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 .
Information Disclosure Statement
The information disclosure statement (IDS) were submitted on 2/14/2024 and 4/15/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12, and 17, with claim 1 being exemplary, recite the following limitations,
“(a) receiving static device data in response to detected first biometric movements; (b) receiving mobile device data in response to detected second biometric movements; (c) applying an analysis algorithm to the static device data to determine static attributes; (d) applying the analysis algorithm to the mobile device data to determine mobile attributes; (e) comparing the static attributes to the mobile attributes; and (f) determining a modification action based on the comparing” [Emphasis added].
Claim 12 further recites the following limitations,
“(g) determining that the static attributes are within a threshold parameter of the mobile attributes; and (h) validating a mobile device based on the determining that the static attributes are within a threshold parameter of the mobile attributes” [Emphasis added].
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that the independent claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories? YES. Independent claims 1, 12, and 17 are directed to a method, method, and system, respectively.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? YES. Independent claims 1, 12, and 17 are directed towards a mathematical concept and a mental process (i.e. an abstract idea).
Regarding claims 1, 12, and 17, limitations (c)-(f), in an emphasized claim 1 above, fall under a mental process as the human mind is capable of determining attributes, comparing data, and determining an action based on the comparison. Furthermore, limitations (c) and (d) also fall under a mathematical concept as applying an analysis algorithm is considered a mathematical calculation. Limitations (g) and (h), in an emphasized claim 12 above, fall under a mental process as the human mind is capable of determining attributes to be above a threshold and validating a device based on that determination.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? NO. Independent claims 1, 12, and 17 do not recite additional elements that integrate the judicial exception into a practical application.
Regarding claims 1, 12, and 17, limitations (a) and (b), in an emphasized claim 1 above, are additional elements, which while not necessarily being an abstract idea, is insignificant extra-solution activity since it is merely data output (see MPEP §2106.05(g)). Claims 1, 12, and 17 further recite a “mobile device” and a “static device”. Claim 17 also recites a “first sensor” and a “second sensor”. These additional elements are not sufficient to recite a practical application of the abstract ideas recited in claims 1, 12, and 17 as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP §2106.05(f)).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO. Independent claim 1, 12, and 17 do not recite additional elements that amount to significantly more than the judicial exception.
Regarding claims 1, 12, and 17, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claim 1 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional compute functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Therefore, independent claims 1, 12, and 17 are directed towards an abstract idea without a practical application or significantly more.
Regarding claim 2, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static device data is generated by a static device and the mobile device data is generated by a mobile device, wherein the mobile device is a wearable device falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 3, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device falls under generic computer elements and functions (see MPEP §2106.05(f)) as resolution and refresh-rate are common properties of computer parts.
Regarding claim 4, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the first biometric movements and the second biometric movements are saccades falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 5, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static device data or the mobile device data is raw data falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 6, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the analysis algorithm is a Velocity-Threshold Identification (I-VT) eye-tracker algorithm falls under a mathematical concept (see MPEP §2106.04(a)(2)(I)).
Regarding claim 7, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the first biometric movements are the same as the second biometric movements, each of the first biometric movements and the second biometric movements detected during performance of a same respective memory saccade task falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 8, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a velocity, an amplitude, a duration, or a latency falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 9, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a saccadic velocity, a saccadic amplitude, a saccadic duration, or a saccadic latency falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 10, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a fixation attribute, a target shown attribute, a maintain fixation attribute, a saccade attribute, or a correction attribute falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 11, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a time to first saccade, a largest first saccade, a largest non-first saccade, total saccades, or a number of saccades within a duration falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 13, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device falls under generic computer elements and functions (see MPEP §2106.05(f)) as resolution and refresh-rate are common properties of computer parts.
Regarding claim 14, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the analysis algorithm is a Velocity-Threshold Identification (I-VT) eye-tracker algorithm falls under a mathematical concept (see MPEP §2106.04(a)(2)(I)).
Regarding claim 15, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the first biometric movements or the second biometric movements are detected during performance of a memory saccade task falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 16, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a time to first saccade, a largest first saccade, a largest non-first saccade, total saccades, or a number of saccades within a duration falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 18, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device falls under generic computer elements and functions (see MPEP §2106.05(f)) as resolution and refresh-rate are common properties of computer parts.
