DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
Regarding 112(b):
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-6, 9-13, 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to Claim 2:
Claim 2 recites the limitation "the comparing processing " in “wherein the comparing processing includes”. There is insufficient antecedent basis for this limitation in the claim. There is no prior of “a comparing processing”. Only one processing was previously recited in the claims in claim 1: “generation program for causing a computer to execute processing comprising…”.
Claim 2 recites the phrase "and at least a part of the first data is training data that includes the first data ". The limitation is indefinite, as the claim notes something is a part of itself. This creates confusion on how to interpret claims utilizing the first data. If the first data comprises itself, then one of ordinary skill in the art would be unable to ascertain the bounds of the first data. Examples of confusion include: Is the first data just a thing of data being stated to contain what is already within, or is the first data a recursive list of data? There is also indefiniteness on how to interpret the limitation reciting the phrase. The recitation “and at least a part of the first data is training data that includes the first data of which a difference between the first deviation and the second deviation satisfies a specific condition” does not make clear how the first data is related to the difference between the deviations. Does the first data contain the deviations? If so, how does the comparing of values created by the deviations possible if the deviations were a part of the data acquired in claim 1? Amending the claims to correct the confusion on what and how first data is used can help prevent the indefiniteness.
In regard to Claim 3:
Claim 3 recites the phrase " wherein at least a part of the first data is training data that includes the first data". The limitation is indefinite, as the claim notes something is a part of itself. This creates confusion on how to interpret claims utilizing the first data. If the first data comprises itself, then one of ordinary skill in the art would be unable to ascertain the bounds of the first data. Examples of confusion include: Is the first data just a thing of data being stated to contain what is already within, or is the first data a recursive list of data? There is also indefiniteness on how to interpret the limitation reciting the phrase. The recitation “wherein at least a part of the first data is training data that includes the first data that corresponds to a case of which the acquired statistical information satisfies a specific condition” does not make clear how the first data could include data for the statistical information when earlier in claim 3 the statistical information is acquired (“acquiring statistical information of each event that corresponds to the first data included in the plurality of pieces of first training data”). Meaning the order of acquiring statistical information does not make sense if the statistical information that is being acquired is somehow in data from before the acquisition of the statistical data, as the first data is from claim 1. Amending the claims to correct the confusion on what and how first data is used can help prevent the indefiniteness.
In regard to Claim 4:
Claim 4 recites the phrase "wherein at least a part of the first data is training data that includes the first data". The limitation is indefinite, as the claim notes something is a part of itself. This creates confusion on how to interpret claims utilizing the first data. If the first data comprises itself, then one of ordinary skill in the art would be unable to ascertain the bounds of the first data. Examples of confusion include: Is the first data just a thing of data being stated to contain what is already within, or is the first data a recursive list of data? There is also indefiniteness on how to interpret the limitation reciting the phrase. The recitation “wherein at least a part of the first data is training data that includes the first data of which the calculated similarity satisfies a specific condition” does not make clear how the first data could include data for satisfying the condition when earlier in claim 4 the calculation for the similarity to meet the condition was calculated (“calculating a similarity between the first data and the second data”). Meaning the order of calculating the similarity does not make sense if the similarity that was calculated is somehow in the first data, even though the similarity contained elements from the first and second data, and how the condition of a similarity fitting a condition is possible if the condition is not calculated until claim 4 where the first data that supposedly already contains elements fitting the condition is introduced in claim 1. Amending the claims to correct the confusion on what and how first data is used can help prevent the indefiniteness.
In regard to Claim 5:
Claim 5 recites the phrase " acquiring a third value by inputting third data included in the plurality of pieces of third training data to a third model that is generated through machine learning based on the plurality of pieces of the generated third training data". The limitation is indefinite, as one of ordinary skill in the art cannot determine what is based on what. An example of the confusion: Is the acquiring a third value “based on the plurality of pieces of the generated third training data” or is the machine learning the third model is generated from “based on the plurality of pieces of the generated third training data”. Noting more specifically what is based on what, possibly by adjusting the structure of the limitation, can help prevent confusion on what is based on what.
The recitation of “third training data” and “generated third training data” also creates confusion and indefiniteness in claim 5. Is “generated third training data” the same as “third training data”? If so, then why are they noted differently in the same claim, as in why does claim 5 note “third training data” and “generated third training data”? Utilizing one term to refer to “third training data” can help prevent confusion as to whether there are one or two things of “third training data”.
