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 .
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 20 is drawn to functional descriptive material recorded on a computer-readable storage media. Normally, the claim would be statutory. However, the specification, at paragraph 79 defines the claimed computer-readable storage media as encompassing statutory media such as a “the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing” but does not specifically define a computer-readable medium as only the given examples. Therefore, according to 1351 OG 212, dated 2/23/2010, computer-readable storage media will be reasonably interpreted to cover both non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. Furthermore, examiner notes that the cited interpretation is valid even if the specification is silent in regards to computer readable media and other such variations.
“A transitory, propagating signal … is not a “process, machine, manufacture, or composition of matter.” Those four categories define the explicit scope and reach of subject matter patentable under 35 U.S.C. § 101; thus, such a signal cannot be patentable subject matter.” (In re Petrus A.C.M. Nuijten; Fed Cir, 2006-1371, 9/20/2007).
Because the full scope of the claim as properly read in light of the disclosure encompasses non-statutory subject matter, the claim as a whole is non-statutory. The examiner suggests amending the claim to include the disclosed tangible computer readable media, while at the same time excluding the intangible media such as signals, carrier waves. Any amendment to the claim should be commensurate with its corresponding disclosure.
Examiner suggests, as seen within 1351 OG 212 dated 2/23/2010, applicant include the limitation, “non-transitory”, within the cited claims to overcome the rejection and to avoid any issues of new matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process abstract ideas without significantly more.
Claim 1 recites:
“monitoring the first drawing process to gather registration metadata relating to the drawn image”, which can be reasonably be interpreted as a human observer viewing a displayed drawing image and mentally performing this monitoring via visual perception;
“providing a description of the drawn image, the providing the description of the drawn image”, which can be reasonably be interpreted as a human observer viewing a displayed drawing image and mentally determining this description via visual perception and providing the description orally or in written form;
“for (the following is recited as intended use and is not required) later authentication by comparison to a description of a later drawn image and metadata relating to the later drawn image”, which can be reasonably be interpreted as a human observer viewing displayed stored and current drawing description and metadata and mentally comparing the stored and current information via visual perception.
This judicial exception is not integrated into a practical application because additional elements of:
“a process of user registration, the process of user registration comprising” are generically recited extra-solution activity;
“receiving a registering user input of a first drawing process of a drawn image into a graphical user interface” are generically recited insignificant extra-solution activity of data gathering;
“comprising applying a first artificial intelligence image analysis method” are generically recited; and
“securely storing the description and a set of the registration metadata” are generically recited extra-solution activity.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements of:
“a process of user registration, the process of user registration comprising” are well-understood, routine, conventional;
“receiving a registering user input of a first drawing process of a drawn image into a graphical user interface” are insignificant extra-solution activity of data gathering;
“comprising applying a first artificial intelligence image analysis method” are well-understood, routine, conventional; and
“securely storing the description and a set of the registration metadata” are well-understood, routine, conventional.
Depending claims do not remedy these deficiencies:
Claim 2 further recites additional elements of limitations pertaining to data gathering and generically recited artificial intelligence (AI) and mental process achievable via visual perception, as explained above. See arguments provided above for claim 1.
Claim 3 further recites additional elements that generically recited limitations pertaining to AI that can be reasonably be interpreted as a client-deployed AI model with description and metadata information communicated to it from the first AI model. Arguments analogous to those provided for associated limitations of claim 1 above are apply here.
Claims 4, 7, 8, and 9 further recite mental process abstract ideas where the limitations can be reasonably be interpreted as being mentally performed by a human observer via visual perception.
Claim 5 further recites additional element limitations that are generically recited and well-understood, routine, conventional.
Claims 6, 10, and 11 further recites limitations that are insignificant extra-solution activity of data gathering.
As per claim(s) 12-18, arguments made in rejecting claim(s) 1, 2, 3, 8, 6, 7, and 10 are analogous, respectively. Claims 12-18 also recite “A computer system, comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising”, which are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Depending claim 19 further recites limitations that are insignificant extra-solution activity of data outputting.
