CTNF 18/541,702 CTNF 101375 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the application and claims filed 12/15/2023. Claims 1-20 are pending and have been examined. Claims 1-20 are rejected. Information Disclosure Statement The information disclosure statement (IDS) submitted on 03/26/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 claimed invention is directed to an abstract idea without significantly more. Claim 1 Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes. Step 2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The claim recites the following abstract ideas: “determining a target work capacity of the user device (...)” (This limitation is a mental process because a person mentally or with a pen and paper can determine a target amount of work to be performed by another (e.g., a person can estimate and write down a target capacity to be consumed in solving a problem). -- Examiner’s Note (EN): The recitation “using a machine-learning (ML) model” is an additional element that is addressed under Step 2A Prong 2 and Step 2B below.) “selecting a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity;” (This limitation is a mental process because a person mentally or with a pen and paper can select a problem from a plurality of problems (e.g., a person can choose one problem from a written list of problems whose difficulty can be adjusted).) “scaling a work capacity of the selected proof-of-work problem to utilize at least the target work capacity;” (This limitation is a mental process a person mentally or with a pen and paper can scale the difficulty of a problem to a target (e.g., a person can make a problem consume more work by adding variables, clauses, or logical operators to a mathematical expression with a pen and paper.) “determining whether the received proof-of-work is a valid solution to the scaled proof-of-work problem.” (This limitation is a mental process because a person mentally or with a pen and paper can determine whether a received solution is a valid solution to a problem (e.g., a person can evaluate an expression using the provided values and observe whether the expression is satisfied).) Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows: “receiving, from a user device, a request for a resource;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines. The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “providing the scaled proof-of-work problem to the user device;” (The limitation amounts to merely transmitting data, which is insignificant extra-solution activity recited at a high level of generality (MPEP 2106.05(g)). -- Examiner’s Note (EN): The limitation is the nominal act of outputting the scaled problem produced by the recited abstract ideas to the user device.) “receiving, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) Step 2B: “receiving, from a user device, a request for a resource;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines. The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “providing the scaled proof-of-work problem to the user device;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “receiving, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 2 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 1 above, which claim 2 depends on. Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites: “responsive to determining the proof-of-work is valid, providing, to the user device, access to the resource.” (Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). -- Examiner’s Note (EN): Providing access to the resource is what happens after the core process is complete. It is the nominal act of outputting or delivering the end result of the validity determination; therefore, this is interpreted as an insignificant post-solution activity.) Step 2B: “responsive to determining the proof-of-work is valid, providing, to the user device, access to the resource.” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 3 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 1 above, which claim 3 depends on. Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites: “responsive to determining the proof-of-work is invalid, denying, to the user device, access to the resource.” (Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). -- Examiner’s Note (EN): Denying access to the resource is what happens after the core process is complete. It is the nominal act of withholding the end result based on the validity determination; therefore, this is interpreted as an insignificant post-solution activity.) Step 2B: “responsive to determining the proof-of-work is invalid, denying, to the user device, access to the resource.” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 4 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 1 above, which claim 4 depends on. Claim 4 further recites: “wherein the plurality of proof-of-work problems comprise a cryptographic problem, a satisfiability problem, and a network transmission problem.” (This limitation falls within the mathematical concepts because it merely further defines the types of problems from which the selection of claim 1 is made. A cryptographic problem comprises a mathematical algorithm (e.g., a one-way hashing function comprising logical operators and hashing constants) and a satisfiability problem comprises a propositional formula comprising variables, logical operators, and parentheses and a network transmission problem comprises a payload of an initial size and an associated number of transmissions.) Step 2A Prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 5 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 4 above, which claim 5 depends on. Claim 5 further recites: “wherein the scaling the work capacity of the cryptographic problem comprises: changing a cryptographic algorithm of the cryptographic problem; and changing logical operators of the cryptographic problem.” (This limitation falls within the mathematical concepts groupings because it involves changing a mathematical algorithm and changing the logical operators of the algorithm.) Step 2A Prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 6 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 4 above, which claim 6 depends on. Claim 6 further recites: “wherein the scaling the work capacity of the satisfiability problem comprises: scaling a number of clauses of the satisfiability problem; scaling a number of variables of the satisfiability problem; and changing logical operators of the satisfiability problem.” (This limitation falls within the mathematical concepts because changing the number of clauses, changing the number of variables, and changing the logical operators of a propositional formula are changes to a mathematical expression. (See paragraph 50)) Step 2A Prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 7 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 4 above, which claim 7 depends on. Claim 7 further recites: “wherein the scaling the work capacity of the network transmission problem comprises: changing a size of a payload of the network transmission problem; and changing a number of transmissions in the network transmission problem.” (This limitation is a mental process because it involves changing a size (a quantity) and changing a number (a count) of a problem. A person mentally or with a pen and paper can change a size of a payload of a problem statement (e.g., write a larger amount of data to be operated on) and change a number of transmissions recited in the problem statement.) Step 2A Prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 8 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 4 above, which claim 8 depends on. Claim 8 further recites: “wherein the plurality of proof-of-work problems further comprises a chained problem, the chained problem comprising a set of scaled proof-of-work problems;” (This limitation is a mental process because a person mentally or with a pen and paper can gather a set of problems to be presented together (e.g., a person can write a problem set comprising a sequence of multiple problems)) Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites: “wherein the proof-of-work received in response to the chained problem comprises a respective proof-of-work for each scaled proof-of-work problem in the set of the scaled proof-of-work problems.” (Data Gather - The limitation merely further characterizes the data received in the receiving step of claim 1, which is mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) Step 2B: “wherein the proof-of-work received in response to the chained problem comprises a respective proof-of-work for each scaled proof-of-work problem in the set of the scaled proof-of-work problems.” