DETAILED ACTION
Claims 21-40 have been examined.
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 U.S.C. § 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.
The invention, as taught in Claims 21-40, is directed to “mental steps” and “mathematical concepts” without significantly more.
The claims recite:
• request to determine respective measures of predictive utility of attributes of a first data set
• measures of predictive utility
• target attribute of the first data set
• first text attribute
• first non-text attribute comprising a numerical attribute, a binary attribute, or a Boolean attribute
• first predictive utility measure of the first text attribute
• second predictive utility measure of the first non-text attribute
• generating respective additional attributes corresponding to the first text attribute and the first non-text attribute
• performing a statistical analysis of individual ones of the respective additional attributes with respect to the target attribute
• determining…, a first predictive utility measure of the first text attribute
• determining…, a second predictive utility measure of the first non-text attribute
• discarding…the first text attribute or the first non-text attribute from the first data set
Claim 21
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “21. A computer-implemented method, comprising…” Therefore, it is a “method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 21 that recite abstract ideas?
YES. The following limitations in Claim 21 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical concepts”:
• request to determine respective measures of predictive utility of attributes of a first data set
• measures of predictive utility
• target attribute of the first data set
• first text attribute
• first non-text attribute comprising a numerical attribute, a binary attribute, or a Boolean attribute
• first predictive utility measure of the first text attribute
• second predictive utility measure of the first non-text attribute
• generating respective additional attributes corresponding to the first text attribute and the first non-text attribute
• performing a statistical analysis of individual ones of the respective additional attributes with respect to the target attribute
• determining…, a first predictive utility measure of the first text attribute
• determining…, a second predictive utility measure of the first non-text attribute
• discarding…the first text attribute or the first non-text attribute from the first data set
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “computing device”
(3) A “programmatic interface”
(4) A “machine learning”
(5) A “presenting”,..., of “the first predictive utility measure and the second predictive utility measure”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
This “receiving” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “computing device” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
This “computing device” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
This “programmatic interface” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
This “machine learning” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “computing device”
(3) A “programmatic interface”
(4) A “machine learning”
(5) A “presenting”,..., of “the first predictive utility measure and the second predictive utility measure”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “computing device” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 21 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 22
Claim 22 recites:
22. The computer-implemented method as recited in claim 21, further comprising:
selecting, at the machine learning service based at least in part on a statistical analysis of a first additional attribute of the respective additional attributes with respect to the target attribute, the first additional attribute as an input variable for training of the machine learning model;
training the machine learning model at the machine learning service using a training data set which includes values of the first additional attribute; and
obtaining, at the machine learning service, one or more predictions for the target attribute using the machine learning model.
Applicant’s Claim 22 merely teaches selecting an attribute as input, training on that input, and “obtaining” predictions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 22 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 23
Claim 23 recites:
23. The computer-implemented method as recited in claim 21, further comprising:
computing, at the machine learning service, a respective first metric with respect to individual ones of a plurality of text token groups of the first text attribute which are present in the first data set, wherein the first metric computed with respect to a particular text token group is indicative of a statistical relationship between the particular text token group and the target attribute; and
identifying, based at least in part on the respective first metrics, a predictive token group list comprising one or more text token groups of the plurality of text token groups, wherein a particular additional attribute corresponding to the first text attribute is generated using at least the predictive token group list.
Applicant’s Claim 23 merely teaches computing a metric and “identifying” a predictive token group (which could be simply naming the token group list). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 23 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 24
Claim 24 recites:
24. The computer-implemented method as recited in claim 23, wherein a particular value of the particular additional attribute, generated with respect to a particular record of the first data set, indicates that a particular text token group of the predictive token group list is present in the first text attribute of the particular record.
Applicant’s Claim 24 merely teaches an indication that a text token group is present. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 24 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 25
Claim 25 recites:
25. The computer-implemented method as recited in claim 21, wherein the first non-text attribute comprises one of: (a) a binary attribute or (b) a numeric attribute.
