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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 4. 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claim 5 is 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.
Claim 5:
The term “substantially equally” in claim 5 is a relative term which renders the claim indefinite. The term “substantially equally” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is not clear what are the meets and bounds of this term. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
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 1-16 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A learning method”, and a method or process is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“A learning method comprising: classifying measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured,” (this is a mental process, a person could mentally evaluate classifying measurement value data that measures performance of an object, see MPEP § 2106.04(a)(2)(III)),
“selecting the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications,” (this is a mental process, a person could mentally evaluate selecting a measurement value data from classifications, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“and performing machine learning on a basis of the selected measurement value data.” (Performing machine learning is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element iii recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2:
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 2 recites the following additional elements:
“The learning method according to claim 1, further comprising: classifying the measurement value data measuring performance of each of a plurality of pieces of equipment equipped in the object on a basis of the situation data.” (this is a mental process, a person could mentally evaluate classifying measurement value data measuring performance of each of a plurality of pieces of equipment equipped in the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 3 recites the following additional elements:
“The learning method according to claim 1, further comprising: classifying the measurement value data on a basis of external situation data each representing a situation of the object due to an external situation of the object.” (this is a mental process, a person could mentally evaluate classifying measurement value data on a basis of external situation data each representing a situation of the object due to an external situation of the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 4 recites the following additional elements:
“The learning method according to claim 1, further comprising: classifying the measurement value data on a basis of internal situation data each representing a situation of the object due to an internal situation of the object..” (this is a mental process, a person could mentally evaluate classifying measurement value data on a basis of internal situation data each representing a situation of the object due to an internal situation of the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 5 recites the following additional elements:
“The learning method according to claim 1, further comprising: substantially equally selecting the measurement value data from each of the classifications.” (this is a mental process, a person could mentally evaluate determining a substantially equal value to select a measurement value data from classifications, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 6 recites the following additional elements:
“The learning method according to claim 1, further comprising: selecting, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.” (this is a mental process, a person could mentally evaluate selecting measurement value data from classifications according to a ratio set for each classification, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 7 recites the following additional elements:
“The learning method according to claim 1, wherein the object is a car, and when the measurement value data are classified on a basis of the situation data, the situation data represents at least one of situations of a situation of a road surface where the car travels and weather at the time of traveling.” (this is a mental process, a person could mentally evaluate the object being a car, and the measurement value data being classified on a basis of situation data, and the situation data representing at least one of situations of a situation of a road surface where the car travels and weather at the time of traveling., see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 8 recites the following additional elements:
“The learning method according to claim 1, wherein the object is a car, and when the measurement value data are classified on a basis of the external situation data each representing the situation of the object due to the external situation of the car, the external situation data is at least one of weather, a temperature, brightness, a time zone, a road surface situation, a steering direction by a driver, and dozing of a driver.” (this is a mental process, a person could mentally evaluate the object being a car, and the measurement value data being classified on a basis of external situation data, and the external situation data is at least one of weather, a temperature, brightness, a time zone, a road surface situation, a steering direction by a driver, and dozing of a driver, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 9:
Regarding claim 9, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 9 recites the following additional elements:
“The learning method according to claim 1, wherein the object is a car, and when the measurement value data are classified on a basis of the internal situation data each representing the situation of the car due to the internal situation of the car, the internal situation data is at least one of a car model, a model number, a purchase date, a repair history, and a total travel distance of the car.” (this is a mental process, a person could mentally evaluate the object being a car, and the measurement value data being classified on a basis of internal situation data, and the internal situation data is at least one of a car model, a model number, a purchase date, a repair history, and a total travel distance of the car, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 10:
Regarding claim 10, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 10 recites the following additional elements:
“and detecting a state of the object according to an output from the model.” (this is a mental process, a person could mentally evaluate detecting the state of an object based on an output, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“A method for detecting a state using the learning method according to claim 1, comprising: inputting the measurement value data newly measured from the object into a model generated by performing the machine learning” (In step 2A, prong 2, this is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)). (In step 2B, this is also considered insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, see receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 11:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A learning device comprising: at least one memory configured to store instructions; and at least one processer configured to execute the instructions to:”, and a learning device or machine is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“classify measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured;” (this is a mental process, a person could mentally evaluate classifying measurement value data that measures performance of an object, see MPEP § 2106.04(a)(2)(III)),
“select the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications;” (this is a mental process, a person could mentally evaluate selecting a measurement value data from classifications, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A learning device comprising: at least one memory configured to store instructions;” (Using a memory to store instructions is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“and at least one processer configured to execute the instructions to:” (Using a processor to execute instructions is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“and perform machine learning on a basis of the selected measurement value data.” (Performing machine learning is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element iii and iv recites generic computer component being used as tool to perform functions of the judicial exception, and additional element v recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 12:
Regarding claim 12, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 12 recites the following additional elements:
“classify the measurement value data measuring performance of each of a plurality of pieces of equipment equipped in the object on a basis of the situation data.” (this is a mental process, a person could mentally evaluate classifying measurement value data measuring performance of each of a plurality of pieces of equipment equipped in the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 13:
Regarding claim 13, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 13 recites the following additional elements:
“classify the measurement value data on a basis of external situation data each representing a situation of the object due to an external situation of the object.” (this is a mental process, a person could mentally evaluate classifying measurement value data on a basis of external situation data each representing a situation of the object due to an external situation of the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 14:
Regarding claim 14, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 14 recites the following additional elements:
“classify the measurement value data on a basis of internal situation data each representing a situation of the object due to an internal situation of the object.” (this is a mental process, a person could mentally evaluate classifying measurement value data on a basis of internal situation data each representing a situation of the object due to an internal situation of the object, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 15:
Regarding claim 15, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 15 recites the following additional elements:
“substantially equally select the measurement value data from each of the classifications.” (this is a mental process, a person could mentally evaluate determining a substantially equal value to select a measurement value data from classifications, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 16:
Regarding claim 16, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis to claim 11. Further, claim 16 recites the following additional elements:
“select, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.” (this is a mental process, a person could mentally evaluate selecting measurement value data from classifications according to a ratio set for each classification, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to” (In step 2A, prong 2, this is considered generic computer component being used as tool to perform functions of the judicial exception, see MPEP § 2106.05(f)). (In step 2B, this is also considered generic computer component being used as tool to perform functions of the judicial exception - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 21:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to:”, and a non-transitory computer-readable medium or machine is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“classify measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured;” (this is a mental process, a person could mentally evaluate classifying measurement value data that measures performance of an object, see MPEP § 2106.04(a)(2)(III)),
“select the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications;” (this is a mental process, a person could mentally evaluate selecting a measurement value data from classifications, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to:” (Using a non-transitory medium comprising instructions is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“and perform machine learning on a basis of the selected measurement value data.” (Performing machine learning is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element iii recites generic computer component being used as tool to perform functions of the judicial exception, and additional element iv recites mere instructions to apply an exception using generic computer, which is not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 1-5, 9, 11-15, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kawahara T. et al, (US. Patent Application Publication 20120246099 A1) effectively filed on September 27, 2012, (hereafter Kawahara), in view of Ishii Y. et al, (Japan Patent Application Publication JP2020035042A) effectively filed on March 5, 2020, (hereafter Ishii).
Claim 1:
Regarding claim 1, Kawahara teaches, “A learning method comprising: classifying measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured,”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
Further, Kawahara teaches, “selecting the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications,”
See Kawahara in paragraph [0052], describing, “The selecting unit 122 performs the process of selecting a plurality of groups each including one or more learning samples from the learning sample storage unit 120 several times. In particular, the selecting unit 122 selects the plurality of groups so that each of the plurality of selected groups includes approximately the same number of categories or samples of learning samples. Specifically, the selecting unit 122 selects the plurality of groups so that the difference between the groups in the number of categories or samples of learning samples included in each of the plurality of selected groups falls within a predetermined range.” Here, Kawahara establishes selecting learning samples, which in the previous limitation was established to be seen as measurement value data, from categories which are the classifications as established in previous limitation, according to a number of the samples for each category.
However, Kawahara did not explicitly teach “and performing machine learning on a basis of the selected measurement value data.”
In the same field of art, Ishii teaches “and performing machine learning on a basis of the selected measurement value data.”
See Ishii on page 3, describing, “The self-encoder 28 is a learning device constructed using various artificial intelligence technologies. In the example of this figure, the self-encoder 28 is configured by a hierarchical neural network including an input layer 50, a hidden layer 52, and an output layer 54.” Here, Ishii establishes a learning device, the self-encoder, which performs machine learning. Further, see Ishii on page 3, describing, “The self-encoder 28 outputs multivariate data equal to the number of dimensions of the input by sequentially executing dimensional compression processing and dimensional restoration processing on the input of the multivariate data. Here, “multivariate data” means data composed of a plurality of variables, and as a specific example, probe data (here, determination target data) acquired from the vehicle information DB 36 through the database processing unit 26. D1).” Here, Ishii establishes the self-encoder taking in multivariate data which can be probe data. Further, see Ishii on page 2, describing, “The probe data includes, for example, data indicating a running state including a time, a position (latitude / longitude), a speed, an acceleration, a yaw rate, an azimuth, and a gradient, an operation state of a vehicle-mounted device, and an operation state of an operation device.” Here, Ishii establishes probe data as measurement value data with the examples.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 2:
Regarding claim 2, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Kawahara teaches, “The learning method according to claim 1, further comprising: classifying the measurement value data measuring performance of each of a plurality of pieces…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…of equipment equipped in the object on a basis of the situation data.”
Further, Ishii teaches “…of equipment equipped in the object on a basis of the situation data.”
See Ishii on page 2, describing, “Collection of Probe Data First, the vehicle 16 sequentially acquires data from various sensors mounted on the own vehicle, and transmits the stored probe data to the data determination device 12 regularly or irregularly.” Here, Ishii establishes the sensors mounted on vehicle as the equipment of the data measured of the object which is the vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 3:
Regarding claim 3, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Kawahara teaches, “The learning method according to claim 1, further comprising: classifying the measurement value data…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…on a basis of external situation data each representing a situation of the object due to an external situation of the object.”