As per claim(s) 20, arguments made in rejecting claim(s) 1 are analogous. Claim 20 also recites, “A computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising”, which are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
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.
Claim(s) 1, 2, 8-11, 12, 13, 15, 18, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20250182529 A1 (Zaghetto).
As per claim 1, Zaghetto teaches a method, comprising:
a process of user registration, the process of user registration comprising (Zaghetto:
para 65: “dataset of users previously registered in the system”;
paras 77, 80, 83, 86: registered users):
receiving a registering user input of a first drawing process of a drawn image into a
graphical user interface (Zaghetto:
paras 44, 51, 79, 127, 132: “pre-registered draw
registered to the system”;
paras 77: “registered images from users”;
para 107: “register their chosen symbol”;
Figs. 1-3 (shown below):
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Paras 52-60: corresponding to Figs. 1-3, shown above.
Para 52: “the input signals to our method are both the free-draw images itself, as well as the time series representing the coordinates of each activated/sensitized point of the sensible surface in a given time t”);
monitoring the first drawing process to gather registration metadata relating to the
drawn image (Zaghetto:
para 64: “dynamic feature extraction (709) to obtain a feature vector that registers the
users' movement behavior (710)”;
Figs. 4-6.
Fig. 7 (shown below): 708-715: mainly 708-710;
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“[0006] This invention has two sets of related works: a) symbol classification using visual feature learning (off-line static approach); and b) behavioral biometrics based on the way in which an individual manipulates interactive surfaces (on-line dynamic approach).”
“[0044] In this sense, we are proposing a method to correctly recognize individuals that uses two complementary approaches: [0045] a) image pattern similarity (hereinafter referred as off-line static approach), based on the matching between a pre-registered draw registered to the system and an actual draw the is being presented; and [0046] b) user unconstrained free-draw behavioral trait (hereinafter referred as on-line dynamic approach), based on the dynamic analysis of users' unique behavior while manipulating the interactive surface in which the free drawing process is being made.”
Para 64: “For On-line Dynamic Approach (715), it is possible to apply a signal decomposition process (708) and dynamic feature extraction (709) to obtain a feature vector that registers the users' movement behavior (710). Another way for obtaining this vector (710) could be using a learning approach for a trained model to capture the dynamic features of the signal.”;
Paras 66-69: “online dynamic approach”:
Para 66: “One possible implementation is making a signal decomposition (708) of the original signal acquired in the acquisition process (702) into vertical and horizontal components and, afterwards, calculate velocity (v) and acceleration (d) derived signals for each component.”;
Para 67: dynamic, position, time, velocity, acceleration;
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);
providing a description of the drawn image, the providing the description of the drawn
image comprising applying a first artificial intelligence image analysis method
(Zaghetto:
Fig. 7 (shown above): 703-707, 716: mainly 706: “User Dataset”: “[0065] By utilizing the embedded
visual vectors (710), we can compare (711) the present authentication subject with the dataset of
users previously registered in the system (706). For instance, if the computed distance (707) to the
closest user is less than a predefined threshold”;
“[0006] This invention has two sets of related works: a) symbol classification using visual feature learning (off-line static approach); and b) behavioral biometrics based on the way in which an individual manipulates interactive surfaces (on-line dynamic approach).”
“[0007] The static approach employs an analysis of the visual features present in images formed by projecting freely drawn symbols onto the image plane. To conduct this analysis, Convolutional Neural Networks (ConvNets) are utilized, which are considered cutting-edge in image representation.”
