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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claims objected to because of the following informalities:
Claim 43 recites “select a fifth sensor from the first third set of sensors and a sixth sensor from the fourth set of sensors based on the correlation data; and authenticate the first device in response to determining that data from the fifth sensor is correlated with data from the sixth sensor,” which should be changed to “select a third sensor from the third set of sensors and a fourth sensor from the fourth set of sensors based on the correlation data; and authenticate the first device in response to determining that data from the third sensor is correlated with data from the fourth sensor”
Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 26, 44-45 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 11,1,19 respectably of copending Application No. 18/988420 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because there is anticipation between the claims of the instant application and copending application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Regarding claim 26 of the instant application, although the conflicting claim is not identical, the claims are not patentably distinct because claim 26 is generic to all that is recited in copending claim 11; that is, the instant application claim is anticipated by the copending claim because it contains all the limitations of application claim 26.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
An illustration of the claim correspondence is as follows:
Instant application
Copending Application No. 18/988420
26. (New) An apparatus comprising:
at least one processor;
and at least one memory storing instructions that, when executed by the at least one processor cause the apparatus at least to:
receive a request to authenticate a first device;
obtain information identifying a first set of sensors associated with the first device;
obtain information identifying a second set of sensors associated with a second device;
obtain correlation data indicating correlations between sensors in the first set of sensors and sensors in the second set of sensors;
select a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data;
and authenticate the first device in response to determining that data from the first sensor is correlated with data from the second sensor.
11. An apparatus (104), comprising:
at least one processor;
and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to:
A- determine (S3) a plurality of candidate processes for the multi-factor user authentication, wherein a candidate process uses at least two authentication factors provided by one or more wearable devices;
B- for one or more of the plurality candidate processes, determine (S4) one or more performance scores respectively to quantify one or more properties that characterize how the method performs in executing said candidate process;
C- determine (S5) a plurality of sets of the one or more candidate processes, wherein a specific set is adapted to execute one or more authentication requests;
D- select (S6) one specific set among the plurality of sets based on the one or more performance scores, according to a selection rule that is based on a performance goal for the method; and
E- execute the one or more authentication requests by performing the selected set.
Candidate processes are described in the specification of the copending application, “candidate process may comprise a combination of at least two authentication-factors provided by at least two different wearable devices 102, so as to provide a multi-device user authentication” (Paragraph 91) and “determining a plurality of sets of the one or more candidate processes, wherein a specific set is adapted to execute one or more authentication requests” (Abstract). Paragraphs 91-92 state, “In one implementation, each or specific candidate process can use at least two device-sensor pairs.” Performance scores are described in the copending application, “In an embodiment, the one or more properties quantified by the one or more performance scores may comprise one or more of: an authentication accuracy” (Paragraphs 22-23). Paragraphs 44-46 in the copending application state, “a specific set is adapted to execute one or more authentication requests; means for selecting one specific set among the plurality of sets based on the one or more performance scores, according to a selection rule that is based on a performance goal for the method; means for executing the one or more authentication requests by performing the selected set.” As a result it can be interpreted that a performance score of authentication accuracy is used to choose sensors when carrying out an authentication request.
Although worded differently, the conflicting claims both recite, in summary, an apparatus that, upon receiving authentications requests, will determine the options that can be used for authentication, these options being sensors or sensor pairings. Then the sensor pairing(s) for authentication are chosen, based on authentication accuracy of the candidate process, or in other words how well sensor data correlates. In the end authentication is to be carried out using the chosen set of sensors.
The limitations of claims 44-45 of the instant application are substantially similar to those of claim 11, and are similarly rejected.
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.
Claims 26-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 26-27, 36-45 recite:
receiving a request to authenticate a first device (extra solution activity, receiving an authentication request is data collection).
obtaining information identifying a first set of sensors associated with the first device (mental process – observation, a human can mentally identify sensors of a first device by observing it).
obtaining information identifying a second set of sensors associated with a second device (mental process – observation, a human can mentally identify sensors of a second device by observing it).
obtaining correlation data indicating correlations between sensors in the first set of sensors and sensors in the second set of sensors (mental process – evaluation. A human could mentally find the relationship sensors of two devices would have with each other depending on sensor type and context of the two devices).
selecting a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data (mental process – judgement. A human could choose the sensors of the two devices based on what they deem to be best among the two groups of sensors, using some sort of mental rating or scoring system).
and authenticating the first device in response to determining that data from the first sensor is correlated with data from the second sensor (mental process – judgement. A human could mentally determine what they deem an acceptable relationship between sensors on a first and second device).
The judicial exception is not integrated into a practical application because claims 26-27, 36-45 do not recite and further limitations that either apply, rely on, or utilize the abstract idea in a manner that imposes meaningful limit on the abstract idea itself. Specifically, claims 26-27, 36-45 do not recite any additional limitations that reflect an improvement to the functioning of a computer, or an improvement to another technology or technical field. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they recited generic computer components, “at least one processor,” “at least one memory” (claim 26), “non-transitory computer readable medium comprising instructions” (claim 45), that when considered individually or in combination with the above abstract idea, do not recite significantly more than the abstract idea itself.
Specifically, the recitations of the “processor,” “memory,” “non-transitory computer readable medium,” are equivalent to components for merely storing (and retrieving) information in memory, which is recognized as well understood, routine, and conventional computer function (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). Thus, the above identified abstract idea recited within claims 26, 44-45, when considered individually and in combination with the above recited well-known, conventional components, fails to recite subject matter that would constitute as significantly more than the abstract idea itself.
