DYNAMIC MODEL SCOPE SELECTION FOR CONNECTED VEHICLES

§101 §102 §103

Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-18 are directed to a method (process) and claim 31 is directed to a non-transitory storage medium (manufacture). Therefore, the claim fall within one of the statutory categories of invention (process, manufacture, composition of matter or machine). As such, the claim is eligible under 35 USC 101. Claim 29 is directed to a computer program (software “per se”). Therefore, the claim does not fall within one of the statutory categories of invention (process, manufacture, composition of matter or machine). As such, the claim is ineligible under 35 USC 101. Claims 1, 29, 31: Regarding claim 1: Step 2A, Prong 1 analysis: Taking claim 1 as a representative claim, the claim recites in part: “generating, using the machine learning model, model cell parameters that define a cell size and a cell geometry;” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. This limitation encompasses analyzing the model cells and determining what size and geometry they should have based on a criteria, which could be done as a mental process by a person having ordinary skill of the art. “generating, using the machine learning model, model cluster parameters that comprise a cluster geometry, a cluster centroid value that identifies a center of the model cluster and a cluster radius value that defines a quantity of model cells from the cluster centroid value to an edge of the model cluster. ” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. This limitation encompasses analyzing the model cells and generating a value that lies at the origin of a 2D hexagon, and a radius value of the of the cluster that identifies the number of cells from the centroid value, to the outer edge of the cluster or hexagon. Such process could be done as a mental process by a person having ordinary skill of the art by manually generating a cell that is at the origin of the shape, and counting how many cells are between said cell and the outer edge of the cluster. Step 2A, Prong 2 analysis: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: “receiving, into a machine learning model, a value for a prediction horizon, Tw, that defines a future time interval for generating predicted data corresponding to an element in a model region;”. As discussed above, the additional elements of receiving, into a machine learning model, a value for a prediction horizon, Tw which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). “receiving machine learning training data that includes data corresponding to previous element activity” As discussed above, the additional elements of receiving, into a machine learning model, a value for a prediction horizon, Tw which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). “a computing device in a network” which is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g). Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: • “a computing device in a network” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. “receiving, into a machine learning model, a value for a prediction horizon, Tw, that defines a future time interval for generating predicted data corresponding to an element in a model region;” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding claim 29: Claim 29 recites the same limitations as claim 1, except for Step 2A, Prong 2 analysis: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: • “A computer program comprising program code” which is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g). • “machine learning dynamic model scope selection for a radio network” which is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: • “A computer program comprising program code” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. • “machine learning dynamic model scope selection for a radio network” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. Regarding claim 31: Claim 31 recites the same limitations as claim 1, except for: Step 2A, Prong 2 analysis: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of: • “A computer program comprising program code” which is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05(g). • “machine learning dynamic model scope selection for a radio network” which is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Step 2B analysis: In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of: • “A computer program comprising program code” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. • “machine learning dynamic model scope selection for a radio network” This limitation is recited at a high level of generality and amount 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. See MPEP 2106.05(f). As explained by the Supreme Court; in order to make a claim directed to a judicia I exception patent-eligible, the additional element or combination of elements must do '"more than simply state the judicial exception] while adding the words 'apply it"'. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, limitations that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not amount to significantly more than the exception itself. Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception. Claim 2: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “a cluster overlap that defines a number of model cells that are included in adjacent model clusters.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses defining or counting a number of model cells, which could be done as a mental process by a person having ordinary skill in the art. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 3: Step 2A, Prong 1 analysis: The claim is directed to the same abstract ideas as identified above. Step 2A, Prong 2 analysis: As discussed above, the additional elements of “a current position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As discussed above, the additional elements of “a previous position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As discussed above, the additional elements of “a future position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B analysis: As discussed above, the additional elements of “a current position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As discussed above, the additional elements of “a previous position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As discussed above, the additional elements of “a future position of the element in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception. Claim 6: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “at least one model cluster comprises a plurality of model clusters, wherein the method further comprises automatically generating the plurality of model clusters in the model region based on the model cluster parameters”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses recursive generation based on parameters. This would be seen as a mental process to a person having ordinary skill of the art given that they would be able to generate said model clusters after considering their parameters. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 7; Claim 7 is rejected under the same basis of claim 6. Claim 8: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: “identifying a single model cell of the plurality of model cells in the model region as a first centroid cell corresponding to a first model cluster of the plurality of model clusters;” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses identifying the central cell of a 2D hexagonal shaped model cluster. This would be seen as a mental process to a person having ordinary skill of the art, since they would be able to view the model cluster and determine which cell is at the origin, and identify it as the first centroid cell. “assigning each of the model cells that are within the cluster radius value of the first centroid cell to be in the first model cluster;” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses assigning cells close to the first central cell to a model cluster. This would be seen as a mental process to a person having ordinary skill of the art, since they would be able to determine if a cell is within the radius of a centroid, and then assigning it to a model cluster. “identifying a second centroid cell based on the first centroid cell, the cluster radius value and the cluster overlap;”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses identifying the second cell based on other values. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to look at the first centroid cell, cluster radius and overlap, and be able to determine the next and/or second centroid cell. “assigning each of the model cells that are within the cluster radius value of the second centroid cell to be in a second model cluster of the plurality of model clusters.;” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses assigning cells close to the second central cell to a model cluster. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to determine if a cell is within the radius of a centroid, and then assigning it to a model cluster. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 9: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “stopping generating model clusters of the plurality of clusters based on the model region being covered with model clusters.”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses ending a recursive cycle based on a condition. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to determine when to stop generating the model clusters based on whether a condition is met or not. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 10: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “removing ones of the at least one model cluster that are without transitions of elements in the machine learning training data.”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses removing a model cluster based on a condition. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to determine if that model cluster contains transitions of elements, and then remove it accordingly. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 11: Step 2A, Prong 1 analysis: The claim teaches the same abstract ideas as identified above. Step 2A, Prong 2 analysis: “training the at least one model cluster using a second order Markov chain model.” is recited at a high-level of generality and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application. Step 2B analysis: “training the at least one model cluster using a second order Markov chain model.” is recited at a high-level of generality and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)) Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not significantly more than the judicial exception. Claim 12: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “further comprising training, the at least one model cluster to generate a transition matrix corresponding to each of the at least one model cluster.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses training a model cluster to recursively generate transition matrixes that corresponds to the model cluster. This would be seen as a mental process to a person having ordinary skill of the art since they would capable of training the model cluster to generate a transition matrix that corresponds to itself. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 13: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: • “responsive to one of the at least one model cluster including a maximum number of transitions of elements, reducing the cluster radius value corresponding to that model cluster.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses reducing a value of a model cluster. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to reduce the radius value themself. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 14: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: “performing an inference operation that is based on the transition matrix and that provides a probable future model cell that the element will be in at Tw and a confidence value corresponding the probable future model cell in response to receiving a current position of the element in the model region and a previous position of the element in the model region.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing an inference operation adapted to a transition matrix. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to consider a transition matrix and perform the inference operation accordingly. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 15: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: “wherein in response to the current position and the previous position being in an overlapping area of multiple ones of the at least one model cluster, the inference operation uses weighted averages corresponding to the multiple ones of the at least one model cluster.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses taking weighted averages that correspond to a model cluster. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to calculate a weighted average and assign it with the correct model cluster. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 16: Step 2A, Prong 1 analysis: The claim is directed to the same abstract ideas as identified above. Step 2A, Prong 2 analysis: • As discussed above, the additional elements of “wherein the probable future model cell comprises a future position of the element in the model region.” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Step 2B analysis: As discussed above, the additional elements of “wherein the probable future model cell comprises a future position of the element in the model region.” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Claim 17: Step 2A, Prong 1 analysis: The claim(s) recite(s) in part: “wherein the probable future model cell comprises a confidence value that corresponds to the future position in the model region.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses identifying a value that corresponds to the future position in the model region. It would be obvious to person having ordinary skill of the art to set the confidence value to adhere to the future position in the region. Step 2A, Prong 2 analysis: There are no additional elements that individually or in combination integrate the judicial element into practical application. Step 2B analysis: There are no additional elements that individually or in combination amount to significantly more than the judicial exception Claim 18: Step 2A, Prong 1 analysis: The claim recites in part: “updating the at least one model cluster and/or a transition matrix responsive to receiving the updated machine learning training data.” As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses updating data. This would be seen as a mental process to a person having ordinary skill of the art since they would be able to analyze the updated machine learning training data and determine if changes to the model cluster or then transition matrix should be made. Step 2A, Prong 2 analysis: • The claim recites the additional element of “receiving updated machine learning training data that includes data corresponding to updated transitions of elements in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Step 2B analysis: • As discussed above, the additional elements of “receiving updated machine learning training data that includes data corresponding to updated transitions of elements in the model region;” which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). Claim Rejections - 35 USC § 102 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. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 1, 3, 4, 5, 13, 29, 31 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by O’Mahony et al., (US 20170227370 A1, O’Mahony hereinafter). Regarding claim 1: O’Mahony teaches the following limitations of claim 1: receiving, into a machine learning model, a value for a prediction horizon, Tw, that defines a future time interval for generating predicted data corresponding to an element in a model region; “In some embodiments, the zone scoring module 165 generates a model for the wait time using the estimated wait times determined over time, and uses those models to predict the wait time when generating zone scores for future points in time.” (O’Mahony) [pg. 4 col. 1 pp.5] O’Mahony teaches selecting a future time offset that generates predictions. It would be obvious to represent the offset as an input horizon parameter. receiving machine learning training data that includes data corresponding to previous element activity in the model region, “the model of provider supply is based on at least one of: a number of providers in the zone… a historical amount of provider supply in the zone.” (O’Mahony) [pg.9 Claim 14] Here, it is explicitly disclosed that the model utilizes historical data to train and compute zone scores as mentioned in the claim. wherein the model region comprises at least one model cluster.” A geographic region, such as a city, neighborhood, or county, etc., is separated into zones and a score is generated for each zone by the travel coordination system. ” (O’Mahony) [pg.1 col. 1 pp.4] Each geographic zone is associated with a surrounding data collection area. This area or region functions as a cluster of zones used jointly for modeling. and wherein each of the at least one model cluster comprises a plurality of model cells that are adjacent to one another; “The zone data store 155 stores information about a geographic region. The geographic region can be a city, neighborhood, county, or any other area in which a provider may travel. The region is partitioned into zones that represent the portions of the geographic region used by the travel coordination system 130 for collecting data about the region,” (O’Mahony) [pg.3 col. 2 pp. 3] Here, Neighborhoods consist of adjacent zones surrounding a central zone, representing model clusters that contain adjacent model cells. generating, using the machine learning model, model cell parameters that define a cell size and a cell geometry, “Zones 400 can be hexagonal (as presented in FIG. 4), rectangular, circular, or any other shape.” (O’Mahony) [pg. 6, col. 2, pp.7] O’Mahony anticipates the use of geometrical shapes to cluster data models, rendering this limitation of claim 1 obvious. and generating, using the machine learning model, model cluster parameters that comprise a cluster geometry, a cluster centroid value that identifies a center of the model cluster and a cluster radius value that defines a quantity of model cells from the cluster centroid value to an edge of the model cluster. “Zones can be hexagonal (as presented in FIG. 4), rectangular, circular, or any other shape.” (O’Mahony) [pg. 6, col. 2, pp.7] Neighborhoods are geometrically