters, and the like. This data may be received from a database, such as goal database 300. Previous user goals 108, previous goal data 120, previous waypoints 116, and previous goal paths 112 may come from the current user or users similarly situated to the users by user interest, pecuniary status, and/or aptitude for task completion. Goal machine learning model 132 may be trained using training data such as prior user goals 108, survey data 136, and goal data 120. Training data may be received from a database, such as training data database 400. The output of the goal machine learning model 132 may be a plurality of user goals 108, a goal selection, goal path 112, and/or waypoints 116.”).
determine at least one process parameter as a function of the at least one future process output (Figure 1: 116);
generate a success expectation for the at least one future process output (“In an embodiment, an action parameter may be used to determine the likelihood of success of a user accomplishing a given user goal 108. An action parameter may be calculated as a function of a user goal 108, survey data 136, and behavioral parameters 124. In a non-limiting example, an action parameter input an element of survey data 136 stating that the user has been unsuccessful in her last three user goals 108.”); and
generate a graphical user interface comprising a plurality of display regions, wherein each display region is configured to present at least one of the at least one future process output, the at least one actualization item, the at least one process parameter, and the success expectation (“GUI 140 may be configured to display a pictorial icon. A “pictorial icon” as used in this disclosure is a graphic illustration displayed on a screen, where the graphic illustration is representative of a category. A “category” as used in this disclosure is a classification of one or more elements to one or more groups. A category may include, but is not limited to, user goal 108, goal path 112, and waypoints 116, and the like.”).
Adams does not teach calculating, for each of a plurality of candidate actualization items, an actualization item score representing a relative importance of the candidate actualization item for completion of the at least one future process output, the actualization item score being generated using a trained actualization item score machine learning model, comparing each actualization item score to a threshold actualization item score representing a minimum importance level for affecting completion of the at least one future process output, selecting the at least one actualization item from the plurality of candidate actualization items when the actualization item score exceeds the threshold actualization item score, and generating an ordered list ranking the selected actualization items based on the actualization item scores.
Bax teaches calculating, for each of a plurality of candidate actualization items, an actualization item score representing a relative importance of the candidate actualization item for completion of the at least one future process output (¶74, actualization items such as content items are ranked), the actualization item score being generated using a trained actualization item score machine learning model (¶74, the model can be considered a machine learning model), comparing each actualization item score to a threshold actualization item score representing a minimum importance level for affecting completion of the at least one future process output (¶74, causation factor above a threshold), selecting the at least one actualization item from the plurality of candidate actualization items when the actualization item score exceeds the threshold actualization item score, and generating an ordered list ranking the selected actualization items based on the actualization item scores (¶74, being above the threshold results in selection and forms the ordered list).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the process of Adams to include calculating, for each of a plurality of candidate actualization items, an actualization item score representing a relative importance of the candidate actualization item for completion of the at least one future process output, the actualization item score being generated using a trained actualization item score machine learning model, comparing each actualization item score to a threshold actualization item score representing a minimum importance level for affecting completion of the at least one future process output, selecting the at least one actualization item from the plurality of candidate actualization items when the actualization item score exceeds the threshold actualization item score, and generating an ordered list ranking the selected actualization items based on the actualization item scores in order to provide helpful suggestions to users.
Regarding claim 2, Adams as modified teaches all of the limitations of claim 1, wherein
the classifier comprises a k-nearest neighbors classifier (“With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like.”)
Regarding claim 3, Adams as modified teaches all of the limitations of claim 1, wherein
classifying the input data comprises normalizing each vector using a vector normalization function (“With continued reference to FIG. 1, generating k-nearest neighbors algorithm … Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,””).
Regarding claim 4, Adams as modified teaches all of the limitations of claim 1, wherein
classifying the input data comprises performing the classification of the input data to the one or more objective groups using a lazy learning algorithm that defers generalization until the input data is received (“Still referring to FIG. 2, machine-learning module 200 may be configured to perform a lazy-learning process 220 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand.”).