In regard to claim 6:
Claim 6 recites the limitation "the comparing processing " in “re-executing the processing of acquiring the second value, the comparing processing, and the generating processing”. There is insufficient antecedent basis for this limitation in the claim. There is no prior of “a comparing processing”. Only one processing was previously recited in the claims in claim 1: “generation program for causing a computer to execute processing comprising…”.
Claim 6 recites the limitation "the generating processing " in “re-executing the processing of acquiring the second value, the comparing processing, and the generating processing”. There is insufficient antecedent basis for this limitation in the claim. There is no prior of “a generating processing”. Only one processing was previously recited in the claims in claim 1: “generation program for causing a computer to execute processing comprising…”.
In regards to claims 9-13 and 16-20:
Claims 9-13 and 16-20 are rejected for being analogous to corresponding claims 2-6, thus containing the same corresponding 112(b) rejections.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
In regards to Claim 1:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a manufacture.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 1 recites the following abstract ideas:
comparing the first value with the second value
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
and generating a plurality of pieces of third training data that does not include at least a part of the first data, based on the plurality of pieces of first training data and the plurality of pieces of second training data, according to a result of the comparison
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgement. Generically reciting the generation of data with a condition, such as not including some data, recites a mental process, as generic manipulation of data is possible to evaluate in the human mind.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 1 recites the following additional elements:
A non-transitory computer-readable storage medium storing a training data generation program for causing a computer to execute processing comprising
At a high level of generality, this is an activity of using a computer parts, such as a computer-readable medium, processor, or memory as an “apply it” use (see MPEP 2106.05(f)).
acquiring a first value by inputting first data included in a plurality of pieces of first training data to a first model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
first model that is generated through machine learning based on the plurality of pieces of first training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)).
acquiring a second value by inputting the first data and second data included in a plurality of pieces of second training data to a second model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
second model that is generated through machine learning based on the plurality of pieces of first training data and the plurality of pieces of second training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 1 recites the following additional elements:
A non-transitory computer-readable storage medium storing a training data generation program for causing a computer to execute processing comprising
At a high level of generality, this is an activity of using a computer parts, such as a computer-readable medium, processor, or memory as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a user and agent device appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea.
acquiring a first value by inputting first data included in a plurality of pieces of first training data to a first model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
first model that is generated through machine learning based on the plurality of pieces of first training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of train or generate a model using training data does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
acquiring a second value by inputting the first data and second data included in a plurality of pieces of second training data to a second model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
second model that is generated through machine learning based on the plurality of pieces of first training data and the plurality of pieces of second training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of train or generate a model using training data does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 2:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 2 recites the following abstract ideas:
processing of comparing a first deviation for an average of the first value and a second deviation for an average of the second value, and at least a part of the first data is training data that includes the first data of which a difference between the first deviation and the second deviation satisfies a specific condition
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgment.
In regards to claim 3:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 3 recites the following additional elements:
acquiring statistical information of each event that corresponds to the first data included in the plurality of pieces of first training data and each event that corresponds to the second data included in the plurality of pieces of second training data, wherein at least a part of the first data is training data that includes the first data that corresponds to a case of which the acquired statistical information satisfies a specific condition
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 3 recites the following additional elements:
acquiring statistical information of each event that corresponds to the first data included in the plurality of pieces of first training data and each event that corresponds to the second data included in the plurality of pieces of second training data, wherein at least a part of the first data is training data that includes the first data that corresponds to a case of which the acquired statistical information satisfies a specific condition
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
In regards to Claim 4:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 4 recites the following abstract ideas:
calculating a similarity between the first data and the second data, wherein at least a part of the first data is training data that includes the first data of which the calculated similarity satisfies a specific condition
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgment.
In regards to claim 5:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 5 recites the following abstract ideas:
comparing the third value with the second value
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
and determining whether or not the third data is suitable as training data according to a result of the comparison
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgement.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 5 recites the following additional elements:
acquiring a third value by inputting third data included in the plurality of pieces of third training data to a third model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
third model that is generated through machine learning based on the plurality of pieces of the generated third training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 5 recites the following additional elements:
acquiring a third value by inputting third data included in the plurality of pieces of third training data to a third model
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
third model that is generated through machine learning based on the plurality of pieces of the generated third training data
At a high level of generality, this is an activity of using machine learning and training data as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of train or generate a model using training data does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to claim 6:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 6 recites the following additional elements:
re-executing the processing of acquiring the second value, the comparing processing, and the generating processing while assuming that the plurality of pieces of the generated third training data is the plurality of pieces of second training data
o This limitation is directed towards the insignificant extra solution activity of repetitive calculations (see MPEP § 2106.05(d)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 6 recites the following additional elements:
re-executing the processing of acquiring the second value, the comparing processing, and the generating processing while assuming that the plurality of pieces of the generated third training data is the plurality of pieces of second training data
This limitation is directed towards the insignificant extra solution activity of repetitive calculations (see MPEP § 2106.05(d)). Repetitive calculations are considered a well understood, routine, and conventional activity acknowledged by the courts (see MPEP § 2106.05(d) subsection 2 example 2 for a computer).
