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 .
DETAILED ACTION
This office action is in response to the claims filed on 12/20/2022.
Claims 1-19 are presented for examination.
Priority
The following claimed benefit is acknowledged: the instant application, filed 12/20/2022 claims priority from foreign application PCT/JP2020/025360, filed 06/26/2020.
Information Disclosure Statement
The information disclosure statements (IDS) filed12/20/2022 and 07/17/2023 are in compliance with the provisions of 37 CFR 1.97 and 1.98. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a non-transitory computer (claims 1-9), system (claims 9-18) and a method (claim 19). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of “Mental Processes/Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
The claim 1 recites:
a) Step 2A: prong 1 analysis:
-“ determining a reference value for each attribute on a basis of the received evaluation value and the number of data for each attribute” this is a mental process, the human mind can determine or assign the reference value for each of attribute, for example, the human can determine that the name attribute is less accurate then the Social security number attribute, since the group of the people may have a same name, (Evaluation/observation).
Step 2A: Prong 2 analysis:
-“ A non-transitory computer-readable storage medium storing a training data generation program for causing a computer”, “and generating training data for machine learning by changing the attribute of at least partial data of the plurality of data according to the reference value for each attribute.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“receiving an evaluation value for a value calculated on a basis of a number of data for each attribute included in a plurality of data;” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ A non-transitory computer-readable storage medium storing a training data generation program for causing a computer”, “and generating training data for machine learning by changing the attribute of at least partial data of the plurality of data according to the reference value for each attribute.” The additional limitation is recited at high level of generality and amounts to no more than mere instructions to apply the judicial exception using a generic computer component (See MPEP 2106.05(f)).
“receiving an evaluation value for a value calculated on a basis of a number of data for each attribute included in a plurality of data;” These/this additional limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As an analysis of the whole, the claim is directed to the abstract idea of “determining a reference value for each attribute on a basis of the received evaluation value and the number of data for each attribute”. Furthermore, the additional claim limitations do not integrate the exception into a practical application, and the additional limitations do not recite an improvement in technology. Therefore, the claim is not patent eligible.
The claim 2 recites:
a) Step 2A: prong 1 analysis:
-“ and in a case where a degree of dispersion of the received evaluation values is equal to or lower than a predetermined value, aggregating the evaluation values received from the respective plurality of evaluators and accept the aggregated evaluation value as an evaluation value agreed by the plurality of evaluators.” This is a mental process, the human mind can combine the evaluation value as evaluation value agreed by the evaluators when the degree of dispersion of the received evaluation values is equal to or lower than a predetermined value, for example, the human can average the rating is 4.5 based on the total evaluation is equal to the predetermined value, (observation/Evaluation).
Step 2A: Prong 2 analysis:
-“ receiving a plurality of the evaluation values from a respective plurality of evaluators” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ receiving a plurality of the evaluation values from a respective plurality of evaluators” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data gathering. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data gathering to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim 4 recites:
a) Step 2A: prong 1 analysis:
-“in a case where the degree of dispersion exceeds the predetermined value, clustering the evaluation values received from the respective plurality of evaluators until the degree of dispersion of the individual evaluation values becomes equal to or lower than the predetermined value, aggregating the evaluation values included in each cluster, and accepting the aggregated evaluation value as each of a plurality of the agreed evaluation values.” This is a mental process, the human cluster or groups the evaluation values until specific point or condition ( the degree of dispersion of the individual evaluation values becomes equal to or lower than the predetermined value)
-“ aggregating the evaluation values included in each cluster, and accepting the aggregated evaluation value as each of a plurality of the agreed evaluation values.” This is a mental process, the human mind can average the rating is 4.5 based on the total evaluation is equal to the predetermined value, (observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 4 recites:
Step 2A: Prong 2 analysis:
-“ presenting each of the agreed evaluation values as an option for the attribute for which the plurality of agreed evaluation values exists to accept a final evaluation value.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception and cannot integrate a judicial exception into a practical application.
b) Step 2B analysis:
-“ presenting each of the agreed evaluation values as an option for the attribute for which the plurality of agreed evaluation values exists to accept a final evaluation value.” These/this limitation(s) are/is recited at a high-level of generality such that it amounts to necessary data displaying. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity of data displaying to a judicial exception do not amount to significantly more than the judicial exception itself .
