DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 10/18/23. Claims 1-15 are pending in this Office 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 .
Claim Objections
Claims 13 and 15 are objected to because of the following informalities: Claims 13 and 15 recite the term "and/or", which is selective language, the examiner suggests using either the "and" term or the "or" term, otherwise the claims should be worded in a clearer fashion to claim both terms. For the purpose of this examination the examiner is selecting the "or" term from this selective language. Appropriate correction is required.
Allowable Subject Matter
Claims 4-5 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Although these claims are allowable over prior art, all other rejections and/or objections (if any) such as 101/112/claim objection must be overcome before the claims are allowed.
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.
2. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 12:
Step 1: Statutory Category ?: Yes. claim 1 recites a method (i.e., a “process”) and Claim 12 recite a system (i.e., a “machine”) which are statutory categories.
Claim 1:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes. Claim 1 recites one or more limitations that can be performed in human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
determining a set of membership probabilities for the test data, wherein the set of membership probabilities comprises a corresponding membership probability associated with each of the plurality of classes, and wherein the corresponding membership probability is indicative of a probability of the test data belonging to a corresponding class of the plurality of classes; and
determining, based on the input and the set of membership probabilities, the set of probable classes, from the plurality of classes, for the test data.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 1 recites additional elements :
“retrieving, from a memory comprising a training dataset, a plurality of classes, and a plurality of corresponding training feature vectors for each of the plurality of classes; receiving an input indicative of a target probability required for the test data” which is data gathering step and pre/post solution activity and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)).
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of data gathering and pre/post solution activity is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception (See MPEP 2106.05(d)) , subsection II). Even when consider in combination, the additional elements do not provide an inventive concept, claim 1 therefore ineligible.
Claim 2 recites additional element of “receiving an input indicative of a type associated with the test data, the type being one of a recognition type or a detection type” which is insignificant extra-solution activity and well-understood, routine , conventional activity (See MPEP 2106.05(d)) , subsection II). Even when consider in combination, the additional elements do not provide an inventive concept, claim 2 therefore ineligible.
Claim 3 recites additional element of “ wherein, based on the type associated with the test data being the recognition type, determining the set of membership probabilities for the test data comprises: selecting a class from the plurality of classes and for each selected class: accessing the plurality of corresponding training feature vectors; determining, based on the plurality of corresponding training feature vectors, a corresponding mean vector; and determining, based on the corresponding mean vector and a test feature vector associated with the test data, a corresponding distance vector associated with the selected class” which is a mathematical calculations . Claim 3 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 3 is not patent eligible.
Claim 4 recites additional element of “for each selected class: determining, based on the corresponding distance vector and the corresponding mean vector, a corresponding difference parameter for each of the plurality of corresponding feature vectors of the selected class, to determine a set of difference parameters associated with the selected class; and determining, based on the set of difference parameters, a standard deviation associated with the selected class” which is a mathematical calculations. Claim 4 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 4 is not patent eligible.
Claim 5 recites additional elements of :
“determining a distribution of the plurality of corresponding training feature vectors with respect to the corresponding mean vector” ( mathematical calculations)
“selecting a probability density function associated with the determined distribution” ( mathematical calculations)
determining the corresponding membership probability of the test feature vector for the selected class based on the probability density function, a magnitude of the corresponding distance vector, and the determined standard deviation( mathematical calculations)
determining the set of membership probabilities of the test feature vector based on the determined corresponding membership probability for each selected class of the plurality of classes.( mathematical calculations)
Claim 5 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 5 is not patent eligible.
Claim 6 recites additional elements of “extracting, from the test data, the test feature vector, wherein the test feature vector is associated with a plurality of features corresponding to the test data; and extracting, from training data associated with the training dataset, the plurality of corresponding training feature vectors for the plurality of classes” . The extracting steps are mathematical calculations steps. Claim 6 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 6 is not patent eligible.