defined regions around a zone. The surrounding data collection area has a defined spatial extent and shape. In some embodiments, the radius of the k-ring based on the amount of data in the area.” (O’Mahony) [pg. 7, col. 1, pp. 1] O’Mahony utilizes k-ring neighborhoods, where k defines how many layers of adjacent zones are included around the central zone. This radius is measured by the number of zones rather than physical distance, directly mapping to the limitation of the claim. Regarding claim 3: O’Mahony teaches the following limitations of claim 3: a current position of the element in the model region; The route scoring module generates a set of candidate routes from the provider's current position to the destination. (O’Mahony) [pg. 5, col. 1, pp. 3]” As anticipated, O’Mahony stores the current position of a driver or element. a previous position of the element in the model region; and the historical supply of providers in the zone. (O’Mahony) [pg. 4, col. 2, pp. 2]” As anticipated, O’Mahony teaches the number of previous drivers in a zone or area. It would be obvious that to keep track of the number of drivers in an area, that you would need to store their previous position, as taught by claim 3. and a future position of the element in the model region. the pickup location for a rider, a route from a pickup location to a destination. (O’Mahony) [pg. 2, col. 2, pp. 2]” It would be obvious that a destination is a user’s future position, therefore the limitation is anticipated by O’Mahony. Claim 4: As anticipated, O’Mahony teaches the following limitations of claim 4: wherein the model region is two-dimensional, “In one embodiment, each zone is a hexagonal section of the region or in other embodiments may be any other tessellating shape or repeating-size-and-shape portion of the map.” (O’Mahony) [pg. 3, col. 2, pp. 3]” The drawings included by O’Mahony also depict the region as being two-dimensional. O’Mahony anticipates the use of a two-dimensional plane to cover a region of a map, as taught by the limitations of claim 4. and wherein the cluster geometry comprises the two-dimensional shape. In one embodiment, each zone is a hexagonal section of the region or in other embodiments may be any other tessellating shape or repeating-size-and-shape portion of the map. (O’Mahony) [pg. 3, col. 2, pp. 3] As anticipated, O’Mahony teaches the use of shapes, although not mentioned here, the shapes are depicted as two-dimensional in the drawings provided by O’Mahony, rendering claim 4 obvious. Regarding claim 5: As anticipated, O’Mahony teaches the following limitations of claim 5: The method of claim 4, wherein the two-dimensional shape comprises a hexagonal shape. In one embodiment, each zone is a hexagonal section of the region or in other embodiments may be any other tessellating shape or repeating-size-and-shape portion of the map. (O’Mahony) [pg. 3, col. 2, pp. 3] As anticipated, O’Mahony teaches the use of shapes, although not mentioned here, the shapes are depicted as two-dimensional in the drawings provided by O’Mahony, rendering claim 5 obvious. Regarding claim 13: As anticipated, O’Mahony teaches the following limitations of claim 13: responsive to one of the at least one model cluster including a maximum number of transitions of elements, reducing the cluster radius value corresponding to that model cluster. Similarly, more-rural areas tend to have lower data density, so a higher k-ring radius may be used. In some embodiments, the data collection areas for different zones can overlap. As an alternative, rather than adjusting the k-ring radius, the size of the zones may increase or decrease according to the density of requests within a zone. (O’Mahony) [pg. 7, col. 1, pp. 1] Although O’Mahony does not explicitly teach the reduction of the cluster radius, it teaches changing the size of the zone which would result in the increase of decrease of the radius, thus rendering claim 13 obvious. Regarding claim 29: As anticipated, O’Mahony teaches the following limitations of claim 29: A computer program comprising program code to be executed by processing circuitry of a computing device adapted for machine learning dynamic model scope selection for a radio network, whereby execution of the program code causes the computing device to perform operations comprising: Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. (O’Mahony) [pg. 8, col. 1, pp. 2] O’Mahony anticipates claim 29 by teaching the same methods of utilizing the program, as claim 29, rendering it obvious. receiving machine learning training data that includes data corresponding to previous element activity in a model region, “the model of provider supply is based on at least one of: a number of providers in the zone… a historical amount of provider supply in the zone.” (O’Mahony) [pg.9, Claim 14] Here, it is explicitly disclosed that the model utilizes historical data to train and compute zone scores as mentioned in the claim. wherein the model region comprises at least one model cluster.” A geographic region, such as a city, neighborhood, or county, etc., is separated into zones and a score is generated for each zone by the travel coordination system. ” (O’Mahony) [pg. 7, col. 1, pp. 1] Each geographic zone is associated with a surrounding data collection area. This area or region functions as a cluster of zones used jointly for modeling, similarly to the claim. and wherein each of the at least one model cluster comprises a plurality of model cells that are adjacent to one another; “The zone data store 155 stores information about a geographic region. The geographic region can be a city, neighborhood, county, or any other area in which a provider may travel. The region is partitioned into zones that represent the portions of the geographic region used by the travel coordination system 130 for collecting data about the region,” (O’Mahony) [pg.3, col. 2, pp.4] Here, Neighborhoods consist of adjacent zones surrounding a central zone, representing model clusters that contain adjacent model cells, as taught by the claim. and generating, using the machine learning model, model cluster parameters that comprise a cluster geometry, “Zones 400 can be hexagonal (as presented in FIG. 4), rectangular, circular, or any other shape.” (O’Mahony) [pg. 