Further, Ishii teaches, “…on a basis of external situation data each representing a situation of the object due to an external situation of the object.”
See Ishii on page 6, describing, “Specific examples of the metadata include a data providing source (for example, a vehicle type, a user layer, and years of use) or a data providing environment (for example, a country, a region, a climate, and a traveling place).” Here, Ishii establishes the data providing environment as the external situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 4:
Regarding claim 4, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Kawahara teaches, “The learning method according to claim 1, further comprising: classifying the measurement value data…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…on a basis of internal situation data each representing a situation of the object due to an internal situation of the object.”
Further, Ishii teaches “…on a basis of internal situation data each representing a situation of the object due to an internal situation of the object.”
See Ishii on page 6, describing, “Specific examples of the metadata include a data providing source (for example, a vehicle type, a user layer, and years of use) or a data providing environment (for example, a country, a region, a climate, and a traveling place).” Here, Ishii establishes the vehicle type, user layer and years of use as the internal situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 5:
Regarding claim 5, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Kawahara teaches, “The learning method according to claim 1, further comprising: substantially equally selecting the measurement value data from each of the classifications.”
See Kawahara in paragraph [0052], describing, “The selecting unit 122 performs the process of selecting a plurality of groups each including one or more learning samples from the learning sample storage unit 120 several times. In particular, the selecting unit 122 selects the plurality of groups so that each of the plurality of selected groups includes approximately the same number of categories or samples of learning samples.” Here, Kawahara establishes substantially equally selecting from each classification with the selection of the data from the different categories or classifications with each group including approximately the same number of categories. Further, see Kawahara in paragraph [0053], describing, “In the present embodiment, the selecting unit 122 performs N (N.gtoreq.0) selection processes of randomly selecting K (K=2) groups from the learning sample storage unit 120 so that each group includes one or more image data. In particular, when performing the selection process, the selecting unit 122 randomly selects K groups so that each of the K groups includes the same number of categories of image data. The value of K may be 2 or more.” Here, Kawahara further establishes substantially equally selecting the same number of classifications from a set number of groups of the same number of categories.
Claim 9:
Regarding claim 9, Kawahara in view of Ishii teaches the limitations of claim 1.
Kawahara does not appear to explicitly teach “The learning method according to claim 1, wherein the object is a car, and when the measurement value data are classified on a basis of the internal situation data each representing the situation of the car due to the internal situation of the car, the internal situation data is at least one of a car model, a model number, a purchase date, a repair history, and a total travel distance of the car.”
However, Ishii teaches “The learning method according to claim 1, wherein the object is a car,”
See Ishii on page 2, describing, “The data determination system 10 is configured to execute a desired process on probe data collected from a traveling four-wheeled vehicle (hereinafter, referred to as a vehicle 16)”.
Further, Ishii teaches, “and when the measurement value data are classified on a basis of the internal situation data each representing the situation of the car due to the internal situation of the car, the internal situation data is at least one of a car model, a model number, a purchase date, a repair history, and a total travel distance of the car.”
See Ishii on page 6, describing, “According to the characteristic table 74, each multiplier is ω = 1 when belonging to a class of 0 to 50%, ω = 0.5 when belonging to a class of 51 to 80%, and 81 to 100. In the case of belonging to the class of%, it is determined that ω = 0.” Further, see Ishii on page 6, describing, “In this way, the learning error calculator 62 determines a multiplier for each sample data according to the characteristic curves 70 to 73 or the characteristic table 74 (step S4). As a result, every time the data population D4 is formed, the multiplier for each sample data is determined adaptively according to the data distribution or the learning progress.” Here, Ishii establishes classifying data using multipliers, which can be measurement value data. Further, see Ishii on page 6, describing, “Incidentally, it is assumed that the existence ratio of the normal value / outlier differs depending on the type of the learning data D3. Therefore, the learning error calculating unit 62 may change the method of setting the multiplier according to the metadata acquired by the data acquiring unit 60 together with the multivariate data. Specific examples of the metadata include a data providing source (for example, a vehicle type, a user layer, and years of use) or a data providing environment (for example, a country, a region, a climate, and a traveling place).” Here, Ishii establishes the vehicle type, user layer and years of use as the internal situation and that this data can be classified with multipliers.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 11:
Regarding claim 11, Kawahara teaches, “classify measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured;”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
Further, Kawahara teaches, “select the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications;”
See Kawahara in paragraph [0052], describing, “The selecting unit 122 performs the process of selecting a plurality of groups each including one or more learning samples from the learning sample storage unit 120 several times. In particular, the selecting unit 122 selects the plurality of groups so that each of the plurality of selected groups includes approximately the same number of categories or samples of learning samples. Specifically, the selecting unit 122 selects the plurality of groups so that the difference between the groups in the number of categories or samples of learning samples included in each of the plurality of selected groups falls within a predetermined range.” Here, Kawahara establishes selecting learning samples, which in the previous limitation was established to be seen as measurement value data, from categories which are the classifications as established in previous limitation, according to a number of the samples for each category.