Claim 7 recites additional elements of:
wherein, based on the type associated with the test data being the detection type, determining the set of membership probabilities for the test data comprises: receiving an input indicative of a system index (insignificant extra-solution activity and is well-understood, routine and conventional); and
determining a normalization factor based on the received system index and the plurality of the corresponding training feature vectors for each of the plurality of classes ( which is mathematical calculations and falls within the mathematical concepts). Claim 7 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 7 is not patent eligible.
Claim 8 recites additional elements of:
“selecting a class from among the plurality of classes; for each selected class, determining the corresponding membership probability for the selected class based on the normalization factor, a test feature vector associated with the test data, the system index, and the plurality of corresponding training feature vectors of the selected class; and determining the set of membership probabilities based on the determined corresponding membership probability for each selected class of the plurality of classes” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 8 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 8 is not patent eligible.
Claim 9 recites additional elements of: “extracting, from the test data, the test feature vector, wherein the test feature vector is associated with a plurality of features corresponding to the test data; and extracting, from training data associated with the training dataset, the plurality of corresponding training feature vectors for the plurality of classes” which are mathematical calculations steps. Claim 9 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 9 is not patent eligible.
Claim 10 recites additional elements of: “wherein determining the set of probable classes comprises: sorting the set of membership probabilities to form a sorted probability array; and selecting the set of probable classes based on the sorted probability array and the target probability, wherein a combined probability of the set of probable classes is greater than the target probability” which is a process that can be performed in the human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper. Claim 10 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 10 is not patent eligible.
Claim 11 recites additional elements of: “providing, via a user device, an output indicating the set of probable classes, wherein the output is one of a visual output or an audio-visual output” ” which is insignificant extra-solution activity and well-understood, routine , conventional activity (See MPEP 2106.05(d)) , subsection II). Even when consider in combination, the additional elements do not provide an inventive concept, claim 11 therefore ineligible.
Claim 12:
Step 2A-Prong 1: Judicial Exception Recited ?: Yes. Claim 12 recites one or more limitations that can be performed in human mind using observation, evaluation, judgment and opinion including with the help of a pen and paper:
determining a set of membership probabilities for the test data, wherein the set of membership probabilities comprises a corresponding membership probability associated with each of the plurality of classes, and wherein the corresponding membership probability is indicative of a probability of the test data belonging to a corresponding class of the plurality of classes; and
determining, based on the input and the set of membership probabilities, the set of probable classes, from the plurality of classes, for the test data.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 12 recites additional elements :
“retrieving, from a memory comprising a training dataset, a plurality of classes, and a plurality of corresponding training feature vectors for each of the plurality of classes; receiving an input indicative of a target probability required for the test data” which is data gathering step and pre/post solution activities and therefore are insignificant extra-solution activities. (See MPEP 2106.05(g)).
The additional element of “a memory; and at least one processor, comprising processing circuitry, communicatively coupled to the memory, at least one processor” amount to no more than mere instructions to apply the exception using generic computer components.
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 12 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of data gathering and pre/post solution activity is well-understood, routine conventional activities previously known to the industry and therefore do not amount to significantly more than the judicial exception (See MPEP 2106.05(d)) , subsection II). The memory, processor and circuitry are at best equivalent of adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 12 therefore is ineligible.
Claims 13-15 recites similar features of claims 2-4 and therefore rejected for the same reasons as indicates in the above rejection of claims 2-4.
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.
Claims 1 and 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Nimavat et al.(US Patent Application Publication 2022/0261687 A1, hereinafter “Nimavat” and further in view of Amershi et al.(US Patent Application Publication 2015/0242761 A1, hereinafter “Amershi”)
As to claim 1, Nimavat teaches a method for predicting a set of probable classes for test data, the method comprising:
retrieving, from a memory comprising a training dataset, a plurality of classes, and a plurality of corresponding training feature vectors for each of the plurality of classes; ( Nimavat par [0028] teaches feature extractor may be configured identify attributes and corresponding values in datasets and generate corresponding feature vectors. The feature extractor may identify entity attributes within training data and/or target data that a trained ML model is directed to analyze).
