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 .
Examiner’s Note
The Examiner strongly encourages Applicant to schedule an interview to discuss issues related to, for example, the rejections noted below under 35 U.S.C § 101, for moving toward allowance. (e.g., elaborating the last limitation toward a practical application)
Providing supporting paragraph(s) for each limitation of amended/new claim(s) in Remarks is strongly requested for clear and definite claim interpretations by Examiner (e.g., to avoid rejections under 35 U.S.C § 112(a) “Lack of written description”)
Applicant can schedule interviews (via Automated Interview Request (AIR)) at any stage of the prosecution (e.g., Non-Final, Final, and After-Final) to discuss any issues related to, for example, rejections under 35 U.S.C § 101 and § 102/103, for moving toward allowance.
Priority
Acknowledgment is made of applicant's claim for the provisional application (62/719,849) filed on 08/20/2018.
Response to Arguments
Applicant's arguments filed on 06/23/2026 have been fully considered but they are not persuasive.
In Remarks, pp. 11-31, Applicant contends:
Accordingly, the present invention combines multiple machine learning models to "minimize bias and variance" and/or "broaden the applicability of the solution" to bring forth an improvement to computer technology.
Furthermore, the components or steps which provide the asserted improvement are present within the claims; namely, "generating, at the computer system, at least one machine learning model relevant to the problem by generating, at the computer system, a new machine learning model based on type, morphology, and parameter information," "determining, at the computer system, a combination of selected and generated models that produces higher accuracy results than the selected and generated models based on a calculated cosine similarity between results of the selected and generated models," and "assembling, at the computer system, a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models."
…
The alleged abstract idea of a mental process would not have the problem of a narrow solution arising from a domain-specific machine learning model, and any problem of the alleged abstract idea would not be solved by combining multiple machine learning models to broaden the applicability of the solution. Therefore, Applicant respectfully submits that "[broadening] the applicability of the solution" is an improvement to computer technology rather than solely an improvement to the alleged judicial exception.
…
One of ordinary skill in the art, then, would realize that mapping each step to one of the three layers based on the task objective would provide the requisite processing power for each step while minimizing the delay of the workflow, as reinforced by para. [O 177], " ... the three layer computing infrastructure ( cloud, gateway, sensors) may provide flexibility and adaptability for the entire workflow."
…
mapping each step of the solution plan to one of the plurality of layers based on the task objective provides an improvement to the functioning of a computer compared to the alternatives. Additionally, while the "three layer computing infrastructure ... may provide flexibility and adaptability ... ", one of ordinary skill in the art upon reading the specification would understand that the three layer computing infrastructure requires the mapping step to realize the improvement (flexibility, adaptability, and minimization of unnecessary delay, as understood by one of ordinary skill in the art upon reading the specification.)
…
The present invention is operable to determine the best layer for execution for each step of the solution plan. The layers which are utilized may be a combination of all three layers, or may be a subset of the three layers, depending on the steps of the solution plan and the task objective.
Examiner’s response:
The examiner understands the applicant’s assertion.
However, it appears that each processing step is just applying the abstract idea to a general field of endeavor with additional elements. In addition, improvements to technology or technical field are not necessarily reflected in the claims. Thus, the claim does not integrate the judicial exception into a practical application, and the claim does not amount to significantly more than the judicial exception.
The examiner understands the applicant’s assertion regarding minimizing bias and variance and higher accuracy.
However, par 110 states “From ensemble learning methods we know that a combination of lower accuracy models may perform better than a higher accuracy model due to overcoming bias” and par 155 says “Model Combination. For any given situation, Selector 452 may not be constrained to using a single model, but may activate a combination of models for ensemble learning, for example, to minimize bias and variance.” Based on the par 110, it appears that it is known that ensemble learning methods minimize bias, and based on the par 155, it appears that “minimizing bias and variance” is mentioned just an example of the ensemble learning, but not a key inventive concept of the whole invention. Assuming, arguendo, that they could be improvements of the invention, it is not clear how “determining, at the computer system, a combination of selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models based on a calculated cosine similarity between results of the selected and generated machine learning models, and assembling, at the computer system, the combination of the selected and generated machine learning models based on the determination of the combination of the selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models” provides the asserted improvements. Basically, it appears that the claim limitations are not detailed enough to provide the asserted improvements.
The examiner understands the applicant’s assertion regarding broadening the applicability of the solution.
