Detailed Action
This Office Action is in response to the remarks entered on 11/26/2025. Claim 1-19, 26-29 and 31 are currently pending.
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.
Claim 1-19, 26-29 and 31 are rejected in view of 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
2A Prong 1:
identifying, (mental process of judgment and evaluation – selecting and combining a plurality of models into a simplified tree structure and determining topics in input data by traversing a decision tree can be done with the aid of pen and paper)
activating, second model, in response to an identification of a first topic within the first instance of input by the first model; (mental process of judgment – selecting a model to use based on the identification result of the first model does not require a computer component and can be done with the aid of pen and paper)
activating, third model in response to an identification of a second topic within the second instance of input by the second model; (mental process of judgment – selecting a model to use based on the identification result of the second model does not require a computer component and can be done with the aid of pen and paper)
outputting, identified by the third model utilizing the third instance of input. (mental process of judging – merely recites a process of traversing a decision tree to classify the input data into smaller categories)
2A Prong 2:
A computer-implemented method, comprising: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
identifying, by a computer, a complex model (mere instructions to apply an exception using a computer MPEP 2106.05(f) – using a computer to perform the model identification process)
applying, by the computer, a first instance of input from the input data only to the first model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
activating, by the computer, the second model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the second model being activated: applying a second instance of input, that is different than the first instance of input, to the first model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data to a model)
applying the second instance of input to the second model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
activating, by the computer, the third model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the third model being activated: applying a third instance of input, that is different than the first instance of input and the second instance of input, to the first model, (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the third instance of input to the second model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data to a model)
applying the third instance of input to the third model; and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data to a model)
outputting, by the computer to a computer memory, an identification of a third topic (mere instructions to apply an exception using a computer MPEP 2106.05(f))
2B:
A computer-implemented method, comprising: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
identifying, by a computer, a complex model (mere instructions to apply an exception using a computer MPEP 2106.05(f) – using a computer to perform the model identification process)
applying, by the computer, a first instance of input from the input data only to the first model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
activating, by the computer, the second model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the second model being activated: applying a second instance of input, that is different than the first instance of input, to the first model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
applying the second instance of input to the second model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
activating, by the computer, the third model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the third model being activated: applying a third instance of input, that is different than the first instance of input and the second instance of input, to the first model, (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the third instance of input to the second model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
applying the third instance of input to the third model; and (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
outputting, by the computer to a computer memory, an identification of a third topic (mere instructions to apply an exception using a computer MPEP 2106.05(f))
Regarding claim 2,
2A Prong 1: Incorporates the rejection of claim 3.
2A Prong 2: wherein the first model includes a first neural network implemented in hardware, wherein the second model includes a second neural network implemented in hardware, wherein the third model includes a third neural network implemented in hardware. (merely says which particular technological field or environment applied as a tool to perform the abstract idea MPEP 2106.05(h))
2B: wherein the first model includes a first neural network implemented in hardware, wherein the second model includes a second neural network implemented in hardware, wherein the third model includes a third neural network implemented in hardware. (merely says which particular technological field or environment applied as a tool to perform the abstract idea MPEP 2106.05(h))
Regarding claim 3,
2A Prong 1: comprising creating the complex model by combining the plurality of simplified models into the tree structure, (mental process of judgment and evaluation – selecting and combining a plurality of models into a tree structure and determining topics in input data can be done in one’s mind without a computer component)
determining relationships between the simplified models, (mental process of observation – anyone who knows the art (e.g., a programmer) can determine relationship between models)
arranging the simplified models into the tree structure, (mental process of judgment – similar to a person arranging the models which does not require a computer component)
2A Prong 2: wherein the tree structure represents an interrelationship between the individual simplified models, wherein creating the complex model further includes: (merely says which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h))
training the simplified models in the arranged tree structure. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning model training)
2B: wherein the tree structure represents an interrelationship between the individual simplified models, wherein creating the complex model further includes: (merely says which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h))
training the simplified models in the arranged tree structure. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning model training)
Regarding claim 4,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: comprising training the first model to identify the first topic using first training data; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
training the second model to identify the second topic using second training data that is different than the first training data; and (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
training the third model to identify the third topic using third training data that is different than the first and second training data, wherein an amount of the first training data is smaller than a total amount of the first, second and third training data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
2B: comprising training the first model to identify the first topic using first training data; (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
training the second model to identify the second topic using second training data that is different than the first training data; and (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
training the third model to identify the third topic using third training data that is different than the first and second training data, wherein an amount of the first training data is smaller than a total amount of the first, second and third training data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic training process of machine learning models)
Regarding claim 5,
2A Prong 1:
deactivating the second and third models and activating a fourth model in response to an identification of a fourth topic within the fourth instance of input by the first model; and (mental process of judgment – not traversing the branch that was not selected, which can be done with the aid of pen and paper)
2A Prong 2: comprising receiving a fourth instance of input that is different than the first, second, and third instances of input; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
applying the fourth instance of input to the first model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
applying a fifth instance of input that is different than the first, second, third, and fourth instances of input to the fourth model. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
2B: comprising receiving a fourth instance of input that is different than the first, second, and third instances of input; (was indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(iv) of gathering statistics)
applying the fourth instance of input to the first model; (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
applying a fifth instance of input that is different than the first, second, third, and fourth instances of input to the fourth model. (mere instructions to apply an exception using a computer MPEP 2106.05(f) – inputting data and processing the input data using the model)
Regarding claim 6,
2A Prong 1: wherein first instance of input is selected from a group consisting of textual data, audio data, and time series data (a mental process, because it merely recites the process of selecting a type of data)
2A Prong 2: The judicial exception is not integrated into a practical application.