receiving an input indicative of a target probability required for the test data; ( Nimavat par [023] one or more clients are configured to submit a target set of requirements. )
determining a set of membership probabilities for the test data, wherein the set of membership probabilities comprises a corresponding membership probability associated with each of the plurality of classes, and wherein the corresponding membership probability is indicative of a probability of the test data belonging to a corresponding class of the plurality of classes ( Nimavat [0061] teaches the system generates a match score for a particular entity in light of a target set of requirements. Nimavat par [0073] teaches row 384A identifies user interface elements 304, 308, 312, 316, and 320 as having a match score from 90% to 100%. Row 388A indicates that user interface element 304 has a match score from 90% to 92%. Row 388B indicates that user interface element 308 has a match score from 93% to 94%. ) and
determining, based on the input and the set of membership probabilities, the set of probable classes, from the plurality of classes, for the test data. ( Nimavat par [0062] generating a GUI that presents individual interface elements, each of which corresponds to an entity, positioned within the GUI based on an entity match score)
Nimavat teaches receiving an input indicative of a target [ probability ] required for the test data but fails to expressly teach a target probability.
However, Amershi teaches a target probability required for the test data. (Amershi par [0051] teaches user may drag the prediction threshold indicator 320 to the left or right to increase or decrease the threshold value 322)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Nimavat and Amershi to achieve the claimed invention. One would have been motivated to make such combination to enable user to quickly identify, prioritize and inspect item-specific errors.(Amershi par [0017])
As to claim 10, Nimavat and Amershi teach the method as claimed in claim 1, wherein determining the set of probable classes comprises: sorting the set of membership probabilities to form a sorted probability array (Nimavat par [0062] teaches ranked array); and selecting the set of probable classes based on the sorted probability array and the target probability, wherein a combined probability of the set of probable classes is greater than the target probability. (Nimavat par [0065] teaches After presenting a GUI as described above in the operation 212, the system may receive user input that re-orders one or more user interface elements corresponding to one or more entities within the GUI (operation 216). More specifically, the system may receive input that moves one interface element from a first location within the GUI (e.g., the ranked list or grid) to a second, different location within the GUI)
As to claim 11, Nimavat and Amershi teach the method as claimed in claim 1, comprising: providing, via a user device, an output indicating the set of probable classes, wherein the output is one of a visual output or an audio-visual output. (Nimavat par [0036] teaches ML engine may generate a graphical user interface presenting a ranked array of individual interface elements corresponding to the analyzed and ranked target data)
Claim 12 merely recites a system to perform the method of claim 1. Accordingly , Nimavat and Amershi teach every limitation of claim 12 as indicates in the above rejection of claims 2-3
Claims 2-3,6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Nimavat and Amershi further in view of Zhang. (US Patent Application Publication 2022/0018221 A1, hereinafter “Zhang”)
As to claim 2, Nimavat and Amershi teach the method as claimed in claim 1 but fail to expressly teach comprising: receiving an input indicative of a type associated with the test data, the type being one of a recognition type or a detection type.
