DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “eye blink detection classifier”, “determination component” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 1-13 and 15-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun et al. (MEMS ultrasonic transducers for safe, low-power and portable eye-blinking monitoring, 2022, Microsystems & Nanoengineering, Vol. 8, No. 63, Pages 1-14), hereinafter “Sun”.
Regarding claim 1, Sun teaches:
An apparatus (See the Abstract.), comprising:
a processor that executes computer-executable components stored in a computer-readable memory, the computer-executable components comprising (See the “PC” in Fig. 5.):
an eye blink detection classifier component that generates a classification of ultrasonic information (See Fig. 8c on page 11, four state classifications of the eye based on signals from ultrasonic transducer arrays in Fig. 5.) as indicative of at least one of liveness, an eye blink, a predetermined set of eye blinks, or a predetermined blinking pattern of a user of a wearable device comprising the apparatus via an eye blink classification algorithm (See Fig. 6, the depicted blink states meet the claimed single “eye blink”, “predetermined set of eye blinks”, and “predetermined blinking pattern” (one eye open, one eye closed).);
a determination component that generates a confirmation of the liveness or a determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device based at least in part on the classification (See page 12, left column: “The first 100 sets of data at the beginning of the experiment are divided into two clusters, which are regarded as labeled data. The midpoint of the two cluster centers is used as the criterion to classify the subsequent data. After that, the classified data are added to the cluster, the cluster center is recalculated, and the criteria are updated.”); and
a user interface (UI) component configured to generate at least one of a data set or instruction configured to facilitate user control of the wearable device based at least in part on the confirmation of the liveness or the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device (See page 2, left column: “In addition, blinking monitoring can be adopted in human–machine interactions…Furthermore, people are able to experience immersive interaction with AR/VR headsets and smart glasses by seamlessly integrating eye blinking monitoring and eye-tracking functions into smart devices.”).
Regarding claim 2, Sun teaches:
The apparatus of claim 1, further comprising: an ultrasonic transceiver that generates the ultrasonic information (See Fig. 5.).
Regarding claim 3, Sun teaches:
The apparatus of claim 2, wherein the ultrasonic information comprises ultrasonic time of flight range information (See page 2, right column: “Based on time-of-flight (TOF) pulse echoes and a dynamic unsupervised learning method, we achieved eye state recognition as a demonstration of portable human blink monitoring.”).
Regarding claim 4, Sun teaches:
The apparatus of claim 1, wherein the ultrasonic transceiver is positioned within the wearable device such that the field of view of the ultrasonic transceiver will sense at least one eye of the user when the wearable device is being worn by the user (See Figs. 5-6.).
Regarding claim 5, Sun teaches:
The apparatus of claim 1, wherein the ultrasonic transceiver is configured to emit ultrasonic pulses and receive echoes as the ultrasonic information (See Fig. 5 and the Abstract; “Low-power pulse-echo ultrasound featuring biosafety is transmitted and received by microelectromechanical system (MEMS) ultrasonic transducers seamlessly integrated on glasses.”), and wherein the eye blink classification algorithm is configured to analyze expected echoes of the ultrasonic pulses on the user (See Fig. 8a.) to provide the confirmation of the liveness of the user or the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern by the user (See Figs. 6 and 8c.).
Regarding claim 6, Sun teaches:
The apparatus of claim 1, wherein the eye blink classification algorithm is based at least in part on a machine learning model trained on captured and classified ultrasonic information received during a plurality of states of use of a test wearable device (See page 12, left column: “To better adapt to different people, a dynamic unsupervised learning method is used for state classification.”).
Regarding claim 7, Sun teaches:
The apparatus of claim 6, wherein the machine learning model is based on an eye blink detection classifier trained on the captured and the classified ultrasonic information received during the plurality of states of use of the test wearable device (See page 12, left column: “The first 100 sets of data at the beginning of the experiment are divided into two clusters, which are regarded as labeled data. The midpoint of the two cluster centers is used as the criterion to classify the subsequent data. After that, the classified data are added to the cluster, the cluster center is recalculated, and the criteria are updated. Compared with supervised learning, this method does not need to label large amounts of data.”).
Regarding claim 8, Sun teaches:
The apparatus of claim 7, wherein the eye blink detection classifier component is configured to generate the classification of the ultrasonic information as indicative of the at least one of the liveness, the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern based at least in part on the eye blink classification algorithm, wherein the eye blink classification algorithm is configured to compute magnitude of samples of ultrasonic information, wherein the eye blink classification algorithm is configured to normalize the magnitude of samples of ultrasonic information as a function of ultrasonic transceiver operating frequency to generate normalized magnitude ultrasonic information (See the normalized voltage in Fig. 6.), wherein the eye blink classification algorithm is configured to compute a set of feature vectors in the normalized magnitude ultrasonic information, and wherein the eye blink classification algorithm is configured to determine a set of feature classification labels and corresponding confidence factors using the eye blink detection classifier (See page 12, left column: “The first 100 sets of data at the beginning of the experiment are divided into two clusters, which are regarded as labeled data. The midpoint of the two cluster centers is used as the criterion to classify the subsequent data. After that, the classified data are added to the cluster, the cluster center is recalculated, and the criteria are updated. Compared with supervised learning, this method does not need to label large amounts of data.” K-means clustering assigns vectors to centroids.).