The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim 5 recites:
a) Step 2A: prong 1 analysis:
-“ determining a value obtained by lowering the value calculated on the basis of the number of data for each attribute at a rate that corresponds to magnitude of the evaluation value as the reference value for each attribute.” This is a mental process, the human mind can determine/assign by reducing the calculated value of each attribute at the rate as the reference value of each attribute, (observation/Evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 6 recites:
a) Step 2A: prong 1 analysis:
-“ changing the attribute of at least partial data of the plurality of data such that a difference between the reference value for each attribute and the value is equal to or less than a predetermined value.” This is a mental process, the human can change the attribute (change to the name attribute) so the difference between reference value and the value is equal to or less than predetermined value (observation/evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 7 recites:
Step 2A: Prong 2 analysis:
-“ wherein the attribute includes an attribute used for determination and an attribute that represents a determination result, and a contribution level of the attribute used for the determination to the determination result is calculated as the value.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
b) Step 2B analysis:
-“ wherein the attribute includes an attribute used for determination and an attribute that represents a determination result, and a contribution level of the attribute used for the determination to the determination result is calculated as the value.” This/these limitation(s) is/are amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception and that it does not integrate the judicial exception into a practical application.
The claim 8 recites:
a) Step 2A: prong 1 analysis:
-“ changing an attribute value of the attribute that represents the determination result to an attribute value that represents a different determination result for at least partial data of the plurality of data such that the contribution level becomes equal to or lower than the reference value for each attribute.” This is a mental process, the human mind can change attribute value, (observation/evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claim 8 recites:
a) Step 2A: prong 1 analysis:
-“ accepting, as the evaluation value, a discrimination level that represents a degree of discriminatory contribution of the attribute used for the determination to the determination result.” This is a mental process, the human mind can accept some evaluation value as the particular value for determination, (observation/evaluation).
Step 2A: Prong 2 analysis and Step 2B analysis
No additional element that provides a practical application or amount to significantly more than the abstract idea.
The claims 10 is rejected for the same reason as the claim 1 since these claims recite the same limitation.
The claims 11 is rejected for the same reason as the claim 2 since these claims recite the same limitation.
The claims 12 is rejected for the same reason as the claim 3 since these claims recite the same limitation.
The claims 13 is rejected for the same reason as the claim 4 since these claims recite the same limitation.
The claims 14 is rejected for the same reason as the claim 5 since these claims recite the same limitation.
The claims 15 is rejected for the same reason as the claim 6 since these claims recite the same limitation.
The claims 16 is rejected for the same reason as the claim 7 since these claims recite the same limitation.
The claims 17 is rejected for the same reason as the claim 8 since these claims recite the same limitation.
The claims 18 is rejected for the same reason as the claim 9 since these claims recite the same limitation.
The claims 19 is rejected for the same reason as the claim 1 since these claims recite the same limitation.
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 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.
Claims 1, 4, 7, 10, 13, and 16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dash et al. (Patent No. US 10362062-hereinafter, Dash) and further in view of Luo et al. (Pub. No. US 20180197087-hereinafter, Luo).
Regarding claim 1, Dash teaches receiving an evaluation value for a value calculated on a basis of a number of data for each attribute included in a plurality of data (Dash [Col. 7, lines 15-22], “Attribute value of A occurs four times out of nine times, attribute value of B occurs four times out of nine times and attribute value of C occurs one time out of nine times. In other words, first value for attribute value of A is 4/9, attribute value of B is 4/9 and attribute value of C is 1/9. Now, referring to FIG. 4A, row 424, corresponding first value for each of the attribute value is shown.” And (Col. 14, claim 1, ], “ generating a first value indicative of occurrence of each of the value for each of the attribute based on a total number of values associated with each of the attribute for the plurality of security entities” Examiner’s note, a first value is calculated for each attribute based on the total number of values associated with the attribute.
