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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a data analysis system with structural components (claims 1 - 12) and a data analysis method (claims 13 - 14). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claims 1,
Step 2A, Prong One
The claim recites in part:
A data analysis system for analyzing to-be-analyzed data, which is data to be analyzed, by using a learned model
For example, a person analyzes data by applying knowledge from past learning and experiences. People have been analyzing data long before computers were invented.
a data acquirer configured to acquire the to be analyzed data
For example, a person records observations from the analyzed data.
a learned model producer configured to produce the learned model by using at least teacher data
For example, a person learns from examples with known answers and develops a mental framework for analyzing future information
As drafted and under its broadest reasonable interpretation, these limitations cover performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
a storage configured to store each of a plurality of learned models as the learned model associated with the teacher data that is used to produce each of the plurality of learned models by the learned model producer, and with a different version(s) of learned model(s) that is/are other learned model(s) of the plurality of learned models and whose analysis purpose is common to each of the plurality of learned models;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The additional elements of:
a display configured to display the plurality of learned models stored in the storage;
a controller configured to control the display to display a selected learned model that is selected from the plurality of learned models displayed on the display, and the different version(s) of learned model(s) that is/are associated with the selected learned model on a common screen.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites a data analysis system, a data acquirer, an analyzer a storage, a display, and a controller which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of a learned model producer amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
a storage configured to store each of a plurality of learned models as the learned model associated with the teacher data that is used to produce each of the plurality of learned models by the learned model producer, and with a different version(s) of learned model(s) that is/are other learned model(s) of the plurality of learned models and whose analysis purpose is common to each of the plurality of learned models;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional elements of:
a display configured to display the plurality of learned models stored in the storage;
a controller configured to control the display to display a selected learned model that is selected from the plurality of learned models displayed on the display, and the different version (s) of learned model (s) that is/are associated with the selected learned model on a common screen.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim further recites a data analysis system, a data acquirer, an analyzer a storage, a display, and a controller which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of a learned model producer amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the storage is configured to store each of the plurality of learned models associated with information on accuracy of a result of analysis of the to-be-analyzed data together with the teacher data, and with the different version (s) of learned model (s) whose analysis purpose is common to each of the plurality of learned models.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the storage is configured to store each of the plurality of learned models associated with information on accuracy of a result of analysis of the to-be-analyzed data together with the teacher data, and with the different version (s) of learned model (s) whose analysis purpose is common to each of the plurality of learned models.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 2, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to the selected learned
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to the selected learned
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 2, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to the selected learned
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to the selected learned
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 4,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 3, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
an input acceptor configured to accept an instruction input for selection of an operator,
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The additional elements of:
wherein the controller is configured to control the display to display the selected learned model that is selected in accordance with the instruction input for the selection accepted by the input acceptor and the information on accuracy of a result of analysis analyzed based on the to-be-analyzed data by the selected learned model, and the learned model (s) whose analysis purpose is common to the selected learned model and the information on accuracy of a result (s) of analysis analyzed based on the to-be-analyzed data by the learned model (s) whose analysis purpose is common to the selected learned model as the information for comparative display of the information.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites an input acceptor and operator which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
an input acceptor configured to accept an instruction input for selection of an operator,
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional elements of:
wherein the controller is configured to control the display to display the selected learned model that is selected in accordance with the instruction input for the selection accepted by the input acceptor and the information on accuracy of a result of analysis analyzed based on the to-be-analyzed data by the selected learned model, and the learned model (s) whose analysis purpose is common to the selected learned model and the information on accuracy of a result (s) of analysis analyzed based on the to-be-analyzed data by the learned model (s) whose analysis purpose is common to the selected learned model as the information for comparative display of the information.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim further recites an input acceptor and operator which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 5,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 3, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the storage is configured to store each of the plurality of learned models associated with first analysis accuracy information that is information on accuracy of a result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to each of the plurality of learned models as the information, and second analysis accuracy information that is information on accuracy of a result of analysis obtained by analyzing the to-be-analyzed data that is different from the to-be- - analyzed data that is used by a common version of learned model (s) whose version is equal to each of the plurality of learned models as the information;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The additional elements of:
the controller is configured to control the display to display the first analysis accuracy information and the second analysis accuracy information for comparative display of the information.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the storage is configured to store each of the plurality of learned models associated with first analysis accuracy information that is information on accuracy of a result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to each of the plurality of learned models as the information, and second analysis accuracy information that is information on accuracy of a result of analysis obtained by analyzing the to-be-analyzed data that is different from the to-be- - analyzed data that is used by a common version of learned model (s) whose version is equal to each of the plurality of learned models as the information;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional elements of:
the controller is configured to control the display to display the first analysis accuracy information and the second analysis accuracy information for comparative display of the information.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
wherein the learned model producer is configured to be able to produce, based on the selected learned model that is selected from the plurality of learned models stored in the storage, a different version of learned model whose analysis purpose is common to the selected learned model.
