DETAILED ACTION
Claims 1-15 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5-8, 10-13 and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Applicant-supplied prior-art reference US 11,386,349 B1 (Prisadnikov et al.).
As to Claim 1, 6 and 11, Prisadnikov et al. anticipate an apparatus; method (Prisadnikov et al. disclose the method - 1:61-65) and a computer program, embodied on a non-transitory computer readable medium (Prisadnikov et al. disclose the processor and memory – 21:32-36), respectively, comprising:
at least one processor (Prisadnikov et al. disclose the processor and memory – 21:32-36); and
at least one memory comprising computer program code (Prisadnikov et al. disclose the processor and memory – 21:32-36), the at least one memory and the computer program code are configured, with the at least one processor, to cause the apparatus at least to
collect time-series data on one or more events occurring on a webpage (Prisadnikov et al. disclose collecting time-series data on user activity {human and non-human bot} behavior on webpages, then determining using FFT transforms, and machine-learning, whether the user is human or a non-human bot – 1:7-9, 1:61-2:10, 3:3-47, 4:47-64; claims 1-3; and Figs. 2B and 3B);
derive classifications of the one or more events to generate labeled data to train a machine learning model, wherein the labeled data comprises a first subset of the one or more events labeled as bot traffic and a second subset of the one or more events labeled as human traffic (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” – 3:3-19. The observed behavior patterns determine labeled user data used to train the machine learning model in order for it to determine human or bot user activity);
perform functional transformations of the time-series data (Prisadnikov et al. disclose collecting time-series data on user activity {human and non-human bot} behavior on webpages, then determining using FFT transforms, and machine-learning, whether the user is human or a non-human bot – 1:7-9, 1:61-2:10, 3:3-47, 4:47-64; claims 1-3; and Figs. 2B and 3B);
determine potential features of the one or more events based on a combination of the labeled data, and results of the functional transformation, wherein the results of the functional transformation comprise numeric data or categorical data (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” – 3:3-19. The observed behavior patterns determine different categories {human or non-human} of labeled user data used to train the machine learning model in order for it to determine human or bot user activity);
train the machine learning model with the potential features (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” – 3:3-19. The observed behavior patterns determine different categories {human or non-human} of labeled user data used to train the machine learning model in order for it to determine human or bot user activity); and
determine, via the machine learning model, bot behavior and non-bot behavior of the one or more events (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” – 3:3-19. The observed behavior patterns determine different categories {human or non-human} of labeled user data used to train the machine learning model in order for it to determine human or bot user activity).
As to Claims 2, 7 and 12, Prisadnikov et al. anticipate the apparatus according to claim 1; the method according to claim 6; and the computer program according to claim 11, respectively, wherein the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to:
receive an array of the one or more events and timing between each of the one or more events (Prisadnikov et al. disclose the vectors {array} used to train the machine-learning model – 4:65–5:26); and
determine, with the machine learning model based on the array, existence of bot behavior or non-bot behavior (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” – 3:3-19).
As to Claims 3, 8 and 13, Prisadnikov et al. anticipate the apparatus according to claim 1; the method according to claim 6; and the computer program according to claim 11, respectively, wherein the functional transformations comprises the at least one memory and the computer program code being further configured, with the at least one processor, to cause the apparatus at least to:
determine whether the one or more events comprises numeric data or categorical data (Prisadnikov et al. disclose collecting time-series data on user activity {human and non-human bot} behavior on webpages, then determining using FFT transforms, and machine-learning, whether the user is human or a non-human bot – 1:7-9, 1:61-2:10, 3:3-47, 4:47-64; claims 1-3; and Figs. 2B and 3B); and
perform a mean transformation or a count transformation based on the determination (Prisadnikov et al. disclose collecting time-series data on user activity {human and non-human bot} behavior on webpages, then determining using FFT transforms, and machine-learning, whether the user is human or a non-human bot – 1:7-9, 1:61-2:10, 3:3-47, 4:47-64; claims 1-3; and Figs. 2B and 3B).
As to Claims 5, 10 and 15, Prisadnikov et al. anticipate the apparatus according to claim 1; the method according to claim 6; and the computer program according to claim 11, respectively, wherein the at least one memory and the computer program code are further configured, with the at least one processor, to cause the apparatus at least to:
apply a validation logic to the one or more events and the machine learning model (Prisadnikov et al. recite: “Particular embodiments described herein relates to systems and methods for automatically determining whether users are likely to be bots or humans based on observed behavior patterns. In particular embodiments, monitored user activities may be captured as time-series data. In particular embodiments, the time-series data may be transformed into a frequency domain to simplify a machine-learning process. In particular embodiments, the resulting machine-learning model may be used to determine whether a group of users are likely to be (predominantly) human users or non-human users. In particular embodiments, the determination may further be used to label individual users as human or non-human, and such labeled user data may in turn be used to train another machine-learning model to determine whether individual users are likely to be human or non-human.” {validating whether human or bot}– 3:3-19.).
Allowable Subject Matter
Claims 4, 9 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Interview Practice
USPTO Automated Interview Request (AIR)
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The USPTO AIR form is available on our website at: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
By submitting this type of interview request, the pending patent application will be in compliance with the written authorization requirement for Internet communication in accordance with MPEP §502.03. This authorization will be in effect until the Applicant provides a written withdrawal of authorization to the Examiner of record.
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Examiner Notes:
A) Prior to conducting any interview (whether using AIR or not), Applicant(s) must submit an agenda including the proposed date and time, all arguments in writing, and proposed claim amendments (if applicable). Any proposed amendments or arguments not presented in the agenda will only be heard by the Examiner, but because the Examiner will not have heard them in advance and been given an equitable opportunity to consider them, no decision will be rendered, nor agreement made. ALL AGENDAS MUST BE RECEIVED BY THE EXAMINER AT LEAST 24 HOURS PRIOR TO THE START OF THE INTERVIEW, OR THE PREVIOUS BUSINESS DAY, WHICHEVER IS LONGER, or the interview may have to be rescheduled.
B) After-final interviews may be granted, but the agenda must be in compliance with MPEP 713.09 which limits the interview only to discussions of proposed amendments, or clarification for appeal. After-final interviews are not to be conducted for the purpose of rehashing previously made arguments. After seeing the agenda, Examiner will decide whether to grant or deny the interview.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD G KEEHN whose telephone number is (571)270-5007. The examiner can normally be reached M-F 9:00am - 5:00pm Eastern.
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/RICHARD G KEEHN/Primary Examiner, Art Unit 2444