Applicant’s Claim 25 merely teaches a mathematical attribute. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 25 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 26
Claim 26 recites:
26. The computer-implemented method as recited in claim 21, further comprising:
presenting, by the machine learning service via the one or more programmatic interfaces, an indication of a text term which meets a correlation criterion with respect to the target attribute, wherein the text term is present in the first text attribute of one or more records of the data set.
Applicant’s Claim 26 merely teaches presenting an “indication of” a text term. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 26 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 27
Claim 27 recites:
27. The computer-implemented method as recited in claim 21, wherein
a generated additional attribute corresponding to the first text attribute comprises a categorical attribute.
Applicant’s Claim 27 merely teaches a categorical attribute. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 27 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 28
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “28. A system, comprising…” Therefore, it is a “system” (or “apparatus”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 28 that recite abstract ideas?
YES. The following limitations in Claim 28 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical concepts”:
• request to determine respective measures of predictive utility of attributes of a first data set
• measures of predictive utility
• target attribute of the first data set
• first text attribute
• first non-text attribute comprising a numerical attribute, a binary attribute, or a Boolean attribute
• first predictive utility measure of the first text attribute
• second predictive utility measure of the first non-text attribute
• generating respective additional attributes corresponding to the first text attribute and the first non-text attribute
• performing a statistical analysis of individual ones of the respective additional attributes with respect to the target attribute
• determining…, a first predictive utility measure of the first text attribute
• determining…, a second predictive utility measure of the first non-text attribute
• discard…the first text attribute or the first non-text attribute from the first data set
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “computing device”
(3) A “programmatic interface”
(4) A “machine learning”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
This “receiving” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “computing device” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
This “computing device” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
This “programmatic interface” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
This “machine learning” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “computing device”
(3) A “programmatic interface”
(4) A “machine learning”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “computing device” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 28 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 29
Claim 29 recites:
29. The system as recited in claim 28, wherein the one or more computing devices include further instructions that upon execution on or across the one or more computing devices further cause the one or more computing devices to:
select, at the machine learning service based at least in part on a statistical analysis of a first additional attribute of the respective additional attributes with respect to the target attribute, the first additional attribute as an input variable for training of the machine learning model;
train the machine learning model at the machine learning service using a training data set which includes values of the first additional attribute; and
obtain, at the machine learning service, one or more predictions for the target attribute using the machine learning model.
Applicant’s Claim 29 merely teaches selecting an attribute as input, training on that input, and “obtaining” predictions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 29 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 30
Claim 30 recites:
30. The system as recited in claim 28, wherein the one or more computing devices include further instructions that upon execution on or across the one or more computing devices further cause the one or more computing devices to:
compute, at the machine learning service, a respective first metric with respect to individual ones of a plurality of text token groups of the first text attribute which are present in the first data set, wherein the first metric computed with respect to a particular text token group is indicative of a statistical relationship between the particular text token group and the target attribute; and
identify, based at least in part on the respective first metrics, a predictive token group list comprising one or more text token groups of the plurality of text token groups, wherein a particular additional attribute corresponding to the first text attribute is generated using at least the predictive token group list.
Applicant’s Claim 30 merely teaches computing a metric and “identifying” a predictive token group (which could be simply naming the token group list). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 30 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 31
Claim 31 recites:
31. The system as recited in claim 30, wherein a particular value of the particular additional attribute, generated with respect to a particular record of the first data set, indicates that a particular text token group of the predictive token group list is present in the first text attribute of the particular record.
Applicant’s Claim 31 merely teaches an indication that a text token group is present. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 31 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 32
Claim 32 recites:
32. The system as recited in claim 28, wherein the first non-text attribute comprises one of: (a) a binary attribute or (b) a numeric attribute.