6, col.2, pp. 5] O’Mahony anticipates the use of geometrical shapes to cluster data models, rendering this limitation of claim 29 obvious. a cluster centroid value that identifies a center of the model cluster and a cluster radius value that defines a quantity of model cells from the cluster centroid value to an edge of the model cluster. “In some embodiments, the radius of the k-ring based on the amount of data in the area.” (O’Mahony) [pg.6, col.2, pp. 8]” O’Mahony utilizes k-ring neighborhoods, where k defines how many layers of adjacent zones are included around the central zone. This radius is measured by the number of zones rather than physical distance, directly mapping to the limitation of the claim. As anticipated, O’Mahony teaches the use of a learning model to predict future time intervals, using historical values, data clusters and data cells. It would be obvious to a person having ordinary skill of the art be to combine these components to predict a future time interval, as taught by claim 29. Regarding Claim 31 A computer program product comprising a non-transitory storage medium including program code to be executed by processing circuitry of a computing device adapted for machine learning dynamic model scope selection for a radio network, whereby execution of the program code causes the computing device to perform operations comprising: Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. (O’Mahony) [pg. 8, col. 1, pp. 2] receiving machine learning training data that includes data corresponding to previous element activity in a model region, “the model of provider supply is based on at least one of: a number of providers in the zone… a historical amount of provider supply in the zone.” (O’Mahony) [Claim 14] Here, it is explicitly disclosed that the model utilizes historical data to train and compute zone scores as mentioned in the claim. wherein the model region comprises at least one model cluster. A geographic region, such as a city, neighborhood, or county, etc., is separated into zones and a score is generated for each zone by the travel coordination system. ” (O’Mahony) [pg. 1, col. 1, pp. 4] Each geographic zone is associated with a surrounding data collection area. This area or region functions as a cluster of zones used jointly for modeling, similarly to the claim. and wherein each of the at least one model cluster comprises a plurality of model cells that are adjacent to one another; “The zone data store stores information about a geographic region. The geographic region can be a city, neighborhood, county, or any other area in which a provider may travel. The region is partitioned into zones that represent the portions of the geographic region used by the travel coordination system 130 for collecting data about the region,” (O’Mahony) [pg. 3, col. 2, pp. 4] Here, Neighborhoods consist of adjacent zones surrounding a central zone, representing model clusters that contain adjacent model cells, as taught by the claim. generating, using a machine learning model, model cell parameters that define a cell size and a cell geometry; “The zone data store stores information about a geographic region. The geographic region can be a city, neighborhood, county, or any other area in which a provider may travel. The region is partitioned into zones that represent the portions of the geographic region used by the travel coordination system 130 for collecting data about the region,” (O’Mahony) [pg. 3, col. 2, pp. 4] Here, Neighborhoods consist of adjacent zones surrounding a central zone, representing model clusters that contain adjacent model cells, as taught by the claim. and generating, using the machine learning model, model cluster parameters that comprise a cluster geometry, “Zones 400 can be hexagonal (as presented in FIG. 4), rectangular, circular, or any other shape.” [pg. 6, col. 2, pp. 5] O’Mahony clearly states using 2D shapes to represent a data cluster. a cluster centroid value that identifies a center of the model cluster and a cluster radius value that defines a quantity of model cells from the cluster centroid value to an edge of the model cluster. “In some embodiments, the radius of the k-ring based on the amount of data in the area.” (O’Mahony) [pg. 3, col. 2, pp. 4] O’Mahony utilizes k-ring neighborhoods, where k defines how many layers of adjacent zones are included around the central zone. This radius is measured by the number of zones rather than physical distance, directly mapping to the limitation of the claim. As anticipated, O’Mahony teaches the use of a learning model to predict future time intervals, using historical values, data clusters and data cells. It would be obvious to a person having ordinary skill of the art be to combine these components to predict a future time interval, as taught by claim 31. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claims 2, 6, 7, 9: Claims 2, 6, 7, 9 are rejected under 35 U.S.C. 103 as being unpatentable over O’Mahony and in further view of Liu et, al. (US 20180232840 A1, Liu hereinafter). Regarding claim 2: O’Mahony teaches all of the limitations of claim 1. However, O’Mahony does not teach wherein the model cluster parameters further comprise a cluster overlap that defines a number of model cells that are included in adjacent model clusters. However, Liu teaches: wherein the model cluster parameters further comprise a cluster overlap that defines a number of model cells that are included in adjacent model clusters. For example, the system generates a similarity score for every pair of adjacent clusters (e.g., clusters that share at least one boundary in the region). (Liu) [pg.1, col.1, pp.6] Liu teaches the use of adjacent clusters, rendering this limitation of claim 2 obvious. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of adjacent model clusters of Liu. Such person would be motivated to do so because this formula for generating the cluster shape component is advantageous, for example, because it can be implemented in a manner that uses less computing power than other methods of quantifying the compactness of a new cluster. (Liu) [pg.5, col.1, pp.4] Regarding claim 6: O’Mahony teaches: wherein the at least one model cluster comprises a plurality of model clusters, The region is partitioned into zones that represent the portions of the geographic region used by the travel coordination system (O’Mahony) [pg. 3, col. 2, pp. 4] Here, O’Mahony teaches partitioning a zone into portions to cover a geographic region, where the zones represent data clusters, or as mentioned model clusters, being split into a plurality of model clusters. Therefore, O’Mahony teaches the model clusters comprising of model clusters, rendering the limitation obvious. O’Mahony fails to teach: wherein the method further comprises automatically generating the plurality of model clusters in the model region based on the model cluster parameters. However, Liu teaches: wherein the method further comprises automatically generating the plurality of model clusters in the model region based on the model cluster parameters. performing an iterative clustering process that joins locations with similar characteristics. An operational parameter is generated for each cluster, and this parameter is used throughout the cluster. This process results in the generation of clusters that cover areas that have relatively uniform characteristics. (Liu)[Abstract] Liu teaches recursively generating clusters with similar attributes, based on the cluster’s parameters, as taught by claim 6. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of iterative generation of clusters taught by Liu. Such person would be motivated to do so because when an operational parameter of the service coordination system is generated or modified for a cluster, the response within the cluster is expected to generate a similar response by the users within the cluster. (Liu) [pg.1, col.1, pp.4] Regarding claim 7: O’Mahony fails to teach: wherein automatically generating the plurality of model clusters in the model region is performed recursively. However, Liu teaches: wherein automatically generating the plurality of model clusters in the model region is performed recursively. performing an iterative clustering process that joins locations with similar characteristics. An operational parameter is generated for each cluster, and this parameter is used throughout the cluster. This process results in the generation of clusters that cover areas that have relatively uniform characteristics. (Liu)[Abstract] Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of iterative generation of clusters taught by Liu. Such person would be motivated to do so because when an operational parameter of the service coordination system is generated or modified for a cluster, the response within the cluster is expected to generate a similar response by the users within the cluster. (Liu) [pg.1, col.1, pp.4] Regarding claim 9 O’Mahony in further view of Liu teaches all of the limitations of claim 9, in addition to: further comprising stopping generating model clusters of the plurality of clusters based on the model region being covered with model clusters. The stop condition can be defined in a number of different ways. Examples of stop conditions include: no cluster pair has a similarity score indicating a degree of similarity greater than a threshold degree of similarity (e.g., if a lower similarity score represents a higher degree of similarity, then this stop condition is satisfied if no cluster pair has a similarity score below a threshold value); the total number of clusters is less than a threshold number of clusters; the number of small clusters (e.g., defined as clusters having a size smaller than a threshold size, or defined as clusters having a number of trip requests lower than a threshold number) is less than a threshold number of small clusters; the percentage of cross-cluster trip requests (e.g., trip requests whose pickup location and destination are in different clusters) is below a threshold percentage. (Liu) [pg.10, col.1, pp.2] Liu teaches the use of multiple methods for stopping the generating of clusters in a model region. Although not explicitly described, it would be obvious that some methods involve determining the coverage of said region to determine if a stop condition has been met. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of stop conditions paired with iterative generation of clusters as taught by Liu. Such person would be motivated to do so because when an operational parameter of the service coordination system is generated or modified for a cluster, the response within the cluster is expected to generate a similar response by the users within the cluster. (Liu) [pg.1, col.1, pp.4] Claim 8 Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over O’Mahony in view of Liu, and in further view of Samavati et, al. (US 20190122430 A6, Samavati hereinafter). Regarding claim 8: O’Mahony teaches the following limitations of claim 8: if the model region is not covered by the plurality of model clusters, identifying (930) a second centroid cell based on the first centroid cell, the cluster radius value and the cluster overlap; For example, the zones may represent geometric shapes that overlap with one another, such as circular boundaries with a given radius and with centers separated by less than the diameter of each circle. (O’Mahony) [pg. 3, col. 2, pp. 4] It would be obvious to a person having ordinary skill of the art, that although O’Mahony does not explicitly recite a second centroid cell or second model cluster, it is implied that there is a second centroid cell and second model cluster because it refers to the centers and clusters in plurality, rendering claim 8 obvious. assigning (920) each of the model cells that are within the cluster radius value of the first centroid cell to be in the first model cluster; For example, the zones may represent geometric shapes that overlap with one another, such as circular boundaries with a given radius and with centers separated by less than the diameter of each circle. (O’Mahony) [pg. 3, col. 2, pp. 4] It would be obvious to a person having ordinary skill of the art, that although O’Mahony does not explicitly recite a first centroid cell or first model cluster, it is implied that there is a first centroid cell and first model cluster because it refers to the centers and clusters in plurality, rendering claim 8 obvious. and assigning each of the model cells that are within the cluster