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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because the phrase “disclosed A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The use of the term GPS, Bluetooth, WiFi,Actigraph GT3X+ which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Objections
Claim 4 is objected to because of the following informalities: The FFT would have been spell out in the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1/20, claims recites the phase “ each data set being converted into a corresponding distance travelled by the user within the suer movement period..”. The claim(s) contains the above limitation which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor because specification, par 0111 discloses “detected by the IMU sensors, can be converted into a distance using parameters such as a user stride length”. This disclosure is not enough for understand the data set is converted into the distance length.
As per claims 1/20, claims recite the phase “ wherein the sensor set is queried..” , specification par 0029 discloses during that period, reduce the rate at which the sensor set is quired. , 00124 discloses the sensor set 17 is queried, 0127 a Wi-Fi module may be periodically queried, 0134] When a predetermined number of position data queries include error values above a prespecified uncertainty threshold, [0135] The sleep preparation procedure includes calculating the size and position of the immobility zone from a sequence of the most recent position data queries. Those paragraph does not disclose enough boundaries of which element is performing this function.
As per claims 1/20 recited the phase “in proximity to a user, this is a relative term. Since, it would have some disclosures or limit or b boundary in the specification.
As per claims 4/15, claims recite the phase “FFT”, specification does not provide enough disclosure how is applying this function and explanation of the FFT function.
The claims 1 and 15 directed to a method for performing a seamless Discrete Wavelet Transformation (DWT), as the specification does not provide an adequate written description of the claimed invention.
The claims use functional language such as "seamless DWT" which covers a generic result. However, the specification describes only one method to achieve this: "maintaining updated sums of DWT coefficients". According to the Federal Circuit, describing one embodiment is not always enough to satisfy the written description requirement for broad claim language.
The court in LizardTech, Inc. v. Earth Resource Mapping, Inc., 424 F.3d 1336, 1346, 76 USPQ2d 1724, 1733 (Fed. Cir. 2005), invalidated claims covering a generic seamless DWT method because the specification only taught a specific method ("maintaining updated sums") and did not show that a more generic method was contemplated. A description of one method does not justify claiming all possible ways to achieve the objective.
In this case, a skilled person reviewing the specification would understand that the applicant possessed only the disclosed embodiment(s), not the full scope of all possible methods for performing a seamless DWT. The specification lacks support for a claim that broadly covers achieving the seamless DWT result beyond the specific method described.
Therefore, the specification does not provide a sufficient written description for the full scope of the claimed invention. It is recommended that the applicant amend the claims to align with the scope of the disclosed embodiments.
All the dependent claims are rejected based on the same rational set forth in the above claims respectively.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1/20, claims recite the phase “each data set being converted into a corresponding distance travelled by the user within the suer movement period.”. The claim(s) contains the above limitation which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor because specification, par 0111 discloses “detected by the IMU sensors, can be converted into a distance using parameters such as a user stride length”. This disclosure is not enough for understand the data set is converted into the distance length. Thus, those claims are indefinite.
As per claims 1/20, claims recite the phase “ wherein the sensor set is queried..” , specification par 0029 discloses during that period, reduce the rate at which the sensor set is queried. , 00124 discloses the sensor set 17 is queried, 0127 a Wi-Fi module may be periodically queried, 0134] When a predetermined number of position data queries include error values above a prespecified uncertainty threshold, [0135] The sleep preparation procedure includes calculating the size and position of the immobility zone from a sequence of the most recent position data queries. Those paragraph does not disclose enough boundaries of which element is performing this function. Thus, those claims are indefinite.
As per claims 1/20 recited the phase “in proximity to a user, this is a relative term. Since, it would have some disclosures or limit or b boundary in the specification. Thus, those claims are indefinite.
As per claims 4/15, claims recite the phase “FFT”, specification does not provide enough disclosure how is applying this function and explanation of the FFT function. Thus, those claims are indefinite.
As pre claims 1/7 and 20, this claim recite the phase “ such as …”. It is not clear what limitation would consider for understanding the boundary of the claim limitation. Thus, claims are indefinite.
All the depended claims are rejected based on the same rationale set forth in the above claims respectively.
Claim Rejections - 35 USC § 103
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) 1-3,5,9,14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata et al US 2011/0081634.