“[0044] In this sense, we are proposing a method to correctly recognize individuals that uses two complementary approaches: [0045] a) image pattern similarity (hereinafter referred as off-line static approach), based on the matching between a pre-registered draw registered to the system and an actual draw the is being presented; and [0046] b) user unconstrained free-draw behavioral trait (hereinafter referred as on-line dynamic approach), based on the dynamic analysis of users' unique behavior while manipulating the interactive surface in which the free drawing process is being made.”;
“[0050] The present invention refers to a method for identifying a user comprising: acquiring a signal representing an authentication drawing input generated by the unconstrained movement of a user on an interactive surface: generating an image that corresponds to the signal: processing the signal to generate a user behavior vector; and authenticating the user by i) comparing the image with an authentication image representing an authentication drawing previously stored by the user; and ii) comparing the user behavior vector with a previously user behavior vector associated with the authentication drawing.”;
“[0051] Generally, the present invention refers to a method to identify individuals based on the unconstrained free-drawing procedure performed on sensible interactive surfaces. In this sense, we are proposing a method to correctly recognize individuals that uses two complementary approaches: a) image pattern similarity (hereinafter referred as off-line static approach), based on the matching between a pre-registered draw registered to the system and an actual draw that is being presented; and b) user unconstrained free-draw behavioral trait (hereinafter referred as on-line dynamic approach), based on the dynamic analysis of users' unique behavior while manipulating the sensible interactive surface in which the free-drawing process is being made.”;
Para 64: “In the Off-line Static Approach (716), firstly the image generation (703) is performed to create a visual representation of the signal {right arrow over (p)}(t). For example, the signal {right arrow over (p)}(t) (702) could be projected in the image plane and a deep metric learning neural network model (DML) could be used to extract an embedded vector (705) in the visual features space.”: this is the description of the drawn image.
Paras 71-73: “Off-Line Static Approach”:
“[0071] Based on all P(x, y) points acquired for each one of the drawn symbols, it is possible to generate a corresponding image for each symbol.”;
“[0072] Given the symbol image I obtained in the step before, the objective is to classify and represent/using a static approach. For this task, we could be training two ConvNet PreActResNet-18 and ConvNetXt-tiny, which have been shown to produce good results in visual representation problems. We use the BCE (Binary Cross Entropy) loss function for the classification task and the Fully Convolutional Network (FCN) for training embedded spaces using Triplet Loss model for the retrieval task.”: this is the description of the drawn image.
Para 73: class labels, embedding space, features: this is the description of the drawn image.); and
securely storing the description and a set of the registration metadata for (the following
is recited as intended use and is not required) later authentication by comparison to a
description of a later drawn image and metadata relating to the later drawn image
(Zaghetto:
“[0042] The proposed invention aims to introduce the idea of combining static image pattern recognition strategies with dynamic drawing behavioral analysis to generate a combined classifier able to distinguish a given input to an electronic surface capturing device as legit (or not) with higher degree of confidence. To illustrate a more practical scenario, we can consider the case in which a touch-pad draw is used as an authentication method to a user to log in to the operating system. In this case, even if some attacker is able to reproduce the draw/symbol itself, this will not be enough for the attacker to gain access to the system, since the on-line analysis of the drawing process will indicate that the drawing dynamics of the attacker differs from that of the legit user.”;
Para 65: “By utilizing the embedded visual vectors (710), we can compare (711) the present authentication subject with the dataset of users previously registered in the system (706). For instance, if the computed distance (707) to the closest user is less than a predefined threshold, that could be calculated during the model training process based in a convenient heuristic strategy (i.e., equal error rate (EER) threshold point where the false rejection rate (FRR) is equal to the false acceptance rate (FAR)) or even arbitrary chosen, we can proceed to select a binary model for classifying the user (713) using the dynamic feature vector (710). Otherwise, the authentication subject is rejected (712). Finally, the binary selected model Mi decides if the subject that is being authenticated in the system is a legit user or not (714).”;
Fig. 7 (shown above): mainly 705-714;
Paras 75-77: comparison with registered data for user authentication.
Para 22: “The main problem addressed by our invention is related to security and user experience during authentication procedures on computational devices…. repeat the same secret information on different systems or even register and keep these information in non-secure locals (e.g.: drawer, personal notebooks)”: Para 25: “secure… secret information leakage problem and convenient to the user, maintaining both security and usability intact”: para 65: “present authentication subject with the dataset of users previously registered in the system (706)”: Fig. 7 (shown above): mainly 706: the embedded vector features and classification label description and the dynamic behavioral metdata information is registered and stored in the user dataset 706, which is not open / public data. This information would be exclusively for user authentication and would not be openly available to the public.).