Regarding claim 19, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the first biometric movements are the same as the second biometric movements, each of the first biometric movements and the second biometric movements detected during performance of a same respective memory saccade task falls under selecting a data type or source (see MPEP §2106.05(g)).
Regarding claim 20, the additional limitations do not integrate the abstract idea into a practical application or add significantly more to the abstract idea. The limitation: wherein the static attributes or the mobile attributes comprise one or more of a fixation attribute, a target shown attribute, a maintain fixation attribute, a saccade attribute, or a correction attribute falls under selecting a data type or source (see MPEP §2106.05(g)).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-14, 16-18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by applicant supplied prior art Dowiasch et al. (Dowiasch, Stefan, Peter Wolf, and Frank Bremmer. "Quantitative comparison of a mobile and a stationary video-based eye-tracker." Behavior research methods 52.2 (2020): 667-680) (hereafter, “Dowiasch”).
Regarding claim 1, Dowiasch discloses a method comprising: receiving static device data in response to detected first biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the stationary ELII as the “static device”); receiving mobile device data in response to detected second biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ESC the “second biometric movements”); applying an analysis algorithm to the static device data to determine static attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the static device data); applying the analysis algorithm to the mobile device data to determine mobile attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the mobile device data); comparing the static attributes to the mobile attributes (Page 673, §Results, Right column, last paragraph, When using the same saccade detector, the saccade related parameters for both systems were much more alike. Examiner considers the saccade related parameters from the respective systems as “static/mobile attributes”); and determining a modification action based on the comparing (Page 677, §Discussion, Our data also showed that a standardized analysis of the raw data with identical parameters across different eye-trackers—for example, for saccade detection—is often more important for consistent results across systems, than the hardware itself. Examiner considers the use of a standardized analysis the “modification action” and the interpretation of the data leading to this conclusion the “determining”).
Regarding claim 2, Dowiasch discloses the method of claim 1, wherein the static device data is generated by a static device and the mobile device data is generated by a mobile device (Page 668, right column, last paragraph, … mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), … stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Method, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker), wherein the mobile device is a wearable device (The EyeSeeCam is a wearable device).
Regarding claim 3, Dowiasch discloses the method of claim 2, wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device (Page 669, §Devices, right column, The ESC is a fully mobile, lightweight eye tracker, which is able to record binocular eye movements with a sampling rate of 230 Hz; Page 670, §Devices, left column The ELII system was operated bin ocularly with 500 Hz. Since the limitation is recited in the alternative, Examiner considers this to disclose the limitation in its entirety).
Regarding claim 4, Dowiasch discloses the method of claim 1, wherein the first biometric movements and the second biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared …, the EyeSeeCam (ESC; Schneider et al., 2009), with …, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the ESC the “second biometric movements”) are saccades (670, §Paradigm, The experiment consisted of a saccade task).
Regarding claim 5, Dowiasch discloses the method of claim 1, wherein the static device data or the mobile device data is raw data (Page 671, §Data analysis, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems).
Regarding claim 6, Dowiasch discloses the method of claim 1, wherein the analysis algorithm is a Velocity-Threshold Identification (I-VT) eye-tracker algorithm (Page 671, §Saccades, The ELII has a build-in online saccade and blink detector, which automatically marks those events in the recorded data. This detector uses a velocity criterion for saccades with a threshold of 30°/s).
Regarding claim 8, Dowiasch discloses the method of claim 1, wherein the static attributes or the mobile attributes comprise one or more of a velocity (Page 673, Table 3 saccade mean velocity), an amplitude (Page 673, Table 3 saccade amplitude), a duration (Fig. 2. Length of saccade segment indicates duration), or a latency (Fig. 2. X-axis shows saccade latency).
Regarding claim 9, Dowiasch discloses the method of claim 1, wherein the static attributes or the mobile attributes comprise one or more of a saccadic velocity (Page 673, Table 3 saccade mean velocity), a saccadic amplitude (Page 673, Table 3 saccade amplitude), a saccadic duration (Fig. 2. Length of saccade segment indicates duration), or a saccadic latency (Fig. 2. X-axis shows saccade latency).