For example, a person learns from examples with known answers and develops mental framework(s) for analyzing future information
As drafted and under its broadest reasonable interpretation, these limitations cover performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
wherein the learned model producer is configured to produce, in a
case in which the learned model is produced while the learned model with common analysis purpose is being selected, a different version of learned model whose analysis purpose is common to the selected learned model by changing at least one of the teacher data and training parameters that are specified to produce the selected learned model.
For example, a person learns from examples with known answers and develops mental framework(s) for analyzing future information
As drafted and under its broadest reasonable interpretation, these limitations cover performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 7, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the controller is configured to control the display, in production of the different version of learned model whose analysis purpose is common to the selected learned model, to selectively display a plurality of versions of learned model that are stored in the storage.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the controller is configured to control the display, in production of the different version of learned model whose analysis purpose is common to the selected learned model, to selectively display a plurality of versions of learned model that are stored in the storage.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the controller is configured to, every when a different
version of learned model whose analysis purpose is common to the selected learned model is produced, add the latest version of learned model as an option.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the controller is configured to, every when a different
version of learned model whose analysis purpose is common to the selected learned model is produced, add the latest version of learned model as an option.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 10,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
an input acceptor configured to accept an input from an operator,
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The additional elements of:
wherein the controller is configured to determine whether a version name that is accepted by the input acceptor is a latest version name named in accordance with a naming convention, and to prevent the learned model producer from producing the learned model if the version name that is accepted by the input acceptor is not the latest version name named in accordance with the naming convention.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
an input acceptor configured to accept an input from an operator,
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional elements of:
wherein the controller is configured to determine whether a version name that is accepted by the input acceptor is a latest version name named in accordance with a naming convention, and to prevent the learned model producer from producing the learned model if the version name that is accepted by the input acceptor is not the latest version name named in accordance with the naming convention.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 11,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the storage is configured to store learned models whose
algorithms of training models are common to each other and that are produced by changing at least one of the teacher data and training parameters that are specified to produce corresponding one of the learned models as different versions of learned models associated with each other.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the storage is configured to store learned models whose
algorithms of training models are common to each other and that are produced by changing at least one of the teacher data and training parameters that are specified to produce corresponding one of the learned models as different versions of learned models associated with each other.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 12 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 102
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 7 and 12 - 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiang (US 2021/0264321).