However, Kawahara did not explicitly teach “A learning device comprising: at least one memory configured to store instructions; and at least one processer configured to execute the instructions to:…and perform machine learning on a basis of the selected measurement value data.”
In the same field of art, Ishii teaches “A learning device comprising: at least one memory configured to store instructions; and at least one processer configured to execute the instructions to:…and perform machine learning on a basis of the selected measurement value data.”
See Ishii on page 2, describing, “The control unit 22 is configured by a processing operation device including a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The control unit 22 functions as a database processing unit 26, a self-encoder 28, a determination processing unit 30, and a learning processing unit 32 by reading and executing a program stored in the storage unit 24.” Here, Ishii establishes a processing unit which can be seen as a processor to execute a program which is also known to consists of instructions, and includes a storage unit to store the program. Further, see Ishii in paragraph [0022], describing, “The storage unit 24 is a non-transitory, computer-readable storage medium. Here, the computer-readable storage medium is a portable medium such as a magneto-optical disk, a ROM, a CD-ROM, a flash memory, or a storage device such as a hard disk built in a computer system.” Here, Ishii establishes that the storage unit can be a memory. Further, see Ishii on page 3, describing, “The self-encoder 28 is a learning device constructed using various artificial intelligence technologies. In the example of this figure, the self-encoder 28 is configured by a hierarchical neural network including an input layer 50, a hidden layer 52, and an output layer 54.” Here, Ishii establishes a learning device, the self-encoder, which performs machine learning. Further, see Ishii on page 3, describing, “The self-encoder 28 outputs multivariate data equal to the number of dimensions of the input by sequentially executing dimensional compression processing and dimensional restoration processing on the input of the multivariate data. Here, “multivariate data” means data composed of a plurality of variables, and as a specific example, probe data (here, determination target data) acquired from the vehicle information DB 36 through the database processing unit 26. D1).” Here, Ishii establishes the self-encoder taking in multivariate data which can be probe data. Further, see Ishii on page 2, describing, “The probe data includes, for example, data indicating a running state including a time, a position (latitude / longitude), a speed, an acceleration, a yaw rate, an azimuth, and a gradient, an operation state of a vehicle-mounted device, and an operation state of an operation device.” Here, Ishii establishes probe data as measurement value data with the examples.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 12:
Regarding claim 12, Kawahara in view of Ishii teaches the limitations of claim 11.
Further, Kawahara teaches, “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to classify the measurement value data measuring performance of each of a plurality of pieces…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…of equipment equipped in the object on a basis of the situation data.”
Further, Ishii teaches “…of equipment equipped in the object on a basis of the situation data.”
See Ishii on page 2, describing, “Collection of Probe Data First, the vehicle 16 sequentially acquires data from various sensors mounted on the own vehicle, and transmits the stored probe data to the data determination device 12 regularly or irregularly.” Here, Ishii establishes the sensors mounted on vehicle as the equipment of the data measured of the object which is the vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 13:
Regarding claim 13, Kawahara in view of Ishii teaches the limitations of claim 11.
Further, Kawahara teaches, “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to classify the measurement value data…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…on a basis of external situation data each representing a situation of the object due to an external situation of the object.”
Further, Ishii teaches, “…on a basis of external situation data each representing a situation of the object due to an external situation of the object.”
See Ishii on page 6, describing, “Specific examples of the metadata include a data providing source (for example, a vehicle type, a user layer, and years of use) or a data providing environment (for example, a country, a region, a climate, and a traveling place).” Here, Ishii establishes the data providing environment as the external situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 14:
Regarding claim 14, Kawahara in view of Ishii teaches the limitations of claim 11.
Further, Kawahara teaches, “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to classify the measurement value data…”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
However, Kawahara did not explicitly teach “…on a basis of internal situation data each representing a situation of the object due to an internal situation of the object.”
Further, Ishii teaches “…on a basis of internal situation data each representing a situation of the object due to an internal situation of the object.”
See Ishii on page 6, describing, “Specific examples of the metadata include a data providing source (for example, a vehicle type, a user layer, and years of use) or a data providing environment (for example, a country, a region, a climate, and a traveling place).” Here, Ishii establishes the vehicle type, user layer and years of use as the internal situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim 15:
Regarding claim 15, Kawahara in view of Ishii teaches the limitations of claim 11.
Further, Kawahara teaches, “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to substantially equally select the measurement value data from each of the classifications.”
See Kawahara in paragraph [0052], describing, “The selecting unit 122 performs the process of selecting a plurality of groups each including one or more learning samples from the learning sample storage unit 120 several times. In particular, the selecting unit 122 selects the plurality of groups so that each of the plurality of selected groups includes approximately the same number of categories or samples of learning samples.” Here, Kawahara establishes substantially equally selecting from each classification with the selection of the data from the different categories or classifications with each group including approximately the same number of categories. Further, see Kawahara in paragraph [0053], describing, “In the present embodiment, the selecting unit 122 performs N (N.gtoreq.0) selection processes of randomly selecting K (K=2) groups from the learning sample storage unit 120 so that each group includes one or more image data. In particular, when performing the selection process, the selecting unit 122 randomly selects K groups so that each of the K groups includes the same number of categories of image data. The value of K may be 2 or more.” Here, Kawahara further establishes substantially equally selecting the same number of classifications from a set number of groups of the same number of categories.