Regarding claim 10, Dowiasch discloses the method of claim 1, wherein the static attributes or the mobile attributes comprise one or more of a fixation attribute (Page 673, Table 3 absolute fixation error), a target shown attribute (Page 673, §Saccades and fixation, there was also a significant interaction between the location of the target and the eye-trackers), a maintain fixation attribute (Page 672, §Fixation, Consequently, small correction saccades and drifts that might occur during that fixation period were included into computation of the average absolute eye position), a saccade attribute (Page 673, Table 3 saccade mean velocity), or a correction attribute (Page 672, §Fixation, Consequently, small correction saccades and drifts that might occur during that fixation period were included into computation of the average absolute eye position).
Regarding claim 11, Dowiasch discloses the method of claim 1, wherein the static attributes or the mobile attributes comprise one or more of a time to first saccade (Fig 2. The x-axis of the figure represents time relative to trial start. Examiner considers the time since trial start for the saccade dataset to also encompass the “time to first saccade”. Since the limitation is recited in the alternative, Examiner considers this to disclose the limitation in its entirety), a largest first saccade, a largest non-first saccade, total saccades, or a number of saccades within a duration.
Regarding claim 12, Dowiasch discloses a method comprising: receiving static device data in response to detected first biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the stationary ELII as the “static device”); receiving mobile device data in response to detected second biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ESC the “second biometric movements”); applying an analysis algorithm to the static device data to determine static attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the static device data); applying the analysis algorithm to the mobile device data to determine mobile attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the mobile device data); determining that the static attributes are within a threshold parameter of the mobile attributes (Fig. 3B, Page 673, §Saccades and Fixation, Furthermore, there was no significant difference between the two systems concerning mean saccade peak velocity [mean ELII: 391 ± 37°/s; mean ESC: 410 ± 48°/s. Examiner considers the ESC standard deviation (± 48°/s) the “mobile threshold” and the ELII mean peak velocity the “static attribute within”); and validating a mobile device based on the determining that the static attributes are within a threshold parameter of the mobile attributes (Page 677, §Discussion, However, here we could show that, despite their different hardware features, their different methods for computing gaze direction and, more general, their different fields of key application, the ESC and the ELII provided indistinguishable data at the population level in almost all cases. Examiner considers the comparison of data between the two eye trackers and the conclusion that they are indistinguishable as “validating a mobile device”).
Regarding claim 13, Dowiasch discloses the method of claim 12, wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device (Page 669, §Devices, The ESC is a fully mobile, lightweight eye tracker, which is able to record binocular eye movements with a sampling rate of 230 Hz; Page 670, §Devices, The ELII system was operated bin ocularly with 500 Hz. Since the limitation is recited in the alternative, Examiner considers this to disclose the limitation in its entirety).
Regarding claim 14, Dowiasch discloses the method of claim 12, wherein the analysis algorithm is a Velocity-Threshold Identification (I-VT) eye-tracker algorithm (Page 671, §Saccades, The ELII has a build-in online saccade and blink detector, which automatically marks those events in the recorded data. This detector uses a velocity criterion for sac cades with a threshold of 30°/s).
Regarding claim 16, Dowiasch discloses the method of claim 12, wherein the static attributes or the mobile attributes comprise one or more of a time to first saccade (Fig 2. The x-axis of the figure represents time relative to trial start. Examiner considers the time since trial start for the saccade dataset to also encompass the “time to first saccade”. Since the limitation is recited in the alternative, Examiner considers this to disclose the limitation in its entirety), a largest first saccade, a largest non-first saccade, total saccades, or a number of saccades within a duration.
