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 .
Response to Arguments
Applicant's arguments filed 11/26/2025 have been fully considered but they are not persuasive. Applicant arguments the prior art does not teach the limitations of: validating the threshold value for each category, wherein validating the threshold values comprises calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
In response: Zorky as modified still does not teach: validating the threshold value for each category, wherein validating the threshold value comprises calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
Nicols, paragraph 45 teaches validate threshold.
Ogawa teaches a system failure can be detected by calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data (column 5, lines 35-40, to define a failure condition when the percentage of incorrectly transmitted block to correctly transmitted block exceeds a predetermined value).
Since each of the category of Zorky has a threshold to be compared to, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: validating the threshold value for each category.
The reason of doing so would have allowed the system of Zorkly to correctly and efficiently classified the messages into different categories.
Since the threshold of Zorky is used to correctly identify a message belong to a particular category, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data as the threshold to determine a successful category match of input data/failure of matching a category to the input message. The reason of doing so would have allowed the system to efficiently identify system failure due to factors that affect a system's failure such as the threshold used in the message classification.
Claim Objections
Claims 6, 7, 13, 14, 20 are objected to, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends. Applicant may cancel the claim(s), or rewrite the claim(s) to further limit the claims they depend on.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zorky (US 7,836,061) in view of Brown (US 2013/0275176) Nichols (US 2016/0306876) and Ogawa (US 4112467).
Regarding claims 1, 8, 15: Zorky teaches: a computer-implemented method/system including a processor and a memory storing instruction/CRM (computer, column 8, line 50-55) for providing automated feedback (classifying electronic text messages, column 1, lines 35-40) to open-ended input data (text message, column 1, lines 35-40), the method comprising: receiving initial input data (electronic text message, column 1, lines 35-40); determining a plurality of categories (creating a hierarchic list of message categories, column 1, lines40-45) associated with the initial input data; determining for each category (each of the message categories, column 1, lines 40-45) in the plurality of categories, a threshold value (threshold, column 2, lines 62-67) and a category likeliness score value (degree of similarity, column 2, lines 62-67); determining a category match between a category and the input data, wherein the category is from the plurality of categories, by calculating when the likeliness score value exceeds the threshold value (if the degree of similarity exceeds a certain threshold for any of the sample message stored in the database, then this category is considered relevant for the particular message, column 2, lines 62-67);
Zorky does not teach: providing a probe request associated each category match; and determining a prediction score associated with each probe request.
Brown, in the area of providing services to customers (paragraph 0006) teaches providing a probe request (questions are generated, paragraph 0069) associated each category match (based on the type of organization being supplied the goods/and or services of the supplier); and determining a prediction score associated with each probe request (a plurality of values associated with the plurality of selection is determined (abstract)).
Since Zorky is also related to services provided by a supplier (page 4, lines 5-10), it would have been obvious to a person with ordinary skill in the art to have modified Zorkly to include: after a service category is matched, to further providing a probe request associated each category match; and determining a prediction score associated with each probe request.
The reason of doing so would have allowed the user to determine a risk associated with a supplier and may subsequently determine whether an additional risk assessment of the supplier is necessary. (paragraph 6, Brown).
Zorky as modified still does not teach: validating the threshold value for each category, wherein validating the threshold value comprises calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
Nicols, paragraph 45 teaches validate threshold.
Ogawa teaches a system failure can be detected by calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data (column 5, lines 35-40, to define a failure condition when the percentage of incorrectly transmitted block to correctly transmitted block exceeds a predetermined value).
Since each of the category of Zorky has a threshold to be compared to, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: validating the threshold value for each category.
The reason of doing so would have allowed the system of Zorkly to correctly and efficiently classified the messages into different categories.
Since the threshold of Zorky is used to correctly identify a message belong to a particular category, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data as the threshold to determine a successful category match of input data/failure of matching a category to the input message. The reason of doing so would have allowed the system to efficiently identify system failure due to factors that affect a system's failure such as the threshold used in the message classification.
Regarding claims 2, 9, 16: Zorky teaches receiving supplemental input data (adding text to a message that has the same color as the background color (i.e., invisible in the human eye, but nonetheless perceived as text by the computer) column 5, lines 40-47).