radius value of the second centroid cell to be in a second model cluster of the plurality of model clusters. For example, the zones may represent geometric shapes that overlap with one another, such as circular boundaries with a given radius and with centers separated by less than the diameter of each circle. (O’Mahony) [pg. 3, col. 2, pp. 4] It would be obvious to a person having ordinary skill of the art, that although O’Mahony does not explicitly recite a second centroid cell or second model cluster, it is implied that there is a second centroid cell and second model cluster because it refers to the centers and clusters in plurality, rendering claim 8 obvious. However, Samavati teaches: identifying a single model cell of the plurality of model cells in the model region as a first centroid cell corresponding to a first model cluster of the plurality of model clusters; The centroid cell 34 has been assigned the index “X0” by appending the identifier “0” to the identifier of the parent cell “X”. Similarly, the vertex children are assigned identifiers “X1” to “X6” by appending identifiers “1” to “6” to the index “X” of the parent cell 32. (Samavati) [pg. 4, col. 2, pp. 2] Samavati teaches identifying the centroid cell as “X0”, denoting it as the first centroid cell. This would be obvious to a person having ordinary skill of the art since it is common to use the number “0” to signify the first index of an array of many other well-known data structures. It would be obvious to a person having ordinary skill of the art to combine the use of multiple data clusters and centroid cells as taught by O’Mahony, with the method of identifying the first centroid cell taught by Samavati. A person would be motivated to do so by appending the identifier “0” to the identifier of the parent cell “ (Samavati) [pg.4, col2, pp.2]to keep track of the first centroid cell, in a plurality of cells and model clusters. Claim 10: Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over O’Mahony and in further view of Ullah et, al. (Simplification of reversible Markov chains by removal of states with low equilibrium occupancy, 2012, Ullah hereinafter.) Regarding claim 10: O’Mahony teaches all of the following limitations of claim 1, except: removing ones of the at least one model cluster that are without transitions of elements in the machine learning training data. However, Ullah teaches: removing ones of the at least one model cluster that are without transitions of elements in the machine learning training data. simple and transparent technique is introduced to remove states with low equilibrium occupancy. (Ullah) [pg. 1, Abstract] It would be obvious that even though Ullah does not explicitly say “without transitions of elements”, low equilibrium occupancy would equate to zero or no elements in a model cluster, thus it would be removed. Therefore, rendering claim 10 obvious. It would be obvious to a person having ordinary skill of the art to combine the use of multiple data clusters and centroid cells as taught by O’Mahony, with the method of removing model clusters that lack transitions of elements from machine learning training data. A person would be motivated to do so, to simplify Markov chains on a large scale, and to reduce the number of parameters given to a machine learning model. (Ullah) [pg.1, Abstract] Claims 11, 12, 14, 16, 17, 18: Claims 11, 12, 14, 15, 16, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over O’Mahony and in further view of Cai et, al. (US9749805B2, Cai hereinafter) Regarding claim 11: O’Mahony teaches all of the limitations of claim 11, except: further comprising training, the at least one model cluster using a second order Markov chain model. However, Cai teaches: further comprising training, the at least one model cluster using a second order Markov chain model. Whenever the UE is entering into a new cell, it is determined if the UE follows its history mobility pattern by checking if the current cell is the predicted one in last cell. In some examples, the GPS is already turned off. The prediction can be done using an order-2 Markov model or some other method. (Cai) [pg. 18, col. 13, line 65] Cai teaches utilizing historical data and a second-order Markov chain to compute predictions, as taught by claim 11, rendering said claim obvious. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of a second-order Markov chain as taught by Cai. Such person would be motivated to do so to assert that the correct historical pattern is being followed. (Cai) [pg. 18, col. 13, line 65] Regarding claim 12: O’Mahony teaches all of the limitations of claim 12, except: at least one model cluster to generate a transition matrix corresponding to each of the at least one model cluster. However, Cai teaches: at least one model cluster to generate a transition matrix corresponding to each of the at least one model cluster. For example, a table could be maintained to record the cell ID sequence which is visited by the mobile user. This table is mainly used to generate the cell transition probability matrix for mobility prediction and store the Intra-cell mobility traces. (Cai) [pg. 18, col. 13, line 65] Cai teaches the use of a transition probability matrix as taught by claim 12, rendering said claim obvious. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of a transition probability matrix as taught by Cai. Such person would be motivated to do so because this table is mainly used to generate the cell transition probability matrix for mobility prediction and store the Intra-cell mobility traces. (Cai) [pg.18, col.14, lines 40-43] Regarding claim 14: O’Mahony teaches all of the limitations of claim 14, except: wherein after training the at least one model cluster, performing an inference operation that is based on the transition matrix and that provides a probable future model cell that the element will be in at Tw and a confidence value corresponding the probable future model cell in response to receiving a current position of the element in the model region and a previous position of the element in the model region. However, Cai teaches: wherein after training the at least one model cluster, performing an inference operation that is based on the transition matrix and that provides a probable