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
The present office action is responsive to communications received on 09/19/2025.
Status of Claims
Claim 1 was amended.
Claims 1-13 are pending.
Response to arguments
With respect to the 35 USC § 103 rejection the arguments are not persuasive; Akula ¶72 teaches “During or after the generation of the traceable image, the image generation module 210 may generate/identify a set of coordinates for the generated traceable image 216 and store [attaching], at circle D, these coordinates in data store 214. In some embodiments, these stored coordinates 216 are indexed according to a unique identifier, which may be based upon a randomly generated number, an output of a function using the generated traceable image as an input (e.g., a hash function, cryptographic function, etc.), a date and/or time, a session identifier of the session state stored by the web server 206 to identify the browser 202 session, and/or a number associated with the received request 128, computing device 104, browser 202, or webpage 204.”. The rejection is maintained.
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.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akula et al. (US 20160055329 A1) hereinafter referred to as Akula in view of Paxton et al. (US 20150347731 A1) hereinafter referred to as Paxton.
With respect to claim 1, Akula discloses: A method of authentication, which comprises the following steps: (i) generating graphic image patterns, (Akula ¶32-33 disclose pre-generated or dynamically “generate the traceable CAPTCHA image.”)
(ii) showing one of the said patterns to the user and prompting user to draw the shown pattern, (Akula ¶32-33 disclose presenting the CAPTCHA pattern to a user for tracing as illustrated in Akula Fig. 1).
(iii) recording a set of key parameters of the user-generated image, (Akula ¶34 “received [recorded] user trace input data [set of key parameters] representing a trace of the presented traceable CAPTCHA image.” Which creates a user generated traced image as illustrated in Akula Fig. 1).
(iv) comparing a set of key parameters of the user-generated image to the relevant set of key parameters of the pattern and generating a successful authentication signal, if their difference is within the confidence range, (Akula ¶34 “in some embodiments the user input trace data is a set of relative coordinates. In some embodiments, a distance value is determined [comparing] based upon the reference set of coordinates and the received set of user trace coordinates [key parameters of the user-generated image to the relevant set of key parameters of the pattern]. In an embodiment, if the distance value is within an error tolerance range [confidence range], the trace is validated [generating a successful authentication signal]”).
wherein the set of key parameters includes at least one time parameter characterizing the dynamics of the path along which the user has generated the image, (Akula ¶31 “the traceable image itself may be modified if no trace input with respect to the displayed image is received within a certain time period after the traceable image has been output or displayed to the user.” Which means that one of the parameters is a time parameter of the user interaction/dynamics with completing the path for the user generated traced image before a time period expires).
and attaching the at least one time parameter to a corresponding stored template, (Akula ¶72 “During or after the generation of the traceable image, the image generation module 210 may generate/identify a set of coordinates for the generated traceable image 216 and store [attaching], at circle D, these coordinates in data store 214. In some embodiments, these stored coordinates 216 are indexed according to a unique identifier, which may be based upon a randomly generated number, an output of a function using the generated traceable image as an input (e.g., a hash function, cryptographic function, etc.), a date and/or time, a session identifier of the session state stored by the web server 206 to identify the browser 202 session, and/or a number associated with the received request 128, computing device 104, browser 202, or webpage 204.”).
Akula does not explicitly disclose: and at the step (i), the said set of key parameters and confidence range limits for each pattern are determined by machine learning based on test images generated manually by previously authenticated users when they were shown such pattern.
However, Paxton in an analogous art discloses: and at the step (i), the said set of key parameters and confidence range limits for each pattern are determined by machine learning based on test images generated manually by previously authenticated users when they were shown such pattern. (Paxton Figs. 8A-B disclose making a determination based on “human noise model” which is a machine learning model based on learned manual human input and comparing it to the current authentication attempt using set of key parameters such as “user solution provided within appropriate time” and/or “does user input path match human input path geometrics” using a percentage range limit. Support is explained in Paxton ¶40 and 48-49).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Akula wherein the step (i), the said set of key parameters and confidence range limits for each pattern are determined by machine learning based on test images generated manually by previously authenticated users when they were shown such pattern as disclosed by Paxton to ensure a threshold level of error while at the same time keeping the authentication secure (see Paxton Figs. 8A-B and Paxton ¶40 and 48-49).