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 9 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 8 above, which claim 9 depends on. Claim 9 further recites: “wherein the scaling the work capacity of the chained problem comprises: scaling a number of scaled proof-of-work problems in the set of the scaled proof-of-work problems.” (This limitation is a mental process because a person mentally or with a pen and paper can change the number of problems in a set (e.g., add problems to, or remove problems from, a written problem set.) Step 2A Prong 2 & Step 2B: The claim does not recite any additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 10 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 1 above, which claim 10 depends on. Step 2A Prong 2 & Step 2B: The claim does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. “wherein the target work capacity comprises a processing capacity, a memory capacity, and a network traffic capacity.” (The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)). -- Examiner’s Note (EN): The limitation merely specifies that the target work capacity determined by the recited abstract idea is expressed in terms of generic capacities (a processing capacity, a memory capacity, and a network traffic capacity) of a generic, off-the-shelf user device.) Therefore, the claim is not patent eligible. Claim 11 Step 1: A process, as above. Step 2A Prong 1: See the rejection of Claim 1 above, which claim 11 depends on. Claim 11 further recites: “...analyzes historical access data comprising one or more previous target work capacities, and wherein the determined target work capacity differs from each of the one or more previous target work capacities.” (This limitation is a mental process because a person mentally or with a pen and paper can analyze historical data comprising one or more previous values (e.g., review a written list of previously determined target capacities) and determine a new value that differs from each previous value (e.g., compare a candidate value against each value on the list and select a value that does not appear on the list).) Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites: “wherein the ML model...” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites the generic, off-the-shelf ML model of claim 1 as a tool to perform the abstract idea of analyzing historical access data and determining a differing target work capacity. The claim does not recite any details of the ML model beyond a generic, off-the-shelf ML model.) Step 2B: “wherein the ML model...” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites the generic, off-the-shelf ML model of claim 1 as a tool to perform the abstract idea of analyzing historical access data and determining a differing target work capacity. The claim does not recite any details of the ML model beyond a generic, off-the-shelf ML model.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 12 Step 1: The claim recites a system; therefore, it is directed to the statutory category of machine. Step 2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The claim recites the following abstract ideas: “determine a target work capacity of the user device (...)” (This limitation is a mental process because a person mentally or with a pen and paper can determine a target amount of work to be performed by another (e.g., a person can estimate and write down a target capacity to be consumed in solving a problem). -- Examiner’s Note (EN): The recitation “using a machine-learning (ML) model” is an additional element that is addressed under Step 2A Prong 2 and Step 2B below.) “select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity;” (This limitation is a mental process because a person mentally or with a pen and paper can select a problem from a plurality of problems (e.g., a person can choose one problem from a written list of problems whose difficulty can be adjusted).) “scale a work capacity of the selected proof-of-work problem to utilize at least the target work capacity;” (This limitation is a mental process because a person mentally or with a pen and paper can scale the difficulty of a problem to a target (e.g., a person can make a problem consume more work by adding variables, clauses, or logical operators to a mathematical expression with a pen and paper). “determine whether the received proof-of-work is a valid solution to the scaled proof-of-work problem.” (This limitation is a mental process because a person mentally or with a pen and paper can determine whether a received solution is a valid solution to a problem (e.g., a person can evaluate an expression using the provided values and observe whether the expression is satisfied).) Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows: “A system comprising: processing circuitry; and memory, including instructions, which when executed by the processing circuitry, causes the processing circuitry to perform operations to:” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites generic, off-the-shelf processing circuitry and memory as tools to perform the recited abstract ideas. The specification describes the processing circuitry and memory at a high level of generality as components of a general-purpose machine). “receive, from a user device, a request for a resource;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines. The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “provide the scaled proof-of-work problem to the user device;” (The limitation amounts to merely transmitting data, which is insignificant extra-solution activity recited at a high level of generality (MPEP 2106.05(g)). -- Examiner’s Note (EN): The limitation is the nominal act of outputting the scaled problem produced by the recited abstract ideas to the user device.) “receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) Step 2B: “A system comprising: processing circuitry; and memory, including instructions, which when executed by the processing circuitry, causes the processing circuitry to perform operations to:” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites generic, off-the-shelf processing circuitry and memory as tools to perform the recited abstract ideas. The specification describes the processing circuitry and memory at a high level of generality as components of a general-purpose machine.) “receive, from a user device, a request for a resource;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines). The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “provide the scaled proof-of-work problem to the user device;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim 13 Claim 13 is a system claim that recites the same limitations as method claim 2. Therefore, claim 13 is rejected using the same rationale as claim 2. Claim 14 Claim 14 is a system claim that recites the same limitations as method claim 3. Therefore, claim 14 is rejected using the same rationale as claim 3. Claim 15 Claim 15 is a system claim that recites the same limitations as method claim 4. Therefore, claim 15 is rejected using the same rationale as claim 4. Claim 16 Claim 16 is a system claim that recites the same limitations as method claim 5. Therefore, claim 16 is rejected using the same rationale as claim 5. Claim 17 Claim 17 is a system claim that recites the same limitations as method claim 6. Therefore, claim 17 is rejected using the same rationale as claim 6. Claim 18 Claim 18 is a system claim that recites the same limitations as method claim 7. Therefore, claim 18 is rejected using the same rationale as claim 7. Claim 19 Claim 19 is a system claim that recites the same limitations as method claim 8. Therefore, claim 19 is rejected using the same rationale as claim 8. Claim 20 Step 1: The claim recites a non-transitory computer-readable storage medium; therefore, it is directed to the statutory category of manufacture. Step 2A Prong 1: 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. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The claim recites the following abstract ideas: “determine a target work capacity of the user device (...)” (This limitation is a mental process because a person mentally or with a pen and paper can determine a target amount of work to be performed by another (e.g., a person can estimate and write down a target capacity to be consumed in solving a problem). -- Examiner’s Note (EN): The recitation “using a machine-learning (ML) model” is an additional element that is addressed under Step 2A Prong 2 and Step 2B below.) “select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity;” (This limitation is a mental process because a person mentally or with a pen and paper can select a problem from a plurality of problems (e.g., a person can choose one problem from a written list of problems whose difficulty can be adjusted).) “scale a work capacity of the selected proof-of-work problem to utilize at least the target work capacity;” (This limitation is a mental process and because a person mentally or with a pen and paper can scale the difficulty of a problem to a target (e.g., a person can make a problem consume more work by adding variables, clauses, or logical operators to a mathematical expression with a pen and paper).) “determine whether the received proof-of-work is a valid solution to the scaled proof-of-work problem.” (This limitation is a mental process because a person mentally or with a pen and paper can determine whether a received solution is a valid solution to a problem (e.g., a person can evaluate an expression using the provided values and observe whether the expression is satisfied).) Step 2A Prong 2: This judicial exception is not integrated into a practical application because the additional elements are as follows: “A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites a generic, off-the-shelf computer and computer-readable storage medium as tools to perform the recited abstract ideas. The specification describes the computer and storage medium at a high level of generality as components of a general-purpose machine.) “receive, from a user device, a request for a resource;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines. The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “provide