Further, dependent claims 27, 36-43, which further limit claim 26 also fail to recite any further limitations that would either integrate the above identified abstract idea into a practical application and fail to recite anything which would constitute as significantly more than the abstract idea itself, as noted for the rejection of claim 26 above.
Further, dependent claims 28-31, 35, which further limit claim 26 also fail to recite any further limitations that would either integrate the above identified abstract idea into a practical application and fail to recite anything which would constitute as significantly more than the abstract idea itself, as noted for the rejection of claim 26 above.
Claim 28 recites “receive first data for a first time period” and “receive second data for the first time period from the second sensor” (mental process – observation. A human would be observing data of the two sensors in the same time frame).
Claim 29 recites: “resample the first data or resample the second data to a common sampling rate” (mental process – observation. If the human observing the sensor data makes a mistake in determining correlation data, they may reattempt the correlation analysis between the sensors, meaning they would be resampling the first and second data).
Claim 30 recites: “determine a value of a time synchronous correlation metric between the first sensor data and the second sensor data” (mental process – evaluation. A human with pen and paper would be able to mark down their self-decided correlation metrics between sensors, marking both observations at the same time).
Claim 31 recites “in response … less than … threshold … determine a value of a time asynchronous correlation metric” (mental process - evaluation and judgement. The threshold is the judgement step, and depending on the judgement another mental evaluation must take place. The evaluation process would be done with sensor data that is not necessarily recorded at the same time, for example the human recording the absolute maximums recorded between the two temperature sensors in the observation step, then evaluating the correlation between the two.).
Claim 35 recites, “wherein the time synchronous correlation metric comprises a dynamic time warping metric” (mathematical calculation done to compare two time series).
Thus, the above identified abstract idea recited within claims 28-31, 35, when considered individually and in combination with the above recited well-known, conventional components, fails to recite subject matter that would constitute as significantly more than the abstract idea itself.
Further, dependent claims 32-34 which further limit claim 26 also fail to recite any further limitations that would either integrate the above identified abstract idea into a practical application and fail to recite anything which would constitute as significantly more than the abstract idea itself, as noted for the rejection of claim 26 above.
Claim 32 recites “train a machine learning model to predict … determine an accuracy of the machine learning model…” (mental process – evaluation and judgement, a human can be trained and evaluated on some accuracy of their judgements on whether two sensors are correlated or not) and “… and in response to determining … accuracy … greater than ... threshold… determine the value of the time asynchronous correlation metric” (mental process – judgement and evaluation. If the human determines their accuracy on determining sensor correlation is high enough for them to judge the sensors, they can provide their evaluation on the sensors).
Claim 33 recites “training data set and the validation data set” (extra solution activity – selecting a particular data source).
Claim 34 recites “machine learning model is caused to generate an output indicating a likelihood…” (mental process – evaluation. A human can calculate a probability based off of correlation scores between data to determine a probability that they are related).
Thus, the above identified abstract idea recited within claims 32-34, when considered individually and in combination with the above recited well-known, conventional components, fails to recite subject matter that would constitute as significantly more than the abstract idea itself.
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.
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.
Claims 26-31, 36-41, 43-45 are rejected under Moline-Markham et al. (US 9961547 B1), hereafter regarded to as Moline in view of Ruby B. Lee and Wei-Han Lee (US 20170227995 A1), hereafter regarded to as Lee.
Regarding claim 26, Moline discloses: an apparatus comprising at least one processor and at least one memory storing instructions that, when executed by the at least one processor cause the apparatus at least to:
receive a request to authenticate a first device (Col. 4 lines 20 - 30 state " The memory stores instructions which, when carried out by the processing circuitry, cause the processing circuitry to: (A) receive first activity data from a mobile device … (C) based on the first activity data received … perform an assessment operation that provides an assessment result" The examiner interprets the activity data step as an authentication request as it leads to authenticating).
obtaining information identifying a first set of sensors associated with the first device (Col. 4 lines 20 - 38 state " receive first activity data from a mobile device, the first activity data identifying physical activity by a user that is currently using the mobile device," Col. 2 lines 12-13 state " first activity data identifying activity by a user … (e.g., physical activity, user input or UI activity, etc.)," Col. 8 lines 50 - 62 state "the activity data 122 identifies activity as sensed by the mobile device … It should be understood that such sensed activity may include user interface input (e.g., touchscreen user gestures such as taps or swipes, button presses, other types of UI activity, combinations of UI input, etc.), motion input (e.g., acceleration, orientation, etc.), as well as other types of input (e.g., GPS data, etc.)." The sensed activity given by the mobile device in the reference, which will be mapped to the first device in the claims, will send its activity data, defined as sensed activity from the sensors on the mobile device, thereby identifying the sensors associated with the first device).
obtaining information identifying a second set of sensors associated with a second device (Col. 4 lines 20-38 state " receive second activity data from an electronic wearable apparatus, the second activity data identifying physical activity by a wearer that is currently wearing the electronic wearable apparatus," the second device is mapped to the wearable device mentioned in the reference. Col 8 lines 50 - 62 state "… other activity data 124 sensed from the electronic wearable apparatus 24. Along these lines, the activity data 122 identifies activity as sensed by the mobile device 22 while the activity data 124 identifies physical activity as sensed by the electronic wearable apparatus 24," the activity data sensed and given by the wearable device identifies its set of sensors, the second set of sensors).