2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Regarding claim 7,
2A Prong 1: wherein in response to the identification of the first topic within the first instance of input, all children of the first model within the tree structure are activated (a mental process as it merely recites the process of operating the child nodes when the root node finishes its computation)
2A Prong 2: The judicial exception is not integrated into a practical application.
2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Regarding claim 8,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the first model includes a root model within the tree structure, the second model includes an intermediate model within the tree structure, and the third model includes a terminal model within the tree structure (merely recites which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h)).
2B: wherein the first model includes a root model within the tree structure, the second model includes an intermediate model within the tree structure, and the third model includes a terminal model within the tree structure (merely recites which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h)).
Regarding claim 9,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the first instance of input includes a first portion of the input data, where the input data is divided into a plurality of chronologically arranged portions (merely recites which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h)).
2B: wherein the first instance of input includes a first portion of the input data, where the input data is divided into a plurality of chronologically arranged portions (merely recites which particular technological field or environment the abstract idea is performed in MPEP 2106.05(h)).
Regarding claim 10,
2A Prong 1:
activating, process of judgment – determining which model to activate based on the identification result of the first model does not require a computer component and can be done in one’s mind)
activating
outputting, by the third model based on the third instance of textual input, (mental process of judging – merely recites a process of traversing a decision tree to classify the input data into smaller categories)
wherein the tree structure is formed by: (mental process of judgment – similar to a person arranging the models which does not require a computer component)
identifying a complex model that determines a plurality of topics in input data, (mental process of judgment – determining whether the model is complex can be done in the human mind)
decomposing the complex model into a number of simplified models (mental process of judgment – determining which model belongs to which set of models can be done with the aid of pencil and paper)
2A Prong 2:
A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more hardware processors to perform a method comprising: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying, by the one or more hardware processors, a first instance of textual input only to a first model within a tree structure, the first instance of textual input being a phrase of multiple words; (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
activating, by the one or more hardware processors, a second model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the second model being activated: reapplying, by the one or more hardware processors, a second instance of textual input that is different than the first instance of input, to the first model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the second instance of textual input to the second model, the second instance of textual input being a phrase of multiple words; (mere instructions to apply an exception using a computer MPEP 2106.05(f))
activating, by the one or more hardware processors, a third model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the third model being activated: reapplying, by the one or more hardware processors, a third instance of textual input that is different than the first instance of textual input and the second instance of textual input, to the first model, (mere instructions to apply an exception using a computer MPEP 2106.05(f))
reapplying the third instance of textual input to the second model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the third instance of textual input to the third model, the third instance of textual input being a phrase of multiple words; and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
outputting, by the one or more hardware processors (mere instructions to apply an exception using a computer MPEP 2106.05(f))
training the number of simplified models in the tree structure using unique sets of training data, wherein the sets of training data are individually smaller than a combined amount of the training data in the sets of training data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning model training)
2B:
A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more hardware processors to perform a method comprising: (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying, by the one or more hardware processors, a first instance of textual input only to a first model within a tree structure, the first instance of textual input being a phrase of multiple words; (mere instructions to apply an exception using a computer MPEP 2106.05(f))
activating, by the one or more hardware processors, a second model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the second model being activated: reapplying, by the one or more hardware processors, a second instance of textual input that is different than the first instance of input, to the first model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the second instance of textual input to the second model, the second instance of textual input being a phrase of multiple words; (mere instructions to apply an exception using a computer MPEP 2106.05(f))
activating, by the one or more hardware processors, a third model (mere instructions to apply an exception using a computer MPEP 2106.05(f))
in response to the third model being activated: reapplying, by the one or more hardware processors, a third instance of textual input that is different than the first instance of textual input and the second instance of textual input, to the first model, (mere instructions to apply an exception using a computer MPEP 2106.05(f))
reapplying the third instance of textual input to the second model, and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
applying the third instance of textual input to the third model, the third instance of textual input being a phrase of multiple words; and (mere instructions to apply an exception using a computer MPEP 2106.05(f))
outputting, by the one or more hardware processors (mere instructions to apply an exception using a computer MPEP 2106.05(f))
training the number of simplified models in the tree structure using unique sets of training data, wherein the sets of training data are individually smaller than a combined amount of the training data in the sets of training data. (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f) – generic machine learning model training)
Claim 11 is a computer program product claim having similar limitation to method claim 2 above. Therefore, it is rejected under the same rationale as of claim 2 above.