the scaled proof-of-work problem to the user device;” (The limitation amounts to merely transmitting data, which is insignificant extra-solution activity recited at a high level of generality (MPEP 2106.05(g)). -- Examiner’s Note (EN): The limitation is the nominal act of outputting the scaled problem produced by the recited abstract ideas to the user device.) “receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (Data Gather - Mere data gathering recited at a high level of generality, and thus is insignificant extra-solution activity (MPEP 2106.05(g)).) Step 2B: “A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites a generic, off-the-shelf computer and computer-readable storage medium as tools to perform the recited abstract ideas. The specification describes the computer and storage medium at a high level of generality as components of a general-purpose machine.) “receive, from a user device, a request for a resource;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “...using a machine-learning (ML) model;” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. -- Examiner’s Note (EN): The claim recites a generic ML model with no additional details or limitations beyond a generic, off-the-shelf ML model. The specification describes the ML model at a high level of generality as any of a list of conventional machine-learning algorithms (e.g., logistic regression, Naïve Bayes, Random Forest, neural networks, support vector machines. The generic ML model is merely used as a tool to perform the abstract idea of determining a target work capacity of the user device.) “provide the scaled proof-of-work problem to the user device;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) “receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem;” (MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer.) The additional elements considered individually or in combination do not amount to significantly more than the judicial exception. Therefore, the claim is not patent eligible. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA 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. Examiner’s Note: Some rejections will include an Examiner’s Note (labeled ‘EN’) to provide additional context or rationale explaining the basis for the rejection. 07-21-aia AIA Claim s 1-3, 10, 11, 12-14, 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Chakraborty ("CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense) in view of Gutzmann (US 9,705,895 B1) . Regarding claim 1, Chakraborty teaches: A method comprising: receiving, from a user device, a request for a resource; (Page 2, "When a new incoming packet is seen, the request packet is forwarded to the request context extractor." – EN: the incoming request packet received from a user seeking access to the protected critical server's content corresponds to receiving a request for a resource.) determining a target work capacity of the user device using a machine-learning (ML) model; (Page 2, "CAPOW uses AI models to learn legitimate network activity patterns and the deviation from the pattern is directly proportional to the difficulty of PoW puzzles to be solved by the user." – EN: the AI/ML model produces a deviation that sets the puzzle difficulty, i.e., the amount of computational work (the target work capacity) the user device must expend.) (…) scaling a work capacity of the selected proof-of-work problem to utilize at least the target work capacity; (Page 2, "The context score is forwarded to the policy component which sets certain parameters, such as, it maps the context score to a puzzle difficulty level." – EN: mapping the context score (target work capacity) to the puzzle's difficulty level scales the selected puzzle's work capacity to utilize the determined target.) providing the scaled proof-of-work problem to the user device; (Page 4, "In CAPOW, when a user deviates from a normal activity pattern, the PoW component issues a PoW puzzle to request proof of legitimacy.") receiving, from the user device, a proof-of-work in response to the scaled proof-of-work problem; and (Page 6, "After solving, the user sends the nonce back to the server for verification." -- EN: the nonce/solution returned by the user device is the proof-of-work received in response to the puzzle.) determining whether the received proof-of-work is a valid solution to the scaled proof- of-work problem. (Page 6, "Puzzle verification is a server-side component that performs straightforward verification of the puzzle solution by performing one hash evaluation... If the sent η value leads to desired number of leading 0's, then the solution is verified.") Chakraborty does not explicitly teach: selecting a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity; However, Gutzmann teaches: selecting a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity; (Col. 21, Line 53-61, "The client puzzles used by the proof-of-work protocol (B.) may include but not be limited to 'Guided Tour Puzzle', single level of difficulty hash pre-image puzzle, two-parameter hash pre-image puzzle, integer square root modulo a large prime, Weaken Fiat-Shamir signatures, Ong-Schnorr-Shamir signature, partial hash inversion, hash sequences, Diffie-Hellman-based puzzles, Mbound, Hokkaido, and Merkle tree based puzzles." Col 14, Line, 15-16, "In turn, the puzzle difficulty is a function of the device's current attacker score." – EN: Gutzmann discloses a plurality of proof-of-work puzzle types from which a puzzle is selected/used.) and, as to "scalable work capacity" ("In turn, the puzzle difficulty is a function of the device's current attacker score.", thus the difficulty (work capacity) of the puzzle is adjustable/scalable, so each puzzle in the plurality has scalable work capacity.) Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with Gutzmann's plurality of selectable, difficulty-adjustable puzzle types. The motivation for doing so would be to give Chakraborty's adaptive framework finer control over the computational difficulty being injected and to ensure a puzzle solution exists, as taught by Gutzmann: "This method provides a more granular control over the computational difficulty than the single difficulty level puzzle. This also improves the chances that a solution to the puzzle exists." (Col. 7, line 67- Col. 8 line 3) Regarding claim 2, Chakraborty in view of Gutzmann teaches all the limitations of claim 1, Chakraborty further teaches: The method of claim 1, further comprising: responsive to determining the proof-of-work is valid, providing, to the user device, access to the resource. (Page 2, "…(4) The difficulty level is passed to the puzzle solver which solves a puzzle of the defined difficulty level using a function func. (5) The computed solution is sent to the verifier. (6) When the solution is correct, the request packet is placed on the server queue for processing…" Page 4, "PoW solver uses a function func to solve the assigned difficulty puzzle (see Figure 1)… This cost indirectly impacts the adversarial intent by throttling the rate of adversarial requests reaching the server queue." – EN: upon determining the proof-of-work is valid/correct, the user's request is placed on the server queue for processing, i.e., access to the resource is provided. The solved request that gets placed on the serve queue is the same request the user device transmitted so the resulting service flows back to that device.) Regarding claim 3, Chakraborty in view of Gutzmann teaches all the limitations of claim 1, Chakraborty further teaches: The method of claim 1, further comprising: responsive to determining the proof-of-work is invalid, (…) (Page 6, "Puzzle verification is a server-side component that performs straightforward verification of the puzzle solution by performing one hash evaluation... If the sent η value leads to desired number of leading 0's, then the solution is verified." Page 2, "When the solution is correct, the request packet is placed on the server queue for processing…" – EN: the verifier returns a binary verified/not-verified result, so a returned solution that does not yield the required leading zeros is determined invalid and because Chakraborty admits only the correct-solution request to the server queue, a request bearing an invalid proof-of-work is withheld from processing.) Chakraborty does not explicitly teach: "denying, to the user device, access to the resource" However, Gutzmann teaches: "denying, to the user device, access to the resource" (FIG. 4F "DROP REQUEST" and Col. 11, line 30-31, "If any of the checks fail, processing is directed to 432.") Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work verification with Gutzmann's dropping of requests bearing invalid proof-of-work solutions. The motivation for doing so would be to filter out requestors that fail to demonstrate legitimacy. See Gutzmann Col. 14, line 8-9, "Either of these protocol behaviors is judged hostile and will eventually result in the blacklisting of the device." Regarding claim 10, Chakraborty in view of Gutzmann teaches all the limitations of claim 1, Chakraborty further teaches: The method of claim 1, wherein the target work capacity comprises a processing capacity, a memory capacity, and a network traffic capacity. (Page 4, "The notion of resource burning cost represents the resource consumption of a user, where the resource could be computational power, memory, network bandwidth, or human capital [14].") Regarding claim 11, Chakraborty in view of Gutzmann teaches all the limitations of claim 1, Chakraborty further teaches: The method of claim 1, wherein the ML model analyzes historical access data (Page 2, "The AI models learn the normal activity pattern from previous activity-logs.") Chakraborty does not explicitly disclose: [historical access data] comprising one or more previous target work capacities, and wherein the determined target work capacity differs from each of the one or more previous target work capacities. However, Gutzmann teaches: [historical access data] comprising one or more previous target work capacities, (Col. 14, line 15-16, "In turn, the puzzle difficulty is a function of the device's current attacker score," Col. 14, line 58-60, "maintain a table of attacker scores (or score ranges) and their corresponding puzzle difficulty levels." – EN: this denotes the puzzle difficulty (the target work capacity) is computed from the device's accumulated attacker score, and the maintained score-to-difficulty mapping means the device's stored history that the system analyzes when setting the next difficulty reflects the previously-assigned difficulties (the previous target work capacities).) and wherein the determined target work capacity differs from each of the one or more previous target work capacities. (Col. 14, line 57-58, As the device increases its attacker score, the puzzle difficulty values increase. As the device decreases its attacker score, the puzzles become easier. – EN: because the device's attacker score is incremented on each successive protocol violation (or reduced over each successive successful iteration, col. 12), the score moves across iterations and the correspondingly raised (or lowered) difficulty makes each newly determined target work capacity differ from each previously assigned target work capacity.) Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's ML model that analyzes historical activity-logs with Gutzmann's attacker-score-driven puzzle difficulty that is varied across successive iterations based on the device's accumulated history. The motivation for doing so would be to progressively throttle (slow down) and limit the user's request rate. See Col. 14, line 26-27, "This puzzle-solving time functions as a throttle or rate-limiter." Regarding claim 12, Chakraborty teaches: receive, from a user device, a request for a resource; (Page 2, "When a new incoming packet is seen, the request packet is forwarded to the request context extractor." – EN: the incoming request packet received from a user seeking access to the protected critical server's content corresponds to receiving a request for a resource.) determine a target work capacity of the user device using a machine-learning (ML) model; (Page 2, "CAPOW uses AI models to learn legitimate network activity patterns and the deviation from the pattern is directly proportional to the difficulty of PoW puzzles to be solved by the user." – EN: the AI/ML model produces a deviation that sets the puzzle difficulty, i.e., the amount of computational work (the target work capacity) the user device must expend.) (…) scale a work capacity of the selected proof-of-work problem to utilize at least the target work capacity; (Page 2, "The context score is forwarded to the policy component which sets certain parameters, such as, it maps the context score to a puzzle difficulty level." – EN: mapping the context score (target work capacity) to the puzzle's difficulty level scales the selected puzzle's work capacity to utilize the determined target.) provide the scaled proof-of-work problem to the user device; (Page 4, "In CAPOW, when a user deviates from a normal activity pattern, the PoW component issues a PoW puzzle to request proof of legitimacy.") receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem; and (Page 6, "After solving, the user sends the nonce back to the server for verification." -- EN: the nonce/solution returned by the user device is the proof-of-work received in response to the puzzle.) determine whether the received proof-of-work is a valid solution to the scaled proof-of-work problem. (Page 6, "Puzzle verification is a server-side component that performs straightforward verification of the puzzle solution by performing one hash evaluation... If the sent η value leads to desired number of leading 0's, then the solution is verified.") Chakraborty does not explicitly teach: “A system comprising: processing circuitry; and memory, including instructions, which when executed by the processing circuitry, causes the processing circuitry to perform operations to:” and “select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity;” However, Gutzmann teaches: A system comprising: processing circuitry; and memory, including instructions, which when executed by the processing circuitry, causes the processing circuitry to perform operations to: (Col. Line 7-13, “Referring now to FIG. 1, the “classifiers” (103) are an array of one or more servers providing an element of the embodiment of the embodiment. Collectively the classification servers comprise the classification service. The classification servers perform the proof-of-work protocol described in “B. Proof of Work Protocol for Device Classification with the requestors.” Col. 21, Line 35-40, “The classification server(103) or service (203) may be executed on any number of HTTP servers, both open Source and commercial products, including but not limited to AOLserver, Apache HTTP Server, Apache Tomcat, Boa, Caucho Resin Server, Caudium, Chero kee HTTP Server, HFS, Hiawatha HTTP…” – EN: Gutzmann's classification servers are computers (machines) that comprise processing circuitry and memory, and the proof-of-work classification software installed on those servers (e.g., as a PHP/Java request filter on a LAMP-stack machine) constitutes instructions stored in the server's memory which, when executed by the server's processing circuitry, cause the processing circuitry to perform the recited proof-of-work operations.) select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity; (Col. 21, Line 53-61, "The client puzzles used by the proof-of-work protocol (B.) may include but not be limited to 'Guided Tour Puzzle', single level of difficulty hash pre-image puzzle, two-parameter hash pre-image puzzle, integer square root modulo a large prime, Weaken Fiat-Shamir signatures, Ong-Schnorr-Shamir signature, partial hash inversion, hash sequences, Diffie-Hellman-based puzzles, Mbound, Hokkaido, and Merkle tree based puzzles." Col 14, Line, 15-16, "In turn, the puzzle difficulty is a function of the device's current attacker score." – EN: Gutzmann discloses a plurality of proof-of-work puzzle types from which a puzzle is selected/used.) and, as to "scalable work capacity" ("In turn, the puzzle difficulty is a function of the device's current attacker score.", thus the difficulty (work capacity) of the puzzle is adjustable/scalable, so each puzzle in the plurality has scalable work capacity.) Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory. The motivation for doing so would be to give Chakraborty's adaptive framework finer control over the computational difficulty being injected and to ensure a puzzle solution exists, as taught by Gutzmann: "This method provides a more granular control over the computational difficulty than the single difficulty level puzzle. This also improves the chances that a solution to the puzzle exists." (Col. 7, line 67- Col. 8 line 3) Regarding claim 13, Chakraborty in view of Gutzmann teaches all the limitations of claim 12, Chakraborty further teaches: responsive to determining the proof-of-work is valid, provide, to the user device, access to the resource. (Page 2, "…(4) The difficulty level is passed to the puzzle solver which solves a puzzle of the defined difficulty level using a function func. (5) The computed solution is sent to the verifier. (6) When the solution is correct, the request packet is placed on the server queue for processing…" Page 4, "PoW solver uses a function func to solve the assigned difficulty puzzle (see Figure 1)… This cost indirectly impacts the adversarial intent by throttling the rate of adversarial requests reaching the server queue." – EN: upon determining the proof-of-work is valid/correct, the user's request is placed on the server queue for processing, i.e., access to the resource is provided. The solved request that gets placed on the serve queue is the same request the user device transmitted so the resulting service flows back to that device.) Chakraborty does not explicitly teach: "wherein the instructions further cause the processing circuitry to:" However, Gutzmann teaches: "wherein the instructions further cause the processing circuitry to:" ( Col. Line 7-13, “Referring now to FIG. 1, the “classifiers” (103) are an array of one or more servers providing an element of the embodiment of the embodiment. Collectively the classification servers comprise the classification service. The classification servers perform the proof-of-work protocol described in “B. Proof of Work Protocol for Device Classification with the requestors.” Col. 21, Line 35-40, “The classification server(103) or service (203) may be executed on any number of HTTP servers, both open Source and commercial products, including but not limited to AOLserver, Apache HTTP Server, Apache Tomcat, Boa, Caucho Resin Server, Caudium, Chero kee HTTP Server, HFS, Hiawatha HTTP…” – EN: Gutzmann's classification servers are computers (machines) that comprise processing circuitry and memory, and the proof-of-work classification software installed on those servers (e.g., as a PHP/Java request filter on a LAMP-stack machine) constitutes instructions stored in the server's memory which, when executed by the server's processing circuitry, cause the processing circuitry to perform the recited proof-of-work operations.) Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory. The motivation for doing so would be to give Chakraborty's adaptive framework finer control over the computational difficulty being injected and to ensure a puzzle solution exists, as taught by Gutzmann: "This method provides a more granular control over the computational difficulty than the single difficulty level puzzle. This also improves the chances that a solution to the puzzle exists." (Col. 7, line 67- Col. 8 line 3) Regarding claim 14, Chakraborty in view of Gutzmann teaches all the limitations of claim 12, Chakraborty further teaches: responsive to determining the proof-of-work is invalid, (…) (Page 6, "Puzzle verification is a server-side component that performs straightforward verification of the puzzle solution by performing one hash evaluation... If the sent η value leads to desired number of leading 0's, then the solution is verified." Page 2, "When the solution is correct, the request packet is placed on the server queue for processing…" – EN: the verifier returns a binary verified/not-verified result, so a returned solution that does not yield the required leading zeros is determined invalid and because Chakraborty admits only the correct-solution request to the server queue, a request bearing an invalid proof-of-work is withheld from processing.) Chakraborty does not explicitly teach: "wherein the instructions further cause the processing circuitry to: (…) deny, to the user device, access to the resource" However, Gutzmann teaches: "wherein the instructions further cause the processing circuitry to:" ( Col. Line 7-13, “Referring now to FIG. 1, the “classifiers” (103) are an array of one or more servers providing an element of the embodiment of the embodiment. Collectively the classification servers comprise the classification service. The classification servers perform the proof-of-work protocol described in “B. Proof of Work Protocol for Device Classification with the requestors.” Col. 21, Line 35-40, “The classification server(103) or service (203) may be executed on any number of HTTP servers, both open Source and commercial products, including but not limited to AOLserver, Apache HTTP Server, Apache Tomcat, Boa, Caucho Resin Server, Caudium, Chero kee HTTP Server, HFS, Hiawatha HTTP…” – EN: Gutzmann's classification servers are computers (machines) that comprise processing circuitry and memory, and the proof-of-work classification software installed on those servers (e.g., as a PHP/Java request filter on a LAMP-stack machine) constitutes instructions stored in the server's memory which, when executed by the server's processing circuitry, cause the processing circuitry to perform the recited proof-of-work operations.) . "deny, to the user device, access to the resource" (FIG. 4F "DROP REQUEST" and Col. 11, line 30-31, "If any of the checks fail, processing is directed to 432.") Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work verification with Gutzmann's dropping of requests bearing invalid proof-of-work solutions as executed by Gutzmann's server system comprising a hardware processor and memory. The motivation for doing so would be to filter out requestors that fail to demonstrate legitimacy. See Gutzmann Col. 14, line 8-9, "Either of these protocol behaviors is judged hostile and will eventually result in the blacklisting of the device." Regarding claim 20, Chakraborty teaches: receive, from a user device, a request for a resource; (Page 2, "When a new incoming packet is seen, the request packet is forwarded to the request context extractor." – EN: the incoming request packet received from a user seeking access to the protected critical server's content corresponds to receiving a request for a resource.) determine a target work capacity of the user device using a machine-learning (ML) model; (Page 2, "CAPOW uses AI models to learn legitimate network activity patterns and the deviation from the pattern is directly proportional to the difficulty of PoW puzzles to be solved by the user." – EN: the AI/ML model produces a deviation that sets the puzzle difficulty, i.e., the amount of computational work (the target work capacity) the user device must expend.) (…) scale a work capacity of the selected proof-of-work problem to utilize at least the target work capacity; (Page 2, "The context score is forwarded to the policy component which sets certain parameters, such as, it maps the context score to a puzzle difficulty level." – EN: mapping the context score (target work capacity) to the puzzle's difficulty level scales the selected puzzle's work capacity to utilize the determined target.) provide the scaled proof-of-work problem to the user device; (Page 4, "In CAPOW, when a user deviates from a normal activity pattern, the PoW component issues a PoW puzzle to request proof of legitimacy.") receive, from the user device, a proof-of-work in response to the scaled proof-of-work problem; and (Page 6, "After solving, the user sends the nonce back to the server for verification." -- EN: the nonce/solution returned by the user device is the proof-of-work received in response to the puzzle.) determine whether the received proof-of-work is a valid solution to the scaled proof-of-work problem. (Page 6, "Puzzle verification is a server-side component that performs straightforward verification of the puzzle solution by performing one hash evaluation... If the sent η value leads to desired number of leading 0's, then the solution is verified.") Chakraborty does not explicitly teach: “A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:” “select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity;” However, Gutzmann teaches: A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to: (Col. 18, line 12-20, “the classification function is provided by installing the proof-of-work protocol, attacker score adjustment, and black/white listing functions as a server request filter… In the present embodiment, the request filter is implemented, without limitation, as a 'PHP auto-prepend' element in a 'LAMP stack' machine (including without limitation LAMP: Linux, Apache, MySQL, PHP)." Col. 21, line 47-52, "The classification server (103) or service (203) and the method for transferring data from the memory cached to a database (F.) may be implemented in various software languages including but not limited to: PHP, Java, Perl, C, C++, C#, J#, Python, TCL, Cold Fusion, Visual Basic, Lua, Ruby, and Visual Basic .NET." – EN: Gutzmann's classification software, written in a programming language and installed on the server machine, is instructions stored on a non-transitory storage medium of that machine (e.g., the storage/memory of the LAMP-stack machine on which the request filter is installed); the stored software therefore corresponds to a non-transitory computer-readable storage medium including instructions that, when executed by a computer (the server machine).) select a proof-of-work problem from a plurality of proof-of-work problems, each proof-of-work problem in the plurality of proof-of-work problems having scalable work capacity; (Col. 21, Line 53-61, "The client puzzles used by the proof-of-work protocol (B.) may include but not be limited to 'Guided Tour Puzzle', single level of difficulty hash pre-image puzzle, two-parameter hash pre-image puzzle, integer square root modulo a large prime, Weaken Fiat-Shamir signatures, Ong-Schnorr-Shamir signature, partial hash inversion, hash sequences, Diffie-Hellman-based puzzles, Mbound, Hokkaido, and Merkle tree based puzzles." Col 14, Line, 15-16, "In turn, the puzzle difficulty is a function of the device's current attacker score." – EN: Gutzmann discloses a plurality of proof-of-work puzzle types from which a puzzle is selected/used.) and, as to "scalable work capacity" ("In turn, the puzzle difficulty is a function of the device's current attacker score.", thus the difficulty (work capacity) of the puzzle is adjustable/scalable, so each puzzle in the plurality has scalable work capacity.) Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with Gutzmann's plurality of selectable, difficulty-adjustable puzzle types embodied as instructions stored on Gutzmann's non-transitory computer-readable medium executed by a processor. The motivation for doing so would be to give Chakraborty's adaptive framework finer control over the computational difficulty being injected and to ensure a puzzle solution exists, as taught by Gutzmann: "This method provides a more granular control over the computational difficulty than the single difficulty level puzzle. This also improves the chances that a solution to the puzzle exists." (Col. 7, line 67- Col. 8 line 3) 07-21-aia AIA Claim s 4, 6, 15, 17 are rejected under 35 U.S.C. § 103 as being unpatentable over Chakraborty et al. ("CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense, hereinafter " Chakraborty ").) in view of Gutzmann et al. (US 9,705,895 B1), hereinafter, "Gutzmann" further in view of Todorović et al., (Proof-of-Useful-Work: BlockChain Mining by Solving Real-Life Optimization Problems), hereinafter "Todorovic" . Regarding claim 4, Chakraborty in view of Gutzmann teaches all the limitations of claim 1, Gutzmann further teaches: The method of claim 1, wherein the plurality of proof-of-work problems comprise a cryptographic problem, (…), and a network transmission problem. (Col. 21, 53-61, "The client puzzles used by the proof-of-work protocol (B.) may include but not be limited to "Guided Tour Puzzle'', single level of difficulty hash pre-image puzzle, two-parameter hash pre-image puzzle, integer square root modulo a large prime, Weaken Fiat-Shamir signatures, Ong-Schnorr-Shamir signature, partial hash inversion, hash sequences. Diffie-Hellman-based puzzles, Mbound, Hokkaido, and Merkle tree based puzzles." – EN: the hash pre-image puzzle corresponds to the cryptographic problem and the guided tour puzzle corresponds to a network transmission problem.