determining a reference value for each attribute on a basis of the received evaluation value and the number of data for each attribute (Dash, [Col.7, lines 36-42 and col.8, lines 1-10], “Having calculated the first value and second value for each of the attribute values, a third value is calculated based on the first value and the second value for each of the attribute value for each of the security entity. In one example, the third value is indicative of significance of the value of the attribute for the security entity. In one example, the third value is calculated using a formula, if second value is greater than the first value, then, third value is equal (second value−first value)*(second value/first value) otherwise equal to zero. This equation may be referred to as Equation 1. Now, referring to FIG. 4E, example table 450 shows the third value for each of the attribute value for each of the security entity. As an example, the first value for attribute value of A is 4/9 (see FIG. 4A, table 420) and the second value for attribute value of A for the security entity E1 is 1/3 (see FIG. 4D, table 440). Now, using Equation 1, we notice that the third value is zero, as the second value is less than the first value, for attribute value of A for security entity E1. This third value is depicted in cell 452 of FIG. 4E, table 450.” Examiner’s note, the third value for each attribute is considered as the reference value, which is calculated based on the first value (evaluation value) and the amount of data of each attribute.)
However, Dash does not teach a non-transitory computer-readable storage medium storing a training data generation program for causing a computer to perform processing comprising: generating training data for machine learning by changing the attribute of at least partial data of the plurality of data according to the reference value for each attribute.
On the other hand, Luo teaches a non-transitory computer-readable storage medium storing a training data generation program for causing a computer to perform processing comprising (Luo, Par.0036], “The memory units/banks can include one or more non-transitory machine-readable storage mediums. The non-transitory machine-readable storage medium can include solid-state memory, magnetic disk, and optical disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (e.g., EPROM, EEPROM, or Flash memory), or any other tangible medium capable of storing information..”)):
generating training data for machine learning by changing the attribute of at least partial data of the plurality of data according to the reference value for each attribute (Luo, [Par.0102-0103], “At block 404, process 400 includes computing system 100, receiving modified content data indicating a change to the baseline content data used to generate the first classification model. The modified content data can correspond to changes in text content of the data item such as changes words, phrases, or n-grams. The modified content data can indicate text or content changes to a document grouping used to create the first classification model. In some implementations, text and content changes have the potential to adversely impact or reduce the accuracy of security classifications generated by the first classification model.[0103] At block 406, process 400 includes system 100 receiving modified metadata indicating a change to the baseline metadata used to generate the first classification model. The modified metadata can correspond to changes to one or more attributes of the data item such as changes in document ownership or department affiliation. The modified metadata can indicate attribute changes to a document grouping used to create the first classification model. In some implementations, metadata or attribute changes have the potential to adversely impact or reduce the accuracy of security classifications generated by the first classification model.” Examiner’s note, the changing of the content data includes changing one or more attributes based on the impact metric value to train the second model .) .
Dash and Luo are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the Dash of the determining a reference value for each attribute on a basis of the received evaluation value and the number of data for each attribute, as taught by Dash to include the generating training data for machine learning by changing the attribute of at least partial data of the plurality of data according to the reference value for each attribute., as taught by Luo. The modification would have been obvious because one of the ordinary skills in art would be motivated to reduce the impact of classification generated by the first classification model, (Luo, [Par. 0103], “At block 406, process 400 includes system 100 receiving modified metadata indicating a change to the baseline metadata used to generate the first classification model. The modified metadata can correspond to changes to one or more attributes of the data item such as changes in document ownership or department affiliation. The modified metadata can indicate attribute changes to a document grouping used to create the first classification model. In some implementations, metadata or attribute changes have the potential to adversely impact or reduce the accuracy of security classifications generated by the first classification model.”).