Claim 21:
Regarding claim 21, Kawahara teaches, “classify measurement value data measuring performance of an object on a basis of situation data each representing a situation of the object when the measurement value data are measured;”
See Kawahara in paragraph [0050], describing, “The learning sample storage unit 120 stores a plurality of learning samples in which respective learning samples are classified to any one of a plurality of categories. In the present embodiment, although a case in which the learning samples are the image data which are expressed by a vector of the dimensionality of Ds (Ds.gtoreq.1 as described above), and in which respective vector elements are brightness values is described as an example, the learning samples are not limited to this. The learning samples may be voice samples or the like if they correspond to the patterns input by the input unit 102.” Here, Kawahara establishes learning samples being classified, the learning samples can be seen as measurement value data in this instance as it shows a measurement of brightness as an example but is not limited to this and the image data being the object, the brightness values of the image data is the performance of the object on a basis of situation data representing a situation.
Further, Kawahara teaches, “select the measurement value data from each of the classifications according to a number of the measurement value data for each of the classifications;”
See Kawahara in paragraph [0052], describing, “The selecting unit 122 performs the process of selecting a plurality of groups each including one or more learning samples from the learning sample storage unit 120 several times. In particular, the selecting unit 122 selects the plurality of groups so that each of the plurality of selected groups includes approximately the same number of categories or samples of learning samples. Specifically, the selecting unit 122 selects the plurality of groups so that the difference between the groups in the number of categories or samples of learning samples included in each of the plurality of selected groups falls within a predetermined range.” Here, Kawahara establishes selecting learning samples, which in the previous limitation was established to be seen as measurement value data, from categories which are the classifications as established in previous limitation, according to a number of the samples for each category.
However, Kawahara did not explicitly teach “A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to:…and perform machine learning on a basis of the selected measurement value data.”
In the same field of art, Ishii teaches “A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to:…and perform machine learning on a basis of the selected measurement value data.”
See Ishii on page 2, describing, “The storage unit 24 is a non-transitory, computer-readable storage medium. Here, the computer-readable storage medium is a portable medium such as a magneto-optical disk, a ROM, a CD-ROM, a flash memory, or a storage device such as a hard disk built in a computer system.” Here, Ishii establishes the storage unit as a non-transitory, computer-readable storage medium. Further, see Ishii in paragraph [0021], describing, “The control unit 22 is configured by a processing operation device including a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit). The control unit 22 functions as a database processing unit 26, a self-encoder 28, a determination processing unit 30, and a learning processing unit 32 by reading and executing a program stored in the storage unit 24.” Here, Ishii establishes a processing unit which can be seen as a processor to execute a program which is also known to consists of instructions, and includes a storage unit to store the program. Further, see Ishii in paragraph [0034], describing, “The self-encoder 28 is a learning device constructed using various artificial intelligence technologies. In the example of this figure, the self-encoder 28 is configured by a hierarchical neural network including an input layer 50, a hidden layer 52, and an output layer 54.” Here, Ishii establishes a learning device, the self-encoder, which performs machine learning. Further, see Ishii in paragraph [0033], describing, “The self-encoder 28 outputs multivariate data equal to the number of dimensions of the input by sequentially executing dimensional compression processing and dimensional restoration processing on the input of the multivariate data. Here, “multivariate data” means data composed of a plurality of variables, and as a specific example, probe data (here, determination target data) acquired from the vehicle information DB 36 through the database processing unit 26. D1).” Here, Ishii establishes the self-encoder taking in multivariate data which can be probe data. Further, see Ishii in paragraph [0027], describing, “The probe data includes, for example, data indicating a running state including a time, a position (latitude / longitude), a speed, an acceleration, a yaw rate, an azimuth, and a gradient, an operation state of a vehicle-mounted device, and an operation state of an operation device.” Here, Ishii establishes probe data as measurement value data with the examples.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Kawahara with the teachings of Ishii by using Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning.
One of ordinary skill in the art would be motivated to do so because by integrating Ishii’s frameworks into the methods of Kawahara, which are both in relation to a learning method, one of ordinary skill in the art would bring “A data determination device [that] includes, in a control unit 22, a learning error calculation unit 62 determining a reconstruction error for each sample data of a data population D4 and calculating a learning error LE for the data population using the reconstruction error for each sample data, and a parameter updating unit 64 updating a learning parameter group 34 so that the calculated learning error is reduced. The learning error calculation unit calculates the learning error by weighting the reconstruction error using a multiplier for each sample data determined according to the data population.” (Ishii, Abstract page 1).
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kawahara T. et al, in view of Ishii Y. et al, and further in view of Galvão et al., "Ratio Selection for Classification Models", available at https://doi.org/10.1023/B:DAMI.0000015913.38787.b3, published on March 2004, (hereafter Galvão).
