DETAILED ACTION
This Office Action is in response for Application # 18/505,687 filed on November 09, 2023 in which claims 1-20 are presented for examination.
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 .
Status of claims
Claims 1-20 are pending, of which claims 1-20 are rejected under 35 U.S.C. 103 and also claims 1-20 are rejected under 35 U.S.C. 101. Claims 1-20 are rejected under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The terms “intelligent” and “best” in claims 1, 12 and 17 are a relative term which renders the claim indefinite. The terms “intelligent” and “best” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner believes the term “intelligent” should be defined as a value that defines the knowledge of the classification component and also term “best” should a degree of how to determine best procedure.
Dependent claims 2-11, 13-16 and 18-20 are also rejected under the same rationale as the independent claims since the dependent claims inherit the deficiencies of the parent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101. because the claims are directed to an abstract idea; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—Claims 1-20 recite a method, system and readable medium respectively.
The analysis of claims 1, 12 and 17 is as follows:
Step 2A, prong one: Does claims 1, 12 and 17 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “identify at least one enquiry message;
determine, based on the at least one enquiry message, at least one case type associated with the at least one enquiry message;
trigger an intelligent classification component, wherein the intelligent classification component is configured to determine at least one procedure for the at least one enquiry message;
trigger a cellular automaton engine, wherein the cellular automaton engine comprises an array of a plurality of cells in a grid-based structure, and wherein the cellular automaton engine is configured to select and perform at least one procedure for the at least one enquiry message;
determine, by the cellular automaton engine, a best procedure based on the selection and performance of the at least one procedure for the at least one enquiry message; and
generate at least one response for the at least one enquiry message based on the determined best procedure by the cellular automaton engine” as drafted, are mental steps based on various processes can be performed in a human mind of generating actionable response for enquiry messages (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “system” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “identify at least one enquiry message;
determine, based on the at least one enquiry message, at least one case type associated with the at least one enquiry message;
trigger an intelligent classification component, wherein the intelligent classification component is configured to determine at least one procedure for the at least one enquiry message;
trigger a cellular automaton engine, wherein the cellular automaton engine comprises an array of a plurality of cells in a grid-based structure, and wherein the cellular automaton engine is configured to select and perform at least one procedure for the at least one enquiry message;
determine, by the cellular automaton engine, a best procedure based on the selection and performance of the at least one procedure for the at least one enquiry message; and
generate at least one response for the at least one enquiry message based on the determined best procedure by the cellular automaton engine” are mere using cellular automation engine (i.e., actionable response); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “generate at least one response for the at least one enquiry message based on the determined best procedure by the cellular automaton engine “, generating response to determine the best procedure are also recited at a high level of generality and merely generally link to respective technological environments (e.g., obtain better procedure) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating response for the best procedure is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 1, 12 and 17 are rejected as being directed to non-patentable subject matter under §101.
The analysis of claims 2-11, 13-16 and 18-20 are as follows:
Step 2A, prong one: Does claims 2-11, 13-16 and 18-20 recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations of “ Claims 2, 13 and 18, cluster the at least one enquiry message in a group based on the at least one case type.
Claims 3, 14 and 19, generate a validation interface component, wherein the validation interface component comprises at least one input message and at least one output message, wherein the at least one input message and the at least one output message are based on the at least one enquiry message; and transmit the validation interface component to a user device, wherein the validation interface component configures a graphical user interface of the user device and shows the at least one input message and the at least one output message.
Claim 4, wherein the validation interface component is generated using an artificial intelligence (Al) based optical character recognition (OCR) component, and wherein the Al based OCR component is configured to validate the at least one input message and the at least one output message.
Claim 5, wherein the validation interface component comprises a plurality of validation interface components, and wherein each validation interface component comprises the at least one input message or the at least one output message.
Claim 6, wherein the validation interface component is overlayed with a legacy application interface component on the graphical user interface of the user device, and wherein the legacy application interface component is based on a legacy application associated with the at least one enquiry message.