Regarding claim 4, Dash teaches presenting each of the agreed evaluation values as an option for the attribute for which the plurality of agreed evaluation values exists to accept a final evaluation value. (Dash, Col. 8, lines 56-65 and Col.9, lines 1-12, Fig.4E], “As another example, the combination with a second highest third value of 4/5 is seen for security entity E4 for an attribute value of X for attribute F3. When compared with other attribute values for attribute F3, we notice that security entity E4 has only the attribute value of X amongst all of the possible attribute values for attribute F3. This indicates a significant deviation from other security entity attribute values for the specific attribute F3. In one example, Security entity E4 with attribute value of X may be selected for further security investigation. As yet another example, evaluating the fourth value for each of the security entity, we notice that a fourth value of 1⅔ for security entity E1, being the highest fourth value indicates that security entity E1 may be a likely candidate for further security investigation. And, security entity E4 with a fourth value of 1 1/20, being the second highest fourth value indicates that security entity E4 may be a likely candidate for further security investigation. In some examples, a threshold value for the fourth value may be set and any security entity with a fourth value greater than the threshold value may be selected for further security investigation. As an example, if a threshold value of 1 is set for the fourth value, then, based on the threshold value, the security entity E1 and E4 will be selected for further security analysis.” Examiner’s note, when any entity with a fourth value greater than the threshold value may be selected for further security investigation, wherein, the fourth value is calculated based on the attribute value.).
However, Dash does not teach the non-transitory computer-readable storage medium according to claim 3,
On the other hand, Luo teaches the non-transitory computer-readable storage medium according to claim 3, (Luo, [Par.0036], “The memory units/banks can include one or more non-transitory machine-readable storage mediums. The non-transitory machine-readable storage medium can include solid-state memory, magnetic disk, and optical disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (e.g., EPROM, EEPROM, or Flash memory), or any other tangible medium capable of storing information.”).),
Regarding claim 7, Dash teaches wherein the attribute includes an attribute used for determination and an attribute that represents a determination result (Dash, Col.8, lines 22-55], “Now, a fourth value is generated for each of the security entity, based on the third value for all of the attribute values for each of the security entity. In one example, all the third values for the security entity are summed to derive the fourth value for the security entity. Now, referring to FIG. 4E, table 450, column 456 shows the fourth value for each of the security entities E1 to E4. For example, the fourth value for security entity E1 is 5/3, security entity E2 is 7/8, security entity E3 is ** and security entity E4 is 1 1/20. (47) Now, evaluating various third values and fourth values, one or more observations can be made. In one example, the third value (security entity, attribute value) may be compared. For example, a high third value for a security entity and attribute value combination may indicate a possible combination of interest for further analysis. In other words, referring to table 450, there are two combinations with a high third value of 2/3. For example, third value (E1, F1=C)=2/3 and third value (E1, F2=D)=2/3. When compared with other attribute values, we notice that attribute values of C and D only appear with reference to security entity E1, and not in other entities. This indicates a significant deviation from other security entity attribute values for the specific attribute F1 and F2. In one example, Security entity E1 with attribute values of C and D may be selected for further security investigation. As one skilled in the art appreciates, in some examples, for security entity E1 with attribute values of C and D may indicate a malware activity, as there is a significant deviation from other security entity attribute values. In some examples, attribute values of C and D may indicate a mutated or uncommon signatures that may look visually normal for a human investigator, but may in fact indicate a likely malware activity.” Examiner’s note, the attribute represents whether the entity is associated with malware activity. )
and a contribution level of the attribute used for the determination to the determination result is calculated as the value (Dash, [Col.8, lines 38-55], “there are two combinations with a high third value of 2/3. For example, third value (E1, F1=C)=2/3 and third value (E1, F2=D)=2/3. When compared with other attribute values, we notice that attribute values of C and D only appear with reference to security entity E1, and not in other entities. This indicates a significant deviation from other security entity attribute values for the specific attribute F1 and F2. In one example, Security entity E1 with attribute values of C and D may be selected for further security investigation. As one skilled in the art appreciates, in some examples, for security entity E1 with attribute values of C and D may indicate a malware activity, as there is a significant deviation from other security entity attribute values. In some examples, attribute values of C and D may indicate a mutated or uncommon signatures that may look visually normal for a human investigator, but may in fact indicate a likely malware activity.” Examiner’s note, the comparison between the third value and the threshold value to determine the appearing of each attribute in each entity to determine whether the entity is indicated the malware activity or not.)
However, Dash does not teach the non-transitory computer-readable storage medium according to claim 1
On the other hand, Lou teaches the non-transitory computer-readable storage medium according to claim 1 (Luo, Par.0036], “The memory units/banks can include one or more non-transitory machine-readable storage mediums. The non-transitory machine-readable storage medium can include solid-state memory, magnetic disk, and optical disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (e.g., EPROM, EEPROM, or Flash memory), or any other tangible medium capable of storing information..”)),
The claim 10 is rejected for the same reason as the claim 1, since these claims recite the same limitations.