Applicant’s Claim 32 merely teaches a mathematical attribute. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 32 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 33
Claim 33 recites:
33. The system as recited in claim 28, wherein the one or more computing devices include further instructions that upon execution on or across the one or more computing devices further cause the one or more computing devices to:
present, by the machine learning service via the one or more programmatic interfaces, an indication of a text term which meets a correlation criterion with respect to the target attribute, wherein the text term is present in the first text attribute of one or more records of the data set.
Applicant’s Claim 33 merely teaches presenting an “indication of” a text term. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 33 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 34
Claim 34 recites:
34. The system as recited in claim 28, wherein a generated additional attribute corresponding to the first text attribute comprises a categorical attribute.
Applicant’s Claim 34 merely teaches a categorical attribute. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 34 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 35
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “35. One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors cause the one or more processors to…” Therefore, it is a “computer-accessible storage medium” (which is not a computer readable medium. That is, accessibility may be for purposes other than reading, such as writing and executing. Therefore, it is not a proper limitation on the claim), which is not a statutory category of invention. Therefore, the answer to the inquiry is: “NO”.
Step 2A (Prong One) inquiry:
Are there limitations in Claim 35 that recite abstract ideas?
YES. The following limitations in Claim 35 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical concepts”:
• request to determine respective measures of predictive utility of attributes of a first data set
• measures of predictive utility
• target attribute of the first data set
• first text attribute
• first non-text attribute comprising a numerical attribute, a binary attribute, or a Boolean attribute
• first predictive utility measure of the first text attribute
• second predictive utility measure of the first non-text attribute
• generating respective additional attributes corresponding to the first text attribute and the first non-text attribute
• performing a statistical analysis of individual ones of the respective additional attributes with respect to the target attribute
• determining…, a first predictive utility measure of the first text attribute
• determining…, a second predictive utility measure of the first non-text attribute
• discard…the first text attribute or the first non-text attribute from the first data set
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “processor”
(3) A “programmatic interface”
(4) A “machine learning”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
This “receiving” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
This “programmatic interface” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
This “machine learning” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “receiving”
(2) A “processor”
(3) A “programmatic interface”
(4) A “machine learning”
A “receiving” is a broad term which is described at a high level. MPEP 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Further, M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); …
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “processor” is a broad term which is described at a high level and includes general purpose computers. Applicant’s Specification recites:
[0061] In at least some embodiments, a server that implements one or more of the techniques described above for obtaining predictive utility indicators (including for example statistics managers and other components of a machine learning service, or servers at which standalone tools used for analyzing data sets are implemented) may include a general-purpose computer system that includes or is configured to access one or more computer-accessible media. FIG. 10 illustrates such a general-purpose computing device 9000. In the illustrated embodiment, computing device 9000 includes one or more processors 9010 coupled to a system memory 9020 (which may comprise both non-volatile and volatile memory modules) via an input/output (I/O) interface 9030. Computing device 9000 further includes a network interface 9040 coupled to I/O interface 9030.
[0062] In various embodiments, computing device 9000 may be a uniprocessor system including one processor 9010, or a multiprocessor system including several processors 9010 (e.g., two, four, eight, or another suitable number). Processors 9010 may be any suitable processors capable of executing instructions. For example, in various embodiments, processors 9010 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processors 9010 may commonly, but not necessarily, implement the same ISA. In some implementations, graphics processing units (GPUs) may be used instead of, or in addition to, conventional processors.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “programmatic interface” is a broad term which is described at a high level. Applicant’s Specification recites:
[0022] FIG. 1 illustrates an example system environment in which the relative predictive utility of different text attributes of a machine learning data set may be determined using derived categorical attributes, according to at least some embodiments. System 100 includes a statistics manager 130 implemented using resources of a network-accessible machine learning service 104. The machine learning service 104 may implement one or more programmatic interfaces 180 (such as web-based consoles, application programming interfaces or APIs, command line tools, graphical user interfaces and like). Clients 185 of the machine learning service 104 may submit various types of requests to the service via the programmatic interfaces 180, and receive corresponding programmatic responses as indicated by arrow 113 in the depicted embodiment. A client 185 may, for example, submit a request to analyze a source data set 110 to determine various metrics, such as predictive utility measures corresponding to respective text attributes of the data set and a prediction target attribute.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
A “machine learning” is a broad term which is described at a high level. Applicant’s Specification recites:
[0052] For various types of machine learning tasks, a client request 711 may indicate parameters that may be used by the MLS to perform the tasks, such as a data source definition (which may indicate a source for a training data set), a feature processing transformation recipe, or parameters to be used for a particular machine learning algorithm.
