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 35-54 are rejected under 35 U.S.C. 101 because:
At step 1:
Claims 35-54 are directed to “Data Pruning tool and related aspects” and thus directed to a statutory category.
At step 2A, Prong One:
The claims 35, 46 recite the following limitation directed to an abstract ideas:
“method for determining one or more regions of interest in a multi-dimensional data set comprising a plurality of parameter sets, each parameter set comprising a parameter set identifier, a plurality of dimensions of selection conditions for assessing a configurable physical entity, and an indication of an assessed characteristic of the configurable physical entity” recites a mental process as gathering parameter sets, each parameter set comprising a parameter set identifier, a plurality of dimensions of selection conditions for assessing a configurable physical entity, and an indication of an assessed characteristic of the configurable physical entity.
“mapping, using a self-organizing map (SOM) model which uses competitive group learning, the multi-dimensional data set onto an edge-connected surface mesh of neurons” recites a mental process as mapping, using a self-organizing map (SOM) model which uses competitive group learning, the multi-dimensional data set onto an edge-connected surface mesh of neurons.
“identifying at least one cluster of neurons on the surface mesh based on a category of the assessed characteristic” recites the mental process as identifying at least one cluster of neurons on the surface mesh based on a category of the assessed characteristic.
“identifying a set of ranges of boundary values for the selection conditions for each cluster, each range of boundary values comprising a maximum and a minimum weight value of the weights representing that selection condition of the neurons in that cluster” recites the mental process identifying a set of ranges of boundary values for the selection conditions for each cluster, each range of boundary values comprising a maximum and a minimum weight value of the weights representing that selection condition of the neurons in that cluster
“determining one or more regions of interest which associate the boundary values of the selection conditions of each cluster with one or more test case identifiers for the test cases represented by the neurons in that cluster” recites the mental process determining one or more regions of interest which associate the boundary values of the selection conditions of each cluster with one or more test case identifiers for the test cases represented by the neurons in that cluster.
Claims 36-45 and 47-51 recites the following limitations directed to an abstract ideas such as an edge-connected SOM surface mesh, self-organizing maps model, collection of adjacent neurons, transforming the edge connected surface mesh to a two dimensional planar surface mesh prior to generating the regions of interest, resizing at least on cluster to have a size matching or exceeding a predefined ratio of one category of the assessed characteristic to another category of the assessed characteristic, reconfiguring the physical entity and repeating the assessment using the selection conditions of each parameter set associated with a region of interest, update each parameter set associated with a region of interest in the multi-dimensional data set with at least the result of the repeated assessment, and iteratively said mapping, etc.
The claim 52 recites the following limitation directed to an abstract ideas:
“analyzing the plurality of test cases using a self-organizing maps model to provide a representation of the test cases on a toroidal mesh comprising a plurality of neurons, each individual test case being allocated to a selected neuron, wherein the self-organizing maps model comprises determining a center neuron of a collection of adjacent neurons as the selected neuron for an individual test case when a collective correlation of the individual test case with the collection of adjacent neurons has a value that is greater than any collective correlation of the individual test case with all other possible collections of adjacent neurons ” recites the mental process analyzing the plurality of test cases using a self-organizing maps model to provide a representation of the test cases on a toroidal mesh comprising a plurality of neurons, each individual test case being allocated to a selected neuron, wherein the self-organizing maps model comprises determining a center neuron of a collection of adjacent neurons as the selected neuron for an individual test case when a collective correlation of the individual test case with the collection of adjacent neurons has a value that is greater than any collective correlation of the individual test case with all other possible collections of adjacent neurons
“converting the toroidal mesh representation into a two-dimensional representation” recites the mental process converting the toroidal mesh representation into a two-dimensional representation.
“associating test results for each of the plurality of test cases with the respective neuron” recites the mental process associating test results for each of the plurality of test cases with the respective neuron.
“identifying one or more clusters of neurons within the two-dimensional representation based on the test results” recites the mental process identifying one or more clusters of neurons within the two-dimensional representation based on the test results.