Claim 6:
Regarding claim 6, Kawahara in view of Ishii teaches the limitations of claim 1.
Neither Ishii or Kawahara appear to explicitly teach “The learning method according to claim 1, further comprising: selecting, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.”
However, in an analogous system and in the same field of art, Galvão teaches, “The learning method according to claim 1, further comprising: selecting, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.”
See Galvão in section 2 Discriminant analysis on page 3, describing, “The most widely used discriminant analysis method is the one developed by Fisher in 1936, which attempts to maximize the ratio of between–groups and within–groups variances. Using vector-matrix notation, the following linear discriminant function
PNG
media_image1.png
18
74
media_image1.png
Greyscale
, also called Z-score, can be derived for the case of binary (two groups) classification (Morrison, 1990):
PNG
media_image2.png
24
138
media_image2.png
Greyscale
(1) where
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media_image3.png
16
98
media_image3.png
Greyscale
is a vector of n classification variables,
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media_image4.png
19
117
media_image4.png
Greyscale
are the sample mean vectors of each group, and
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media_image5.png
19
28
media_image5.png
Greyscale
is the common sample covariance matrix. Notice that, in this work, each classification variable is a ratio of two quantities.” Here, Galvão establishes a formula for the ratio set of classifications. Further, see Galvão in section 2.1 Selection of classification variables on page 5, describing, “Suppose that a set of variables
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media_image6.png
19
86
media_image6.png
Greyscale
is to be assessed with respect to discriminating information and collinearity. In the approach proposed here, the discriminating information is considered by obtaining a Z-score model and evaluating the classification accuracy when this model is applied to the modelling set itself.” Here, Galvão establishes the variables as measurement value data for classifications. Further, see Galvão in section 2.1 Selection of classification variables on page 4, describing, “it may be more appropriate to select a subset of the available variables for inclusion in the classification model.” Here, Galvão establishes a selecting of variables for the classifications.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Ishii and Kawahara, with the teachings of Galvão by using Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning, and Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Galvão’s teachings of ratio selection for classification models.
One of ordinary skill in the art would be motivated to do so because by integrating Galvão’s frameworks into the methods of Ishii and Kawahara, which are all in relation to a learning method, one of ordinary skill in the art would bring “Two different selection methods [that] are considered. The first one employs a pre-selection technique to discard some of the ratios on the basis of a multivariate relevance index, and tests combinations of the remaining ratios with respect to amount of discriminating information and collinearity. The second one uses a genetic algorithm, which is a search technique inspired by the mechanisms of natural selection and evolution (Goldberg, 1989), to avoid the need for a pre-selection of ratios. In the example considered in this work, which involves 60 failed and continuing British firms during a recent period (1997–2000), both methods compare favorably with the use of ratios commonly found in the financial distress literature.” (Galvão, Introduction page 3).
Claim 16:
Regarding claim 16, Kawahara in view of Ishii teaches the limitations of claim 11.
Neither Ishii or Kawahara appear to explicitly teach “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to select, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.”
However, in an analogous system and in the same field of art, Galvão teaches, “The learning device according to claim 11, wherein the at least one processer configured to execute the instructions to select, according to a ratio set for each of the classifications, the measurement value data from each of the classifications.”
See Galvão in section 2 Discriminant analysis on page 3, describing, “The most widely used discriminant analysis method is the one developed by Fisher in 1936, which attempts to maximize the ratio of between–groups and within–groups variances. Using vector-matrix notation, the following linear discriminant function
PNG
media_image1.png
18
74
media_image1.png
Greyscale
, also called Z-score, can be derived for the case of binary (two groups) classification (Morrison, 1990):
PNG
media_image2.png
24
138
media_image2.png
Greyscale
(1) where
PNG
media_image3.png
16
98
media_image3.png
Greyscale
is a vector of n classification variables,
PNG
media_image4.png
19
117
media_image4.png
Greyscale
are the sample mean vectors of each group, and
PNG
media_image5.png
19
28
media_image5.png
Greyscale
is the common sample covariance matrix. Notice that, in this work, each classification variable is a ratio of two quantities.” Here, Galvão establishes a formula for the ratio set of classifications. Further, see Galvão in section 2.1 Selection of classification variables on page 5, describing, “Suppose that a set of variables
PNG
media_image6.png
19
86
media_image6.png
Greyscale
is to be assessed with respect to discriminating information and collinearity. In the approach proposed here, the discriminating information is considered by obtaining a Z-score model and evaluating the classification accuracy when this model is applied to the modelling set itself.” Here, Galvão establishes the variables as measurement value data for classifications. Further, see Galvão in section 2.1 Selection of classification variables on page 4, describing, “it may be more appropriate to select a subset of the available variables for inclusion in the classification model.” Here, Galvão establishes a selecting of variables for the classifications.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Ishii and Kawahara, with the teachings of Galvão by using Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning, and Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Galvão’s teachings of ratio selection for classification models.