In regards to claim 7:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 7 recites the following additional elements:
applying a model generated through machine learning based on the plurality of pieces of the generated third training data to a model that is operated by a system
At a high level of generality, this is an activity of using a model an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 7 recites the following additional elements:
applying a model generated through machine learning based on the plurality of pieces of the generated third training data to a model that is operated by a system
At a high level of generality, this is an activity of using a model an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “applying a model” that was generate using machine learning does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 8:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a process.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 8 recites the same abstract ideas as analogous claim 1.
Claim 8 is noted to be interpreted as a computer containing hardware according to paragraph 133 of the specification: “As illustrated in FIG. 20, a computer 200 includes a CPU 201 that executes various types of arithmetic processing, an input device 202 that receives data input, a monitor 203, and a speaker 204. Furthermore, the computer 200 includes a medium reading device 205 that reads a program or the like from a storage medium, an interface device 206 to be connected to various devices, and a communication device 207 to be connected to and communicate with an external device in a wired or wireless manner. Furthermore, the information processing device 1 includes a RAM 208 that temporarily stores various types of information, and a hard disk device 209. Furthermore, each of the units (201 to 209) in the computer 200 is connected to a bus 210.”.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 8 recites the same additional elements as analogous claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 8 recites the same additional elements as analogous claim 1.
In regards to Claim 9:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 9 recites the same abstract ideas as analogous claim 2.
In regards to Claim 10:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 10 recites the same additional elements as analogous claim 3.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 10 recites the same additional elements as analogous claim 3.
In regards to Claim 11:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 11 recites the same abstract ideas as analogous claim 4.
In regards to Claim 12:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 12 recites the same abstract ideas as analogous claim 5.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 12 recites the same additional elements as analogous claim 5.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 12 recites the same additional elements as analogous claim 5.
In regards to Claim 13:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 13 recites the same additional elements as analogous claim 6.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 13 recites the same additional elements as analogous claim 6.
In regards to Claim 14:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 14 recites the same additional elements as analogous claim 6.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 14 recites the same additional elements as analogous claim 6.
In regards to Claim 15:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a machine.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 15 recites the same abstract ideas as analogous claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 15 recites the same additional elements as analogous claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 15 recites the same additional elements as analogous claim 1.
In regards to Claim 16:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 16 recites the same abstract ideas as analogous claim 2.
In regards to Claim 17:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 17 recites the same additional elements as analogous claim 3.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 17 recites the same additional elements as analogous claim 3.
In regards to Claim 18:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 18 recites the same abstract ideas as analogous claim 4.
In regards to Claim 19:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 19 recites the same abstract ideas as analogous claim 5.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 19 recites the same additional elements as analogous claim 5.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 19 recites the same additional elements as analogous claim 5.
In regards to Claim 20:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 20 recites the same additional elements as analogous claim 6.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 20 recites the same additional elements as analogous claim 6.
In regards to Claim 21:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 21 recites the same additional elements as analogous claim 6.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 21 recites the same additional elements as analogous claim 6.
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.
Claims 1-4, 8-11, 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al (“Statistically Significant Detection of Linguistic Change”), referred to as Kulkarni in this document, and further in view of Parthasarathy et al (US 20210157704 A1), referred to as Parthasarathy in this document.
Regarding Claim 1:
Kulkarni teaches:
acquiring a first value by inputting first data included in a plurality of pieces of first training data to a first model that is generated through machine learning based on the plurality of pieces of first training data
acquiring a second value by inputting the first data and second data included in a plurality of pieces of second training data to a second model that is generated through machine learning based on the plurality of pieces of first training data and the plurality of second training data
comparing the first value with the second value
[Kulkarni 3.3 Distributional Method page 3]: “Thus, vector representations of words appearing in similar contexts will be close to each other. Recent developments in representation learning (deep learning) [5] have enabled the scalable learning of such models [first model that is generated through machine learning based on the plurality of pieces of first training data][second model that is generated through machine learning based on the plurality of pieces of first training data and the plurality of second training data]. We use a variation of these models [28] to learn word vector representation [acquiring a first value by inputting first data][acquiring a second value by inputting the first data and second data] (word embeddings) that we track across time. Specifically, we seek to learn a temporal word embedding φt : V, Ct 7→ Rd. Once we learn a representation of a specific word for each time snapshot corpus [included in a plurality of pieces of first training data to a first model] [included in a plurality of pieces of second training data to a second model], we track the changes [comparing the first value with the second value] of the representation across the embedding space to quantify the meaning shift of the word (as shown in Figure 1).”