As per claim 2, Zaghetto teaches the method of claim 1, further comprising a process of user
authentication, comprising:
receiving a user input of a later drawing process of the later drawn image into the graphical user
interface (Zaghetto: See arguments and citations offered in rejecting claim 1 above);
monitoring the later drawing process to gather the metadata relating to the later drawn image
(Zaghetto: See arguments and citations offered in rejecting claim 1 above);
providing the description of the later drawn image, the providing the description of the later
drawn image comprising applying the first artificial intelligence image analysis method
(Zaghetto: See arguments and citations offered in rejecting claim 1 above); and
authenticating the user input by comparing the description of the later drawn image and a set of
the metadata relating to the later drawn image to the securely stored description and set of the
registration metadata (Zaghetto: See arguments and citations offered in rejecting claim 1
above).
As per claim 8, Zaghetto teaches the method of claim 1, wherein the monitoring the first drawing
process comprises monitoring selected input parameters for the first drawing process, drawing input
characteristics, (only one alternative is required) or a combination thereof (Zaghetto: See arguments and citations offered in rejecting claim 1 above: velocity, acceleration, hiatus, timing;
Para 69; equation 4).
As per claim 9, Zaghetto teaches the method of claim 8, wherein the drawing input characteristics are selected from the group consisting of (the following is a Markush group, which is a closed list of alternatives; only one listed item is required) a start area in the graphical user interface, an end area in the graphical user interface, a direction of movement of a portion of the drawing input, and a number of strokes used in the drawing input (Zaghetto: See arguments and citations offered in rejecting claim 8 above).
As per claim 10, Zaghetto teaches the method of claim 1, further comprising providing a grid structure in the graphical user interface for the first drawing process, the grid structure comprising selectable drawing input characteristics (Zaghetto: See arguments and citations offered in rejecting claim 1 above: the grid structure is the coordinate system of the GUI screen, e.g. pixel locations: See Figs. 1-3).
As per claim 11, Zaghetto teaches the method of claim 1, further comprising providing a set of preset requirements for the first drawing process (Zaghetto: See arguments and citations offered in rejecting claim 1 above: preset requirements can include limitations of the dimensions of the input screen).
As per claim(s) 12, 13, 15, and 18, arguments made in rejecting claim(s) 1, 2, 8, and 10 are analogous, respectively. Zaghetto also teaches a computer system, comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising (Zaghetto: See arguments and citations offered in rejecting claim 1 above;
paras 28, 29, 92-99). Note that claim 15 has a slightly non-analogous dependency relative to corresponding claim 8 in that claim 15 depends from intervening claim 13 while claim 8 depends directly from independent claim 1. Never-the-less, the single reference of Zaghetto is applied for teaching all limitations for these claims.
As per claim 19, Zaghetto teaches the computer system of claim 12, wherein the system further comprises a fading display of a drawing line that is configured to fade in a defined time period (Zaghetto: See arguments and citations offered in rejecting claim 12 above;
para 59: “To clarify, besides from showing on the screen the complete draw to the user (and potentially to an attacker), the system may apply a kind of fadeout effect in order to partially show the lines and curves drawn by the user during the draw procedure. This fadeout effect intends to erase parts of the draw input before a time threshold and maintain parts drawn after this threshold. With this king of strategy, we can make it possible to give a visual feedback to users, potentially improving their experience and yet maintaining, in some level, the secrecy of the complete free draw.”).
As per claim(s) 20, arguments made in rejecting claim(s) 1 is analogous. Zaghetto also teaches a computer program product comprising: one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to perform operations comprising (Zaghetto: See arguments and citations offered in rejecting claim 1 above;
paras 28, 29, 92-99).
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) 3-5, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto as applied to claims 1, 2, 12, and 13 above, and further in view of Official Notice.