However, Zhang teaches receiving an input indicative of a type associated with the test data, the type being one of a recognition type or a detection type. (Zhang par [0037] teaches a test data classification sequence for a test data vector begins with classifier 232 or model select unit 235 determining the set of variable types in the test vector. For example, as part of a model request, classifier 232 may perform the determination or may transmit the test vector to be analyzed by model select unit 235. In response to model select unit 235 determining, directly or via indication from classifier 232, the set of variable types, model select unit 235 compares the set with the sets of variable types for each of the models )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of S Nimavat and Amershi and Zhang to achieve the claimed invention. One would have been motivated to make such combination to accurately and efficiently process test data.(Zhang par [0023])
As to claim 3, Nimavat and Amershi and Zhang teach the method as claimed in claim 2, wherein, based on the type associated with the test data being the recognition type, determining the set of membership probabilities for the test data comprises: selecting a class from the plurality of classes and for each selected class: accessing the plurality of corresponding training feature vectors; determining, based on the plurality of corresponding training feature vectors, a corresponding mean vector; and determining, based on the corresponding mean vector and a test feature vector associated with the test data, a corresponding distance vector associated with the selected class.( Zhang par [0044] teaches determining a similarity between a probability distribution for the test data set and the probability distributions for the multiple machine-learning models comprises calculating distance between data points in the probability distribution for the test data set and data points in the probability distributions for the machine-leaning models. Calculating mean vector is well known in the art)
As to claim 6, Nimavat and Amershi and Zhang teach the method as claimed in claim 3, comprising: extracting, from the test data, the test feature vector, wherein the test feature vector is associated with a plurality of features corresponding to the test data; and extracting, from training data associated with the training dataset, the plurality of corresponding training feature vectors for the plurality of classes. (Nimavat par [0028] teaches feature extractor may be configured identify attributes and corresponding values in datasets and generate corresponding feature vectors. The feature extractor may identify entity attributes within training data and/or target data that a trained ML model is directed to analyze).
As to claim 13-14, see the above rejection of claims 2-3.
Claims 7-9 rejected under 35 U.S.C. 103 as being unpatentable over Nimavat, Amershi , Zhang and further in view of Siddiqui et al. (US Patent Application Publication 2024/0371187 A1, hereinafter “Siddiqui”)
As to claim 7, Nimavat and Amershi and Zhang teach the method as claimed in claim 2 but fail to teach wherein, based on the type associated with the test data being the detection type, determining the set of membership probabilities for the test data comprises: receiving an input indicative of a system index; and determining a normalization factor based on the received system index and the plurality of the corresponding training feature vectors for each of the plurality of classes.
However, Siddiqui teaches wherein, based on the type associated with the test data being the detection type, determining the set of membership probabilities for the test data comprises: receiving an input indicative of a system index; and determining a normalization factor based on the received system index and the plurality of the corresponding training feature vectors for each of the plurality of classes. (Siddiqui par [0034] teaches for each class in the subset of classes under consideration for a given word, a normalized probability P.sub.i.sup.w that the NLP model 100 assigns that class to the word may be determined and these normalized probabilities P.sub.i.sup.w may be summed)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Nimavat and Amershi and Zhang with the teaching of Siddiqui to achieve the claimed invention. One would have been motivated to make such combination to cause the model to put more probability on the correct classes.(Siddiqui par [0028])
As to claim 8, Nimavat and Amershi , Zhang and Siddiqui teach the method as claimed in claim 7, comprising: selecting a class from among the plurality of classes; for each selected class, determining the corresponding membership probability for the selected class based on the normalization factor, a test feature vector associated with the test data, the system index, and the plurality of corresponding training feature vectors of the selected class; and determining the set of membership probabilities based on the determined corresponding membership probability for each selected class of the plurality of classes. (Siddiqui par [0047] teaches a normalized probability P.sub.c.sup.w that the NLP model 100 assigns the class c to the word may be determined for each class c among the assigned labels, i.e., for each different annotated class represented by the annotation data 130-0, 130-1 for the word. The normalized probabilities P.sub.c.sup.w may be summed, resulting in a quantity that may be thought of as the normalized probability of the model's predicted class membership of the word being among the different annotated classes)
As to claim 9, Nimavat and Amershi , Zhang and Siddiqui teach the method as claimed in claim 8, comprising: extracting, from the test data, the test feature vector, wherein the test feature vector is associated with a plurality of features corresponding to the test data; and extracting, from training data associated with the training dataset, the plurality of corresponding training feature vectors for the plurality of classes. (Siddiqui par [0023] teaches For each item of input data 110, the model output data 120 may represent the predicted class membership as a vector or other set of probabilities that the item of input data 110 belongs to each of a plurality of classes (e.g., bird, vehicle, mammal, etc.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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