Claims 7, 15 and 20, wherein the intelligent classification component comprises a reinforcement-based learning model, computer-readable code is configured to cause the at least one processing device to perform the following operations: apply the at least one enquiry message to a reinforcement-based learning model; and determine, by the reinforcement-based learning model, the at least one procedure for the at least one enquiry message, wherein the at least one procedure is based on at least one mapping of the at least one procedure and required data.
Claims 8 and 16, wherein the reinforcement-based learning model is trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of historical data classifications associated with a set of historical enquiry messages and a set of historical procedures; create a first training dataset comprising the set of historical data classifications, the set of historical enquiry messages, and the set of historical procedures; and train the reinforcement-based learning model in a first stage using the first training dataset.
Claim 9, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of reinforcement messages associated with the set of historical classifications, the set of historical enquiry messages, and the set of historical procedures; create a second training dataset comprising the set of reinforcement messages; and train the reinforcement-based learning model in a second stage using the second training dataset.
Claim 10, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of standard procedures associated with an attribute of the set of historical enquiry messages; create a standards training dataset comprising the set of standard procedures; and train the reinforcement-based learning model in a derivative stage using the standards training dataset.
Claim 11, wherein the computer-readable code is causing the at least one determine, by the cellular automaton engine, whether the best procedure requires unknown data; generate, by the cellular automaton engine and based on the determination the best procedure requires unknown data, a request for the unknown data, wherein the request comprises a storage component identifier associated with the unknown data; transmit the request for the unknown data to a storage component associated with the storage component identifier; and receive the unknown data from the storage component” as drafted, are mental steps based on various processes can be performed in a human mind of generating actionable response for enquiry messages (acts of thinking, decision making). These limitations, therefore fall within the human mind processes group and with a pen & paper.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application as just stated as related to the technical field of computer science . Although the claim recites that the recited functionality includes “method”, “system” and “readable medium”, these computer components are recited at a high-level of generality such that it amounts to no more than a mere instructions to apply the exception using generic computer component. In addition, the claim recites “
Claims 2, 13 and 18, cluster the at least one enquiry message in a group based on the at least one case type.
Claims 3, 14 and 19, generate a validation interface component, wherein the validation interface component comprises at least one input message and at least one output message, wherein the at least one input message and the at least one output message are based on the at least one enquiry message; and transmit the validation interface component to a user device, wherein the validation interface component configures a graphical user interface of the user device and shows the at least one input message and the at least one output message.
Claim 4, wherein the validation interface component is generated using an artificial intelligence (Al) based optical character recognition (OCR) component, and wherein the Al based OCR component is configured to validate the at least one input message and the at least one output message.
Claim 5, wherein the validation interface component comprises a plurality of validation interface components, and wherein each validation interface component comprises the at least one input message or the at least one output message.
Claim 6, wherein the validation interface component is overlayed with a legacy application interface component on the graphical user interface of the user device, and wherein the legacy application interface component is based on a legacy application associated with the at least one enquiry message.
Claims 7, 15 and 20, wherein the intelligent classification component comprises a reinforcement-based learning model, computer-readable code is configured to cause the at least one processing device to perform the following operations: apply the at least one enquiry message to a reinforcement-based learning model; and determine, by the reinforcement-based learning model, the at least one procedure for the at least one enquiry message, wherein the at least one procedure is based on at least one mapping of the at least one procedure and required data.
Claims 8 and 16, wherein the reinforcement-based learning model is trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of historical data classifications associated with a set of historical enquiry messages and a set of historical procedures; create a first training dataset comprising the set of historical data classifications, the set of historical enquiry messages, and the set of historical procedures; and train the reinforcement-based learning model in a first stage using the first training dataset.
Claim 9, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of reinforcement messages associated with the set of historical classifications, the set of historical enquiry messages, and the set of historical procedures; create a second training dataset comprising the set of reinforcement messages; and train the reinforcement-based learning model in a second stage using the second training dataset.
Claim 10, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations: collect a set of standard procedures associated with an attribute of the set of historical enquiry messages; create a standards training dataset comprising the set of standard procedures; and train the reinforcement-based learning model in a derivative stage using the standards training dataset.