obtaining correlation data indicating correlations between sensors in the first set of sensors and sensors in the second set of sensors (Col 13 lines 37-55 state "CSAW provides a continuous quantitative estimate of the confidence that a phone's current user is in fact the phone's owner, assuming that the phone's owner is wearing the owner's watch. For each user-phone interaction, CSAW correlates two different observations by two distinct devices: 1) an observation derived from motion data from sensors in the watch; and 2) an observation derived from motion and input sensors in the phone. CSAW feeds these observations to an agent that estimates the likelihood that the two observations correspond to the same interaction," col 13 lines 8 -20 state "CSAW correlates wrist motion with phone motion and phone input, and continuously produces a score indicating its confidence that the person holding (and using) the phone is the person wearing the wristband," the confidence score CSAW obtains by correlating sensors in the first set of sensors and the second set of sensors is what will be considered as correlation data between the sensor sets).
authenticating the first device in response to determining that data from the first sensors are correlated with data from the second sensors (Col. 10 lines 25-38 state " the policy application circuitry 110 can compare the confidence score 160 to a predetermined threshold to decide whether the overall control circuitry 100 considers the user of the mobile device 22 and the wearer of the electronic wearable apparatus 24 to be the same person. That is, if the confidence score 160 is higher than the predefined threshold … same person. However, if the confidence score 160 is lower than the predefined threshold … not to be the same person," the CSAW's correlation data is used in the decision for authenticating the user).
However, Moline does not teach selecting a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data. Lee teaches the selecting of a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data (Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication." Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Moline's system for continuous mobile device authentication using a wearable apparatus by enhancing the system to select a sensor from the mobile and wearable devices based on fisher score correlation data, taught by Lee, in order to achieve higher authentication performance. The motivation would be to achieve better authentication performance in a system that utilizes a smartwatch and smartphone's sensor data. (Lee, paragraph 67, “The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone”).
Regarding claim 27, Moline in view of Lee teaches the apparatus according to claim 26. Moline further teaches the device’s configuration to generate correlation data by learning correlations between the first set of sensors and the second set of sensors (Column 9, lines 19-32 states, " based on the activity data 122, 124, the motion correlation circuitry 104 provides a motion correlation score 140 indicating a numerical measure of correlation between motion of the mobile device 22 and motion the electronic wearable apparatus 24." Activity data was earlier described as sensor data, meaning that the motion correlation score generated by the motion correlation circuitry is generated from the first and second set of sensors.).
Regarding claim 28, Moline in view of Lee teaches the apparatus according to claim 27. Moline further teaches:
the learning of the correlations between the first set of sensors and the second set of sensors further comprising the receiving first data for a first time period from the first sensors. receive second data for the first time period from the second sensors (Col 18. lines 27-41 state "When the phone is in motion or in use, 30 this module receives two continuous streams of motion data from the watch (Wm) and the phone (Pm) … the input data streams are segmented using sliding windows of size w m with overlap fraction om; CSAW uses om=0.5 and wm=2 seconds when the phone is locked and wm=4 seconds when it is unlocked. For a window ending at time t, the module 40 outputs a correlation score cm(t) indicating how well the two motions correlate," the data streams of the two device sensors are segmented into windows based on time).
determining a correlation between the first sensors and the second sensors based on the first data and the second data (Col 18. lines 27-41 state, " … the input data streams are segmented using sliding windows of size w m with overlap fraction om; CSAW uses om=0.5 and wm=2 seconds when the phone is locked and wm=4 seconds when it is unlocked. For a window ending at time t, the module 40 outputs a correlation score cm(t) indicating how well the two motions correlate," the correlation scores are given for the window of time).
However, Moline does not specifically teach the process for two sensors, one from each set of sensors. Lee teaches the usage of one sensor from each set of sensors for determining a correlation between the first and second data (Paragraph 61 states, "Before training the authentication model, it is useful to understand which sensors may be of value in distinguishing users." Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication." Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline’s system for continuous mobile device authentication using a wearable apparatus by enhancing Moline’s system to select a sensor from each of the mobile device and wearable apparatus based on Fisher score correlation data, as taught by Lee, in order to achieve an optimal authentication performance. The motivation would be to improve Moline with Lee since Fisher Scores are used in an embodiment to select the most promising sensors for user authentication, and use Fisher Scores to rule out less useful sensors. (Lee Paragraph 62-63, " Fisher scores (FS) were used to help select the most promising sensors for user authentication." "In this embodiment, it is seen that the magnetometer, orientation sensor and light sensor have lower FS scores than the accelerometer and gyroscope because they are influenced by the environment. This can introduce various background noise unrelated to the user's behavioral characteristics").
Regarding claim 29, Moline in view of Lee teaches the apparatus according to claim 28. However, Moline does not specifically teach a resampling of sensor data to a common sampling rate. Lee teaches the learning of the correlations between the first set of sensors and the second set of sensors further comprises at least one of resample the first data or resample the second data to a common sampling rate (Paragraph 105 states, "As seen in Tables VIII and IX, the average improvement from one sensor to two sensors is approximately 7.4% in the first data set and 14.6% in the second data set when the sampling rate is 20 seconds," the tables VIII and IX showing other sampling rates and accuracies, the sampling rates being the same, the accuracies on the tables being in the high 90s for two sensors and a high rate of sampling. Paragraph 69 states, "In cases where the sensor measurements originally obtained are too large to process directly, a re-sampling process can be utilized. This can be done for several reasons, including to reduce the computational complexity, or to reduce the effect of noise by averaging the data points." Paragraph 97 states, "An application was developed to monitor the average CPU and memory utilization of the smartphone and smartwatch while running the authentication app which continuously requested sensor data at a rate of 50 Hz on a Nexus 5 smartphone and a Moto 360 smartwatch." What can be understood is that the sampling rate between sensors is the same between the different devices, and that resampling would have to resample the first or second data to a common sampling rate).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline’s system for continuous mobile device authentication using a wearable apparatus by enhancing Moline’s system to resample data of the first or second sensors if the sensor measurements are too large to process directly, as taught by Lee. The motivation is that resampling can reduce effect of noise and computational complexity on sensed data, increasing computational speed and decreasing the chance to overtrain the model (Paragraph 69, "This can be done for several reasons, including to reduce the computational complexity, or to reduce the effect of noise by averaging the data points").