With respect to claim 2, Akula in view of Paxton disclose: The method according to claim 1 wherein the said time parameter is the average speed along the path. (Paxton ¶43 teaches “the length of the input path, the average speed of the input”)
With respect to claim 3, Akula in view of Paxton disclose: The method according to claim 1 wherein the said time parameter is the acceleration projected on the initial part of the path. (Paxton ¶23 “The characteristic of the object 194 may be any of: the initial location, the initial orientation, the initial speed, the initial direction of motion, the (initial) color, the (initial) size, the (initial) orientation, the (initial) shape, and the (initial) context of the object 194.”).
With respect to claim 4, Akula in view of Paxton disclose: The method according to claim 1 wherein the said time parameter is the average number of points on the path for selected time intervals. (Akula ¶101 and 152 disclose using “mean, media or mode” of point distances and corresponding time required to complete the trace of each path).
With respect to claim 5, Akula in view of Paxton disclose: The method according to claim 1 wherein the said time parameter represents the time intervals during which the selected parts of the image were generated by the user. (Akula ¶106 teaches “the time intervals are configured an amount of time in which a mostly complete trace is not provided [selected parts of the image were generated by the user]. For example, in an embodiment, if after 10 seconds less than thirty percent of a “full” trace has been completed the image is modified. In some embodiments, this threshold value (i.e., “after 10 seconds”) may be flexibly configured to allow for more or less time to provide the trace.” ).
With respect to claim 6, Akula in view of Paxton disclose: The method according to claim 1 wherein the said time parameter is a mean and/or mean root square deviation of the length of the time intervals between the selected points of the path. (Akula ¶101 and 152 disclose using a “mean” between traced points in a path as understood by the examiner).
With respect to claim 7, Akula in view of Paxton disclose: The method according to claim 1 wherein the said set of key parameters includes an area under the path projected on a selected coordinate axis. (Applicant specifications provides limited details therefore the claim is interpreted under BRI. Paxton ¶43 recites “The user interaction pattern may be a snippet of the user interaction, such as a particular section of a cursor path provided within the graphical interface (e.g., a portion of the cursor path shown in FIG. 1) or the geometry of the input path (e.g., the angle of a direction change or the linearity of a portion of the input), [area under the path projected on a selected coordinate axis] as shown in FIGS. 10A-10C; alternatively, the user interaction pattern may be a characteristic of a portion or the entirety of the input path, such as average noise in the input path, the length of the input path, the average speed of the input, or where along the input path a mouse click is generated. However, the user interaction pattern may be of any other characteristic of the user interaction.”).
With respect to claim 8, Akula in view of Paxton disclose: The method according to claim 1 wherein the said set of key parameters includes a path length. (Akula ¶88 and 93 disclose length of finger stroke as a factor).
With respect to claim 9, Akula in view of Paxton disclose: The method according to claim 1 wherein the said confidence range limits include upper threshold value and lower threshold value, and at step (iv), a successful authentication signal is generated, if the said difference of key parameters of the user-generated image and pattern is within the said upper threshold value and lower threshold value. (Akula ¶34 teaches using “tolerance range” and “error range” which is interpreted as an upper and lower threshold for successful authentication of user drawn/traced pattern).
With respect to claim 10, Akula in view of Paxton disclose: The method according to claim 1 wherein, at step (ii), the pattern is shown with noise. (Akula ¶107 teaches “noise may be introduced into the traceable image”).
With respect to claim 11, Akula in view of Paxton disclose: The method according to claim 1 wherein, at step (ii), the pattern is shown as an animation. (Akula ¶115 discloses that traceable image can move or rescale over time which is interpreted as animation).
With respect to claim 12, Akula in view of Paxton disclose: The method according to claim 1 wherein a successful authentication signal is used as a CAPTCHA pass signal. (Akula ¶57 discloses “pass the CAPTCHA”).
With respect to claim 13, Akula in view of Paxton disclose: The method according to claim 1 wherein a successful authentication signal is used as an unlock signal. (Akula ¶8 recites “then the user trace is considered validated and the user may be provided access to a resource protected by the CAPTCHA.” Wherein Akula ¶178 discloses the protected resource could be a storage which is interpreted as unlock access to a storage when authentication is completed).
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANY S GADALLA whose telephone number is (571)272-2322. The examiner can normally be reached Mon to Fri 8:00AM - 4:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Colin can be reached at (571) 272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HANY S. GADALLA/Primary Examiner, Art Unit 2493