Regarding claim 17, Dowiasch discloses a system comprising: a static device comprising at least one first sensor to detect first biometric movements (Page 668, right column, stationary but head-mounted laboratory eye-tracker, the EyeLink II) ; a mobile device comprising at least one second sensor to detect second biometric movements (Page 668, right column, one of the most sophisticated mobile eye-trackers available, the EyeSeeCam) ; a processor; and a computer-readable data storage device storing instructions that, when executed by the processor (Page 671, §Data analysis, Eye movement parameters were analyzed offline using MATLAB 2015a. MATLAB is a software program that runs on a computer, which inherently includes a computer readable storage device and a processor), cause the system to: receive static device data based on the first biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the stationary ELII as the “static device”); receive mobile device data based on the second biometric movements (Page 668, right column, last paragraph, In this study we quantitatively compared one of the most sophisticated mobile eye-trackers available, the EyeSeeCam (ESC; Schneider et al., 2009), with a commonly used stationary but head-mounted laboratory eye-tracker, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ESC the “second biometric movements”); apply an analysis algorithm to the static device data to determine static attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the static device data); apply the analysis algorithm to the mobile device data to determine mobile attributes (Page 671, §Saccades, right column, last paragraph, we also analyzed the raw eye-position data recorded by both systems with the same preprocessing algorithms. Examiner considers the preprocessing algorithms the “analysis algorithm”. Since it is applied to both systems, this encompasses the mobile device data); compare the static attributes to the mobile attributes (Page 673, §Results, Right column, last paragraph, When using the same saccade detector, the saccade related parameters for both systems were much more alike. Examiner considers the saccade related parameters from the respective systems as “static/mobile attributes”); and determine a modification action based on the comparing (Page 677, §Discussion, Our data also showed that a standardized analysis of the raw data with identical parameters across different eye-trackers—for example, for saccade detection—is often more important for consistent results across systems, than the hardware itself. Examiner considers the use of a standardized analysis the “modification action” and the interpretation of the data leading to this conclusion the “determining”).
Regarding claim 18, Dowiasch discloses the system of claim 17, wherein the static device has at least one of a higher resolution or a higher refresh-rate than the mobile device (Page 669, §Devices, right column, The ESC is a fully mobile, lightweight eye tracker, which is able to record binocular eye movements with a sampling rate of 230 Hz; Page 670, §Devices, left column The ELII system was operated bin ocularly with 500 Hz. Since the limitation is recited in the alternative, Examiner considers this to disclose the limitation in its entirety).
Regarding claim 20, Dowiasch discloses the system of claim 17, wherein the static attributes or the mobile attributes comprise one or more of a fixation attribute fixation attribute (Page 673, Table 3 absolute fixation error), a target shown attribute (Page 673, §Saccades and fixation, there was also a significant interaction between the location of the target and the eye-trackers), a maintain fixation attribute (Page 672, §Fixation, Consequently, small correction saccades and drifts that might occur during that fixation period were included into computation of the average absolute eye position), a saccade attribute (Page 673, Table 3 saccade mean velocity), or a correction attribute (Page 672, §Fixation, Consequently, small correction saccades and drifts that might occur during that fixation period were included into computation of the average absolute eye position).
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 7, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over applicant supplied prior art Dowiasch et al. (Dowiasch, Stefan, Peter Wolf, and Frank Bremmer. "Quantitative comparison of a mobile and a stationary video-based eye-tracker." Behavior research methods 52.2 (2020): 667-680) (hereafter, “Dowiasch”) in view of Abegg et al. (Abegg, Mathias, Hyung Lee, and Jason JS Barton. "Systematic diagonal and vertical errors in antisaccades and memory-guided saccades." Journal of Eye Movement Research 3.3 (2010): 15) (hereafter, “Abegg”).
Regarding claim 7, Dowiasch discloses the method of claim 1, wherein the first biometric movements are the same as the second biometric movements, each of the first biometric movements and the second biometric movements detected during performance of a same respective (Page 668, right column, last paragraph, In this study we quantitatively compared …, the EyeSeeCam (ESC; Schneider et al., 2009), with …, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the ESC the “second biometric movements”) [memory saccade task].
However, Dowiasch fails to disclose memory saccade task.
Abegg teaches memory saccade task (Page 3, §Methods, left column, For the memory guided saccades, grey rather white stimuli were used. They were presented during 300 ms, followed by a grey blank screen for 300 ms. This was done to avoid afterimages. The blank screen was followed by a fixation light that disappeared after 1.7 seconds; resulting in a 2-second memory period).
Both Dowiasch and Abegg are analogous to the claimed invention because they are in field of measuring eye movements. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the memory saccade task of Abegg into the eye tracking system of Dowiasch. The suggestion/motivation for doing so would have been that in combination, each element merely performs the same function as it does separately and the results of the combination would have been predictable to one of ordinary skill in the art.
This method of improving Dowiasch was within the ordinary ability of one of ordinary skill in the art based on the teachings of Abegg.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Dowiasch with the teachings of Abegg to obtain the invention as specified in claim 7.