“associating one or more regions of interest in the multi-dimensional input space with the one or more identified clusters of neurons within the two-dimensional representation” recites mental process associating one or more regions of interest in the multi-dimensional input space with the one or more identified clusters of neurons within the two-dimensional representation.
The claim 53 recites the following limitation directed to an abstract ideas:
“determining one or more regions of interest in a multi-dimensional data set of test cases using a group learning, self-organizing map (SOM) model to determine at least one region of interest comprising a set of boundary values of a plurality of radio settings for testing the transceiver where a first configuration of the DPD results in the transceiver failing a transceiver peak power spectrum output test” recites the mental process determining one or more regions of interest in a multi-dimensional data set of test cases using a group learning, self-organizing map (SOM) model to determine at least one region of interest comprising a set of boundary values of a plurality of radio settings for testing the transceiver where a first configuration of the DPD results in the transceiver failing a transceiver peak power spectrum output test.
“reconfiguring the DPD with a different set of linearization parameters” recites the mental reconfiguring the DPD with a different set of linearization parameters.
“retesting the transceiver with the reconfigured DPD using radio settings of each test case in the at least one region of interest” recites the mental retesting the transceiver with the reconfigured DPD using radio settings of each test case in the at least one region of interest.
“updating the multi-dimensional data set of test cases with at least a new test result for each retested test case” recites the mental updating the multi-dimensional data set of test cases with at least a new test result for each retested test case.
“repeating said determining using the same SOM model configuration to determine if there are one or more regions where a new configuration of the DPD results in the transceiver failing the transceiver peak power spectrum output test” recites the mental process repeating said determining using the same SOM model configuration to determine if there are one or more regions where a new configuration of the DPD results in the transceiver failing the transceiver peak power spectrum output test.
“configuration to determine if there are one or more regions where a new configuration of the DPD results in the transceiver failing the transceiver peak power spectrum output test” recites the mental process configuration to determine if there are one or more regions where a new configuration of the DPD results in the transceiver failing the transceiver peak power spectrum output test
At step 2A, Prong Two:
The claims recite the following additional elements:
That the content management system includes “server” “resource”, which are high level recitation of generic computer component s and functions and represent mere instruction to apply to a computer as in MPEP 2106.05 (f) which does not provide integration into a practical application.
At step 2B
The conclusions for the mere implementation using a generic computer and mere field of use are carried over and to not provide significantly more.
Allowable Subject Matter
Claims 35-53 would be allowed. (if rewritten to overcome the rejection under 35 USC § 101 and to include all of the limitations of the base claim and any intervening claims)
The following is a statement of reason for the indication of allowable subject matter:
With respect to claims 35-51, James Malone et al. “Data Mining using Rule Extracting from Kohonen Self-Organizing Maps” discloses a computer-implemented method for determining one or more regions of interest in a multi-dimensional data set (fig. 10, section 3.2); mapping, using a self-organizing map (SOM) model which uses competitive group learning, the multi-dimensional data set onto an (i.e., “Very little work has appeared in the literature regarding rule extraction from unsupervised/SOM type networks [24]. This is surprising considering the importance of unsupervised methods when applied to exploratory data analysis applications. Bahamonde used the SOM to cluster symbolic rules for their semantic similarity”(sec. 3) and fig. 2); identifying at least one cluster of neurons based on a category of the assessed characteristic (sec. 3.2 “this process is repeated until the number of boundaries is sufficient to identify all of the clusters (i.e., the number of classes required), wherein the category refers to the class in the wording); identifying a set of ranges of boundary values for the selection conditions for each cluster each range of boundary values comprising a maximum and a minimum weight value of the weights representing that selection condition of the neurons in that cluster (fig. 3 shows rule extraction algorithm using the train SOM and each classes, calculate boundary from total component umtrix and sec. 3.3 shows if a match is found between a component’s boundary and that of total U-mattrix boundary then that component is considered important and is of significance to the clustering process); and determining one or more regions of interest which associate the boundary values of the selection conditions of each cluster with one or more test case identifiers for the test cases represented by the neurons in that cluster (sec. 3.3 discloses single rule is formed by simply extracting gthe values from the positions of the previously extracted boundaries and then taking the mean of these values “thus entailing using the test data and the corresponding feature values associated to the boundary values) but James Malone does not discloses each parameter set comprising a parameter set identifier, a plurality of dimensions of selection conditions for assessing a configurable physical entity, and an indication of an assessed characteristic of the configurable physical entity, the multi-dimensional data set onto an edge-connected surface mesh of neurons identifying at least one cluster of neurons on the surface mesh based on a category of the assessed characteristic; each range of boundary values comprising a maximum and a minimum weight value of the weights representing that selection condition of the neurons in that cluster.