As per claim 1, Pickering discloses a movement verification system for determining user movement that is characterized by a sequence of repeated user actions, the system comprising:
a user mobile device position able in proximity to a user so as to register the movement of that user (0030, a mobile device of the merchant 130 is proximity to the user and 0053 the motion capture system may comprise one or more image and/or depth sensors, coupled to a smart phone or a laptop/desktop computer of the user. The user makes a signature move comprising a sequence of movements that may be analyzed for comparison with a unique electronic signature), the user mobile device comprising a sensor set and configured to generate from that sensor set an unverified set of movement data resulting from user movement (par 0030, in close physical proximity to each other local POS terminal 135 may be a mobile device of the merchant 130, and 0053 the motion capture system may comprise one or more image and/or depth sensors, coupled to a smart phone or a laptop/desktop computer of the user. The user makes a signature move comprising a sequence of movements that may be analyzed for comparison with a unique electronic signature, 0032 The user movement signature authentication server 160 may enable consumers to initially set up, as to register the user, their unique electronic signatures using the movement-based authentication 33, and 0035 motion capture system 200 may comprise one or more sensors 210A, 210B, and 210C. and 0035 The sensors 210A, 210B, and 210C may be placed at specific locations within a three-dimensional space, i.e. unverified set of movement data, , such that the features and/or movement patterns, i.e. unverified set of data of a user 220 may be accurately detected and tracked and 0021 generating a unique electronic signature based on user movements, i.e. sensor set an unverified set of movement data, in a three-dimensional space); and
a movement verifier, including a processor and implemented by a movement verification function performed at least in part by the processor (0041, At step 410, the user movement signature authentication server 160, i.e. movement verifier, may detect one or more features associated with a user based on one or more 2D images and 0047 at step 515 the user movement signature authentication server may prepare the received contextual data, i.e. user movements, for model training, then splitting the contextual data into two parts where one part is for training a model , a movement verification function, and the other part is for validating the trained model, and the machine learning model may be trained using the vectors, i.e. a movement verification function, generated, and in step 515. de-duplicating, normalizing, compressing (e.g., Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), the user movement signature authentication server is using the Discrete Fourier Transform (DFT), i.e. the DFT is the version of that transform for discrete (sampled) signals, i.e. user movement data from the sensor and the vectors are extensively used for the verification and analysis of movement across various fields for the biometric security and wherein The Fast Fourier Transform (FFT) is an efficient algorithm used to compute the DFT and the Discrete Fourier Transform (DFT) is fundamentally involved with frequency. It is a mathematical tool that converts a finite sequence of samples from the time (or spatial) domain into a sequence of components in the frequency domain), in communication with the device configured to perform the steps of:
receiving said unverified set of movement data ([0046] At step 510, the user movement signature authentication server 160 may receive contextual data, i.e. unverified of movement data, form the sensors for the training a model using generative vectors, i.e. a movement verification function ); and
applying a movement verification function that compares the unverified set of movement data against a model so as to verify user movement characterized by a sequence of repeated user actions (0046 The contextual data may comprise data relating to features and/or movement patterns of the user. The contextual data may be associated with known genuine and/or forged features, and/or known genuine and/or forged movement patterns, and may be used as training data , movement patterns may be to train, i.e. applying a movement verification function, the machine learning model to more accurately distinguish between genuine and forged features/movement patterns. And 0048 A trained machine learning model could analyze contextual data to determine whether certain feature(s) and/or movement pattern(s) , i.e. movement characterized by a sequence of repeated user actions , are genuine (i.e., the feature(s) and/or movement pattern(s) match the genuine feature(s) and/or movement pattern(s)) or forged (i.e., the feature(s) and/or movement pattern(s) do not match the genuine feature(s) and/or movement pattern(s), or more closely resemble the forged feature(s) and/or movement pattern(s)). At step 525, the user movement signature authentication server 160 may store the trained machine learning model in a local or remote storage. The trained machine learning model may then be used by the user movement signature authentication server 160 to verify the features and/or movement patterns of the user.),
wherein the sensor set (0037 One or more sensors 210A, 210B, and 210C (FIG. 2) may generate a plurality of 2D and 3D images of the user making the signature move)
wherein the sensor set is queried over a user movement period to generate corresponding time-referenced data sets ( 0041 the user movement signature authentication server 160 may also identify features, i.e. queried, of the constructed skeletal structure, such as, e.g., a height, arm span, body segment lengths of the skeletal structure, i.e. movement patterns, 0049 the detected movement patterns may be normalized for authentication purposes and Normalization may involve relativizing the magnitude of signals received from various sensors, and/or relativizing the magnitude of the various signals received from a single sensor. 0047 The sensor data is processed by the DFT and it is a transform that converts a signal from the time domain to the frequency domain. The DFT provides frequency-domain features that reveal a signal's underlying sinusoidal components For example, if detected movement patterns of a user are faster or slower than the movement patterns of the unique electronic signature (for example, if the user is making the movements faster or slower than usual), the user may still be authenticated if the detected movement patterns consistently reflect the increased amplitude/speed. The amplitude value at the specific point of the user movement in time and this is the original signal's representation over time, i.e. time-referenced data sets),