As to claim 1, Xiang figures 1, 4Aand 4B shows and teaches a data analysis system for analyzing to-be-analyzed data, which is data to be analyzed, by using a learned model, the data analysis system (paragraph [0015]…a machine learning model registry system is disclosed. The machine learning model registry system may be utilized to deploy a first version of a machine learning model and a first version of an access module to server machines) comprising:
a data acquirer configured to acquire the to-be-analyze data (paragraph [0022]…The client device 122 may access the various data and applications provided by other entities in the networked system 120 via web client 124 (e.g., a browser, such as the Internet Explorer® browser developed by Microsoft® Corporation of Redmond, Wash. State) or one or more client applications 126)(Examiner’s Note: “client device 122 ” reads on “data acquirer”);
an analyzer configured to analyze the to-be-analyzed data by using the learned model (paragraph [0019]… The machine learning model registry system 118 includes a networked system 120, according to example embodiments. According to one embodiment, the machine learning model registry system 118 is configured to receive, train, acceptance test, and deploy access modules 121 and to receive, train, validate, and deploy machine learning models 123)(Examiner’s Note: “networked system 122” reads on “an analyzer”);
a learned model producer configured to produce the learned model by using at least teacher data (paragraph [0024]…the scheduler module 135 trains the machine learning models 123 based on training data information (e.g., sample data) to make predictions or decisions without the machine learning models 123 being explicitly programmed to perform the task ; paragraph [0028]…At operation “4,” scheduler module 135 (e.g., cron job), at the training server machine 128, serializes the trained machine learning model 123 and communicates it to the distributed server machine 130)(Examiner’s Note: “trained server machine 128” reads on “learned model producer” ; “training data information (e.g., sample data)” reads on “at least teacher data”);
a storage configured to store each of a plurality of learned models as the learned model associated with the teacher data that is used to produce each of the plurality
of learned models by the learned model producer, and with a different version(s) of learned model (s) that is/are other learned model(s) of the plurality of learned models and whose analysis purpose is common to each of the plurality of learned models (paragraph [0030]…The registry server machine 132 includes a registry module 140 and is communicatively coupled to the machine learning model registry 125. The machine learning model registry 125 may include one or more databases. For example, the one or more databases may be stored in and retrieved from high speed, volatile, and non-volatile storage. In one example, the one or more databases may be stored in a cloud-based storage. The machine learning model registry 125 stores the model training information 138, as previously described, access model information 142, and the deployment information 144. The access model information 142 describes a version of an access module 121, as described further in this disclosure. Each element of the deployment information 144 describes a version of an access module 121 and one or more versions of machine learning models 123 that interoperate with the access module 121, as described further in this disclosures)(Examiner’s Note: “The machine learning model registry 125 stores the model training information 138, as previously described, access model information 142, and the deployment information 144” reads on “a storage configured to store each of a plurality of learned models as the learned model associated with the teacher data that is used to produce each of the plurality of learned models by the learned model producer” ; “The access model information 142 describes a version of an access module 121, as described further in this disclosure. Each element of the deployment information 144 describes a version of an access module 121 and one or more versions of machine learning models 123 that interoperate with the access module 121, as described further in this disclosure” reads on “with a different version(s) of learned model (s) that is/are other learned model(s) of the plurality of learned models and whose analysis purpose is common to each of the plurality of learned models”);
a display configured to display the plurality of learned models stored in the storage (paragraph [0035]…the registry server machine 132 further provides a user interface for the client devices 122. The user interface may be utilized to compare the performances of different versions of a machine learning model 123 and different machine learning models 123)(Examiner’s Note: “user interface” reads on “display configured to display”); and
a controller configured to control the display to display a selected learned model that is selected from the plurality of learned models displayed on the display, and
the different version (s) of learned model (s) that is/are associated with the selected learned model on a common screen (paragraph [0049]…FIG. 4A is a diagram illustrating a user interface 400, according to an embodiment, to promote a machine learning model. The user interface 400 may be utilized by the “promote” command. The user interface 400 includes a first input box 402 to receive the name of the model (e.g., “Home Valuation”) and a second input box 404 to receive the model version (e.g., “2”). For example, the input box 402 may be embodied as a pull down menu including model name information 202 from each of the model training information 138 stored in the machine learning model registry 125. Further for example, the input box 404 may be embodied as a pull down menu including model version identifiers 220 from each of the model training information 138 stored in the machine learning model registry 125 and associated with the selected model name information 202) (Examiner’s Note: “The user interface 400 may be utilized by the “promote” command. The user interface 400 includes a first input box 402 and a second input box 404” reads on “a controller configured to control the display to display a selected learned model that is selected from the plurality of learned models displayed on the display, and the different version (s) of learned model (s) that is/are associated with the selected learned model on a common screen”).