The claim 13 is rejected for the same reason as the claim 4, since these claims recite the same limitations.
The claim 16 is rejected for the same reason as the claim 7, since these claims recite the same limitations.
The claim 19 is rejected for the same reason as the claim 1, since these claims recite the same limitations.
Claims 2, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dash et al. (Patent No. US 10362062-hereinafter, Dash) and further in view of Lou et al. (PUB No. US 20180197087 -hereinafter, Lou) and further in view of Ronen et al. (Pub. No. US 20210312362-hereinafter, Ronen) .
Regarding claim 2, Dash in view of Lou teaches the non-transitory computer-readable storage medium according to claim 1 , the processing further comprising, but it does not teach receiving a plurality of the evaluation values but it does not teach receiving a plurality of the evaluation values from a respective plurality of evaluators; and in a case where a degree of dispersion of the received evaluation values is equal to or lower than a predetermined value, aggregating the evaluation values received from the respective plurality of evaluators and accept the aggregated evaluation value as an evaluation value agreed by the plurality of evaluators.
On the other hand, Ronen teaches receiving a plurality of the evaluation values from a respective plurality of evaluators (RONEN, [Par.0029], “As an example, FIG. 2 is a diagram illustrating a classifier tree 200 configured for classifying an activity based on associated attributes according to an embodiment. The classifier tree 200 includes two nodes that evaluate attributes 202 and 204 and three leaf nodes associated with outcomes 206, 208, and 210”) ;
and in a case where a degree of dispersion of the received evaluation values is equal to or lower than a predetermined value, aggregating the evaluation values received from the respective plurality of evaluators (RONEN, [Par.0028], “In some examples, an attribute 118 is indicative of an activity outcome category 116 when a defined quantity or percentage of activities with the attribute 118 of a value, value within a value range, or value on one side of a value threshold are associated with the activity outcome category 116.” And [Par.0029], “As an example, FIG. 2 is a diagram illustrating a classifier tree 200 configured for classifying an activity based on associated attributes according to an embodiment. The classifier tree 200 includes two nodes that evaluate attributes 202 and 204 and three leaf nodes associated with outcomes 206, 208, and 210” Examiner’s note, when the value of the attribute is within the range (equal to the threshold) that attribute is indicate the outcome category that corresponding to the aggregating the evaluation values received from the respective plurality of evaluators.),
and accept the aggregated evaluation value as an evaluation value agreed by the plurality of evaluators (RONEN, [Par.0028], “In some examples, an attribute 118 is indicative of an activity outcome category 116 when a defined quantity or percentage of activities with the attribute 118 of a value, value within a value range, or value on one side of a value threshold are associated with the activity outcome category 116.” And [Par.0029], “As an example, FIG. 2 is a diagram illustrating a classifier tree 200 configured for classifying an activity based on associated attributes according to an embodiment. The classifier tree 200 includes two nodes that evaluate attributes 202 and 204 and three leaf nodes associated with outcomes 206, 208, and 210” Examiner’s note, the attribute is indicate the outcome category that corresponding to evaluation value agreed by the plurality of evaluators, because the outcome node is evaluated/classified by the plurality of nodes (evaluators). )
Dash, Lou and Ronen are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of Dash and Lou of the non-transitory computer-readable storage medium, to include the receiving a plurality of the evaluation values from a respective plurality of evaluators; and in a case where a degree of dispersion of the received evaluation values is equal to or lower than a predetermined value, aggregating the evaluation values received from the respective plurality of evaluators and accept the aggregated evaluation value as an evaluation value agreed by the plurality of evaluators, as taught by Ronen. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve the outcome, (Ronen [Par.0015], “The described method and system provide action item information for events, deals, or other activities that the user can use to inform decisions about next steps that are likely to enhance or improve outcomes of the activities using machine learning and associated classification techniques.”).
The claim 11 is rejected for the same reason as the claim 2, since these claims recite the same limitations.