As per claim 3, Zaghetto teaches the method of claim 2. Zaghetto does not teach providing the description of the later drawn image further comprising: applying a second artificial intelligence method and updating the securely stored description and set of registration metadata to migrate to the second artificial intelligence method. Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage of adopting a new model without the user having the hassle of having to reregistering. The teachings of the prior art could have been incorporated into Zaghetto in that a second AI model is deployed on a client device and the stored description and metadata are communicated to it.
As per claim 4, Zaghetto teaches the method of claim 2. Zaghetto does not teach the authenticating the user input comprises comparing a confidence of the description of the later drawn image with a threshold confidence. Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage of efficiently not needing an exact match. The teachings of the prior art could have been incorporated into Zaghetto in that the user input comprises comparing a confidence of the description of the later drawn image with a threshold confidence.
As per claim 5, Zaghetto teaches the method of claim 1. Zaghetto does not teach the securely storing comprises generating hashes of the description of the drawn image and the registration metadata.
Examiner provides Official Notice that these limitations were well known prior to filing.
One of ordinary skill in the art, prior to filing, would have recognized the advantage of hashing being a simple and robust security option for match comparison as opposed to the added complexity and key-exposure risk of encryption. The teachings of the prior art could have been incorporated into Zaghetto in that the securely storing comprises generating hashes of the description of the drawn image and the registration metadata.
As per claim(s) 14, arguments made in rejecting claim(s) 3 are analogous. Zaghetto also teaches a computer system, comprising: a processor set; one or more computer-readable storage media; and program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising (Zaghetto: See arguments and citations offered in rejecting claim 1 above;
paras 28, 29, 92-99). Note that claim 15 has a slightly non-analogous dependency relative to corresponding claim 8 in that claim 15 depends from intervening claim 13 while claim 8 depends directly from independent claim 1. Never-the-less, the single reference of Zaghetto is applied for teaching all limitations for these claims.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto as applied to claims 1 and 12 above, and further in view of US 20250086263 A1 (Messegee).
As per claim 6, Zaghetto teaches the method of claim 1, wherein the receiving the registering user input
of the first drawing process comprises
Zaghetto does not teach receiving a repeated input of the drawn image for confirmation. Messegee
teaches limitations (Messegee:
“[0025] FIG. 2 shows a flow chart with an example implementation of the method. The user inputs the
symbol or mark, consisting of one or more points and/or lines, a total of five times to establish a new
mark that will be associated with that user's account, when a new user is registering with the system
(202). Multiple drawings of the same mark by that user offers the system an opportunity to
characterize the mark as drawn by that specific user to set a baseline for later comparison. When the
user returns to the system for subsequent visits, the user is asked to draw the mark only once.”;
Fig. 2 (shown below): mainly 202;
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).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Messegee into Zaghetto since both Zaghetto and Messegee suggest a practical solution and field of endeavor of dynamic, behavioral, drawing-based authentication sysems extracting coordinate data and apply a CNN and registering metadata that characterizes the user’s drawing in general and Messegee additionally provides teachings that can be incorporated into Zaghetto in that the registration involves the user providing repeated drawing as to “offers the system an opportunity to characterize the mark as drawn by that specific user to set a baseline for later comparison” (Messegee: para 25). The teachings of Messegee can be incorporated into Zaghetto in that the registration involves the user providing repeated drawing. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
As per claim(s) 16, arguments made in rejecting claim(s) 6 are analogous. Zaghetto also teaches A computer system, comprising: a processor set; one or more computer-readable storage media; and
program instructions stored on the one or more computer-readable storage media to cause the processor set to perform operations comprising (Zaghetto: See arguments and citations offered in rejecting claim 1 above;
paras 28, 29, 92-99).
Allowable Subject Matter
Claims 7 and 17 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Limitations pertaining to “providing one or more high-level text descriptions; and providing words of the one or more high-level text descriptions separately and with synonyms”, in conjunction with other limitations present in the claim(s), distinguish over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
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Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677
Prosecution Insights