However, par 156 says “Accordingly, cos θ may be used as a metric of congruence between different models. However, embodiments may also use less correlated models, which learn different things, to broaden the applicability of the solution.” Based on the par 156, it appears that the invention just may use another option of “less correlated models”, and the “less correlated models” may broaden the applicability of the solution. However, the specification does not provide what the “less correlated models” are and how the “less correlated models” broaden the applicability of the solution. In addition, it appears that the claims do not specify and elaborate the “less correlated models”. Thus, even assuming, arguendo, it is considered improvements, still it is not clear how the claims reflect the asserted improvements.
The examiner understands the applicant’s assertion regarding the plurality of layers and "[providing] flexibility and adaptability," “minimizing the delay of the workflow”, “provide the requisite processing power for each step while minimizing the delay of the workflow”, “determine the best layer for execution for each step of the solution plan”.
It appears that minimizing the delay of the workflow for the inventive system may be an improvement by determining the best layer for execution for each step of the solution plan and providing the requisite processing power for each step while providing flexibility and adaptability. However, the claim 1 just recites “mapping each step of the series of processing steps of the processing flow to one of a plurality of layers of the selected computing infrastructure, the plurality of layers comprising a sensors layer, a gateway layer, and a cloud layer, wherein the sensors layer is deployed on an edge computing layer, wherein the gateway layer is equipped with a computing capability suitable for executing a neural network, and wherein the cloud layer is equipped with a computing capability suitable for training the neural network and/or executing simulation tasks”. Basically, the limitation does not provide how to determine a best layer for execution for each step of the solution plan, but it just says “mapping each step of the series of processing steps of the processing flow to one of a plurality of layers of the selected computing infrastructure”. In addition, it is not clear why/how determining the best layer provides flexibility and adaptability in terms of the technologies of the inventive system.
The examiner understands the applicant’s assertion regarding Step 2B.
However, as rejected as rejected under Claim Rejections - 35 USC § 101, when read individually and/or in combination as a whole, it appears that the claim does not provide improvements for the whole invention. Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The Remarks states that the claimed invention provides improvements for each technology mentioned above, but details of how the claimed invention provides improvements for those technologies is missing. Showing details of how the claimed invention provides improvements for a specific technology toward a practical application may help overcome the rejections under 35 USC § 101.
It does not appear that the limitations clearly show e.g., improvements in computer technology and improvements to other technical fields. Rather, it appears that the improvements in Remarks are about just improving the abstract ideas of the independent claims. It doesn’t seem that the specification and/or the independent claims clearly show how the inventive concept of the claims enables improvements and how they are tied together. The applicant may need to amend the claims to show how the claim languages and improvements are tied together.
To find a valid improvement to a technology, MPEP 2106.04(d)(1) says the specification must explain the improvement and that the claim must reflect the disclosed improvement. Furthermore, the improvement should not be merely a consequence of the abstract idea. See MPEP 2106.05(a). An improvement in the abstract idea itself is not an improvement to technology.
For at least these reasons, Applicant's arguments are not convincing.
However, it appears that elaborating the last limitation toward a practical application may overcome the existing 101 rejections. The Examiner encourages Applicant to schedule an interview to discuss issues related to, for example, the rejections noted below under 35 U.S.C § 101.
Allowable Subject Matter
Claims 1, 4, 6-7, 10, 12-13, 16, 18 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112 and 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 4, 6-7, 10, 12-13, 16, 18 are considered allowable since when reading the claims in light of the specification, none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specific in the independent claim including at least:
From independent claim 1, 7, 13:
generating, at the computer system, at least one machine learning model relevant to the problem by generating, at the computer system, a new machine learning model based on type, morphology, and parameter information, wherein the at least one machine learning model relevant to the problem is further generated by:
determining, at the computer system, a combination of selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models based on a calculated cosine similarity between results of the selected and generated machine learning models, and
assembling, at the computer system, the combination of the selected and generated machine learning models based on the determination of the combination of the selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models;
selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem, wherein the selected computing infrastructure comprises a mesh of interconnected applications, wherein the selected computing infrastructure includes at least one framework configured to perform model tuning and hyperparameters optimization using at least Bayesian optimization and metaheuristics, at least one container configured to run stateful services, and at least one graphics processing unit, and wherein each application comprises analog and digital input, event ingestion processing, event consumption processing, event generation processing, and analog and digital output; and
The closest prior art of record, Sun et al. (US 2017/0011308 A1) teaches receiving a description of a problem and applying machine learning to automatically solve the problem. The system receives a description of a problem, and assigning, by a clustering engine, the problem to a class, and identifying, by a correlation engine, a database associated with the class. The system also provides a suggestion for solving the problem, based on the retrieved data.