Claims 3, 5, 12, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dash et al. (Patent No. US 10362062-hereinafter, Dash) and further in view of Lou et al. (PUB No. US 20180197087 -hereinafter, Lou) and further in view of Ronen et al. (Pub. No. US 20210312362-hereinafter, Ronen) and further in view of Ryoichi et al. (Pub. No. US 20050073918-hereinafter, Ryoichi) .
Regarding claim 3, Dash in view of Lou teaches the non-transitory computer-readable storage medium according to claim 2, but it does not teach the processing further comprising: in a case where the degree of dispersion exceeds the predetermined value, clustering the evaluation values received from the respective plurality of evaluators until the degree of dispersion of the individual evaluation values becomes equal to or lower than the predetermined value, aggregating the evaluation values included in each cluster, and accepting the aggregated evaluation value as each of a plurality of the agreed evaluation values.
On the other hand, Ishikawa teaches the processing further comprising: in a case where the degree of dispersion exceeds the predetermined value, clustering the evaluation values received from the respective plurality of evaluators until the degree of dispersion of the individual evaluation values becomes equal to or lower than the predetermined value (Ishikawa , [Par.0022], “and when the duty ratio is higher than the predetermined range, the threshold value changing unit adds the amount value to the threshold value in sequence until the duty ratio falls within the predetermined range, and sets the second value of the threshold value to a threshold value with which the duty ratio falls within the predetermined”).
aggregating the evaluation values included in each cluster, and accepting the aggregated evaluation value as each of a plurality of the agreed evaluation values (Ishikawa , [Par.0022], “and when the duty ratio is higher than the predetermined range, the threshold value changing unit adds the amount value to the threshold value in sequence until the duty ratio falls within the predetermined range, and sets the second value of the threshold value to a threshold value with which the duty ratio falls within the predetermined”).
Dash, Lou, Ronen and Ishikawa are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of Dash and Lou of the non-transitory computer-readable storage medium, to include where the degree of dispersion exceeds the predetermined value, clustering the evaluation values received from the respective plurality of evaluators until the degree of dispersion of the individual evaluation values becomes equal to or lower than the predetermined value, aggregating the evaluation values included in each cluster, and accepting the aggregated evaluation value as each of a plurality of the agreed evaluation values., as taught by Ishikawa. The modification would have been obvious because one of the ordinary skills in art would be motivated to aggregating the evaluation value into the cluster, (Ishikawa , [Par.0022], “and when the duty ratio is higher than the predetermined range, the threshold value changing unit adds the amount value to the threshold value in sequence until the duty ratio falls within the predetermined range, and sets the second value of the threshold value to a threshold value with which the duty ratio falls within the predetermined”).
Regarding claim 5, Dash in view of Lou teaches the non-transitory computer-readable storage medium according to claim 1, the processing further comprising: but it does not teach determining a value obtained by lowering the value calculated on the basis of the number of data for each attribute at a rate that corresponds to magnitude of the evaluation value as the reference value for each attribute,
On the other hand, Ronen teaches determining a value obtained by lowering the value calculated on the basis of the number of data for each attribute at a rate that corresponds to magnitude of the evaluation value as the reference value for each attribute (Ranon, [par.0028], “In some examples, an attribute 118 is indicative of an activity outcome category 116 when a defined quantity or percentage of activities with the attribute 118 of a value, value within a value range, or value on one side of a value threshold are associated with the activity outcome category 116. For instance, a quantity of advertisements attribute exceeding a threshold may be indicative of an associated event attracting enough people to have a successful outcome. In such a case, the forest generator 122 may identify that 75% of events in a successful outcome category exceeded the quantity of advertisements threshold, exceeding a required percentage threshold of 70%. Thus, the quantity of advertisements attribute with the associated threshold would be used in generating the random forest classifier 124 as an attribute 118 indicative of the desired outcome category 116. Other attributes and/or associated values, ranges, or thresholds may not be present in 70% or greater past activities that have the desired outcome category 116 such that those other attributes are not considered sufficiently indicative of the desired outcome category 116. In some examples, attributes 118 may be assigned weight factors or importance factors based on the degree to which they are indicative of a desired outcome (e.g., an attribute that is indicative of a desired outcome 90% of the time is assigned greater weight factor than an attribute that is indicative of a desired outcome 75% of the time)” Examiner’s note, the attribute has designed outcome of 75% has the weight factor less than the attribute has the desire outcome of 90%.)