Claim 11, wherein the computer-readable code is causing the at least one determine, by the cellular automaton engine, whether the best procedure requires unknown data; generate, by the cellular automaton engine and based on the determination the best procedure requires unknown data, a request for the unknown data, wherein the request comprises a storage component identifier associated with the unknown data; transmit the request for the unknown data to a storage component associated with the storage component identifier; and receive the unknown data from the storage component” are mere using cellular automation engine (i.e., actionable response); the computers that perform those functions and the mental steps are recited at a high level of generality that do not impose a meaningful limitation on the judicial exception and are insufficient to integrate the mental steps into a practical application. Although the claim recites the additional functionality “generate at least one response for the at least one enquiry message based on the determined best procedure by the cellular automaton engine “, generating response to determine the best procedure are also recited at a high level of generality and merely generally link to respective technological environments (e.g., obtain better procedure) and therefore likewise amounts to no more than a mere instructions to apply the exception using generic computer components and is insufficient to integrate the steps into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No— The recitation in the preamble is insufficient to transform a judicial exception to a patentable invention because the preamble elements are recited at a high level of generality that simply links to a field of use, see MPEP 2106.05(h). The claimed extra-solution of operation based on generating response for the best procedure is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II)(i). Similarly, the gathering and determining are also recited at a high level of generality and merely generally link to respective technological environments. The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Taken alone, their additional elements do not amount to significantly more than the above- identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claims 2-11, 13-16 and 18-20 are rejected as being directed to non-patentable subject matter under §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alexander Bobotek US 2012/0028606 A1 (hereinafter ‘Bobotek’) in view of Poff et al. US 2022/0318545 A1 (hereinafter ‘Poff’).
As per claim 1, Bobotek disclose, A system (Bobotek: paragraph 0036: disclose a local system) for dynamically determining procedures for electronic communications using cellular automaton processing (Bobotek: paragraph 0097: disclose able to take (e.g., automatically ‘dynamically’ execute ‘automation’) a desired action ‘determining procedures’ in response to identified spam mobile ‘cellular’ messages ‘electronic communications’), the system comprising:
a memory device (Bobotek: paragraph 0225: disclose memory components) with computer-readable program code stored thereon (Bobotek: paragraph 0226: disclose computer-readable device);
at least one processing device, wherein executing the computer-readable code is configured to cause the at least one processing device to perform the following operations (Bobotek: paragraph 0036: disclose components can execute from various computer readable media having various data structures stored thereon):
identify at least one enquiry message (Bobotek: paragraph 0011: disclose identify abusive mobile messages. Examiner argues that the enquiry message and abusive messages are the same for the computer and only human can distinguish the difference, therefore examiner equates enquiry message to abusive message);
determine, based on the at least one enquiry message, at least one case type associated with the at least one enquiry message (Bobotek: paragraph 0011: disclose message abuse detector component (MADC) distinguish between spam mobile messages (and different types of spam mobile messages, including, for example, general commercial spam mobile messages, virus-containing mobile messages, phishing mobile messages, etc.));
trigger an intelligent classification component (Bobotek: paragraph 0144: disclose triggering evaluation of the sent mobile messages to identify whether they are abusive), wherein the intelligent classification component is configured to determine at least one procedure for the at least one enquiry message (Bobotek: paragraph 0148: disclose a response component that can perform or take a desired action(s) ‘procedure’ (e.g., abuse management action(s)) in response to identification or classification of a mobile message(s));
trigger a cellular automaton engine (Bobotek: paragraph 0170: disclose necessary component of cellular automation engine which is triggered when needed by MADC also can include a data store that can store data structures (e.g., user data, metadata); code structure(s) (e.g., modules, objects, hashes, classes, procedures) or instructions; information relating to mobile messaging associated with UEs operating in the core network, information relating to operations of the MADC, whitelists of respective subscribers, blacklists (e.g., of respective subscribers or global blacklist), predefined message abuse criteria (and associated predefined message abuse rules), predefined message routing rules, etc., to facilitate controlling operations associated with the MADC, etc), and wherein the cellular automaton engine is configured to select and perform at least one procedure for the at least one enquiry message (Bobotek: paragraph 0173: disclose mobile device with cellular automation engine use abusive message reporter component can facilitate providing, via an interface, an abusive mobile message button or menu and/or a region where additional information relating to the abuse report can be received so that the user can generate an abuse report for a mobile message the user deems abusive);
determine, by the cellular automaton engine, a best procedure based on the selection and performance of the at least one procedure for the at least one enquiry message (Bobotek: paragraph 0173: disclose abusive message reporter component also can facilitate communicating the abuse report from the mobile device to the MADC for processing. An abuse report (e.g., abusive mobile message report) ‘best procedure’ of the user also can include other information, such as originating address of the mobile message, termination address of the mobile message, time of the mobile message, etc, where MADC selects the abusive message reports as the best procedure based on need ‘performance’ for abuse report); and
generate at least one response for the at least one enquiry message based on the determined best procedure by the cellular automaton engine (Bobotek: paragraph 0200: disclose desired action(s) can be executed (e.g., automatically) in response to identifying or classifying the originating address as abusive. The desired action(s) (e.g., abuse management action) can be many of the listed actions ‘procedure’ by the MADC ‘cellular automation engine’).