Regarding claim 30, Moline in view of Lee teaches the apparatus according to claim 28. Moline teaches determining the correlation between the first sensors and the second sensors based on the first sensor data and the second sensor data further comprises determining a value of a time synchronous correlation metric between the first sensor data and the second sensor data (Col 18 lines 43-64 state, "For each segmented window, a correlation feature vector F.sub.c with features from time and frequency domains is computed." and "Two (cross-correlation and correlation-coefficient) measure similarity between two signals and how they vary together in time domain," The time domain similarity measurements are the time synchronous metrics between the first and second sensor data).
Regarding claim 31, Moline in view of Lee teaches the apparatus according to claim 30. Moline further teaches the determining of the correlation between the first sensor and the second sensor based on the first sensor data and the second sensor data further comprising: in response to the determining that the value of the time synchronous correlation metric is less than the first threshold, determine a value of a time asynchronous correlation metric between the first sensor data and the second sensor data (Col. 18 lines 43-64 state, "…and a third (coherence) measures similarity and variance in the frequency domain" Col. 18 lines 65-67 to Col. 19 lines 1-10 state "To determine the correlation score, uses a model that estimates the probability that a given feature vector F.sub.c represents two motions that correlate" and "For a given feature vector, the classifier computes probability estimates for the two labels (0 and 1); outputs the classifier's probability estimate for label 1 as its correlation score c.sub.m(t)." Since the signal calculations are calculated at the same time, if the time domain correlation scores are too low, then the frequency domain score will be used as the main classifier, the frequency domain correlation is interpreted as the time asynchronous metric between the data of the two sensors).
Regarding claim 36, Moline in view of Lee teaches the apparatus according to claim 26. Moline teaches that the first sensor and the second sensor measure different characteristics (Col. 14 lines 38-50 state, "CSAW correlates two different observations by two distinct devices: 1) an observation derived from motion data from sensors in the watch; and 2) an observation derived from motion and input sensors in the phone," Col. 16 lines 40 to 55 state, "When the owner uses her watch-hand to provide input to the phone, while holding the phone in the other hand, CSA W authenticates the user by correlating the user's taps and swipes with the user's wrist movement. There may be disparate data types. Here, data for phone input may be different than for wrist movement (acceleration and rotational velocity). CSAW aims to correlate these two data sources despite their different data types" The sensors on the two devices are interpreted to measure the different characteristics by them being different data types and different observations from the sensors on the two devices).
Regarding claim 37, Moline in view of Lee teaches the apparatus according to claim 26. Moline teaches the request to authenticate the first device comprises the information identifying the first set of sensors associated with the first device (Col. 4 lines 20 - 30 state " The memory stores instructions which, when carried out by the processing circuitry, cause the processing circuitry to: (A) receive first activity data from a mobile device … (C) based on the first activity data received … perform an assessment operation that provides an assessment result" The examiner interprets the activity data step as identifying the first set of sensors as earlier defined, as well as the authentication request).
Regarding claim 38, Moline in view of Lee teaches the apparatus according to claim 26. Lee teaches selecting of the first sensor from the first set of sensors and the second sensor from the second set of sensors further comprises identifying a combination of sensors from the first set of sensors and the second set of sensors that are associated with a highest correlation metric value in the correlation data (Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication. “Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance). The motivation to combine Lee to Moline to reject claim 38 is the same motivation used in the rejection of claim 26 above.
Regarding claim 39, Moline in view of Lee teaches the apparatus according to claim 38. Lee teaches:
the first set of sensors comprises the first sensor and a third sensor (Paragraph 63 references table V for the fisher scores of sensors on the smartphone and watch, where there is more than one sensor in the smartphone section of the table).
the identifying of a combination of sensors from the first set of sensors and the second set of sensors that are associated with a highest correlation metric value comprises selecting the first sensor from the first set of sensors in response to the determining that a first value of a correlation metric between the first sensor and the second sensor is greater than a second value of the correlation metric between the third sensor and the second sensor (Paragraph 62 states, "In one embodiment, Fisher scores (FS) were used to help select the most promising sensors for user authentication. FS is one of the most widely used supervised feature selection methods due to its excellent performance," Table V shows Acc(x), Gyr(x), and many more sensors on both the phone and watch sections, showing that the correlation metric is calculated considering all the sensors, since fisher scores compare the features/sensors they are using with all the other features/sensors). The motivation to combine Lee to Moline to reject claim 39 is the same motivation used in the rejection of claim 38 above.
Regarding claim 40, Moline in view of Lee teaches the apparatus according to claim 39. Lee further teaches:
the second set of sensors comprises the second sensor and a fourth sensor (Paragraph 63 references table V for the fisher scores of sensors on the smartphone and watch, where there is more than one sensor in the watch section of the table).
the identifying of a combination of sensors from the first set of sensors and the second set of sensors that are associated with a highest correlation metric value further comprising selecting the first sensor from the first set of sensors and the second set of sensors in response to the determining that a first value of a correlation metric between the first sensor and the second sensor is greater than, a second value of the correlation metric between the third sensor and the second sensor, a third value of the correlation metric between the third sensor and the fourth sensor; and a fourth value of a correlation metric between the first sensor and the fourth sensor (Paragraph 62 states, "In one embodiment, Fisher scores (FS) were used to help select the most promising sensors for user authentication. FS is one of the most widely used supervised feature selection methods due to its excellent performance," Table V shows Acc(x), Gyr(x), and many more sensors on both the phone and watch sections, showing that the correlation metric is calculated considering all the sensors, since fisher scores compare the features/sensors they are using with all the other features/sensors). The motivation to combine Lee to Moline to reject claim 40 is the same motivation used in the rejection of claim 39 above.