As to claim 2, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system according to claim 1, wherein the storage is configured to store each of the plurality of learned models associated with information on accuracy of a result of analysis of the to-be-analyzed data together with the teacher data, and with the different version (s) of learned model (s) whose analysis purpose is common to each of the plurality of learned models (paragraph [0035]…The user interface may be utilized to compare the performances of different versions of a machine learning model 123 and different machine learning models 123. In addition, if the user wants to update the prediction service with a new version of the machine learning model 123, then the user may execute a “promote” command. The promote command receives a machine learning model identifier that identifies the machine learning model 123 and a version of the machine learning model 123 that, in turn, is communicated to the machine learning model registry system 118 that, in turn, automatically identifies a version of an access module 121 that interoperates with the identified version of the machine learning model 123 and deploys the identified version of the access module 121 and the identified version of the machine learning model 123 to one or more API service server machines 134, as described further below)(Examiner’s Note: “the user interface may be utilized to compare the performances of different versions of a machine learning model 123 and different machine learning models 123” reads on “plurality of learned models associated with information on accuracy of a result of analysis of the to-be-analyzed data together with the teacher data, and with the different version (s) of learned model (s) whose analysis purpose is common to each of the plurality of learned models”).
As to claim 3, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to the selected learned model for comparative display of the information (paragraph [0053]…the input boxes for selecting a graph may include a metrics input box 430 and a parameters input box 432. The metrics input box 430 may be utilized to select a metric from performance metrics information 232 in the machine learning model 123 for plotting the predictions in the graph. The parameters input box 432 may be utilized to select a hyperparameter from hyperparameter information 230 in the machine learning model 123 to plot the predictions in the graph) (Examiner’s Note: “The metrics input box 430 may be utilized to select a metric from performance metrics information 232 in the machine learning model 123 for plotting the predictions in the graph” reads on “the controller is configured to control the display to display the information on the accuracy of the result of analysis by the selected learned model and the information on the accuracy of the result of analysis by the different version (s) of learned model (s)”).
As to claim 4, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, further comprising an input acceptor configured to accept an instruction input for selection of an operator, wherein the controller is configured to control the display to display the selected learned model that is selected in accordance with the instruction input for the selection accepted by the input acceptor and the information on accuracy of a result of analysis analyzed based on the to-be-analyzed data by the selected learned model, and the learned model (s) whose analysis purpose is common to the selected learned model and the information on accuracy of a result (s) of analysis analyzed based on the to-be- analyzed data by the learned model (s) whose analysis purpose is common to the selected learned model as the information for comparative display of the information (paragraph 0053]…The input boxes for selecting a graph may include a metrics input box 430 and a parameters input box 432. The metrics input box 430 may be utilized to select a metric from performance metrics information 232 in the machine learning model 123 for plotting the predictions in the graph. The parameters input box 432 may be utilized to select a hyperparameter from hyperparameter information 230 in the machine learning model 123 to plot the predictions in the graph)(Examiner’s Note: “input boxes for selecting a graph may include a metrics input box 430 and a parameters input box 432” reads on “an input acceptor configured to accept an instruction input for selection of an operator”).