Note 2: the original text message is the original input and added text that is invisible to human but detected by the machine can be viewed as supplemental input data.
Regarding claims 3, 10, 17: Zorky teaches determining a change in the initial input data and supplemental input data, wherein determining the change comprises identifying at least one of: a change in data input rate, input stoppage, or character change (note 3)
Note 3: column 5, lines 38-47 teaches certain preselected words...modified to take into account spammer "trick" such as replacing the letter "o" with a zero in some words. Obviously, in order to take into to take into account spammer "trick" such as replacing the letter "o" with a zero in some words, such changed character in the input data or supplemental input data by the spammer need to be determined by the system.
Regarding claims 4, 11, 18; Brown teaches: generating at least one secondary prompt request (paragraph 51, risk assessment information 110 include follow up questions specifically tailored to risk categories for which supplier 104a has a high level of risk).
Therefore, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to further include: generating at least one secondary prompt request.
The reason of doing so would have more accurately reflect the risk of a supplier and a user can have more information to make a determination of whether to use the supplier or not.
Note: 4, as discussed in rejection 1, 8, 15, and 3, 10, 17, the combination of Zorky and Brown is using the combination of initial input data and supplemental input data to determine a category of the service supplier. The secondary prompt request is associated with the risk score of the service supplier (paragraph 51 of Brown) and hence the secondary request is associated with the combination of the initial input data and supplemental input data.
Regarding claims 5, 12, 19: Brown teaches, wherein determining the prediction score associated with each probe request (paragraph 50, data 106 which includes selections made in response to questions ...determines values to assign to the selections) further comprises providing a portion of the initial input data or supplemental input data that matches the probe request (see rejections of claims 1, 8, 15, the provided initial input data is used to match a service supplier to a particular category/type. Paragraph 69, Brown, further teaches questions selected for inclusion in the risk information may depend on...supplier type. Therefore, the initial input data matches a type of service supplier which matches the probe request/questions).
Regarding claims 6, 13: Zorky teaches threshold value to be compare to for each category (each of the message category, column 1, lines 40-50, threshold column 2, line 65). Zorky does not teach: validating the threshold value for each category. Nicols, paragraph 45 teaches validate threshold. Since each of the category has a threshold to be compared to, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: validating the threshold value for each category. The reason of doing so would have allowed the system of Zorkly to correctly and efficiently classified the messages into different categories
Regarding claims 7, 14: Zorky does not teach: The computer-implemented method of claim 6, wherein validating the threshold value comprises calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
Ogawa teaches a system failure can be detected by calculatinga percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data (column 5, lines 35-40, to define a failure condition when the percentage of incorrectly transmitted block to correctly transmitted block exceedsa predetermined value).
Since the threshold Zorky is used to correctly identify a message belong to a particular category, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: calculatinga percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
The reason of doing so would have allowed the system to efficiently identify system failure due to factors that affect a system's failure such as the threshold used in the message classification.
Regarding claim 20: Zorky teaches threshold value to be compare to for each category (each of the message category, column 1, lines 40-50, threshold column 2, line 65)..
Zorky does not teach: validating the threshold value for each category.
Nicols, paragraph 45 teaches validate threshold. Since each of the category has a threshold to be compared to, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: validating the threshold value for each category.
The reason of doing so would have allowed the system of Zorkly to correctly and efficiently classified the messages into different categories.
Zorky as modified still does not teach: The computer-implemented method of claim 6, wherein validating the threshold value comprises calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
Ogawa teaches a system failure can be detected by calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data (column 5, lines 35-40, to define a failure condition when the percentage of incorrectly transmitted block to correctly transmitted block exceeds a predetermined value).
Since the threshold Zorky is used to correctly identify a message belong to a particular category, it would have been obvious to a person with ordinary skill in the art to have modified Zorky to include: calculating a percentage ratio of a correctly associated category with input data to an incorrectly associated data with the input data.
The reason of doing so would have allowed the system to efficiently identify system failure due to factors that affect a system's failure such as the threshold used in the message classification.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KING Y POON/ Supervisory Patent Examiner, Art Unit 2617