***
The output 716 of the feature processing transformations may in turn be used as input for a selected machine learning algorithm 766 (with corresponding algorithm parameters 754), which may be executed using yet another set of resources from pool 785. A wide variety of machine learning algorithms may be supported natively by the MLS, including for example regression algorithms, classification algorithms (such as random forest algorithms), neural network algorithms, and the like. In at least one embodiment, the MLS may be designed to be extensible – e.g., clients may provide or register their own modules (which may be specified as user-defined functions) for input record handling, feature processing, or for implementing additional machine learning algorithms than are supported natively by the MLS.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 35 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 36
Claim 36 recites:
36. The one or more non-transitory computer-accessible storage media as recited in claim 35, storing further program instructions that when executed on or across the one or more processors further cause the one or more processors to:
select, at the machine learning service based at least in part on a statistical analysis of a first additional attribute of the respective additional attributes with respect to the target attribute, the first additional attribute as an input variable for training of the machine learning model;
train the machine learning model at the machine learning service using a training data set which includes values of the first additional attribute; and
obtain, at the machine learning service, one or more predictions for the target attribute using the machine learning model.
Applicant’s Claim 36 merely teaches selecting an attribute as input, training on that input, and “obtaining” predictions. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 36 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 37
Claim 37 recites:
37. The one or more non-transitory computer-accessible storage media as recited in claim 35, storing further program instructions that when executed on or across the one or more processors further cause the one or more processors to:
compute, at the machine learning service, a respective first metric with respect to individual ones of a plurality of text token groups of the first text attribute which are present in the first data set, wherein the first metric computed with respect to a particular text token group is indicative of a statistical relationship between the particular text token group and the target attribute; and
identify, based at least in part on the respective first metrics, a predictive token group list comprising one or more text token groups of the plurality of text token groups, wherein a particular additional attribute corresponding to the first text attribute is generated using at least the predictive token group list.
Applicant’s Claim 37 merely teaches computing a metric and “identifying” a predictive token group (which could be simply naming the token group list). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 37 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 38
Claim 38 recites:
38. The one or more non-transitory computer-accessible storage media as recited in claim 37, wherein a particular value of the particular additional attribute, generated with respect to a particular record of the first data set, indicates that a particular text token group of the predictive token group list is present in the first text attribute of the particular record.
Applicant’s Claim 38 merely teaches an indication that a text token group is present. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 38 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 39
Claim 39 recites:
39. The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein the first non-text attribute comprises one of: (a) a binary attribute or (b) a numeric attribute.
Applicant’s Claim 39 merely teaches a mathematical attribute. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 39 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 40
Claim 40 recites:
40. The one or more non-transitory computer-accessible storage media as recited in claim 35, storing further program instructions that when executed on or across the one or more processors further cause the one or more processors to:
present, by the machine learning service via the one or more programmatic interfaces, an indication of a text term which meets a correlation criterion with respect to the target attribute, wherein the text term is present in the first text attribute of one or more records of the data set.