As to claim 5, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the storage is configured to store each of the plurality of learned models associated with first analysis accuracy information that is information on accuracy of a result of analysis by the different version (s) of learned model (s) whose analysis purposes is/are common to each of the plurality of learned models as the information, and second analysis accuracy information that is information on accuracy of a result of analysis obtained by analyzing the to-be-analyzed data that is different from the to-be- -analyzed data that is used by a common version of learned model (s) whose version is equal to each of the plurality of learned models as the information; and
the controller is configured to control the display to display the first analysis accuracy information and the second analysis accuracy information for comparative display of the information (paragraph [0052]…The input boxes for selecting filters may include a date range input box 420, an environmental input box 422, a name input box 424, a created by input box 426, and a logical comparison input box 428. The date range input box 420 may be utilized to select a date range to plot the predictions in the graph. The environmental input box 422 may be utilized to select a hyperparameter from the hyperparameter information 230 in the machine learning model 123 to plot the predictions in the graph. The name input box 424 may be utilized to select a name to label the graph. The created by input box 426 may be utilized to select a user-name to label the graph. The logical comparison input box 428 may be utilized to select elements (e.g., cities) for plotting the predictions in the graph. Other elements for logically comparing may include states, counties, countries, regions, and so forth ; paragraph [0053],,,The input boxes for selecting a graph may include a metrics input box 430 and a parameters input box 432. The metrics input box 430 may be utilized to select a metric from performance metrics information 232 in the machine learning model 123 for plotting the predictions in the graph. The parameters input box 432 may be utilized to select a hyperparameter from hyperparameter information 230 in the machine learning model 123 to plot the predictions in the graph).
As to claim 6, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the learned model producer is configured to be able to produce, based on the selected learned model that is selected from the plurality of learned models stored in the storage, a different version of learned model whose analysis purpose is common to the selected learned model (paragraph [0051]…The input boxes for selecting a model may be utilized to identify a machine learning model 123 that has been trained (e.g., a machine learning model artifact). The input boxes to select the model include a namespace input box 416, a model type input box 418, and a model version input box 419. The namespace input box 416 may be utilized to select a model name from the model name information 202 in the machine learning model 123. The model type input box 418 may be utilized to select a model type from model type information 204 in the machine learning model 123. The model version input box 419 may be utilized to select a model version number from the model version identifier 220 in the machine learning model 123).
As to claim 6, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the learned model producer is configured to produce, in a case in which the learned model is produced while the learned model with common analysis purpose is being selected, a different version of learned model whose analysis purpose is common to the selected learned model by changing at least one of the teacher data and training parameters that are specified to produce the selected learned model (paragraph [0051]…The input boxes for selecting a model may be utilized to identify a machine learning model 123 that has been trained (e.g., a machine learning model artifact). The input boxes to select the model include a namespace input box 416, a model type input box 418, and a model version input box 419. The namespace input box 416 may be utilized to select a model name from the model name information 202 in the machine learning model 123. The model type input box 418 may be utilized to select a model type from model type information 204 in the machine learning model 123. The model version input box 419 may be utilized to select a model version number from the model version identifier 220 in the machine learning model 123).
As to claim 7, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the learned model producer is configured to produce, in a case in which the learned model is produced while the learned model with common analysis purpose is being selected, a different version of learned model whose analysis purpose is common to the selected learned model by changing at least one of the teacher data and training parameters that are specified to produce the selected learned model.
(paragraph [0051]…The input boxes for selecting a model may be utilized to identify a machine learning model 123 that has been trained (e.g., a machine learning model artifact). The input boxes to select the model include a namespace input box 416, a model type input box 418, and a model version input box 419. The namespace input box 416 may be utilized to select a model name from the model name information 202 in the machine learning model 123. The model type input box 418 may be utilized to select a model type from model type information 204 in the machine learning model 123. The model version input box 419 may be utilized to select a model version number from the model version identifier 220 in the machine learning model 123 ; paragraph [0052]…The input boxes for selecting filters may include a date range input box 420, an environmental input box 422, a name input box 424, a created by input box 426, and a logical comparison input box 428. The date range input box 420 may be utilized to select a date range to plot the predictions in the graph. The environmental input box 422 may be utilized to select a hyperparameter from the hyperparameter information 230 in the machine learning model 123 to plot the predictions in the graph. The name input box 424 may be utilized to select a name to label the graph. The created by input box 426 may be utilized to select a user-name to label the graph. The logical comparison input box 428 may be utilized to select elements (e.g., cities) for plotting the predictions in the graph. Other elements for logically comparing may include states, counties, countries, regions, and so forth).