It is noted, however, Bobotek did not specifically detail the aspects of
wherein the cellular automaton engine comprises an array of a plurality of cells in a grid-based structure as recited in claim 1.
On the other hand, Poff achieved the aforementioned limitations by providing mechanisms of
wherein the cellular automaton engine comprises an array of a plurality of cells in a grid-based structure (Poff: paragraph 0094: disclose DPMC can map the respective relationships between the respective cells (e.g., candidate cells) onto a grid structure, such as a grid structure of a spreadsheet in the format of the desired spreadsheet application).
Bobotek and Poff are analogous art because they are from the “same field of endeavor” and both from the same “problem-solving area”. Namely, they are both from the field of “Mobile Systems”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the systems of Bobotek and Poff because they are both directed to mobile systems and both are from the same field of endeavor. The skilled person would therefore regard it as a normal option to include the restriction features of Poff with the method described by Bobotek in order to solve the problem posed.
The motivation for doing so would have been to provide a contextual overview relating to electronic document processing (Poff: paragraph 0003).
Therefore, it would have been obvious to combine Poff with Bobotek to obtain the invention as specified in instant claim 1.
As per claim 2, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Bobotek disclose, wherein the computer-readable code is configured to cause the at least one processing device to perform the following operation cluster the at least one enquiry message in a group based on the at least one case type (Bobotek: paragraph 0139: disclose subset ‘cluster’ of mobile messages is/are abusive ‘case type’ and paragraph 0152: disclose cluster analysis, which implies that the messages are grouped based on message type).
As per claim 3, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Bobotek disclose, wherein the computer-readable code is configured to cause the at least one processing device to perform the following operations:
generate a validation interface component, wherein the validation interface component comprises at least one input message and at least one output message, wherein the at least one input message and the at least one output message are based on the at least one enquiry message (Bobotek: paragraph 0134: disclose an interface with an abusive message ‘input message’ button or menu and/or a region where additional information relating to the abuse report can be received so that UE users can generate and transmit an abuse report for a mobile message the user deems abusive, wherein the abuse report can be transmitted from the UE to the MADC, where the abusive report is the output message ); and
transmit the validation interface component to a user device, wherein the validation interface component configures a graphical user interface of the user device and shows the at least one input message and the at least one output message (Bobotek: paragraph 0016: disclose mobile communication device users can be provided with an abusive message ‘input message’ reporter component (e.g., abuse report button and/or menu) that can be employed by the user to report an abusive message. For example, the abuse report button and/or menu can be provided to the user via a user interface and abuse report is considered as output message).
As per claim 4, most of the limitations of this claim have been noted in the rejection of claims 1 and 3 above.
It is noted, however, Bobotek did not specifically detail the aspects of
wherein the validation interface component is generated using an artificial intelligence (Al) based optical character recognition (OCR) component, and wherein the Al based OCR component is configured to validate the at least one input message and the at least one output message as recited in claim 4.