each data set being converted into a corresponding distance travelled by the user within the user movement period (par 0047 The sensor data is processed by the DFT and it is a transform that converts a signal from the time domain to the frequency domain. The DFT provides frequency-domain features that reveal a signal's underlying sinusoidal components those signal comprise of the user movement data ), at least one distance derived from the at least one inertial measurement unit being compared to at least another distance derived from the at least one positioning module as a crosscheck to assist verification of user movement performed during the user movement period (0047 Data preparation may involve randomizing or sequencing the ordering of the contextual data, i.e. user movement, visualizing the contextual data to identify relevant relationships between different variables, identifying any data imbalances, splitting the contextual data into two parts, i.e. a crosscheck for verification, where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, compressing (e.g., Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT)), representing similarity or dissimilarity between sample features/movement patterns and genuine features/movement patterns in a vector form, labeling instances (e.g., vectors) as genuine and/or forged, correcting errors in the contextual data and par 0047 The sensor data is processed by the DFT and it is a transform that converts a signal from the time domain to the frequency domain. The DFT provides frequency-domain features that reveal a signal's underlying sinusoidal components those signal comprise of the user movement data).
Pikering does not disclose sensor comprises at least one inertial measurement unit, such as an accelerometer or a gyroscope, and at least one reference-based positioning module, such as a GPS module, wherein the at least one positioning module is queried at a frequency that is at least ten times lower than the query frequency of the at least one inertial measurement unit.
However, Kurata discloses sensor comprises at least one inertial measurement unit, such as an accelerometer or a gyroscope, and at least one reference-based positioning module, such as a GPS module, (0119 he behaviour/situation analysis system 10 shown in FIG. 1, is actually realised by a server or a client device a mobile terminal 0096 the location sensor, a GPS (Global Positioning System) is used and 0100 the behaviour/situation analysis system 10 is mainly configured from a motion sensor 102, a location sensor 104, a time/calendar information acquisition unit 106, a movement/state recognition unit 108, a geo-categorisation unit 110,, i.e. all units are the measurement unit of the mobile terminal and a behaviour/situation recognition unit 112. Furthermore, an application AP and a service SV that use a behaviour/situation pattern that is detected by the behaviour/state recognition unit 112 are prepared for the behaviour/situation analysis system 10. Furthermore, a result of usage of the behaviour/situation pattern by the application AP and profile information of a user may be input to the behaviour/situation recognition unit 112. and 0007 provided a behaviour pattern analysis system which includes a mobile terminal including a movement sensor that detects a movement of a user and outputs movement information, a current location information acquisition unit that acquires information on a current location, a building information acquisition unit that acquires information on a building existing at a location indicated by the information acquired by the current location information acquisition unit or information on buildings existing at the current location and in a vicinity of the current location, a first behaviour pattern detection unit that analyses the movement information output from the movement sensor, and detects a first behaviour pattern corresponding to the movement information from multiple first behaviour patterns obtained by classifying behaviours performed by the user over a relatively short period of time, and a transmission unit that transmits, to a server, the information on a building or buildings acquired by the building information acquisition unit and the first behaviour pattern detected by the first behaviour pattern detection unit, and a server including a reception unit that receives, from the mobile terminal, the information on a building or buildings and the first behaviour pattern, and a second behaviour pattern detection unit that analyses the information on a building or buildings and the first behaviour pattern received by the reception unit, and detects a second behaviour pattern corresponding to the information on a building or buildings and the first behaviour pattern from multiple second behaviour patterns obtained by classifying behaviours performed by the user over a relatively long period of time);
wherein the at least one positioning module is queried at a frequency that is at least ten times lower than the query frequency of the at least one inertial measurement unit.( fig.1, par 0096 location sensor 104, the latitude and longitude of the current location can be detected from an RFID) (Radio Frequency Identification). 0156 When the sensor data is input, the movement/state recognition unit 108 removes, from x-acc, y-acc and z-acc, a frequency outside a frequency range at which the user is recognized to be walking or running, the frequency is lower than the outside a frequency range. It can be seen as the location sensor 104 can queried a lower frequency than the outside frequency range of the movement/state recognition unit 108 of the mobile terminal)
Pickering and Kurata are both considered to be analogous to the claimed invention because they are in the same field of detecting the user movement behavior.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata and provide analyses the frequency of acceleration data(par 0145).