Regarding claim 41, Moline in view of Lee teaches the apparatus according to claim 26. Moline teaches the second device already being authenticated (Col. 6 lines 13-15 state, "The electronic wearable apparatus 24 is constructed and arranged to be worn by the legitimate user," meaning that the second device is already authenticated).
Regarding claim 43, Moline in view of Lee teaches the apparatus according to claim 26. Moline teaches:
instructions, when executed by the at least one processor, further causing the apparatus to delay a period of time after authenticating the first device (col 10 lines 48-57 state, " As another example, the control circuitry 100 may operate to control de-authentication, i.e., locking the mobile device 22 following successful initial authentication. Here, the control circuitry 100 performs continuous authentication while the mobile device 22 is being used. If the control circuitry 100 determines that the user of the mobile device 22 and the wearer of the electronic wearable apparatus 24 are no longer the same person, the control circuitry 100 outputs a result signal 180 that denies further mobile device access," The device having a continuously reauthenticating system means there is time delay for reauthentication.)
obtain information identifying a third set of sensors associated with the first device (Col. 4 lines 20 - 38 state " receive first activity data from a mobile device, the first activity data identifying physical activity by a user that is currently using the mobile device," Col. 2 lines 12-13 state " first activity data identifying activity by a user … (e.g., physical activity, user input or UI activity, etc.)," Col. 8 lines 50 - 62 state "the activity data 122 identifies activity as sensed by the mobile device … It should be understood that such sensed activity may include user interface input (e.g., touchscreen user gestures such as taps or swipes, button presses, other types of UI activity, combinations of UI input, etc.), motion input (e.g., acceleration, orientation, etc.), as well as other types of input (e.g., GPS data, etc.)." Since there is a continuous authentication system being used as mentioned above, it would repeat the steps required for authenticating as described in the specification, with the third set of sensors associated with the first device being the same sensors as the first set of sensors, however this time they are being used after a delayed period of time.).
obtain information identifying a fourth set of sensors associated with a second device that is authenticated (Col. 4 lines 20-38 state " receive second activity data from an electronic wearable apparatus, the second activity data identifying physical activity by a wearer that is currently wearing the electronic wearable apparatus," the second device is mapped to the wearable device mentioned in the reference. Col 8 lines 50 - 62 state "… other activity data 124 sensed from the electronic wearable apparatus 24. Along these lines, the activity data 122 identifies activity as sensed by the mobile device 22 while the activity data 124 identifies physical activity as sensed by the electronic wearable apparatus 24," the activity data sensed and given by the wearable device identifies its set of sensors, now the fourth set of sensors since there is continuous authentication).
obtain correlation data indicating correlations between sensors in the third set of sensors and sensors in the fourth set of sensors (Col 13 lines 37-55 state "CSAW provides a continuous quantitative estimate of the confidence that a phone's current user is in fact the phone's owner, assuming that the phone's owner is wearing the owner's watch. For each user-phone interaction, CSAW correlates two different observations by two distinct devices: 1) an observation derived from motion data from sensors in the watch; and 2) an observation derived from motion and input sensors in the phone. CSAW feeds these observations to an agent that estimates the likelihood that the two observations correspond to the same interaction," col 13 lines 8 -20 state "CSAW correlates wrist motion with phone motion and phone input, and continuously produces a score indicating its confidence that the person holding (and using) the phone is the person wearing the wristband," the confidence score CSAW obtains by correlating sensors in the first set of sensors and the second set of sensors is what will be considered as correlation data between the sensor sets).
and authenticate the first device in response to determining that data from the fifth sensor is correlated with data from the sixth sensor (Col. 10 lines 25-38 state " the policy application circuitry 110 can compare the confidence score 160 to a predetermined threshold to decide whether the overall control circuitry 100 considers the user of the mobile device 22 and the wearer of the electronic wearable apparatus 24 to be the same person. That is, if the confidence score 160 is higher than the predefined threshold … same person. However, if the confidence score 160 is lower than the predefined threshold … not to be the same person," the CSAW's correlation data is used in the decision for authenticating the user).
However, Moline does not explicitly teach selecting a sensor from the third set of sensors nor selecting a sensor from the fourth set of sensors. Lee teaches selecting a fifth sensor from the first third set of sensors and a sixth sensor from the fourth set of sensors based on the correlation data (Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication." Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Moline's system for continuous mobile device authentication using a wearable apparatus by enhancing the system to select a sensor from the mobile and wearable devices based on fisher score correlation data, taught by Lee, in order to achieve higher authentication performance. The motivation would be to achieve better authentication performance in a system that utilizes a smartwatch and smartphone's sensor data. (Lee, paragraph 67, “The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone”).