Applicant’s Claim 40 merely teaches presenting an “indication of” a text term. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 40 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Reason for not Rejecting Claims 21, 28, and 35
The closest prior art of Göpfert, et al., Measurement Extraction with Natural Language Processing: A Review, Findings of the Association for Computational Linguistics: EMNLP 2022, 11 DEC 2022, pp. 2191-2215 does not expressly teach:
“discarding by the machine learning service and based at least in part on the determined first predictive utility measure or the determined second predictive utility measure”
Response to Arguments
Applicant's arguments filed 02 JAN 2026 have been fully considered but they are not persuasive. Specifically, Applicant argues:
Argument 1
Similarly, Applicant's recited subject matter (including the discarding of the attribute) is eligible because it is an advancement analysis and selection of attributes for machine learning models (also an improvement in AI technology).
The argued “discarding of the attribute” data is merely the mental step of ignoring data.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 2
Additionally, Applicant's recited subject matter is eligible at least because the subject matter is an improvement to computer technology in that the subject matter results in reduced memory consumption and improves runtime performance of the model, and may also reduce the probability of overfitting. Detailed Description, paragraph 16.
The claims improve the text attribute corpus. This reduces the amount of work the computer must do, but it does not improve the computer, itself.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 3
Additionally, Applicant respectfully submits that the present claims are not directed to mathematical concepts, or mental processes. Rather, the claims here are directed to implementations of a computer system. See, e.g., Spec. at Figs. 1, 2, 3, 7, 8. As explained in the specification, the claimed subject matter in this case focuses on technique of analyzing data in machine learning systems to quickly determine the relative predictive utilities of individual attributes (textual or non-textual attributes). See Spec. at [0015] and FIG. 9. The technique involves, inter alia, normalizing the textual attributes to corresponding categorical attributes, which is not something that a human would perform to analyze textual data. As the specification further explains, the inventive technique allows the computer system to quickly identify textual attributes that can be discarded based on their low predictive utility. See id. at [0016]. Such discarding reduces the size of the training dataset, which in turn reduces training time of the machine learning system, reduces the resources needed to collect and store input data, and improves the memory consumption and runtime performance of resulting text analysis models. See id. Accordingly, the claimed solution in this case is something that is designed to improve computer functioning, and not contemplated as a human activity or pure mental process. Thus, the claimed subject matter does not fall under any of the recognized exceptions under section 101. Accordingly, Applicant respectfully requests that the rejections of the claims be withdrawn for this basis alone.
Examples of abstract ideas in the claims are the following:
• first text attribute comprising a numerical attribute, a binary attribute, or a Boolean attribute (i.e., mental or mathematical step)
• determining…, a first predictive utility measure of the first text attribute (i.e., a mental step)
There are more listed in the rejection, above.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 4
Even if, arguendo, Applicant's recited subject matter recites a mathematical concept (not ceded) Applicant's recited subject matter is patent-eligible for reasons similar to those in Section 101 Examples Example 4: Global Positioning System. There, where the claim recited mathematical operations (e.g., calculating pseudo-ranges and absolute times, and the mathematical model), the claim described that a programmed CPU acts in concert with the recited features of the mobile device to enable the mobile device to determine and display its absolute position through interaction with a remote server and multiple remote satellites. The example is patent-eligible because these meaningful limitations placed upon the application of the claimed mathematical operations "show that the claim is not directed to performing mathematical operations on a computer alone." Similarly, applicant's additional features of a programmatic interface (for receiving requests to determine respective measures of predictive utility, with respect to a target attribute of a plurality of attributes of records of a first data set) in combination with the generating respective additional attributes, and determining based on the statistical analysis, the first and second predictive utility measures, and in further combination with discarding, by the machine learning service and based at least in part on the first predictive utility measure or the second predictive utility measure, the first text attribute or the first non-text attribute from the first data set to create a training data set to train a machine learning model to provide predictions with respect to the target attribute, all work together in concert with the statistical analysis to enable the system to quickly determine the relative predictive utilities of individual attributes (textual and non-textual attributes) and discard attributes that do not contribute to accuracy of predictions made by a model.