Kshepakaran et al. (US 20170364637 A1) teaches various deep learning and machine learning applications by taking advantage of big data, using exemplary deep learning algorithms, neural networks and/or artificial intelligence and/or expert systems and/or convolutional neural networks, to identify potential automated recommendations and/or electronic alerts.
Claesen et al. (Hyperparameter Search in Machine Learning) teaches a wide variety of optimization methods for hyperparameter search, including particle swarm optimization, genetic algorithms, coupled simulated annealing and racing algorithms. Software packages are released which implement various dedicated optimization methods for hyperparameter search, including Bayesian methods and metaheuristic optimization approaches.
Shintre et al. (US10225277B1) teaches application programming interface (API) queries of a model inversion attack against the machine learning classifier to protect the user data point from being exposed by the model inversion attack. The machine learning classifier may be exposed to the API as a Machine Learning as a Service (MLaaS) offering to enable API queries against the machine learning classifier.
WETMORE et al. (US 20140057232 A1) teaches an Arduino system which includes digital inputs and outputs, analog inputs and outputs, serial receiver, serial transmission, and power (both 5V and 3.3V). The microcontroller system is programmed with custom software for controlling the various elements of the system for memory enhancement.
TEIG et al. (US 20180025268 A1) teaches configurable machine learning assemblies for autonomous operation in personal devices. Example systems implement machine learning based on neural networks that draw low power for use in diverse devices. The onboard machine learning assemblies can be powered by batteries, and once onboard a small personal device can learn to perform object recognition and autonomous decision-making without access to outside resources.
Ross et al. (US 2018/0232663 A1) teaches a system that gathers machine learning models from various sources, and the machine learning models are identified by the input format that they receive, and an output label that they identify from the input format. In addition, the system can improve the accuracy of the machine learning model by combining the existing machine learning models in serial, in parallel, or hierarchically, to create a resulting machine learning model.
However, none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specific in the independent claim including at least:
the limitations recited above from the independent claim 1, 7, 13,
as in the claims for the purpose of providing an intelligent adaptive system that combines input data types, processing history and objectives, research knowledge, and situational context to determine the most appropriate mathematical model, choose the computing infrastructure, and propose the best solution for a given problem based on a selected computing infrastructure.
In addition, the dependent claim(s) is/are also considered allowable since the dependent claim(s) is/are dependent on the independent claim(s) above which is/are allowable.
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, 4, 6-7, 10, 12-13, 16, 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
The limitations of
“…, the method comprising:
…;
generating, …, a description of the problem, wherein the description conforms to defined format, by: determining, by a planner component, a processing flow for the problem …;
…;
determining, …, a combination of selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models based on a calculated cosine similarity between results of the selected and generated machine learning models, and
assembling, …, the combination of the selected and generated machine learning models based on the determination of the combination of the selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models;
selecting, …, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem…;
mapping each step of the series of processing steps of the processing flow to one of a plurality of layers of the selected computing infrastructure, …; and
…;
…”, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements (“implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor”, “at the computer system”, “by running at least one heuristic search algorithm on a bidirectional graph”, “executing, at the computer system, the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem”, “executing, at the computer system, the at least one machine learning model relevant to the problem using the one of the plurality of layers of the selected computing infrastructure corresponding to the each step of the series of processing steps to generate at least one recommendation relevant to the problem ") – using a computing system and a machine learning model to process data. The computing system and the machine learning model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.05(f).