Regarding claim 44, Moline discloses: a method comprising:
receiving a request to authenticate a first device (Col. 4 lines 20 - 30 state " The memory stores instructions which, when carried out by the processing circuitry, cause the processing circuitry to: (A) receive first activity data from a mobile device … (C) based on the first activity data received … perform an assessment operation that provides an assessment result" The examiner interprets the activity data step as an authentication request as it leads to authenticating).
obtaining information identifying a first set of sensors associated with the first device (Col. 4 lines 20 - 38 state " receive first activity data from a mobile device, the first activity data identifying physical activity by a user that is currently using the mobile device," Col. 2 lines 12-13 state " first activity data identifying activity by a user … (e.g., physical activity, user input or UI activity, etc.)," Col. 8 lines 50 - 62 state "the activity data 122 identifies activity as sensed by the mobile device … It should be understood that such sensed activity may include user interface input (e.g., touchscreen user gestures such as taps or swipes, button presses, other types of UI activity, combinations of UI input, etc.), motion input (e.g., acceleration, orientation, etc.), as well as other types of input (e.g., GPS data, etc.)." The sensed activity given by the mobile device in the reference, which will be mapped to the first device in the claims, will send its activity data, defined as sensed activity from the sensors on the mobile device, thereby identifying the sensors associated with the first device).
obtaining information identifying a second set of sensors associated with a second device (Col. 4 lines 20-38 state " receive second activity data from an electronic wearable apparatus, the second activity data identifying physical activity by a wearer that is currently wearing the electronic wearable apparatus," the second device is mapped to the wearable device mentioned in the reference. Col 8 lines 50 - 62 state "… other activity data 124 sensed from the electronic wearable apparatus 24. Along these lines, the activity data 122 identifies activity as sensed by the mobile device 22 while the activity data 124 identifies physical activity as sensed by the electronic wearable apparatus 24," the activity data sensed and given by the wearable device identifies its set of sensors, the second set of sensors).
obtaining correlation data indicating correlations between sensors in the first set of sensors and sensors in the second set of sensors (Col 13 lines 37-55 state "CSAW provides a continuous quantitative estimate of the confidence that a phone's current user is in fact the phone's owner, assuming that the phone's owner is wearing the owner's watch. For each user-phone interaction, CSAW correlates two different observations by two distinct devices: 1) an observation derived from motion data from sensors in the watch; and 2) an observation derived from motion and input sensors in the phone. CSAW feeds these observations to an agent that estimates the likelihood that the two observations correspond to the same interaction," col 13 lines 8 -20 state "CSAW correlates wrist motion with phone motion and phone input, and continuously produces a score indicating its confidence that the person holding (and using) the phone is the person wearing the wristband," the confidence score CSAW obtains by correlating sensors in the first set of sensors and the second set of sensors is what will be considered as correlation data between the sensor sets).
authenticating the first device in response to determining that data from the first sensors are correlated with data from the second sensors (Col. 10 lines 25-38 state " the policy application circuitry 110 can compare the confidence score 160 to a predetermined threshold to decide whether the overall control circuitry 100 considers the user of the mobile device 22 and the wearer of the electronic wearable apparatus 24 to be the same person. That is, if the confidence score 160 is higher than the predefined threshold … same person. However, if the confidence score 160 is lower than the predefined threshold … not to be the same person," the CSAW's correlation data is used in the decision for authenticating the user).
However, Moline does not teach selecting a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data. Lee teaches the selecting of a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data (Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication." Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Moline's system for continuous mobile device authentication using a wearable apparatus by enhancing the system to select a sensor from the mobile and wearable devices based on fisher score correlation data, taught by Lee, in order to achieve higher authentication performance. The motivation would be to achieve better authentication performance in a system that utilizes a smartwatch and smartphone's sensor data. (Lee, paragraph 67, “The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone”).
Regarding claim 45, Moline discloses: A non-transitory computer readable medium comprising program instructions that, when executed by an apparatus, cause the apparatus to perform at least the following:
receive a request to authenticate a first device (Col. 4 lines 20 - 30 state " The memory stores instructions which, when carried out by the processing circuitry, cause the processing circuitry to: (A) receive first activity data from a mobile device … (C) based on the first activity data received … perform an assessment operation that provides an assessment result" The examiner interprets the activity data step as an authentication request as it leads to authenticating).
obtaining information identifying a first set of sensors associated with the first device (Col. 4 lines 20 - 38 state " receive first activity data from a mobile device, the first activity data identifying physical activity by a user that is currently using the mobile device," Col. 2 lines 12-13 state " first activity data identifying activity by a user … (e.g., physical activity, user input or UI activity, etc.)," Col. 8 lines 50 - 62 state "the activity data 122 identifies activity as sensed by the mobile device … It should be understood that such sensed activity may include user interface input (e.g., touchscreen user gestures such as taps or swipes, button presses, other types of UI activity, combinations of UI input, etc.), motion input (e.g., acceleration, orientation, etc.), as well as other types of input (e.g., GPS data, etc.)." The sensed activity given by the mobile device in the reference, which will be mapped to the first device in the claims, will send its activity data, defined as sensed activity from the sensors on the mobile device, thereby identifying the sensors associated with the first device).
obtaining information identifying a second set of sensors associated with a second device (Col. 4 lines 20-38 state " receive second activity data from an electronic wearable apparatus, the second activity data identifying physical activity by a wearer that is currently wearing the electronic wearable apparatus," the second device is mapped to the wearable device mentioned in the reference. Col 8 lines 50 - 62 state "… other activity data 124 sensed from the electronic wearable apparatus 24. Along these lines, the activity data 122 identifies activity as sensed by the mobile device 22 while the activity data 124 identifies physical activity as sensed by the electronic wearable apparatus 24," the activity data sensed and given by the wearable device identifies its set of sensors, the second set of sensors).
obtaining correlation data indicating correlations between sensors in the first set of sensors and sensors in the second set of sensors (Col 13 lines 37-55 state "CSAW provides a continuous quantitative estimate of the confidence that a phone's current user is in fact the phone's owner, assuming that the phone's owner is wearing the owner's watch. For each user-phone interaction, CSAW correlates two different observations by two distinct devices: 1) an observation derived from motion data from sensors in the watch; and 2) an observation derived from motion and input sensors in the phone. CSAW feeds these observations to an agent that estimates the likelihood that the two observations correspond to the same interaction," col 13 lines 8 -20 state "CSAW correlates wrist motion with phone motion and phone input, and continuously produces a score indicating its confidence that the person holding (and using) the phone is the person wearing the wristband," the confidence score CSAW obtains by correlating sensors in the first set of sensors and the second set of sensors is what will be considered as correlation data between the sensor sets).