Claim 12 is a computer program product claim having similar limitation to method claim 3 above. Therefore, it is rejected under the same rationale as of claim 3 above.
Claim 13 is a computer program product claim having similar limitation to method claim 4 above. Therefore, it is rejected under the same rationale as of claim 4 above.
Claim 14 is a computer program product claim having similar limitation to method claim 5 above. Therefore, it is rejected under the same rationale as of claim 5 above.
Regarding claim 15,
2A Prong 1: Incorporates the rejection of claim 10.
2A Prong 2: wherein first, second, and third instances of textual input are collectively a single continuous string of text from the input (a field of use and technological environment MPEP 2106.05(h)).
2B: wherein first, second, and third instances of textual input are collectively a single continuous string of text from the input (a field of use and technological environment MPEP 2106.05(h)).
Regarding claim 16,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: in response to the identification of the first topic within the first instance of input by the first model, all children of the first model are provided the second instance of input, and (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
in response to the identification of the second topic within the second instance of input by the second model, all children of the second model are provided the third instance of input. (insignificant extra-solution activity MPEP 2106.05(g)(iii) of gathering statistics)
2B: in response to the identification of the first topic within the first instance of input by the first model, all children of the first model are provided the second instance of input, and (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(i) of transmitting or receiving data over a network)
in response to the identification of the second topic within the second instance of input by the second model, all children of the second model are provided the third instance of input. (indicated as an insignificant extra-solution activity MPEP 2106.05(g)(iii) in Step 2A Prong 2. Therefore, the limitation is re-evaluated in Step 2B as well understood, routine and conventional activity MPEP 2106.05(d)(II)(i) of transmitting or receiving data over a network)
Regarding claim 17,
2A Prong 1: the first model is associated with the first topic and searches for the first topic within the first instance of input, the second model is associated with the second topic and searches for the second topic within the first instance of input, and the first model is associated with the third topic and searches for the third topic within the first instance of input (a mental process of observation – analogous to a group of person searching for different topics which does not require a computer component)
2A Prong 2: The judicial exception is not integrated into a practical application.
2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Regarding claim 18,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: the first instance of input includes a first portion of a plurality of sequentially organized instances of input, the second instance of input includes a second portion of the plurality of sequentially organized instances of input occurring immediately after the first instance of input, and the third instance of input includes a third portion of the plurality of sequentially organized instances of input occurring immediately after the second instance of input (particular technological field or environment MPEP 2106.05(h), as the limitation merely recites the input data is divided into several groups and sequentially organized)
2B: the first instance of input includes a first portion of a plurality of sequentially organized instances of input, the second instance of input includes a second portion of the plurality of sequentially organized instances of input occurring immediately after the first instance of input, and the third instance of input includes a third portion of the plurality of sequentially organized instances of input occurring immediately after the second instance of input (particular technological field or environment MPEP 2106.05(h), as the limitation merely recites the input data is divided into several groups and sequentially organized)
Regarding claim 19,
2A Prong 1: Claim 19 is a system claim having similar limitation to method claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
2A Prong 2: A system, comprising: a plurality of neural networks implemented in hardware; a hardware processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: A system, comprising: a plurality of neural networks implemented in hardware; a hardware processor; and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Regarding claim 26,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the input data is a single textual document, wherein the first, second and third instances of input are arranged sequentially in the textual document and are processed according to the sequential arrangement. (directed to field of use and technological environment MPEP 2106.05(h))
2B: wherein the input data is a single textual document, wherein the first, second and third instances of input are arranged sequentially in the textual document and are processed according to the sequential arrangement. (directed to field of use and technological environment MPEP 2106.05(h))
Regarding claim 27,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the input data is an audio recording (directed to field of use and technological environment MPEP 2106.05(h)).
2B: wherein the input data is an audio recording (directed to field of use and technological environment MPEP 2106.05(h)).
Regarding claim 28,
2A Prong 1: Incorporates the rejection of claim 1.
2A Prong 2: wherein the instances of data are sequential strings of data from a same textual document (directed to field of use and technological environment MPEP 2106.05(h)).
2B: wherein the instances of data are sequential strings of data from a same textual document (directed to field of use and technological environment MPEP 2106.05(h)).