On the other hand, Poff achieved the aforementioned limitations by providing mechanisms of
wherein the validation interface component is generated using an artificial intelligence (Al) based optical character recognition (OCR) component, and wherein the Al based OCR component is configured to validate the at least one input message and the at least one output message (Poff: paragraph 0046: disclose character recognition component can perform OCR analysis or another desired type of character recognition analysis on the image data representative of the bordered candidate cells).
As per claim 5, most of the limitations of this claim have been noted in the rejection of claims 1 and 3 above. In addition, Bobotek disclose, wherein the validation interface component comprises a plurality of validation interface components, and wherein each validation interface component comprises the at least one input message or the at least one output message (Bobotek: Fig 5A and Fig 5B: disclose interface for input message and output message).
As per claim 6, most of the limitations of this claim have been noted in the rejection of claims 1 and 3 above. In addition, Bobotek disclose, wherein the validation interface component is overlayed with a legacy application interface component on the graphical user interface of the user device, and wherein the legacy application interface component is based on a legacy application associated with the at least one enquiry message (Bobotek: Fig 5A and Fig 5B: disclose interface for input message and output message. Examiner argues that a computer can’t distinguish legacy application and it can be only done in a human mind).
As per claim 7, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Bobotek disclose, wherein the intelligent classification component comprises a reinforcement-based learning model, computer-readable code is configured to cause the at least one processing device to perform the following operations:
apply the at least one enquiry message to a reinforcement-based learning model (Bobotek: paragraph 0152: disclose reinforced learning --to historic and/or current data associated with the systems and methods disclosed herein to facilitate rendering an inference(s) related to the systems); and
determine, by the reinforcement-based learning model, the at least one procedure for the at least one enquiry message, wherein the at least one procedure is based on at least one mapping of the at least one procedure and required data (Bobotek: paragraph 0128: disclose predefined message abuse rules, comprising one or more mobile message classification rules).
As per claim 8, most of the limitations of this claim have been noted in the rejection of claims 1 and 7 above. In addition, Bobotek disclose, wherein the reinforcement-based learning model is trained by the computer-readable code is causing the at least one processing device to perform the following operations:
collect a set of historical data classifications associated with a set of historical enquiry messages and a set of historical procedures (Bobotek: paragraph 0209: disclose additional information (e.g., historical information, CDRs, reputation, honeypot reports, and/or mobile message content, etc., associated with the originating address) can be obtained as well);
create a first training dataset comprising the set of historical data classifications, the set of historical enquiry messages, and the set of historical procedures (Bobotek: paragraph 0012: disclose MADC also can analyze historical information (e.g., call data records (CDRs), abuse reports, or other negative or positive information); and
train the reinforcement-based learning model in a first stage using the first training dataset (Bobotek: paragraph 0153: disclose numerous methodologies for learning from data and then drawing inferences from the models so constructed, e.g., Hidden Markov Models (HMMs) and related prototypical dependency models).
As per claim 9, most of the limitations of this claim have been noted in the rejection of claims 1, 7 and 8 above. In addition, Bobotek disclose, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations:
collect a set of reinforcement messages associated with the set of historical classifications, the set of historical enquiry messages, and the set of historical procedures (Bobotek: paragraph 0209: disclose additional information (e.g., historical information, CDRs, reputation, honeypot reports, and/or mobile message content, etc., associated with the originating address) can be obtained as well);
create a second training dataset comprising the set of reinforcement messages (Bobotek: paragraph 0012: disclose MADC also can analyze historical information (e.g., call data records (CDRs), abuse reports, or other negative or positive information); and
train the reinforcement-based learning model in a second stage using the second training dataset (Bobotek: paragraph 0153: disclose numerous methodologies for learning from data and then drawing inferences from the models so constructed, e.g., Hidden Markov Models (HMMs) and related prototypical dependency models).