authenticating the first device in response to determining that data from the first sensors are correlated with data from the second sensors (Col. 10 lines 25-38 state " the policy application circuitry 110 can compare the confidence score 160 to a predetermined threshold to decide whether the overall control circuitry 100 considers the user of the mobile device 22 and the wearer of the electronic wearable apparatus 24 to be the same person. That is, if the confidence score 160 is higher than the predefined threshold … same person. However, if the confidence score 160 is lower than the predefined threshold … not to be the same person," the CSAW's correlation data is used in the decision for authenticating the user).
However, Moline does not teach selecting a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data. Lee teaches the selecting of a first sensor from the first set of sensors and a second sensor from the second set of sensors based on the correlation data (Paragraph 62 states, "… Fisher scores (FS) were used to help select the most promising sensors for user authentication." Paragraph 63 states, "Table V shows the FS for different sensors that are widely implemented in smartphones and smartwatches." Paragraph 65 states, "… two sensors were selected, the accelerometer and gyroscope, because they have higher FS scores and furthermore, are the most common sensors built into current smartphones and smartwatches." Paragraph 67 states, "The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone." Selecting one sensor from a smartphone and smartwatch, based on correlation data using fisher scores in order to achieve highest performance).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify Moline's system for continuous mobile device authentication using a wearable apparatus by enhancing the system to select a sensor from the mobile and wearable devices based on fisher score correlation data, taught by Lee, in order to achieve higher authentication performance. The motivation would be to achieve better authentication performance in a system that utilizes a smartwatch and smartphone's sensor data. (Lee, paragraph 67, “The combination of the smartphone and smartwatch has the lowest FRR and FAR, and achieves the best authentication performance than using each alone”).
Claims 32-34, are rejected under Moline in view of Lee in further view of Molony and Dumitriu (US 20220093255 A1), hereafter regarded by Molony.
Regarding claim 32, Moline in view of Lee teaches the apparatus according to claim 31. Moline teaches:
determining of the value of the time asynchronous correlation metric between the first sensor data and the second sensor data further comprises training a machine learning model to predict if at least two input data samples from a training data set are associated with the same user at the same time (Col 17 lines 17-25 state, " CSAW receives a steady stream of motion data from the phone (P.sub.m) and the watch (W.sub.m), and touchscreen input data from the phone (P.sub.i) … The resulting correlation metrics are considered by the scoring engine that actually computes C(t)." Col 18 lines 27 - 64 state, "For each segmented window, a correlation feature vector F.sub.c with features from time and frequency domains is computed,"and " a third (coherence) measures similarity and variance in the frequency domain," and "To determine the correlation score, uses a model that estimates the probability that a given feature vector F.sub.c represents two motions that correlate. A random-forest binary classifier trained to classify a feature vector as 0 (not correlated) or 1 (correlated) is used." The asynchronous metric is taken from the two sensors’ data within the same time window, this being used to determine whether the user of both devices is the same).
determining the value of the time asynchronous correlation metric using the machine learning model, the first sensor data and the second sensor data (Col 18 lines 43 to 60 states, "a third (coherence) measures similarity and variance in the frequency domain," which is the value of the time asynchronous correlation metric).
However, Moline does not specifically teach machine learning model accuracy with validation data sets nor determining asynchronous correlation metric after determining accuracy is greater than a second threshold. Lee teaches determining an accuracy of the machine learning model based on a validation data set (Paragraph 67 states, " Given a window size, for each target user, the system can utilize multi-fold (e.g., 10-fold, etc.) cross-validation for training and testing. In one embodiment, a 10-fold cross-validation was used, i.e., 9/10 data was used as the training data, and 1/10 used as the testing data.")
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline’s system for continuous mobile device authentication using a wearable apparatus by enhancing Moline’s system with Lee by setting the correlation metric computation between signals in Moline to use a validation set for determination. The motivation is that cross validation reduces false acceptance and rejection rates on classifiers by preventing overfitting, in this case this would increase authentication accuracy of a system using training data, yielding predictable results.
However, Moline in view of Lee does not specifically teach in response to the determining that the accuracy of the machine learning model is greater than a second threshold: determine the value of the time asynchronous correlation metric using the machine learning model, the first sensor data and the second sensor data. Molony teaches in response to the determining that the accuracy of the machine learning model is greater than a second threshold: determine the value of the … metric using the machine learning model… (Paragraph 46 states, "The proposed classification for each individual in the test dataset 122 is compared to a known classification for that individual, and an accuracy score for the model is determined based on the results. If the model 120 satisfies a threshold accuracy condition on the test dataset 122, then the model 120 is output 110 for use … Otherwise, the model 120 may be returned to the training operation 104 for further refinement," The model having to satisfy a threshold accuracy on data before being permitted to classify data is equivalent to, since machine learning classification models use their own metric, to being allowed to use a machine learning model to calculate a correlation metric if it reaches a certain accuracy threshold)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline in view of Lee's combined system for continuous mobile device authentication using a wearable apparatus by enhancing Moline in view of Lee’s system with Molony's system that calculates correlation between signals if the machine learning model trained meets an accuracy requirement. The motivation is that if the model can’t reach an accuracy threshold, it may need further refinement to be reliable in classification, especially when the classification is used for authentication purposes.