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with the enumerated set of alternative client puzzle types of Gutzmann, including the hash pre-image puzzle (the cryptographic problem) and the Guided Tour Puzzle (the network transmission problem). The motivation for doing so would be the slow down attackers. every puzzle in Gutzmann's set forces the requesting device to spend time and resources before it can send another request, so adding these puzzle types to Chakraborty's framework gives the defender more ways to slow an attacker down. Gutzmann discloses this benefit: "The time required to find a solution to the proof-of-work puzzle is time that cannot be used by the requestor to make HTTP requests against the server. This puzzle-solving time functions as a throttle or rate-limiter." (Gutzmann, col. 14, line. 24–28) Chakraborty in view of Gutzmann does not explicitly teach: "a satisfiability problem" However, Todorovic teaches: "a satisfiability problem" (Page 17, "The maximum satisfiability problem (MAX-SAT) represents the optimization variant of SAT problem in which the objective is to find a model that maximizes the number of satisfied clauses." Page 18, "In evaluating our COCP, we consider instances of MAX-SAT problem and address them using the sequential and parallel local search-based metaheuristics developed by L. Matijević and M. Todorović.") Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types with the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović. The motivation for doing so would be the asymmetric-workload benefit. A satisfiability problem makes the solver do a large amount of work, while the issuer can check the returned answer almost instantly. See Abstract of Todorović: "As is the case with the majority of consensus protocols, PoUW exhibits the property of asymmetry. It is difficult to find a solution for the considered CO problem; however, once a solution is found, its verification is straightforward." Regarding claim 6, Chakraborty in view of Gutzmann further in view of Todorovic teaches all the limitations of claim 4, Todorovic further teaches, The method of claim 4, wherein the scaling the work capacity of the satisfiability problem comprises: scaling a number of clauses of the satisfiability problem, (Page 17, "A formula, represented as a conjunction over disjunctions of its literals, is known as a conjunctive normal form (CNF). Each disjunction of literals in a CNF is referred to as a clause." Page 17, "The maximum satisfiability problem (MAX-SAT) represents the optimization variant of SAT problem in which the objective is to find a model that maximizes the number of satisfied clauses." Page 21, Section 4.2, "Six examples of MAX-SAT (f600, f1000, f2000, hole8, hole10, pr150_75)" scaling a number of variables of the satisfiability problem; and (Page 17, "SAT problems are defined as those in which we need to decide whether the given formula F, including n Boolean variables X = {x1, x2, · · · xn}, is satisfiable…" and Page 17, "Therefore, in order to find the truth assignment that satisfies formula F on n variables, in the worst case, we need to check 2^n valuations.") changing logical operators of the satisfiability problem. (Page 17, "Propositional formulas are constituted of basic building blocks, such as propositional (Boolean) variables and logical operators, for example, negation (¬), conjunction ( ∧ ), and disjunction ( ∨ ). Propositional formulas are used to articulate different statements that can have unique truth values (TRUE or FALSE).") Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types with the scaling clauses, variables, and changing logical operators of Todorovic. The motivation for doing so would be to control the amount of work the issued instance imposes on the requesting device. See Todorovic, Page 30, "This phase would enable us to balance the work of miners by requiring them to solve one hard instance or a group of several easy instances," and Page 30, "…empirical hardness models, which take into account the parameters/features of the corresponding hard CO problem instances." Regarding claim 15, Chakraborty in view of Gutzmann teaches all the limitations of claim 12, Gutzmann further teaches: The system of claim 12, wherein the plurality of proof-of-work problems comprise a cryptographic problem, (…), and a network transmission problem. (Col. 21, 53-61, "The client puzzles used by the proof-of-work protocol (B.) may include but not be limited to "Guided Tour Puzzle'', single level of difficulty hash pre-image puzzle, two-parameter hash pre-image puzzle, integer square root modulo a large prime, Weaken Fiat-Shamir signatures, Ong-Schnorr-Shamir signature, partial hash inversion, hash sequences. Diffie-Hellman-based puzzles, Mbound, Hokkaido, and Merkle tree based puzzles." – EN: the hash pre-image puzzle corresponds to the cryptographic problem and the guided tour puzzle corresponds to a network transmission problem.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework with the enumerated set of alternative client puzzle types of Gutzmann implemented on its server system comprising a hardware processor and memory, including the hash pre-image puzzle (the cryptographic problem) and the Guided Tour Puzzle (the network transmission problem). The motivation for doing so would be the slow down attackers. every puzzle in Gutzmann's set forces the requesting device to spend time and resources before it can send another request, so adding these puzzle types to Chakraborty's framework gives the defender more ways to slow an attacker down. Gutzmann discloses this benefit: "The time required to find a solution to the proof-of-work puzzle is time that cannot be used by the requestor to make HTTP requests against the server. This puzzle-solving time functions as a throttle or rate-limiter." (Gutzmann, col. 14, line. 24–28) Chakraborty in view of Gutzmann does not explicitly teach: "a satisfiability problem" However, Todorovic teaches: "a satisfiability problem" (Page 17, "The maximum satisfiability problem (MAX-SAT) represents the optimization variant of SAT problem in which the objective is to find a model that maximizes the number of satisfied clauses." Page 18, "In evaluating our COCP, we consider instances of MAX-SAT problem and address them using the sequential and parallel local search-based metaheuristics developed by L. Matijević and M. Todorović.") Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory with the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović. The motivation for doing so would be the asymmetric-workload benefit. A satisfiability problem makes the solver do a large amount of work, while the issuer can check the returned answer almost instantly. See Abstract of Todorović: "As is the case with the majority of consensus protocols, PoUW exhibits the property of asymmetry. It is difficult to find a solution for the considered CO problem; however, once a solution is found, its verification is straightforward." Regarding claim 17, Chakraborty in view of Gutzmann further in view of Todorovic teaches all the limitations of claim 15, Todorovic further teaches, The system of claim 15, wherein the scaling the work capacity of the satisfiability problem comprises: change a number of clauses of the satisfiability problem; (Page 17, "A formula, represented as a conjunction over disjunctions of its literals, is known as a conjunctive normal form (CNF). Each disjunction of literals in a CNF is referred to as a clause." Page 17, "The maximum satisfiability problem (MAX-SAT) represents the optimization variant of SAT problem in which the objective is to find a model that maximizes the number of satisfied clauses." Page 21, Section 4.2, "Six examples of MAX-SAT (f600, f1000, f2000, hole8, hole10, pr150_75)" change a number of variables of the satisfiability problem; and (Page 17, "SAT problems are defined as those in which we need to decide whether the given formula F, including n Boolean variables X = {x1, x2, · · · xn}, is satisfiable…" and Page 17, "Therefore, in order to find the truth assignment that satisfies formula F on n variables, in the worst case, we need to check 2^n valuations.") change logical operators of the satisfiability problem. (Page 17, "Propositional formulas are constituted of basic building blocks, such as propositional (Boolean) variables and logical operators, for example, negation (¬), conjunction ( ∧ ), and disjunction ( ∨ ). Propositional formulas are used to articulate different statements that can have unique truth values (TRUE or FALSE).") Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory with the scaling clauses, variables, and changing logical operators of Todorovic. The motivation for doing so would be to control the amount of work the issued instance imposes on the requesting device. See Todorovic, Page 30, "This phase would enable us to balance the work of miners by requiring them to solve one hard instance or a group of several easy instances," and Page 30, "…empirical hardness models, which take into account the parameters/features of the corresponding hard CO problem instances." 07-21-aia AIA Claim s 5, 7, 16, 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Chakraborty et al. ("CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense, hereinafter " Chakraborty ") in view of Gutzmann et al. (US 9,705,895 B1), hereinafter, "Gutzmann" in view of Todorović et al., (Proof-of-Useful-Work: BlockChain Mining by Solving Real-Life Optimization Problems), hereinafter "Todorovic" further in view of Krueger et al. (US 2020/0380475 A1), hereinafter "Krueger" . Regarding claim 5, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 4, Krueger teaches: The method of claim 4, wherein the scaling the work capacity of the cryptographic problem comprises: changing a cryptographic algorithm of the cryptographic problem; and (Para 759, "In this embodiment, the hash H(DB.j) of a data block DB.j is calculated as H(DB.j)=SHA256 (H (MDS.j)+H(DB.i)+ RN.j), wherein the SHA256 hash function can be replaced with any other suitable hash function." and Para 764, "Known kryptological hash functions are " Snefru " , " N - Hash " , " FFT - Hash " , " MD4 " , " MD5 " , " SHA " ( in par ticular " SHA - 0 " , " SHA - 1 " , " SHA - 256 " , " SHA - 384 " and " SHA - 512 " ) , " RIPEMD " , " HAVAL " , " TIGER " , " PANAMA " , " WHIRLPOOL " , " SMASH " , " FORK - 256 " , " SHA - 3 " , " BLAKE " , or other hash functions based on the Merkle - Damgard construction or on the Sponge construction") changing logical operators of the cryptographic problem. (Para 759, "The operation " + " can be understood as arithmetic addition of numbers or as concatenation of strings ( by converting numbers to strings before the concatenation") . Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the cryptographic hash algorithms and operations of Krueger's nonce-based proof of work. The motivation for doing so would be to force the requesting device to solve the puzzle by brute force while the issuer checks the answer with a single hash calculation. See Krueger Para 155, "Since a hash function is a one-way function, the only way to determine the nonce is to try different nonces, possibly a huge number of nonces, but a verification of the execution of the consensus algorithm can be done by calculating the hash function only once." Regarding claim 7, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 4, Krueger teaches: The method of claim 4, wherein the scaling the work capacity of the network transmission problem comprises: changing a size of a payload of the network transmission problem; and (Para 141, "In particular, a network-bound proof of work can comprise down loading certain data stored on different network nodes." Para 138, "In particular, for a network bound proof of work more communication actions or communications actions with a higher latency have to be performed for performing the action than for verifying that the action actually has been performed ." – EN: the data the device must download is the payload of the network transmission problem, so changing what data must be downloaded for each issued proof-of-work changes the size of the payload.) changing a number of transmissions in the network transmission problem. (Para 141, "In particular, a network-bound proof of work can comprise communication with several network nodes, wherein the access of one or more network nodes is delayed by network latency or by the accessed networks." Para 761, ""…a network-bound proof of work (in particular, in order to determine DET-FDB the further data block, either a lot of network nodes or difficult to access network nodes need to be accessed)…" – EN: each communication with a network node is a transmission, so requiring the device to contact more or fewer nodes changes the number of transmissions.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the payload and transmission change of the network transmission problem of Krueger. The motivation for doing so would be to slow the requesting device with network delays that its own computing power can't speed up, thus limiting how many requests it can make in a given time. See in Krueger, Para 137, "In general, a proof of work is a measure required by an entity to perform an action in a system, in particular by a computer to perform an action within a network, to reduce the possible number of actions in a certain time interval." Regarding claim 16, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 15, Krueger teaches: The system of claim 15, wherein the scaling the work capacity of the cryptographic problem comprises: change a cryptographic algorithm of the cryptographic problem; and (Para 759, "In this embodiment, the hash H(DB.j) of a data block DB.j is calculated as H(DB.j)=SHA256 (H (MDS.j)+H(DB.i)+ RN.j), wherein the SHA256 hash function can be replaced with any other suitable hash function." and Para 764, "Known kryptological hash functions are " Snefru " , " N - Hash " , " FFT - Hash " , " MD4 " , " MD5 " , " SHA " ( in par ticular " SHA - 0 " , " SHA - 1 " , " SHA - 256 " , " SHA - 384 " and " SHA - 512 " ) , " RIPEMD " , " HAVAL " , " TIGER " , " PANAMA " , " WHIRLPOOL " , " SMASH " , " FORK - 256 " , " SHA - 3 " , " BLAKE " , or other hash functions based on the Merkle - Damgard construction or on the Sponge construction") change logical operators of the cryptographic problem. (Para 759, "The operation " + " can be understood as arithmetic addition of numbers or as concatenation of strings ( by converting numbers to strings before the concatenation") . Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the cryptographic hash algorithms and operations of Krueger's nonce-based proof of work. The motivation for doing so would be to force the requesting device to solve the puzzle by brute force while the issuer checks the answer with a single hash calculation. See Krueger Para 155, "Since a hash function is a one-way function, the only way to determine the nonce is to try different nonces, possibly a huge number of nonces, but a verification of the execution of the consensus algorithm can be done by calculating the hash function only once." Regarding claim 18, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 15, Krueger teaches: The system of claim 15, wherein the scaling the work capacity of the network transmission problem comprises: change a size of a payload of the network transmission problem; and (Para 141, "In particular, a network-bound proof of work can comprise down loading certain data stored on different network nodes." Para 138, "In particular, for a network bound proof of work more communication actions or communications actions with a higher latency have to be performed for performing the action than for verifying that the action actually has been performed." – EN: the data the device must download is the payload of the network transmission problem, so changing what data must be downloaded for each issued proof-of-work changes the size of the payload.) change a number of transmissions in the network transmission problem. (Para 141, "In particular, a network-bound proof of work can comprise communication with several network nodes, wherein the access of one or more network nodes is delayed by network latency or by the accessed networks." Para 761, ""…a network-bound proof of work (in particular, in order to determine DET-FDB the further data block, either a lot of network nodes or difficult to access network nodes need to be accessed)…" – EN: each communication with a network node is a transmission, so requiring the device to contact more or fewer nodes changes the number of transmissions.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the payload and transmission change of the network transmission problem of Krueger. The motivation for doing so would be to slow the requesting device with network delays that its own computing power can't speed up, thus limiting how many requests it can make in a given time. See in Krueger, Para 137, "In general, a proof of work is a measure required by an entity to perform an action in a system, in particular by a computer to perform an action within a network, to reduce the possible number of actions in a certain time interval." 