Doing so would provide information about sensor data, thereby to improve the detection of the movement of the user.
As per claim 2. Pickering and Kurata disclose the system of claim 1, Pickering discloses wherein the user mobile device is configured to process at least a portion of the unverified movement data prior to transmission by the user mobile device to the movement verifier(par 0030, in close physical proximity to each other (e.g., during a meeting between the merchant 130 and the consumer, at a brick-and-mortar store of the merchant 130, etc.)., local POS terminal 135 may be a mobile device of the merchant 130, 0032 The user movement signature authentication server 160 may enable consumers to initially set up, as to register the user, their unique electronic signatures using the movement-based authentication 33, and 0035 motion capture system 200 may comprise one or more sensors 210A, 210B, and 210C).
As per claim 3, Pickering and Kurata disclose the system of claim 2, Pickering discloses wherein the processing, by the user mobile device, of the unverified movement data comprises shifting the unverified movement data from the time domain to the frequency domain (0047 The sensor data is processed by the DFT and it is a transform that converts a signal from the time domain to the frequency domain. The DFT provides frequency-domain features that reveal a signal's underlying sinusoidal components for example, if detected movement patterns of a user are faster or slower than the movement patterns of the unique electronic signature (for example, if the user is making the movements faster or slower than usual), the user may still be authenticated if the detected movement patterns consistently reflect the increased amplitude/speed. The amplitude value at the specific point of the user movement in time and this is the original signal's representation over time, i.e. time-referenced data sets).
As per claim 5. Pickering and Kurata disclose the system of claim 1, Pickering discloses wherein the user mobile device is configured to pre-categories the unverified set of movement data prior to transmission by the user mobile device to the movement verifier (0044 determining a match between the one or more detected features and the one or more stored features, and/or between the one or more determined movement patterns and the unique electronic signature (step 435 in FIG. 4) may be performed using a machine learning model (e.g., machine learning classifier, i.e. pre-categories). In other words, the match determination may be performed using a machine learning model. For instance, the user movement signature authentication server 160, i.e. movement verifier, may train a machine learning model to identify i) the detected features that match the stored features and ii) the movement patterns that match the unique electronic signature).
As per claim 9. Pickering and Kurata disclose the system of claim 1, Pickering discloses wherein the movement verification function comprises passing the unverified movement data through a neural network, the neural network being trained by training data (0038 the body points and segments may be inferred using a machine learning model. To facilitate the machine-learning based feature detection, the movement-based authentication data entry interface implemented with the motion capture system 200 may initially be used to collect a large of amount of training data (e.g., 3D images of user movements), in order to analyze body points and segments across an entire user base. Other computing systems equipped with depth and image sensor(s) may also be used to provide training data. These computing systems may allow human evaluator(s) to manually identify and validate body points and segments in 3D images, and also manually connect validated body points in each 3D image to construct a skeletal structure. The collected data may be used to train a machine learning model to be representative of body points and segments across an entire user base. The machine learning model may be continuously or periodically updated as more training data become available, and the updated machine learning model may be periodically provided to the user movement signature authentication server 160 ), and the training data including model movement data sets augmented with trusted desired output values corresponding to the presence and timing, within that model movement data set, of candidate repeated user actions( 0045 training a machine learning model, according to one aspect of the present disclosure. In particular, the steps of method 500 may be performed by the user movement signature authentication server 160. The trained machine learning model may be used to analyze features and/or movement patterns of a user, and to determine whether the features and/or movement patterns match stored features and/or a unique electronic signature (comprising signature movement patterns, i.e., genuine movement patterns) previously set up by the user, respectively and 0047 Data preparation may involve randomizing or sequencing the ordering of the contextual data, visualizing the contextual data to identify relevant relationships between different variables, identifying any data imbalances, splitting the contextual data into two parts where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, compressing (e.g., Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), etc.), representing similarity or dissimilarity between sample features/movement patterns and genuine features/movement patterns ).