Regarding claim 33, Moline in view of Lee and in further view of Molony teaches the apparatus according to claim 32. Lee teaches the training data set and the validation data set comprising data from the first sensor and the second sensor (Paragraph 67 states, " Given a window size, for each target user, the system can utilize multi-fold (e.g., 10-fold, etc.) cross-validation for training and testing. In one embodiment, a 10-fold cross-validation was used, i.e., 9/10 data was used as the training data, and 1/10 used as the testing data.”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline’s system for continuous mobile device authentication using a wearable apparatus by enhancing Moline’s system with Lee by setting the correlation metric computation between signals in Moline to use a validation set for determination. The motivation is that cross validation reduces false acceptance and rejection rates on classifiers by preventing overfitting on training data, in this case this would increase authentication accuracy of a system using training data, yielding predictable results.
Regarding claim 34, Moline in view of Lee and in further view of Molony teaches the apparatus according to claim 32. Moline teaches: that the machine learning model is caused to generate an output indicating a likelihood that the at least two input data samples are associated with the same person at the same time (Col 18 lines 43-60 state, "For a given feature vector, the classifier computes probability estimates for the two labels (0 and 1); outputs the classifier's probability estimate for label 1 as its correlation score c.sub.m(t).")
wherein the time asynchronous correlation metric corresponds to the likelihood (Col. 18 lines 43-60 state, "and a third (coherence) measures similarity and variance in the frequency domain").
Claims 35 is rejected under Moline in view of Lee in further view of Mahmood (US 20220391487 A1).
Regarding claim 35, Moline in view of Lee teaches the apparatus according to claim 30. Moline teaches time synchronous correlation (Col 18 lines 43 to 60 states, "Two (cross-correlation and correlation-coefficient) measure similarity between two signals and how they vary together in time domain," time domain correlation is a time synchronous correlation).
However, Moline and Lee do not specifically mention using dynamic time warping for temporal correlation. However, Mahmood teaches a time synchronous correlation metric comprises a dynamic time warping metric (Paragraph 98 states, "The similarity measure may involve the use of a distance function which will be understood as referring to a function used to calculate a distance between the extracted feature set and a predetermined feature set … The similarity measure may involve the user of a dynamic time warping (DTW) function which will be understood as referring to a function that measures the distance between two time series.").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline in view of Lee’s system for continuous mobile device authentication using a wearable apparatus by enhancing Moline in view of Lee’s system to use dynamic time warping as a means to compare signals in the time domain and produce a correlation output, as taught by Mahmood. The motivation is that dynamic time warp is a distance measurement technique for two time series, the benefit being that delay in sensor readings won't result in a false negative like it would with something like Euclidean distance, where delay substantially influences a metric between two time series.
Claims 42 is rejected under Moline in view of Lee in further view of Huang et al. (US 20240403285), hereafter referred to as Huang.
Regarding claim 42, Moline in view of Lee teaches the apparatus according to claim 26. Moline teaches:
determining that correlation data comprises information indicating a type of correlation between the first sensor and the second sensor, and a first value of the correlation metric between the first sensor and the second sensor (Col 18 lines 43-60 state, " For each segmented window, a correlation feature vector F.sub.c with features from time and frequency domains is computed … For a given feature vector, the classifier computes probability estimates for the two labels (0 and 1); outputs the classifier's probability estimate for label 1 as its correlation score c.sub.m(t)," the first correlation metric is the probability label generated on the feature vectors)
identify the type of correlation between the first sensor and the second sensor; calculate a second value of the correlation metric between the first sensor and the second sensor based on the type of correlation. Col 18 lines 43-60 state, "For each segmented window, a correlation feature vector F.sub.c with features from time and frequency domains is computed," since the type of correlation, time or frequency, is used in the feature vector for calculate the metric, it is calculated based on the type of correlation. Additionally, the time and frequency domain features identify the type of correlation between the sensors).
However, Moline in view of Lee does not specifically teach determining that the data from the first sensor is correlation with data from the second sensor in response to the determining that the second value of the correlation metric is within a third threshold of the first value of the correlation metric. Huang teaches determining that the data from the first sensor is correlation with data from the second sensor in response to the determining that the second value of the correlation metric is within a third threshold of the first value of the correlation metric. (Paragraph 41 states, "it may be determined whether a difference between the anomaly period average correlation metric and the trace-back period average correlation metric of the target time-series data pair 302 is greater than a predetermined correlation difference threshold. If it is determined at 330 that the difference is greater than the correlation difference threshold, it may be determined at 342 that the target time-series data pair 302 has abnormal correlation in the anomaly period, and the target time-series data pair 302 may be identified as an abnormal time-series data pair," The correlation metrics of the time series data pairs are compared with previous ones, and if they are greater than a certain correlation threshold, there is an anomaly that is determined between the time series, abnormal is interpreted as meaning uncorrelated)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Moline in view of Lee's system for continuous mobile device authentication using a wearable apparatus by enhancing Moline in view of Lee's system to compare concurrent feature vector classifications with each other to judge whether the change between the probabilities of the two values in a time frame is abnormal or not, as taught by Huang. The motivation would be to add a notifier to the authentication system that a critical and immediate change has occurred in the correlation of the sensed data, possibly meaning the system should be double checked for computational accuracy or that a user is not currently in possession of their mobile device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUHAMMAD H RASUL whose telephone number is (571)272-4613. The examiner can normally be reached Monday - Friday 7:30 - 5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rupal Dharia can be reached at 571-272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.H.R./Examiner, Art Unit 2492 /RUPAL DHARIA/Supervisory Patent Examiner, Art Unit 2492