One of ordinary skill in the art would be motivated to do so because by integrating Galvão’s frameworks into the methods of Ishii and Kawahara, which are all in relation to a learning method, one of ordinary skill in the art would bring “Two different selection methods [that] are considered. The first one employs a pre-selection technique to discard some of the ratios on the basis of a multivariate relevance index, and tests combinations of the remaining ratios with respect to amount of discriminating information and collinearity. The second one uses a genetic algorithm, which is a search technique inspired by the mechanisms of natural selection and evolution (Goldberg, 1989), to avoid the need for a pre-selection of ratios. In the example considered in this work, which involves 60 failed and continuing British firms during a recent period (1997–2000), both methods compare favorably with the use of ratios commonly found in the financial distress literature.” (Galvão, Introduction page 3).
Claim(s) 7-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kawahara T. et al, in view of Ishii Y. et al, and further in view of Ishikawa M. et al, (US. Patent Application Publication 20170358154 A1) effectively filed on December 14, 2017, (hereafter Ishikawa).
Claim 7:
Regarding claim 7, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Ishii teaches “The learning method according to claim 1, wherein the object is a car,”
See Ishii on page 2, describing, “The data determination system 10 is configured to execute a desired process on probe data collected from a traveling four-wheeled vehicle (hereinafter, referred to as a vehicle 16)”.
However, neither Ishii or Kawahara appear to explicitly teach “and when the measurement value data are classified on a basis of the situation data, the situation data represents at least one of situations of a situation of a road surface where the car travels and weather at the time of traveling.”
In an analogous system and in the same field of art, Ishikawa teaches, “and when the measurement value data are classified on a basis of the situation data, the situation data represents at least one of situations of a situation of a road surface where the car travels and weather at the time of traveling.”
See Ishikawa in paragraph [0027] describing, “The environment information can include, for example, a weather, a temperature, an atmospheric pressure, humidity, an altitude, a time zone, a road surface condition, a traffic condition, a road condition such as an inclination and a curve, a surrounding environment such as an urban area and mountain, road information such as a toll road and a notional road.” Here, Ishikawa establishes the data of a situation of environment representing both road surface and weather at a time of traveling. Further, see Ishikawa in paragraph [0008] describing, “In order to attain the above object, the anomality candidate information analysis apparatus of the present invention is an anomality candidate information analysis apparatus for analyzing anomality candidate information of a monitoring object including a storage unit that stores monitoring object information regarding the monitoring object, environment information regarding an environment around the monitoring object”. Here, Ishikawa establishes the environment situation data for an object. Further, see Ishikawa in paragraph [0060] describing, “In a case where the learning result of the driving characteristic is newly obtained thereafter, the clustering unit 94 calculates the driving behavior, calculates whether the obtained calculation result of the driving behavior is classified into which past driver type, and assigns the driving characteristic type. The assignment of the driving characteristic type is performed for both the learning result of the normal driving characteristic and the learning result of the anomaly driving characteristic. The assigned learning results are associated with the other information and stored in the storage unit 30 of the monitoring center 20.” Here, Ishikawa establishes classifying information from obtained driving characteristics. Further, see Ishikawa in paragraph [0061] describing, “The learning results of the plurality of driving characteristics collected from the plurality of vehicles 10 are learning results obtained in the different environments.” Here, Ishikawa establishes the driving characteristics being associated with environment information which is linked to situation data making the calculated or measurement value data be classified on a basis of an environment situation which is established to include road surface information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Ishii and Kawahara, with the teachings of Ishikawa by using Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning, and Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishikawa’s teachings of an anomality candidate information analysis apparatus that uses monitoring object information regarding a vehicle, environment information regarding an environment around the vehicle, operator information regarding an operator of the vehicle, and anomality candidate information detected in the vehicle in association with each other.
One of ordinary skill in the art would be motivated to do so because by integrating Ishikawa’s frameworks into the methods of Ishii and Kawahara, which are all in relation to a learning method, one of ordinary skill in the art would bring “an anomality candidate information analysis apparatus capable of early confirming that an unexpected anomality occurs in a monitoring object such as a vehicle, and a behavior prediction device used for the apparatus.” (Ishikawa, paragraph [0007]).
Claim 8:
Regarding claim 8, Kawahara in view of Ishii teaches the limitations of claim 1.
Further, Ishii teaches “The learning method according to claim 1, wherein the object is a car,”
See Ishii on page 2, describing, “The data determination system 10 is configured to execute a desired process on probe data collected from a traveling four-wheeled vehicle (hereinafter, referred to as a vehicle 16)”.
However, neither Ishii or Kawahara appear to explicitly teach “and when the measurement value data are classified on a basis of the external situation data each representing the situation of the object due to the external situation of the car, the external situation data is at least one of weather, a temperature, brightness, a time zone, a road surface situation, a steering direction by a driver, and dozing of a driver.”
In an analogous system and in the same field of art, Ishikawa teaches, “and when the measurement value data are classified on a basis of the external situation data each representing the situation of the object due to the external situation of the car, the external situation data is at least one of weather, a temperature, brightness, a time zone, a road surface situation, a steering direction by a driver, and dozing of a driver.”