As per claim 14. Pickering and Kurata discloses the system of claim 9, Pickering disclose wherein the training data comprises data sets pre-transformed into the frequency domain (0047 Data preparation may involve randomizing or sequencing the ordering of the contextual data, visualizing the contextual data to identify relevant relationships between different variables, identifying any data imbalances, splitting the contextual data into two parts where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, compressing (e.g., Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT), etc.), representing similarity or dissimilarity between sample features/movement patterns and genuine features/movement patterns wherein the Discrete Fourier Transform (DFT) is fundamentally involved with frequency. It is a mathematical tool that converts a finite sequence of samples from the time (or spatial) domain into a sequence of components in the frequency domain).
As per claim 20, this claim is rejected based on the same rational set forth in the claim 1.
Claim(s) 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata et al US 2011/0081634 and Sutou et al EP 3358552.
As per claim 4, Pickering and Kurata disclose the system of claim 3, Pickering’s par 0047 discloses, authentication server is using the Discrete Fourier Transform (DFT), i.e. the DFT is the version of that transform for discrete (sampled) signals, i.e. user movement data from the sensor, the combination fails to disclose wherein the processing of the unverified movement data comprises applying a FFT function by the movement verifier.
However, Sutou discloses wherein the processing of the unverified movement data comprises applying a FFT function by the movement verifier (0203/0291 where sensor data of the millimeter wave radar 22 are sensor data from which a movement amount of an object can be determined by performing FFT in twice, it is possible to determine a movement amount of an object (for example, a movement amount in the z direction) from results of FFT of sensor data of the millimeter wave radar 22 performed twice and detect the moving object on the basis of the movement mount).
Pickering and Kurata and Sutou are considered to be analogous to the claimed invention because they are in the same field of using the sensor.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Sutou and provide analyses the frequency of acceleration data (par 0145).
Doing so would provide information about sensor data, thereby to improve the detection of the movement of the user.
As per claim 15, this claim is rejected based on the same rational set forth in the claim 4.
Claim(s) 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata in view of Toth et al US 2020/0184065.
As per claim 6. Pickering and Kurata disclose the system of claim 1, Pickering discloses user movement are verifying the authentication server using the training model, but fails to discloses wherein the user mobile device is configured to include one or more additional parameters within the unverified data set, the movement verifier applying the movement verification function in response to the value of the one or more additional parameters (emphasis added).
However, Toth discloses wherein the user mobile device is configured to include one or more additional parameters within the unverified data set, the movement verifier applying the movement verification function in response to the value of the one or more additional parameters (0054 at step 208 client authentication computing platform 110 may process one or more authentication events using the first user-specific authentication model, the second user-specific authentication model, one or more other user-specific authentication models, and/or the population-level authentication model. In addition to using one or more authentication models (e.g., to evaluate and/or confirm whether actual, measured user activity data in a particular session is valid relative to corresponding parameters of the one or more authentication models), client authentication computing platform 110 also may receive and/or validate one or more authentication credentials. In addition, based on evaluating actual, measured user activity data against corresponding parameters of one or more authentication models and/or based on validating one or more authentication credentials, client authentication computing platform 110 may grant and/or deny access to a portal hosted by account portal computing platform 120 and/or other secured information resources).
Pickering and Kurata and Toth are both considered to be analogous to the claimed invention because they are in the same field of using the sensor.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Toth and provide an authentication model.
Doing so would provide secured information, thereby improved the secured information protection against financial and reputational damage so that ensures compliance with regulations, and maintains customer trust.
As per claim 7. Pickering and Kurata and Toth disclose the system of claim 6, Toth discloses wherein the one or more additional parameters comprise: information about the hardware configuration of the user mobile device, and/or biometric information about the user, such as stride length ( 0042 biometric credentials and 0063 graphical user interface 400 may include text and/or other information prompting a user of client computing device 140 to login using one or more specific authenticators of a plurality of authenticators implemented by client authentication computing platform 110 (e.g., “To maintain your account security, we would like for you to login to your user account using your biometric authenticators. Please click here to launch mobile banking and login.”).).
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and kurata in view of Xue EP 3217600.
As per claim 8. Pickering and Kurata disclose the system of claim 1, the combination does not explicitly disclose wherein the user mobile device is configured to implement a power-saving strategy by inferring periods of inactivity, and in response, during that period, reduce the rate at which the sensor set is queried.