07-21-aia AIA Claim s 8, 9, 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Chakraborty et al. ("CAPoW: Context-Aware AI-Assisted Proof of Work based DDoS Defense, hereinafter " Chakraborty ") in view of Gutzmann et al. (US 9,705,895 B1), hereinafter, "Gutzmann" in view of Todorović et al., (Proof-of-Useful-Work: BlockChain Mining by Solving Real-Life Optimization Problems), hereinafter "Todorovic" further in view of Juels et al. (US 7,197,639 B1), hereinafter, "Juels" Regarding claim 8, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 4 Juels teaches: The method of claim 4, wherein the plurality of proof-of-work problems further comprises a chained problem, the chained problem comprising a set of scaled proof-of-work problems; and (Col. 16, line 28-31, "If the variable (m) represents the number of sub-puzzles contained in a one puzzle, and the j'th sub-puzzle in P_i is denoted by P_i[j], then a puzzle P_i consists of sub-puzzles P_i[1], P_i[2], . . . , P_i[m]." Col. 14, line 66 - Col. 15. Line 1, "By adjusting the number of revealed bits in the input data 550, the puzzle generator 130 can adjust the computational intensity of each puzzle." – EN: Juels' single issued puzzle is itself a set of m sub-puzzles, each an independently solvable proof-of-work problem whose work capacity is scaled by the number of concealed bits, and therefore is a chained problem comprising a set of scaled proof-of-work problems.) wherein the proof-of-work received in response to the chained problem comprises a respective proof-of-work for each scaled proof-of-work problem in the set of the scaled proof-of-work problems. (Col. 15, line 65-66, "The answer to each Sub-puzzle is a portion of an answer for the entire puzzle." Col. 20, line 60-64, "Complete verification of a correct puzzle containing Sub puzzles of this type, requires (m+1) hash computations, one hash computation to compute (W) and another (m) hash computations to verify all sub-puzzle solutions." – EN: the client's returned solution contains a separate answer for each of the m sub-puzzles, each individually verified by the server, i.e., a respective proof-of-work for each problem in the set.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the multi-sub-puzzle composite problem of Juels. The motivation for doing so would be to deter attackers who guess rather than solve, without adding work for legitimate users. See Juels, Col. 15, line 63-65: "A puzzle comprised of multiple sub-puzzles penalizes the guessing adversary beyond that of a puzzle containing one sub-puzzle of the same total size." Regarding claim 9, Chakraborty in view of Gutzmann in view of Todorovic in view of Juels teaches all the limitations of claim 8, Juels further teaches: The method of claim 8, wherein the scaling the work capacity of the chained problem comprises: scaling a number of scaled proof-of-work problems in the set of the scaled proof-of-work problems. (Col. 19, line 6-10, "Since a puzzle contains (m) sub-puzzles, the expected or statistical average number of time steps for a client 110 (or adversary) to solve a puzzle P will be m·2^(k−1), while the maximum number of time steps will be m·2^k" Col 21. Line 60-67, "In the simple implementation of the client puzzle protocol described above, the security parameters K and (m) are fixed ... Note, though, that it is possible for the server 120 to scale puzzle sizes and thus impose variably sized computational loads on the client." – EN: because the puzzle's total work is m·2^(k−1), directly proportional to the number m of sub-puzzles, Juels' teaching to vary the otherwise-fixed parameter m to impose variably sized computational loads corresponds to scaling the work capacity of the chained problem by scaling the number of problems in the set under BRI.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorovic with the varying the number (m) of sub-puzzles in the set of Juels. The motivation for doing so would be to adjust the computational burden imposed on a requesting device in proportion to the threat it presents. See Juels Col. 22, line 8-11," Through modification of client puzzle parameters, server performance can be caused to degrade gracefully, and in proportion to the severity of the threat to the server." Regarding claim 19, Chakraborty in view of Gutzmann in view of Todorovic teaches all the limitations of claim 15, Juels teaches: The system of claim 15, wherein the plurality of proof-of-work problems further comprises a chained problem, the chained problem comprising a set of scaled proof-of-work problems; and (Col. 16, line 28-31, "If the variable (m) represents the number of sub-puzzles contained in a one puzzle, and the j'th sub-puzzle in P_i is denoted by P_i[j], then a puzzle P_i consists of sub-puzzles P_i[1], P_i[2], . . . , P_i[m]." Col. 14, line 66 - Col. 15. Line 1, "By adjusting the number of revealed bits in the input data 550, the puzzle generator 130 can adjust the computational intensity of each puzzle." – EN: Juels' single issued puzzle is itself a set of m sub-puzzles, each an independently solvable proof-of-work problem whose work capacity is scaled by the number of concealed bits, and therefore is a chained problem comprising a set of scaled proof-of-work problems.) wherein the proof-of-work received in response to the chained problem comprises a respective proof-of-work for each scaled proof-of-work problem in the set of the scaled proof-of-work problems. (Col. 15, line 65-66, "The answer to each Sub-puzzle is a portion of an answer for the entire puzzle." Col. 20, line 60-64, "Complete verification of a correct puzzle containing Sub puzzles of this type, requires (m+1) hash computations, one hash computation to compute (W) and another (m) hash computations to verify all sub-puzzle solutions." – EN: the client's returned solution contains a separate answer for each of the m sub-puzzles, each individually verified by the server, i.e., a respective proof-of-work for each problem in the set.) Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Chakraborty's context-aware AI-assisted proof-of-work framework and Gutzmann's plurality of selectable, difficulty-adjustable puzzle types implemented on Gutzmann's server system comprising a hardware processor and memory and the satisfiability-based proof-of-work problems (MAX-SAT instances) of Todorović with the multi-sub-puzzle composite problem of Juels. The motivation for doing so would be to deter attackers who guess rather than solve, without adding work for legitimate users. See Juels, Col. 15, line 63-65: "A puzzle comprised of multiple sub-puzzles penalizes the guessing adversary beyond that of a puzzle containing one sub-puzzle of the same total size." Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAYMUR RAHMAN ALI whose telephone number is (571)272-0007. The examiner can normally be reached Mon-Fri. 9:30-6:30 pm. Examiner can be reached through email at Naymur.Ali@uspto.gov 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, Alexey Shmatov can be reached at (571)270-3428. 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. /NAYMUR RAHMAN ALI/Examiner, Art Unit 2123 /ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123 Application/Control Number: 18/541,702 Page 2 Art Unit: 2123 Application/Control Number: 18/541,702 Page 3 Art Unit: 2123 Application/Control Number: 18/541,702 Page 4 Art Unit: 2123 Application/Control Number: 18/541,702 Page 5 Art Unit: 2123 Application/Control Number: 18/541,702 Page 6 Art Unit: 2123 Application/Control Number: 18/541,702 Page 7 Art Unit: 2123 Application/Control Number: 18/541,702 Page 8 Art Unit: 2123 Application/Control Number: 18/541,702 Page 9 Art Unit: 2123 Application/Control Number: 18/541,702 Page 10 Art Unit: 2123 Application/Control Number: 18/541,702 Page 11 Art Unit: 2123 Application/Control Number: 18/541,702 Page 12 Art Unit: 2123 Application/Control Number: 18/541,702 Page 13 Art Unit: 2123 Application/Control Number: 18/541,702 Page 14 Art Unit: 2123 Application/Control Number: 18/541,702 Page 15 Art Unit: 2123 Application/Control Number: 18/541,702 Page 16 Art Unit: 2123 Application/Control Number: 18/541,702 Page 17 Art Unit: 2123 Application/Control Number: 18/541,702 Page 18 Art Unit: 2123 Application/Control Number: 18/541,702 Page 19 Art Unit: 2123 Application/Control Number: 18/541,702 Page 20 Art Unit: 2123 Application/Control Number: 18/541,702 Page 21 Art Unit: 2123 Application/Control Number: 18/541,702 Page 22 Art Unit: 2123 Application/Control Number: 18/541,702 Page 23 Art Unit: 2123 Application/Control Number: 18/541,702 Page 24 Art Unit: 2123 Application/Control Number: 18/541,702 Page 25 Art Unit: 2123 Application/Control Number: 18/541,702 Page 26 Art Unit: 2123 Application/Control Number: 18/541,702 Page 27 Art Unit: 2123 Application/Control Number: 18/541,702 Page 28 Art Unit: 2123 Application/Control Number: 18/541,702 Page 29 Art Unit: 2123 Application/Control Number: 18/541,702 Page 30 Art Unit: 2123 Application/Control Number: 18/541,702 Page 31 Art Unit: 2123 Application/Control Number: 18/541,702 Page 32 Art Unit: 2123 Application/Control Number: 18/541,702 Page 33 Art Unit: 2123 Application/Control Number: 18/541,702 Page 34 Art Unit: 2123 Application/Control Number: 18/541,702 Page 35 Art Unit: 2123 Application/Control Number: 18/541,702 Page 36 Art Unit: 2123 Application/Control Number: 18/541,702 Page 37 Art Unit: 2123 Application/Control Number: 18/541,702 Page 38 Art Unit: 2123 Application/Control Number: 18/541,702 Page 39 Art Unit: 2123 Application/Control Number: 18/541,702 Page 40 Art Unit: 2123 Application/Control Number: 18/541,702 Page 41 Art Unit: 2123 Application/Control Number: 18/541,702 Page 42 Art Unit: 2123 Application/Control Number: 18/541,702 Page 43 Art Unit: 2123 Application/Control Number: 18/541,702 Page 44 Art Unit: 2123 Application/Control Number: 18/541,702 Page 45 Art Unit: 2123 Application/Control Number: 18/541,702 Page 46 Art Unit: 2123 Application/Control Number: 18/541,702 Page 47 Art Unit: 2123 Application/Control Number: 18/541,702 Page 48 Art Unit: 2123 Application/Control Number: 18/541,702 Page 49 Art Unit: 2123 Application/Control Number: 18/541,702 Page 50 Art Unit: 2123
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