Dash, Lou and Ronen are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of Dash and Lou of the non-transitory computer-readable storage medium, to include the determining a value obtained by lowering the value calculated on the basis of the number of data for each attribute at a rate that corresponds to magnitude of the evaluation value as the reference value for each attribute, as taught by Ronen. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve the outcome, (Ronen [Par.0015], “The described method and system provide action item information for events, deals, or other activities that the user can use to inform decisions about next steps that are likely to enhance or improve outcomes of the activities using machine learning and associated classification techniques.”).
The claim 12 is rejected for the same reason as the claim 3, since these claims recite the same limitations.
The claim 14 is rejected for the same reason as the claim 5, since these claims recite the same limitations.
Claims 6, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Dash et al. (Patent No. US 10362062-hereinafter, Dash) and further in view of Lou et al. (PUB No. US 20180197087 -hereinafter, Lou) and further in view of Lakshmanan et al. (Patent. No. US 11068796 -hereinafter, Lakshmanan).
Regarding claim 6, Dash in view of Lou teaches the non-transitory computer-readable storage medium according to claim 1, but it does not teach the processing further comprising: changing the attribute of at least partial data of the plurality of data such that a difference between the reference value for each attribute and the value is equal to or less than a predetermined value.
On the other hand, Lakshmanan teaches changing the attribute of at least partial data of the plurality of data such that a difference between the reference value for each attribute and the value is equal to or less than a predetermined value (Lakshmanan, Col. 1, lines 40-47], “A method for pruning process execution logs includes learning a predictive model from a set of execution traces that characterize a process, wherein the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces.”).
Dash, Lou and Lakshmanan are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the combined teaching of Dash and Lou of the non-transitory computer-readable storage medium, to include changing the attribute of at least partial data of the plurality of data such that a difference between the reference value for each attribute and the value is equal to or less than a predetermined value, as taught by Lakshmanan. The modification would have been obvious because one of the ordinary skills in art would be motivated to improve changing the attribute, (Lakshmanan, Col. 1, lines 40-47], “A method for pruning process execution logs includes learning a predictive model from a set of execution traces that characterize a process, wherein the predictive model determines a likelihood of a given instance reaching a specified outcome; identifying attributes in the predictive model that fall below a threshold measure of relevance to the specified outcome using a processor; and removing the identified attributes from the set of execution traces.”).
The claim 15 is rejected for the same reason as the claim 6, since these claims recite the same limitations.
Claims 8, 9, 17, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dash et al. (Patent No. US 10362062-hereinafter, Dash) in view of Lou et al. (Pub. No. US 20180197087-hereinafter, Lou) and further in view of Chu et al. (Patent No. US 8832116-hereinafter, chu).
Regarding claim 8, Dash in view of Luo teaches the non-transitory computer-readable storage medium according to claim 7 ((Luo, Par.0036], “The memory units/banks can include one or more non-transitory machine-readable storage mediums. The non-transitory machine-readable storage medium can include solid-state memory, magnetic disk, and optical disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (e.g., EPROM, EEPROM, or Flash memory), or any other tangible medium capable of storing information..”),
However, neither Dash nor Lou teaches changing an attribute value of the attribute that represents the determination result to an attribute value that represents a different determination result for at least partial data of the plurality of data such that the contribution level becomes equal to or lower than the reference value for each attribute
On the other hand, Chu teaches changing an attribute value of the attribute that represents the determination result to an attribute value that represents a different determination result for at least partial data of the plurality of data such that the contribution level becomes equal to or lower than the reference value for each attribute (Chu, [COL.8, LINES 10-29], “) The update module 330 updates stored business information and adds new business information about business entities based on scored instance attributes (i.e., attributes, attribute values, and confidence scores) acquired from the mobile application logs. The update module 330 updates the stored business information by replacing stored attribute values with low accuracy scores (e.g., lower than a threshold value) with corresponding instance attribute values (i.e., obtained from the mobile application logs) having higher confidence scores or having confidence scores that are higher than a threshold value. In addition, if a quantum of stored business information from the aggregate information sources 120 is missing, the update module 330 adds the corresponding instance attribute value obtained from the mobile application logs if the confidence score for that instance attribute value is high (e.g., higher than a threshold value), and sets the accuracy score for the stored quantum of business information equal to the confidence score.”).