In particular, the claim recites an additional element(s) (“stored”) – the act of storing data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of storing data is recited at a high-level of generality (i.e., as a generic act of storing performing a generic act function of storing data) such that it amounts no more than a mere act to apply the exception using a generic act of storing. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites additional elements (“receiving from data channels, at the computer system, data relating to a problem to be solved”, “obtaining, at the computer system, at least one machine learning model relevant to the problem by generating, at the computer system, a new model based on type, morphology, and parameter information, wherein the at least one machine learning model relevant to the problem is further obtained by:”) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element(s) (“generating, at the computer system, at least one machine learning model relevant to the problem by generating, at the computer system, a new machine learning model based on type, morphology, and parameter information, wherein the at least one machine learning model relevant to the problem is further generated by”). The additional element is recited at such a high level without any details as to how a model is generated such that it amounts to only the idea of a solution or outcome because it fails to recite details of how a solution to a problem is accomplished, and, therefore, represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element (“the received data comprising feedback from a measured output”, “the processing flow comprising a series of processing steps”, “wherein the selected computing infrastructure comprises a mesh of interconnected applications, wherein the selected computing infrastructure includes at least one framework configured to perform model tuning and hyperparameters optimization using at least Bayesian optimization and metaheuristics, at least one container configured to run stateful services, and at least one graphics processing unit, and wherein each application comprises analog and digital input, event ingestion processing, event consumption processing, event generation processing, and analog and digital output”, “the plurality of layers comprising a sensors layer, a gateway layer, and a cloud layer, wherein the sensors layer is deployed on an edge computing layer, wherein the gateway layer is equipped with a computing capability suitable for executing a neural network, and wherein the cloud layer is equipped with a computing capability suitable for training the neural network and/or executing simulation tasks”). This is a recitation of a particular type or source of model/data/device to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/ data/device is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. See MPEP 2106.05(f).
As discussed above, the claim recites the additional element(s) of storing data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g) – storing data. However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
As discussed above, the claim recites the additional element(s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
The additional elements regarding training are recited at such a high level without any details as to how a model is generated such that it amounts to only the idea of a solution or outcome because it fails to recite details of how a solution to a problem is accomplished, and, therefore, represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). Accordingly, this additional element does not amount to significantly more than the abstract idea. The claim is directed to an abstract idea.
This is a recitation of a particular type or source of model/ data/device to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data/device is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 4
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
The limitations of
“wherein the at least one machine learning model relevant to the problem is further obtained by at least one of:
selecting, …, at least one model from among previously used processed models stored at the computer system; and
selecting, …, at least one model from among models obtained from public sources, proprietary sources, or both”, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements (“at the computer system”) – using a computing system to process data. The computing system in each step is recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.05(f).
In particular, the claim recites an additional element(s) (“stored”) – the act of storing data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of storing data is recited at a high-level of generality (i.e., as a generic act of storing performing a generic act function of storing data) such that it amounts no more than a mere act to apply the exception using a generic act of storing. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. See MPEP 2106.05(f).
As discussed above, the claim recites the additional element (“stored”) at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g) – storing data. However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
Regarding claim 6
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a method; therefore, it falls into the statutory category of processes.
Step 2A Prong 1:
The limitations of
“wherein the combination of the selected and generated machine learning models that produces higher accuracy results than the selected and generated machine learning models may be determined …”, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites additional elements (“by selected and trained heuristics or by a machine learning model”) – using a machine learning model to process data. The machine learning model in each step is recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. See MPEP 2106.05(f).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. See MPEP 2106.05(f).
Regarding claim 7
The claim recites “A computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:” to perform precisely the method of Claim 1. As performance of an abstract idea on generic computer components (see MPEP 2106.05(f)) and “Storing and retrieving information in memory” (see MPEP 2106.05(g) on Insignificant Extra-Solution Activity, and MPEP 2106.05(d) on Well-Understood, Routine, Conventional Activity) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, the claim is rejected for reasons set forth in the rejection of Claim 1.
Regarding claim 10
The claim is rejected for the reasons set forth in the rejection of Claim 4 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 12
The claim is rejected for the reasons set forth in the rejection of Claim 6 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 13
The claim recites “A computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer system, to cause the computer system to perform a method comprising:” to perform precisely the method of Claim 1. As performance of an abstract idea on generic computer components (see MPEP 2106.05(f)) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, the claim is rejected for reasons set forth in the rejection of Claim 1.
Regarding claim 16
The claim is rejected for the reasons set forth in the rejection of Claim 4 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 18
The claim is rejected for the reasons set forth in the rejection of Claim 6 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
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 SEHWAN KIM whose telephone number is (571)270-7409. The examiner can normally be reached Mon - Thu 7:00 AM - 5:00 PM.
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, Michael J Huntley can be reached on (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SEHWAN KIM/Examiner, Art Unit 2129
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