future model cell that the element will be in at Tw and a confidence value corresponding the probable future model cell in response to receiving a current position of the element in the model region and a previous position of the element in the model region. In some embodiments, second-order Markov model is used to predict the wireless device's next cell and to determine if the wireless device follows its history mobility pattern. This model uses probability transition matrix to make the prediction, which need not maintain all history cell ID sequence. (Cai) [pg. 13, col. 14, line 53] Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the use of a transition probability matrix as taught by Cai. Such person would be motivated to do so because a transition matrix does not need to maintain all history. Therefore, it is more efficient…from a computing and memory space perspective (Cai) [pg. 13, col. 4, line 59]. Regarding Claim 15: O’Mahony teaches all of the limitations of claim 15 including: wherein in response to the current position and the previous position being in an overlapping area of multiple ones of the at least one model cluster, the inference operation uses weighted averages corresponding to the multiple ones of the at least one model cluster. In some embodiments, the travel coordination system uses a weighted average or an integral of the zone scores along a route to determine a score for the route. (O’Mahony) [pg. 1, col.2, pp.4] In further embodiments, each zone may overlap with one another. For example, the zones may represent geometric shapes that overlap with one another, such as circular boundaries with a given radius and with centers separated by less than the diameter of each circle. (O’Mahony) [pg. 3, col. 2, pp. 4] As anticipated, O’Mahony teaches the use of weighted averages corresponding to model clusters and the overlapping of model clusters, as taught by claim 15. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the use of a transition probability matrix as taught by Cai with the overlapping of cluster zones and taking weighted averages of said zones, as taught by O’Mahony. Such person would be motivated to do so the travel coordination system selects the route with the highest score. (O’Mahony) [pg. 1, col.2, pp.4] Regarding claim 16: O’Mahony teaches all of the limitations of claim 16, except: wherein the probable future model cell comprises a future position of the element in the model region. However, Cai teaches: wherein the probable future model cell comprises a future position of the element in the model region. After processing the collected history information may be stored with specified format for future pattern matching and positioning. (Cai) [pg. 17, col. 12, line 1] Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the storing of a future position, as taught by Cai. Such person would be motivated to do so to determine points or places where the predictions have a higher probability to be erroneous. (Cai) [pg. 17, col. 12, lines 10-13] Regarding claim 17: Cai teaches all of the limitations of claim 17 in addition to: wherein the probable future model cell comprises a confidence value that corresponds to the future position in the model region. Examine difference between prediction and comparison value at checkpoints. As an example, the comparison value may be obtained by a GPS unit of the wireless device. (Cai) [pg. 19, col. 16, lines 64-65] It would be obvious to a person having ordinary skill of the art, that Cai teaches a prediction value, as similarly taught in claim 17 as a “confidence value that corresponds to the future position in the model region” thus rendering claim 17 obvious. Before the effective filing date, it would have been obvious to a person having ordinary skill of the art to combine the predicting of future time intervals of O’Mahony, with associating a value that corresponds to a future position. A person would be motivated to do this to create a method for managing a positioning method based on prediction. (Cai) [pg. 12, col. 2, lines 46-47] As well as assert the correctness of the prediction by comparing it to actual data. Regarding claim 18: O’Mahony teaches all of the limitations of claim 16, except: claim 1, further comprising: receiving updated machine learning training data that includes data corresponding to updated transitions of elements in the model region; and updating the at least one model cluster and/or a transition matrix responsive to receiving the updated machine learning training data. However, Cai teaches: claim 1, further comprising: receiving updated machine learning training data that includes data corresponding to updated transitions of elements in the model region; The wireless device may update the mobility pattern by adding the collected mobility patterns to a database comprising mobility patterns. (Cai) [pg. 22, col. 22, lines 27-29] As anticipated, Cai teaches updating data that is then fed into a database or learning model, rendering claim 18 obvious. and updating the at least one model cluster and/or a transition matrix responsive to receiving the updated machine learning training data. The wireless device 110 may, e.g. in case of GPS measurements, update the mobility pattern by adding the collected mobility patterns to a database comprising mobility patterns. Expressed differently, the wireless device 110 may update the mobility pattern database by adding information from the GPS measurements, such as GPS coordinates. (Cai) [pg. 20, col. 18, line 22-24] Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine the predicting of future time intervals of O’Mahony, with the storing of a future position, as taught by Cai. Such person would be motivated to do so to indicate one practical example of an accurate and reliable positioning method. (Cai) [pg. 17, col. 11, lines 35-36] Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYAAN AYAZ SHEIKH whose telephone number is (571)272-4643. The examiner can normally be reached MON-FRI 7:30-5pm. 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, Omar Fernandez can be reached at (571) 272-2589. 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. /AYAAN AYAZ SHEIKH/Examiner, Art Unit 2128 /OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128