Dash, Lou and Chu are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the Dash of the non-transitory computer-readable storage medium, as taught by Dash to include the changing an attribute value of the attribute that represents the determination result to an attribute value that represents a different determination result for at least partial data of the plurality of data such that the contribution level becomes equal to or lower than the reference value for each attribute, as taught by Chu. The modification would have been obvious because one of the ordinary skills in art would be motivated to get the current updated information, (Chu, [Col.8, lines 28-39], “Because the scored instance attributes acquired from the mobile application logs are based on recent user activities and are cycled rapidly (e.g., daily), the information is likely to be more accurate and up-to-date than the stored business information provided by the aggregate information sources 120, which is typically updated on much longer time scales (e.g., every six months). Accordingly, the scored instance attributes obtained from the mobile application logs can be used to measure the accuracy of the corresponding stored business information (e.g., telephone numbers, addresses) received from the aggregate information sources 120 and/or to update the stored business information.”).
Regarding claim 9, Dash in view of Luo teaches the non-transitory computer-readable storage medium according to claim 7 (Luo, Par.0036], “The memory units/banks can include one or more non-transitory machine-readable storage mediums. The non-transitory machine-readable storage medium can include solid-state memory, magnetic disk, and optical disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (e.g., EPROM, EEPROM, or Flash memory), or any other tangible medium capable of storing information..”)),
However, neither Dash nor Lou teaches the processing further comprising: accepting, as the evaluation value, a discrimination level that represents a degree of discriminatory contribution of the attribute used for the determination to the determination result.
On the other hand, Chu teaches the processing further comprising: accepting, as the evaluation value, a discrimination level that represents a degree of discriminatory contribution of the attribute used for the determination to the determination result (Chu, Col. 8, lines 16-20], “the update module 330 updates stored business information and adds new business information about business entities based on scored instance attributes (i.e., attributes, attribute values, and confidence scores) acquired from the mobile application logs. The update module 330 updates the stored business information by replacing stored attribute values with low accuracy scores (e.g., lower than a threshold value) with corresponding instance attribute values (i.e., obtained from the mobile application logs) having higher confidence scores or having confidence scores that are higher than a threshold value.” Examiner’s note, attribute values with the low accuracy score is considered as the evaluation value. The range of the confidence score from 0-1 is considered as the discrimination level, which determine the accuracy of the attributes, whether the attribute is probably inaccurate or the attribute is almost accurate.) .
Dash, Lou and Chu are analogous in arts because they have the same field of endeavor of generating the data including the attribute data.
Accordingly, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the claimed invention to modify the Dash of the non-transitory computer-readable storage medium, as taught by Dash to include the accepting, as the evaluation value, a discrimination level that represents a degree of discriminatory contribution of the attribute used for the determination to the determination result, as taught by Chu. The modification would have been obvious because one of the ordinary skills in art would be motivated to get the current updated information, (Chu, [Col.8, lines 28-39], “Because the scored instance attributes acquired from the mobile application logs are based on recent user activities and are cycled rapidly (e.g., daily), the information is likely to be more accurate and up-to-date than the stored business information provided by the aggregate information sources 120, which is typically updated on much longer time scales (e.g., every six months). Accordingly, the scored instance attributes obtained from the mobile application logs can be used to measure the accuracy of the corresponding stored business information (e.g., telephone numbers, addresses) received from the aggregate information sources 120 and/or to update the stored business information.”).
The claim 17 is rejected for the same reason as the claim 8, since these claims recite the same limitations.
The claim 18 is rejected for the same reason as the claim 9, since these claims recite the same limitations.
Conclusion
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/E.T./Examiner, Art Unit 2128
/RYAN C VAUGHN/Primary Examiner, Art Unit 2125