Again, Applicant's claims merely enhance data. As Applicant argues:
…quickly determine the relative predictive utilities of individual attributes (textual and non-textual attributes) and
discard attributes that do not contribute to accuracy of predictions made by a model.
Both of these actions are mental steps.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 5
Additionally, knowing the predictive utility of input variables for a predictive machine learning model can result in more accurate predictions than if all of the attributes were used as input variables. Detailed Description, paragraphs 42, 60. This, plus the resulting improvement to existing technology (a process for selecting more accurate attributes used for machine learning models) makes the claim eligible.
Again, Applicant's claims merely enhance data.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 6
Additionally, the Office Action at pages 8-9 cites to a portion of the specification that states that the "processors 9010" may be "any suitable processors capable of executing instructions," and may be "general-purpose" processors. However, this does not mean that the claimed subject matter is not an improvement to computer functionality, and not patent- eligible. Many types of patent-eligible computer inventions are implemented using general purpose hardware, for example, the "self-referential tables" in Enfish. Indeed, in Enfish, the Federal Circuit stated that claims to patentable improvements to computer functionality are not necessarily doomed because they do not relate to "physical components" in the computer. See Enfish at 17. "Much of the advancement made in computer technology consists of improvements to software Id. at 17-18. Thus, a software invention can be patentable, even if it can be implemented using general purpose processors.
A genera purpose computer is an additional element that is well-understood, routine, and conventional. Thus, it does not add eligibility to the data simply being processed.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 7
Moreover, the claims recite specific technical improvements that transform the computer system's ability to process and analyze text data for machine learning applications. The invention provides a concrete technical solution to the problem of efficiently processing text datasets by generating additional attributes through statistical analysis to determine predictive utility. Applicant's Detailed Description describes that a higher measure of predictive utility indicates a greater accuracy of predictions made using the respective target attribute. Paragraph 15. This technical approach enables the system to dramatically reduce computational resource requirements and improve processing efficiency in ways that were not previously possible (e.g., by finding attributes with a greater accuracy of predictions).
Again, Applicant's argued “specific technical improvements” merely enhance data.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 8
Furthermore, the claims recite a technological improvement that goes beyond merely implementing abstract ideas on a computer. The invention addresses technical challenges specific to computer-based machine learning systems processing text datasets. The claimed generation of additional attributes and statistical analysis to determine predictive utility (a higher measure of predictive utility indicates a greater accuracy of predictions made using the respective target attribute) enables the computer system to process text data more efficiently and with more accuracy than previous approaches. This improved efficiency is achieved through technical means - the specific steps of attribute generation and correlation-based analysis - rather than through abstract mental steps. The resulting improvements in system performance, including reduced memory footprint and enhanced processing speed, demonstrate that the claimed invention improves the functioning of the computer itself.
The argued “generation of additional attributes (i.e., mental and mathematical steps) and statistical analysis (i.e., mathematical steps)” are abstract ideas practiced on a computer.
Applicant’s argument is unpersuasive.
The rejections stand.
Argument 9
The dependent claims of the present application are also patent-eligible due to their inclusion of the patent-eligible subject matter of their respective parent claims, and also for the subject matter they add. All of these dependent claims also satisfy the "significantly more" requirement under steps one and two of the Alice test. Accordingly, Applicant respectfully requests withdrawal of the current § 101 rejections.
The dependent claims have been rejected on their own merits (see rejections above. Further, the independent claims do not contain eligible matter. Therefore, there is no such matter that may be incorporated by reference to the dependent claims.
Applicant’s argument is unpersuasive.
The rejections stand.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiries concerning this communication or earlier communications from the examiner should be directed to Wilbert L. Starks, Jr., who may be reached Monday through Friday, between 8:00 a.m. and 5:00 p.m. EST. or via telephone at (571) 272-3691 or email: Wilbert.Starks@uspto.gov.
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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
30 MAR 2026