Regarding claim 29,
2A Prong 1: Incorporates the rejection of claim 10.
2A Prong 2: wherein the input data is a single textual document (directed to field of use and technological environment MPEP 2106.05(h)).
2B: wherein the input data is a single textual document (directed to field of use and technological environment MPEP 2106.05(h)).
Regarding claim 31,
2A Prong 1: Incorporates the rejection of claim 6.
2A Prong 2: wherein the first model includes a root model within the tree structure, the second model includes an intermediate model within the tree structure, and the third model includes a terminal model within the tree structure, wherein the first instance of input includes a first portion of the input data, wherein the input data is divided into a plurality of chronologically arranged portions (directed to field of use and technological environment MPEP 2106.05(h)).
2B: wherein the first model includes a root model within the tree structure, the second model includes an intermediate model within the tree structure, and the third model includes a terminal model within the tree structure, wherein the first instance of input includes a first portion of the input data, wherein the input data is divided into a plurality of chronologically arranged portions (directed to field of use and technological environment MPEP 2106.05(h)).
Response to Arguments
Response to arguments under 35 U.S.C. 101
Applicant’s arguments filed 11/26/2025 have been fully considered but they are not persuasive.
Arguments: [Remarks, page 13-14] Applicant cites Ex parte Desjardins, a case involving a machine learning model trained on multiple tasks and asserts that since Ex parte Desjardins improves the functioning of a computer, the present application improves the functioning of a computer [0002], [0053] and [0071]. [Remarks, page 15-17] Applicant also asserts that Example 47 of “July 2024 Subject Matter Eligibility Examples” and the spec [0053] supports that the claim as a whole integrates the exception into a practical application.
Response: Examiner respectfully disagrees. First, Director Squires’ statement regarding Ex parte Desjardins, Enfish, LLC v. Microsoft Corp. and McRo, Inc. v. Bandai Namco Games America Inc. are not analogous to the present invention. MPEP 2106.04(d)(1) states that “The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.” The court deemed the cited cases patent eligible because the specification sets forth the improvement and the claim reflect it. For example, Claim 1 in Ex parte Desjardins discloses computing, assigning, training, and adjusting process with sufficient details so the improvement is apparent to a person with ordinary skill in the art. Especially, “assigning, using the approximation, a value to each of the plurality of parameters, the value being the respective measure of the importance of the parameter to the first machine learning task and approximating a probability that the first value of the parameter after the training on the first machine learning task is a correct value of the parameter given the first training data used to train the machine learning model on the first machine learning task;” and “training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” cannot be performed in one’s mind and clearly reflects the improvements recited in the specification.
However, the present application paragraphs [0002], [0053] and [0071] fails to disclose the improvement as the paragraphs fails to disclose how the simplification process is performed, how the method trains each neural network, and/or how the simplification process reduces the amount of required storage space and processor utilization. The cited spec [0053] merely discloses a conclusion that the simplification reduces an amount of storage space, processor utilization, and memory usage to train and implement the topic-identification models.
Example 47 provides guidance on how to evaluate whether the claim recites an abstract idea. As the applicant explained in the remarks, the evaluation is performed based on MPEP 2106.04(d)(1) and 2106.05(a). As discussed above, MPEP 2106.04(d)(1) states that a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art cannot be said to improve the technology.
The applicant’s argument of “For example, if the complex model has M inputs and N outputs, raining data on the order of MxN is necessary for training the complex model. By decomposing the complex model into M simplified models each having one input, training data on the order of M+N is necessary for training and simplified model” does not make sense because the ‘decomposing’ involves dividing a large model into a plurality of smaller models and each smaller models still requires training data on the order of M+N thereby the total amount of order of training data required will be (Number of simplified models)x(M+N). This still requires a burden of training data therefore the improvement is not apparent to a person with ordinary skill in the art. Since the improvement is not apparent to a person with ordinary skill in the art, the claim is not directed to the judicial exception.
Lastly, even though the claims were amended to recite ‘identifying, by a computer’, ‘applying, by the computer’, ‘activating, by the computer’ … the computers are recited at a high level of generality which directs to mere instructions to apply the abstract idea using a generic computer component. Example 47 claim 2 further supports that even the claim recites ‘by a computer …’ the claim is not eligible if the computer component is recited at a high level of generality.
Accordingly, arguments to claims 1, 10 and 19 are not persuasive. Therefore, arguments to claims 2-9, 11-18, 26-29 and 31 depend from claims 1, 10 and 19 are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Quinlan, “Simplifying decision trees”, 1999 (This prior art is pertinent as the prior art teaches simplification method for a decision tree)
THIS ACTION IS MADE FINAL. 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 JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 7:30AM – 4:30PM ET.
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, Abdullah Kawsar can be reached at (571)270-3169. 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.
/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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