As per claim 10, most of the limitations of this claim have been noted in the rejection of claims 1, 7 and 8 above. In addition, Bobotek disclose, wherein the reinforcement-based learning model is further trained by the computer-readable code is causing the at least one processing device to perform the following operations:
collect a set of standard procedures associated with an attribute of the set of historical enquiry messages (Bobotek: paragraph 0209: disclose additional information (e.g., historical information, CDRs, reputation, honeypot reports, and/or mobile message content, etc., associated with the originating address) can be obtained as well);
create a standards training dataset comprising the set of standard procedures (Bobotek: paragraph 0012: disclose MADC also can analyze historical information (e.g., call data records (CDRs), abuse reports, or other negative or positive information); and
train the reinforcement-based learning model in a derivative stage using the standards training dataset (Bobotek: paragraph 0153: disclose numerous methodologies for learning from data and then drawing inferences from the models so constructed, e.g., Hidden Markov Models (HMMs) and related prototypical dependency models).
As per claim 11, most of the limitations of this claim have been noted in the rejection of claim 1 above. In addition, Bobotek disclose, determine, by the cellular automaton engine, whether the best procedure requires unknown data;
generate, by the cellular automaton engine and based on the determination the best procedure requires unknown data, a request for the unknown data, wherein the request comprises a storage component identifier associated with the unknown data (Bobotek: paragraph 0121: disclose malicious entity can use a communication device (e.g., UE) to send malware (e.g., spamming malware) to or install control malware in a UE of an innocent and/or unknowing user, wherein the malware is used to configure the UE of the innocent and/or unknowing user to send);
transmit the request for the unknown data to a storage component associated with the storage component identifier (Bobotek: paragraph 0116: disclose mobile message sender who is known or unknown to the user); and
receive the unknown data from the storage component (Bobotek: paragraph 0121: disclose having mobile messaging services of the innocent and/or unknowing user being suspended or blocked).
As per claim 12, Bobotek disclose, A computer program product for dynamically determining procedures for electronic communications using cellular automaton processing (Bobotek: paragraph 0097: disclose able to take (e.g., automatically ‘dynamically’ execute ‘automation’) a desired action ‘determining procedures’ in response to identified spam mobile ‘cellular’ messages ‘electronic communications’), wherein the computer program product comprises at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions which when executed by a processing device are configured to cause the processing device to perform the following operations (Bobotek: paragraph 0226: disclose computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices): remaining limitations in this claim 12 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 13, limitations of this claim are similar to claim 2. Therefore, examiner rejects claim 13 limitations under the same rationale as claim 2.
As per claim 14, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 14 limitations under the same rationale as claim 3.
As per claim 15, limitations of this claim are similar to claim 7. Therefore, examiner rejects claim 15 limitations under the same rationale as claim 7.
As per claim 16, limitations of this claim are similar to claim 8. Therefore, examiner rejects claim 16 limitations under the same rationale as claim 8.
As per claim 17, Bobotek disclose, A computer implemented method (Bobotek: paragraph 0149: disclose method) for dynamically determining procedures for electronic communications using cellular automaton processing, the computer implemented method comprising (Bobotek: paragraph 0226: disclose computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices): remaining limitations in this claim 17 are similar to the limitations in claim 1. Therefore, examiner rejects these remaining limitations under the same rationale as limitations rejected under claim 1.
As per claim 18, limitations of this claim are similar to claim 2. Therefore, examiner rejects claim 18 limitations under the same rationale as claim 2.
As per claim 19, limitations of this claim are similar to claim 3. Therefore, examiner rejects claim 19 limitations under the same rationale as claim 3.
As per claim 20, limitations of this claim are similar to claim 7. Therefore, examiner rejects claim 20 limitations under the same rationale as claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. US 2017/0223168 A1 disclose “ELECTRONIC DEVICE AND METHOD FOR MANAGING OPERATION THEREOF WHILE OPERATING VEHICLE”
US Pub. US 2015/0133176 A1 disclose “METHOD OF ANIMATING MOBILE DEVICE MESSAGES”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAVAN MAMILLAPALLI whose telephone number is (571)270-3836. The examiner can normally be reached on M-F. 8am - 4pm, EST.
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, Ann J Lo can be reached on (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PAVAN MAMILLAPALLI/
Primary Examiner, Art Unit 2159