See Ishikawa in paragraph [0027] describing, “The environment information can include, for example, a weather, a temperature, an atmospheric pressure, humidity, an altitude, a time zone, a road surface condition, a traffic condition, a road condition such as an inclination and a curve, a surrounding environment such as an urban area and mountain, road information such as a toll road and a notional road.” Here, Ishikawa establishes the data of external situation of environment which includes weather, temperature, time zone and road surface. Further, see Ishikawa in paragraph [0060] describing, “In a case where the learning result of the driving characteristic is newly obtained thereafter, the clustering unit 94 calculates the driving behavior, calculates whether the obtained calculation result of the driving behavior is classified into which past driver type, and assigns the driving characteristic type. The assignment of the driving characteristic type is performed for both the learning result of the normal driving characteristic and the learning result of the anomaly driving characteristic. The assigned learning results are associated with the other information and stored in the storage unit 30 of the monitoring center 20.” Here, Ishikawa establishes classifying information from obtained driving characteristics. Further, see Ishikawa in paragraph [0061] describing, “The learning results of the plurality of driving characteristics collected from the plurality of vehicles 10 are learning results obtained in the different environments.” Here, Ishikawa establishes the driving characteristics being associated with environment information which is linked to external situation data making the calculated or measurement value data be classified on a basis of external situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Ishii and Kawahara, with the teachings of Ishikawa by using Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning, and Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishikawa’s teachings of an anomality candidate information analysis apparatus that uses monitoring object information regarding a vehicle, environment information regarding an environment around the vehicle, operator information regarding an operator of the vehicle, and anomality candidate information detected in the vehicle in association with each other.
One of ordinary skill in the art would be motivated to do so because by integrating Ishikawa’s frameworks into the methods of Ishii and Kawahara, which are all in relation to a learning method, one of ordinary skill in the art would bring “an anomality candidate information analysis apparatus capable of early confirming that an unexpected anomality occurs in a monitoring object such as a vehicle, and a behavior prediction device used for the apparatus.” (Ishikawa, paragraph [0007]).
Claim 10:
Regarding claim 10, Kawahara in view of Ishii teaches the limitations of claim 1.
Neither Ishii or Kawahara appear to explicitly teach “A method for detecting a state using the learning method according to claim 1, comprising: inputting the measurement value data newly measured from the object into a model generated by performing the machine learning and detecting a state of the object according to an output from the model.”
However, in an analogous system and in the same field of art, Ishikawa teaches, “A method for detecting a state using the learning method according to claim 1, comprising: inputting the measurement value data newly measured from the object into a model generated by performing the machine learning and detecting a state of the object according to an output from the model.”
See Ishikawa in paragraph [0047] describing, “One autoencoder 52a is, for example, a layer in which a characteristic extraction is performed from the environment information which is transmitted from the monitoring center 20 to the vehicle 10. Another autoencoder 52b is, for example, a layer in which the characteristic extraction is performed form the detection value of the vehicle sensor 51 excluding the detection value of an in-vehicle camera or the laser radar. The convolution layer 52c is, for example, a layer in which the characteristic extraction is performed from the detection value of the in-vehicle camera or the laser radar among the detection values of the vehicle sensor 51. The characteristics extracted by the autoencoders 52a and 52b and the convolution layer 52c are input to the recurrent neural network 52d.” Here, Ishikawa establishes inputting characteristics extracted from environment information, which is seen as the measurement value data, into a neural network which is a machine learning model while also detecting different states of the vehicle which is the object. Inputting into a recurrent neural network applies new vales being input. Further, see Ishikawa in paragraph [0048] describing, “For example, the recurrent neural network 52d calculates and outputs the prediction value of the detection value of the vehicle sensor 51 after a time k. For example, the recurrent neural network 52d is used for predicting the detection value of the vehicle sensor 51 of the vehicle 10 that is sequentially controlled by the driver. Here, for example, the sequential control of the vehicle 10 by the driver includes controlling the velocity and an inter-vehicle distance of the vehicle by recognizing an external environment such as the weather, the road surface condition, and the inter-vehicle distance and an internal environment such as the velocity or the engine of the host vehicle by the driver.” Here, Ishikawa further establishes detecting a state by outputting a prediction of the detection from the neural network. The sequential control of the vehicle by the driver is being interpreted as the state being detected of the object as an example.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Ishii and Kawahara, with the teachings of Ishikawa by using Ishii’s teachings of a data determination device, a method, and a program for determining measurement value data using machine learning, and Kawahara’s teachings of classifying and selecting measurement value data, and incorporate with Ishikawa’s teachings of an anomality candidate information analysis apparatus that uses monitoring object information regarding a vehicle, environment information regarding an environment around the vehicle, operator information regarding an operator of the vehicle, and anomality candidate information detected in the vehicle in association with each other.
One of ordinary skill in the art would be motivated to do so because by integrating Ishikawa’s frameworks into the methods of Ishii and Kawahara, which are all in relation to a learning method, one of ordinary skill in the art would bring “an anomality candidate information analysis apparatus capable of early confirming that an unexpected anomality occurs in a monitoring object such as a vehicle, and a behavior prediction device used for the apparatus.” (Ishikawa, paragraph [0007]).
Conclusion
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/HASSAN RAMADAN SESAY/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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