Regarding claim 15, Dowiasch discloses the method of claim 12, wherein the first biometric movements or the second biometric movements are detected during performance of a (Page 668, right column, last paragraph, In this study we quantitatively compared …, the EyeSeeCam (ESC; Schneider et al., 2009), with …, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the ESC the “second biometric movements”) [memory saccade task].
However, Dowiasch fails to disclose memory saccade task.
Abegg teaches memory saccade task (Page 3, §Methods, left column, For the memory guided saccades, grey rather white stimuli were used. They were presented during 300 ms, followed by a grey blank screen for 300 ms. This was done to avoid afterimages. The blank screen was followed by a fixation light that disappeared after 1.7 seconds; resulting in a 2-second memory period).
Both Dowiasch and Abegg are analogous to the claimed invention because they are in field of measuring eye movements. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the memory saccade task of Abegg into the eye tracking system of Dowiasch. The suggestion/motivation for doing so would have been that in combination, each element merely performs the same function as it does separately and the results of the combination would have been predictable to one of ordinary skill in the art.
This method of improving Dowiasch was within the ordinary ability of one of ordinary skill in the art based on the teachings of Abegg.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Dowiasch with the teachings of Abegg to obtain the invention as specified in claim 15.
Regarding claim 19, Dowiasch discloses the system of claim 17, wherein the first biometric movements are the same as the second biometric movements, each of the first biometric movements and the second biometric movements detected during performance of a same respective (Page 668, right column, last paragraph, In this study we quantitatively compared …, the EyeSeeCam (ESC; Schneider et al., 2009), with …, the EyeLink II (ELII); Page 669, §Methods, Each subject had to perform the same standardized sequence of paradigms twice, each time with a different eye tracker. Examiner considers the eye movements when recording with the ELII the “first biometric movements” and the ESC the “second biometric movements”) [memory saccade task].
However, Dowiasch fails to disclose memory saccade task.
Abegg teaches memory saccade task (Page 3, §Methods, left column, For the memory guided saccades, grey rather white stimuli were used. They were presented during 300 ms, followed by a grey blank screen for 300 ms. This was done to avoid afterimages. The blank screen was followed by a fixation light that disappeared after 1.7 seconds; resulting in a 2-second memory period).
Both Dowiasch and Abegg are analogous to the claimed invention because they are in field of measuring eye movements. It would have been obvious to a person of ordinary skill before the effective filing date of the claimed invention to incorporate the memory saccade task of Abegg into the eye tracking system of Dowiasch. The suggestion/motivation for doing so would have been that in combination, each element merely performs the same function as it does separately and the results of the combination would have been predictable to one of ordinary skill in the art.
This method of improving Dowiasch was within the ordinary ability of one of ordinary skill in the art based on the teachings of Abegg.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify Dowiasch with the teachings of Abegg to obtain the invention as specified in claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Deane et al. 2022 (Deane, Oliver, Eszter Toth, and Sang-Hoon Yeo. "Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data." Behavior Research Methods 55.3 (2023): 1372-1391) discloses a machine learning algorithm for processing eye tracking data (Page 1372, Abstract, The system achieves this by first running footage recorded on a head-mounted camera through a deep learning-based object detection algorithm called Masked Region-based Convolutional Neural Network (Mask R-CNN)).
Ehinger et al. (Ehinger, Benedikt V., et al. "A new comprehensive eye-tracking test battery concurrently evaluating the Pupil Labs glasses and the EyeLink 1000." PeerJ 7 (2019): e7086) discloses a comparison between a mobile and static eye tracker (Page 3, Consequently, we recorded the participants’ gaze with two video-based eye-trackers at the same time: the stationary EyeLink 1000 (SR research) and the mobile Pupil Labs glasses (Pupil Labs, Berlin, Germany)).
Samadani (US 2019/0192063) discloses a comparison of mobile and static eye tracking data (¶0209, FIG. 11 demonstrates the test-retest reliability of a stationary to portable tracker. Subjects (n=24) demonstrated high test-retest reliability between separate eyetracking sessions on the stationary tracker and the portable tracker (FIG. 10)).
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/X.D./ Examiner, Art Unit 2676
/Henok Shiferaw/ Supervisory Patent Examiner, Art Unit 2676