However, Xue discloses wherein the user mobile device is configured to implement a power-saving strategy by inferring periods of inactivity, and in response, during that period, reduce the rate at which the sensor set is queried (0071, 0107 the mobile personal station establishes a connection according to the most power-saving connection mode; and the mobile personal station re-establishes a connection to the body device according to the networking mode when the networking mode is different from the most power-saving connection mode ).
Pickering and Kurata and Xue are both considered to be analogous to the claimed invention because they are in the same field of using the sensor.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Xue and provide an authentication model.
Doing so would provide secured information, thereby improved the secured information protection against financial and reputational damage so that ensures compliance with regulations, and maintains customer trust.
Claim(s) 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata and Kim US 2021/0004705.
As per claim 10. Pickering and Kurata disclose the system of claim 9, the combination does not explicitly disclose wherein the training data is generated from paired data sets generated by devices that are collocated at a trusted user producing user movement characterized by a sequence of repeated user actions.
However, Kim discloses wherein the training data is generated from paired data sets generated by devices that are collocated at a trusted user producing user movement characterized by a sequence of repeated user actions(0017 the user behavior predicting method includes recording a user behavior as movement data (M(t)), action data (A(t)), and site data (S(t)) together with time information (t) through information received from at least one of a sensor, an external signal receiver, and an application executor of a user equipment, generating a user behavior predictive model by learning a probability correlation, i.e. paring, between M(t1), A(t1), and S(t1) at any time point (t1) and M(t1+Δt), A(t1+Δt), and S(t1+Δt) after a certain time (t1+Δt) and an operation associated, i.e. pairing, with the user behavior by the user equipment based on the generated user behavior predictive model, when the user behavior is sensed based on the information received from the sensor and the processor).
Pickering and Kurata and Kim are both considered to be analogous to the claimed invention because they are in the same field of using the sensor.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Kim and provide a probability correlation between user operations.
Doing so would provide an operation associated with the user behavior by the user equipment based on connected to the user device, thereby improved the trust of the user device.
As per claim 11. Pickering and Kurata and Kim disclose the system of claim 10, Kim discloses wherein the paired data sets from which the training data is generated, are pre-processed to improve the pairing between them (0017 generating a user behavior predictive model by learning a probability correlation. paired, between M(t1), A(t1), and S(t1) at any time point (t1) and M(t1+Δt), A(t1+Δt), and S(t1+Δt) after a certain time (t1+Δt) and 0026 the generating the user behavior predictive model, the probability correlation between the M(t.sub.1), A(t.sub.1), and S(t.sub.1) of the any time point (t.sub.1) and the M(t.sub.1+Δt), A(t.sub.1+Δt), and S(t.sub.1+Δt) after a certain time ).
As per claim 12. Pickering and Kurata and Kim discloses the system of claim 11, Kim discloses wherein the pre-processing comprises time-aligning the paired data sets (0002 a predicted user behavior by predicting a user behavior, and more particularly, to a user behavior predicting method and a device for executing a predicted user behavior, which may predict a user behavior at a certain time later from any time point, i.e. time-aligning, based on the correlation, i.e. paring, between data according to the movement, action, and par 0017, generating a user behavior predictive model by learning a probability correlation between M(t1), A(t1), and S(t1) at any time point (t1) and M(t1+Δt), A(t1+Δt), and S(t1+Δt) after a certain time (t1+Δt).).
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata and Moses et al US 2015/0242819.
As per claim 13. Pickering and Kurata disclose the system of claim 9, Pickering discloses, par 0045 training a machine learning model that includes neural networks are a specific type or subset of machine learning (ML) models. Does not explicitly disclose wherein the training data is segmented into epochs for training the neural network via k-fold cross-validation (emphasis added).
However, Moses discloses training the neural network via k-fold cross-validation (0044 The neural network may be trained/tuned according to methods known in the art, including K-fold cross validation).
Pickering and Kurata and Moses are both considered to be analogous to the claimed invention because they are in the same field of using the sensor.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Moses and provide validation of the data.
Doing so would provide outlier resistant modeling, thereby improve the boosting of the cross validation.
Claim(s) 16 -18 are rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata and Broch et al US 2016/0044511.