Kulkarni does not explicitly teach:
A non-transitory computer-readable storage medium storing a training data generation program for causing a computer to execute processing comprising
generating a plurality of pieces of third training data that does not include at least a part of the first data, based on the plurality of pieces of first training data and the plurality of pieces of second training data, according to a result of the comparison
acquiring a second value by inputting the first data and second data
Parthasarathy teaches:
A non-transitory computer-readable storage medium storing a training data generation program for causing a computer to execute processing comprising
[Parthasarathy 0073]: “FIG. 13 is a block diagram of a system for implementing machines that implement the present technology. System 1300 of FIG. 13 may be implemented in the contexts of the likes of machines that implement application program [program] monitoring system 110, machines that host applications 130 and 136, network server 145, manager 160, servers 170 and 180, datastore 190, and client device 195. The computing system [computer] 1300 of FIG. 13 includes one or more processors [processors] 1310 and memory [memory] 1320. Main memory 1320 stores, in part, instructions and data for execution by processor 1310. Main memory 1320 can store the executable code when in operation. The system 1300 of FIG. 13 further includes a mass storage device 1330, portable storage medium drive(s) 1340, output devices 1350, user input devices 1360, a graphics display 1370, and peripheral devices 1380.”
[Parthasarathy Claim 9]: “A non-transitory [non-transitory computer readable storage medium] computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for automatically continuously monitoring an application, the method comprising…”
acquiring a second value by inputting the first data and second data
[Parthasarathy 0049]: “A first block of data collected during the monitoring but previous to the recent chunk of data is selected at step 420. In some instances, the first block may be the most recent 15 minutes of streaming data received just prior to the recent chunk for the period of time at step 410. Hence, if data has been collected for an application for 120 minutes, the recent chunk may be the most recent 15 minutes [acquiring a second value by inputting the first data and second data as while Kulkarni teaches multiple data via snapshots, here Parthasarathy shows that the data within the second data (recent chunk and past data) can include data from the first data (past data) which shows data containing the past data and recent data being looked at as a collection of data (where the collection of data is the whole timeframe or the 120 minutes)], and the first block may be the data associated with 15-30 minutes back into the collected data.”
generating a plurality of pieces of third training data that does not include at least a part of the first data, based on the plurality of pieces of first training data and the plurality of pieces of second training data, according to a result of the comparison
[Parthasarathy 0050]: “The recent chunk is compared to the selected block of past data at step 425. In some instances, the past data is data collected while monitoring the application for a period of time at step 410, but modified to remove data that has been determined to be unacceptable. For example, if within the 120 minutes a portion of data between 15 minutes ago and 30 minutes ago was determined to be unacceptable, then the past data would include 105 minutes of data determined to be acceptable [generating a plurality of pieces of third training data that does not include at least a part of the first data, based on the plurality of pieces of first training data and the plurality of pieces of second training data, according to a result of the comparison where containing only acceptable elements as a result of a comparison between two things of data (recent chunk and past data, akin to the second data and first data) is seen as teaching the limitation for manipulating data to make the third training data as a data is generated and data can be training data or the same operations could be performed on training data as data], and would not include the 15 minutes of data determined to be unacceptable.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Kulkarni and Parthasarathy. Kulkarni and Parthasarathy are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Kulkarni and Parthasarathy to incorporate the removal of data from a set of data, such as a set of data containing recent and past elements, in order to ensure the data is still considered acceptable ([Parthasarathy 0045]: “A concept drift analysis may determine if there is a shift in the streaming data values that should be considered an acceptable trend over a longer term. More details for performing a concept drift analysis is discussed with respect to the method of FIG. 8.”). One of ordinary skill in the art would have also been motivated to combine Kulkarni and Parthasarathy to incorporate computer parts and storage in order to utilize hardware to implement the method ([Parthasarathy 0073]: “FIG. 13 is a block diagram of a system for implementing machines that implement the present technology. System 1300 of FIG. 13 may be implemented in the contexts of the likes of machines that implement application program…).
Regarding Claim 2:
The non-transitory computer-readable storage medium of claim 1 is taught by Kulkarni and Parthasarathy.