Regarding claim 6, Adams as modified teaches all of the limitations of claim 1, wherein generating the graphical user interface comprises:
generating and presenting a user prompt requesting a user response; and receiving a user input comprising a modification of at least one of the at least one future process output, the at least one actualization item, the at least one process parameter, and the success expectation (“In some embodiments, processor 104 may be configured to produce classification output results including the classified goal ranking to user goals 108 in a selectable format by user, including at least the ranked user goals 108 with the success score displayed by each user goal 108. For example, user may select to output classified goal ranking to user goals 108 in a pie chart, wherein the goal ranking to user goals 108 are divided, and color coded in selectable classification bins, showing the number of user goals 108 that fall into a classification.”)
Regarding claim 7, Adams as modified teaches all of the limitations of claim 1, wherein
the graphical user interface comprises a layered structure configured to provide additional information associated with one or more of the at least one of the at least one future process output, the at least one actualization item, the at least one process parameter, and the success expectation (“GUI 140 may be configured to display a pictorial icon. A “pictorial icon” as used in this disclosure is a graphic illustration displayed on a screen, where the graphic illustration is representative of a category. A “category” as used in this disclosure is a classification of one or more elements to one or more groups. A category may include, but is not limited to, user goal 108, goal path 112, and waypoints 116, and the like.”).
Regarding claim 8, Adams as modified teaches all of the limitations of claim 1, wherein
generating the graphical user interface comprises: transmitting information related to the at least one of the at least one future process output, the at least one actualization item, the at least one process parameter, and the success expectation over a communication network that is communicatively connected to the at least a processor (“GUI 140 may be configured to display a pictorial icon. A “pictorial icon” as used in this disclosure is a graphic illustration displayed on a screen, where the graphic illustration is representative of a category. A “category” as used in this disclosure is a classification of one or more elements to one or more groups. A category may include, but is not limited to, user goal 108, goal path 112, and waypoints 116, and the like.” – The GUI and the processor communicate with each other over some channel, or “network”).
Regarding claims 11-14 and 16-18, Adams as modified according to corresponding claims 1-4 and 6-8 performs the method of claims 11-14 and 16-18 under normal operation.
Claim(s) 5, 10, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adams (US11599592B1) in view of Bax (US20210182700A1), further in view of Kozlowski (US20230120897A1).
Regarding claim 5, Adams as modified teaches all of the limitations of claim 5 but does not teach
wherein classifying the input data comprises: classifying elements of the training data into categories based on a cryptographic return; and selecting a subset of the training data as a function of the categories.
Kozlowski teaches wherein classifying the input data comprises: classifying elements of the training data into categories based on a cryptographic return; and selecting a subset of the training data as a function of the categories (¶108).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Adams such that classifying includes classifying the input data comprises: classifying elements of the training data into categories based on a cryptographic return; and selecting a subset of the training data as a function of the categories in order to integrate the decentralized fiat of Adams (Figure 1, 144) into the classification process thereby resulting in a more robust reward structure.
Regarding claim 10, Adams as modified teaches all of the limitations of claim 1, but does not teach wherein the at least a processor comprises a secure computing module configured to detect and respond to software-based and hardware-based attacks.
Kozlowski teaches wherein the at least a processor comprises a secure computing module configured to detect and respond to software-based and hardware-based attacks (¶96).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Adams to have a processor which comprises a secure computing module configured to detect and respond to software-based and hardware-based attacks in order to provide a more secure product.
Regarding claims 15 and 20, Adams as modified according to corresponding claims 5 and 10 performs the method of claims 15 and 20 under normal operation.
Response to Arguments
Applicant’s arguments filed 04/03/2026 have been fully considered.
Applicant’s remarks are moot in view of the new grounds of rejection necessitated by amendment.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCHYLER S SANKS whose telephone number is (571)272-6125. The examiner can normally be reached 06:30 - 15:30 Central Time, M-F.
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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