As per claim 16. Pickering and Kurata discloses the system of claim 1, Pickering par 0041 discloses the user movement signature authentication server 160 may determine a movement pattern, i.e. it can be seen as a token, of each of the one or more detected body points based on the one or more 3D images of the user and the movement pattern signature can be seen as the verified data of the training data, Pickering does not explicitly mention about the generating token of the verified data, the combination does not explicitly disclose further comprising a token generator, wherein the movement verifier is configured to transmit the corresponding set of verified movement data to the token generator, and in response, the token generator generates a token represented by a data structure that includes at least one of: a representation of the verified movement data, and an identifier unique to the user and/or user mobile device from which that verified movement data originates.
However, Kato discloses a token generator, wherein the movement verifier is configured to transmit the corresponding set of verified movement data to the token generator, and in response, the token generator generates a token represented by a data structure that includes at least one of: a representation of the verified movement data, and an identifier unique to the user and/or user mobile device from which that verified movement data originates (0018 a “token” may be any set of data. In some embodiments, token 112 may be a cryptographic token, i.e., a set of encrypted data. In various embodiments, the form and/or format of the token 112 and/or the manner in which it is provided to the mobile app 102 may vary. In some embodiments, the form/format of the token 112 may depend on the method by which mobile app 102 is configured to obtain authorization to access the third party service 106, e.g., OAuth flow, SAML flow, Basic Authentication, etc. In various embodiments, the app/device will provide the token back to the authentication server based on how the app is configured to authenticate).
Pickering and Kurata and Broch are both considered to be analogous to the claimed invention because they are in the same field of verifying data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Broch and provide authentication token.
Doing so would provide access controlling of the resource, thereby improving restriction of accessing the resource.
As per claim 17. Pickering and Kurata and Broch disclose the system of claim 16, Broch discloses wherein access to a first set of data within the token, or first set of transactional functions that can be performed on the token, are cryptographically restricted to said user and/or user mobile device (0018 token 112 may be a cryptographic token, i.e., a set of encrypted data. In various embodiments, the form and/or format of the token 112 and/or the manner in which it is provided to the mobile app 102 may vary. In some embodiments, the form/format of the token 112 may depend on the method by which mobile app 102 is configured to obtain authorization to access the third-party service 106).
As per claim 18. Pickering and Kurata and Broch disclose the system of claim 16, further comprising a token management system, implemented at least in part by the processor, wherein the token generator is configured to transmit the token to the token management system(0027 generated and provide a responsive communication to the third party service, e.g., a response including one or both of information extracted and/or derived from information comprising the token and an indication of a result of an authorization determination made at the MDM server 108 (i.e. a token management system ).
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Pickering US 2020/0364721 and Kurata and Broch et al US 2016/0044511 and Lam et al US 2020/0052903.
As per claim 19. Pickering and Kurata and Broch disclose the system of claim 18, the combination does not wherein the token management system is configured to store the token in a cryptographically-secure, publicly-accessible distributed ledger.
However, Lam discloses wherein the token management system is configured to store the token in a cryptographically-secure, publicly-accessible distributed ledger(0058 an identifier issued by a governmental entity, such as a driver's license, a social security number, or a passport number, a cryptographic key or token generated by management system 130 (e.g., and tied to corresponding login credentials, authentication credentials, and other unique digital identifiers within the authentication blocks of the distributed ledger).
Pickering and Kurata and Broch and Lam are both considered to be analogous to the claimed invention because they are in the same field of token.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Pickering to incorporate the teachings of Kurata, including the teaching of Broch, including the teaching of Lam and provide a distributed token.
Doing so would provide access controlling of the resource, thereby improving restriction of accessing the resource.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Blahnik et al US 2016/0058337, 0006 a sensor configured to detect movement associated with the electronic device and generate activity data based on the detected movement; a display; a non-transitory computer readable storage medium comprising instructions for: determining that a physical activity has been performed by a user wearing the electronic device, based on the activity data received from the sensor; determining whether the physical activity corresponds to a first type based on a first set of criteria and determining whether the physical activity corresponds to a second type based on a second set of criteria; in response to determining that the physical activity corresponds to the first type, updating a first value stored in a memory based on the activity data; in response to determining that the physical activity corresponds to the second type, updating a second value stored in the memory based on the activity data; displaying a first indicator representative of the first value, the first value representing an aggregate amount of the first type of physical activity detected from the sensor over a period of time, and displaying a second indicator representative of the second value, the second value representing an aggregate amount of the second type of physical activity detected from the sensor over the period of time; and one or more processors operatively coupled to the sensor.
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