Kulkarni teaches:
the comparing processing includes processing of comparing a first deviation for an average of the first value and a second deviation for an average of the second value
at least a part of the first data is training data that includes the first data of which a difference between the first deviation and the second deviation satisfies a specific condition
[Kulkarni Algorithm 1 page 5]
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[Kulkarni 4 Change Point Detection page 5]: “This corresponds to calculating the shift in mean [the comparing processing includes processing of comparing a first deviation for an average of the first value and a second deviation for an average of the second value] between two parts of the time series pivoted at time point j. Change points can be thus identified by detecting significant shifts [at least a part of the first data is training data that includes the first data of which a difference between the first deviation and the second deviation satisfies a specific condition as a significant shift is interpreted as “a specific condition”] in the mean.”
As a result of the 112(b) rejection, the limitation for part of the first data is interpreted as noting the requirement of a condition being met involving first data.
Regarding Claim 3:
The non-transitory computer-readable storage medium of claim 1 is taught by Kulkarni and Parthasarathy.
Kulkarni teaches:
acquiring statistical information of each event that corresponds to the first data included in the plurality of pieces of first training data and each even that corresponds to the second data included in the plurality of pieces of second training data
[Kulkarni Introduction page 1]: “Frequency based statistics [acquiring statistical information of each event that corresponds to the first data included in the plurality of pieces of first training data and each even that corresponds to the second data included in the plurality of pieces of second training data where this is showing the frequency based statistic as a method for acquiring statistical information of the events in the data, where the idea of first and second training data sets were already taught in claim 1] to capture sudden changes in word usage”
wherein at least a part of the first data is training data that includes the first data that corresponds to a case of which the acquired statistical information satisfies a specific condition
[Kulkarni 3.1 Frequency Based Method page 3]: “Frequency based methods can capture linguistic shift, as changes in frequency [wherein at least a part of the first data is training data that includes the first data that corresponds to a case of which the acquired statistical information satisfies a specific condition where the condition could be the change in frequency] can correspond to words acquiring or losing senses”
As a result of the 112(b) rejection, the limitation for part of the first data is interpreted as noting the requirement of a condition being met involving first data.
Regarding Claim 4:
The non-transitory computer-readable storage medium of claim 1 is taught by Kulkarni and Parthasarathy.
Kulkarni teaches:
[Kulkarni 3.3 Distributional Method page 3]: "The distributional hypothesis states that words appearing in similar contexts are semantically similar [13]. Distributional methods learn a semantic space that maps words to continuous vector space [calculating a similarity between the first data and the second data, wherein at least a part of the first data is training data that includes the first data of which the calculated similarity satisfies a specific condition where the condition of the similarity is appearing in similar contexts] Rd, where d is the dimension of the vector space. Thus, vector representations of words appearing in similar contexts will be close to each other."
Support for the above quote noting similarity is given in [Kulkarni 7 Related Work page 10]: "Gulordava and Baroni [15] propose a distributional similarity approach to detecting semantic change in the Google Book Ngram corpus between 2 time periods."
As a result of the 112(b) rejection, the limitation for part of the first data is interpreted as noting the requirement of a condition being met involving first data.
Regarding Claim 8:
Claim 8 is considered analogous to the teachings of claim 1.
Regarding Claim 9:
The method of claim 8 is taught by Kulkarni and Parthasarathy.
Claim 9 is considered analogous to the teachings of claim 2.
Regarding Claim 10:
The method of claim 8 is taught by Kulkarni and Parthasarathy.
Claim 10 is considered analogous to the teachings of claim 3.
Regarding Claim 11:
The method of claim 8 is taught by Kulkarni and Parthasarathy.
Claim 11 is considered analogous to the teachings of claim 4.
Regarding Claim 15:
Claim 15 is considered analogous to the teachings of claim 1.
Regarding Claim 16:
The training data apparatus of claim 15 is taught by Kulkarni and Parthasarathy.
Claim 16 is considered analogous to the teachings of claim 2.
Regarding Claim 17:
The training data apparatus of claim 15 is taught by Kulkarni and Parthasarathy.
Claim 17 is considered analogous to the teachings of claim 3.
Regarding Claim 18:
The training data apparatus of claim 15 is taught by Kulkarni and Parthasarathy.
Claim 18 is considered analogous to the teachings of claim 4.
Claims 5-7, 12-14, and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al (“Statistically Significant Detection of Linguistic Change”), referred to as Kulkarni in this document, and further in view of Parthasarathy et al (US 20210157704 A1), referred to as Parthasarathy in this document., and further in view of Ryan et al (US 20200387797 A1), referred to as Ryan in this document.
Regarding Claim 5:
The non-transitory computer-readable storage medium of claim 1