With respect to claim 52, James Malone et al. discloses a computer-implemented method for determining regions of interest in a multi- dimensional input space, wherein the multi-dimensional input space represents a plurality of test cases for use with each of one or more radio settings of a transceiver device, the method comprising:
analyzing the plurality of test cases using a self-organizing maps model to provide a representation of the test cases, (sec. 3.3 discloses single rule is formed by simply extracting gthe values from the positions of the previously extracted boundaries and then takin gthe mean of these values “thus entailing using the test data and the corresponding feature values associated to the boundary values), associating test results for each of the plurality of test cases with the respective neuron; identifying one or more clusters of neurons within the two-dimensional representation based on the test results ((sec. 3.2 “this process is repeated until the number of boundaries is sufficient to identify all of the clusters (i.e., the number of classes required), wherein the category refers to the class in the wording); and but
James Malone does not discloses analyzing the plurality of test cases using a self-organizing maps model to provide a representation of the test cases on a toroidal mesh comprising a plurality of neurons, wherein the self-organizing maps model comprises determining a center neuron of a collection of adjacent neurons as the selected neuron for an individual test case when a collective correlation of the individual test case with the collection of adjacent neurons has a value that is greater than any collective correlation of the individual test case with all other possible collections of adjacent neurons, converting the toroidal mesh representation into a two-dimensional representation; associating test results for each of the plurality of test cases with the respective neuron; identifying one or more clusters of neurons within the two-dimensional representation based on the test results; and associating one or more regions of interest in the multi-dimensional input space with the one or more identified clusters of neurons within the two-dimensional representation.
With respect to claim 53, James Malone et al. discloses a method of testing a transceiver having configurable digital pre-distortion (DPD), the method comprising:
determining one or more regions of interest in a multi-dimensional data set of test cases using a group learning, self-organizing map (SOM) model to determine at least one region of interest comprising a set of boundary values ((i.e., “Very little work has appeared in the literature regarding rule extraction from unsupervised/SOM type networks [24]. This is surprising considering the importance of unsupervised methods when applied to exploratory data analysis applications. Bahamonde used the SOM to cluster symbolic rules for their semantic similarity”(sec. 3) and fig. 2)) , updating the multi-dimensional data set of test cases with at least a new test result for each retested test case; and repeating said determining using the same SOM model configuration to determine if there are one or more regions where a new configuration of the DPD results in the transceiver failing the transceiver peak power spectrum output test ((sec. 3.3 discloses single rule is formed by simply extracting gthe values from the positions of the previously extracted boundaries and then taking the mean of these values “thus entailing using the test data and the corresponding feature values associated to the boundary values)) but James Malone does not discloses analyzing determining one or more regions of interest in a multi-dimensional data set of test cases using a group learning, self-organizing map (SOM) model to determine at least one region of interest comprising a set of boundary values of a plurality of radio settings for testing the transceiver where a first configuration of the DPD results in the transceiver failing a transceiver peak power spectrum output test, ; reconfiguring the DPD with a different set of linearization parameters; retesting the transceiver with the reconfigured DPD using radio settings of each test case in the at least one region of interest;
Conclusion
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/HUNG T VY/Primary Examiner, Art Unit 2163 December 2, 2025