As to claim 8, Xiang figures 1, 4Aand 4B shows and teaches the data analysis system, wherein the controller is configured to control the display, in production of the different version of learned model whose analysis purpose is common to the selected learned model to selectively display a plurality of versions of learned model that are stored in the storage (paragraph [0050]… FIG. 4B is a diagram illustrating an example user interface 410, according to an embodiment, to search for model training information 138 and compare machine learning models 123. The user interface 410 includes multiple input boxes 412 for receiving input that is utilized to identify one or more elements of machine learning models 123 to generate a chart 414 enabling a comparison (e.g., mean squared errors) of two machine learning models 123 over time for two cities (e.g., Dallas and Phoenix). The input boxes 412 may be embodied as pull down menus that are utilized to select a model, select filters, and select a graph).
Claim 12 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 9 – 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiang (US 2021/0264321) in view of Barfield (US 2020/0249936).
As to claim 9, Xiang teaches a controller.
Xiang fails to explicitly show/teach wherein the controller is configured to, every when a different version of learned model whose analysis purpose is common to the selected learned model is produced, add the latest version of learned model as an option.
However, Barfield teaches a controller is configured to, every when a different version of learned model whose analysis purpose is common to the selected learned model is produced, add the latest version of learned model as an option (paragraph [0040]…Component 244 defines storing algorithm models that may be uploaded to platform 200 by the user and may contain multiple versions of models for a single algorithm. Component 246 indicates storage of the algorithm code; like algorithm model storage, it may contain multiple algorithm versions for a particular algorithm model. paragraph [0055]… select algorithms pre-made for particular data types in a repository, or test and edit models in the creation step through a user interface).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Xiang’s controller to be configured to, every when a different version of learned model whose analysis purpose is common to the selected learned model is produced, add the latest version of learned model as an option, as in Barfield, for the purpose of creating applications encompassing a user supplied algorithm.
As to claim 10, Barfield teaches an input acceptor configured to accept an input
from an operator, wherein the controller is configured to determine whether a
version name that is accepted by the input acceptor is a latest version name named in accordance with a naming convention, and to prevent the learned model producer from producing the learned model if the version name that is accepted by the input acceptor is not the latest version name named in accordance with the naming convention (paragraph [0055]… a user may select different platform types, geographic choices, scaling configuration for servers, and types of server scaling. With Algorithms deployed, a user may configure endpoints, version algorithms, re-train or add updated models or architectures to algorithms deployed, or log particular aspects of the code based user supplied algorithms. The user may also manage billing; re-deploy the APIs containing algorithm defined by the user; or monitor statistics such as algorithm accuracies, usage, or errors of the algorithm with a managing component of the user interface).
It would have been obvious for an input acceptor configured to accept an input
from an operator, wherein the controller is configured to determine whether a
version name that is accepted by the input acceptor is a latest version name named in accordance with a naming convention, and to prevent the learned model producer from producing the learned model if the version name that is accepted by the input acceptor is not the latest version name named in accordance with the naming convention, for the same reasons as above.
As to claim 11, Barfield teaches the data analysis system, wherein the storage is configured to store learned models whose algorithms of training models are common to each other and that are produced by changing at least one of the teacher data and training parameters that are specified to produce corresponding one of the learned models as different versions of learned models associated with each other (paragraph [0040]…Component 244 defines storing algorithm models that may be uploaded to platform 200 by the user and may contain multiple versions of models for a single algorithm. Component 246 indicates storage of the algorithm code; like algorithm model storage, it may contain multiple algorithm versions for a particular algorithm model. paragraph [0055]… select algorithms pre-made for particular data types in a repository, or test and edit models in the creation step through a user interface).
It would have been obvious for one having ordinary skill in the art, at the time the invention was made for, Barfield storage is configured to store learned models whose algorithms of training models are common to each other and that are produced by changing at least one of the teacher data and training parameters that are specified to produce corresponding one of the learned models as different versions of learned models associated with each other, for the same reason as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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