Regarding claim 9, Sun teaches:
The apparatus of claim 8, wherein the determination component is configured to generate the confirmation of the liveness or the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern based at least in part on the set of feature classification labels and corresponding confidence factors (See page 12, left column: “K-means clustering is performed on the first 100 groups of TOF data obtained from the experiment”).
Regarding claim 10, Sun teaches:
The apparatus of claim 9, wherein the determination component is further configured to generate the confirmation or determination of the liveness or the at least one of the eye blink or the predetermined set of eye blinks of the user of the wearable device to determine a donned or a doffed state of the wearable device, based at least in part on at least one of an eye blink detection classifier component classification of the eye blink or the predetermined set of eye blinks (Detection/classification of one of the four states in Fig. 8c implies that the system first determines that the glasses are worn.).
Regarding claim 11, Sun teaches:
The apparatus of claim 1, wherein the eye blink detection classifier component, for a plurality of measurement vectors associated with the ultrasonic information, is configured to generate a classification prediction of at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device (See page 12, left column: “The first 100 sets of data at the beginning of the experiment are divided into two clusters, which are regarded as labeled data. The midpoint of the two cluster centers is used as the criterion to classify the subsequent data. After that, the classified data are added to the cluster, the cluster center is recalculated, and the criteria are updated. Compared with supervised learning, this method does not need to label large amounts of data.” K-means clustering assigns vectors to centroids.).
Regarding claim 12, Sun teaches:
The apparatus of claim 1, wherein the eye blink detection classifier component is further configured to distinguish between a natural eye blink, characterized by at least one of a nominal eye blink speed, duration, or interval, and at least one of a voluntary long blink, the predetermined set of eye blinks comprising a plurality of eye blinks, or the predetermined blinking pattern (See Fig. 8c, state 3 “All closed” for the depicted duration meeting the claimed “natural eye blink”, and states 2 and 4 meeting the claimed “plurality of eye blinks” and “predetermined blinking pattern”.).
Regarding claim 13, Sun teaches:
The apparatus of claim 12, wherein the predetermined blinking pattern comprises the plurality of eye blinks, wherein characteristics of each eye blink of the plurality of eye blinks differ from a successive eye blink of the plurality of eye blinks in at least one of blink speed, blink duration, interval to next eye blink, or which one or both of the left eye blink or right eye blink (See states 2 and 4 in Fig. 8c.).
Regarding claim 15, Sun teaches:
A method (See the Abstract.), comprising:
generating, by an ultrasonic transceiver operatively coupled to a processor, ultrasonic information associated with a user of a wearable device (See Figs. 5-6.);
generating a classification, via an eye blink detection classifier component associated with a memory coupled to the processor, of the ultrasonic information (See Fig. 8c on page 11, four state classifications of the eye based on signals from ultrasonic transducer arrays in Fig. 5.) as indicative of at least one of liveness, an eye blink, a predetermined set of eye blinks, or a predetermined blinking pattern of the user of the wearable device according to an eye blink classification algorithm executed by the processor (See Fig. 6, the depicted blink states meet the claimed single “eye blink”, “predetermined set of eye blinks”, and “predetermined blinking pattern” (one eye open, one eye closed).);
generating a confirmation, via a determination component associated with the memory, of the liveness or a determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device based at least in part on the classification (See page 12, left column: “The first 100 sets of data at the beginning of the experiment are divided into two clusters, which are regarded as labeled data. The midpoint of the two cluster centers is used as the criterion to classify the subsequent data. After that, the classified data are added to the cluster, the cluster center is recalculated, and the criteria are updated.”); and
generating, via a user interface (UI) component associated with the memory, at least one of a data set or instruction configured to facilitate user control of the wearable device based at least in part on the confirmation of the liveness or the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device (See page 2, left column: “In addition, blinking monitoring can be adopted in human–machine interactions…Furthermore, people are able to experience immersive interaction with AR/VR headsets and smart glasses by seamlessly integrating eye blinking monitoring and eye-tracking functions into smart devices.”).
Sun teaches claim 16 for the reasons given in the treatment of claim 8.
Sun teaches claim 17 for the reasons given in the treatment of claim 9.
Sun teaches claim 18 for the reasons given in the treatment of claim 10.
Sun teaches claim 19 for the reasons given in the treatment of claim 12.
Sun teaches claim 20 for the reasons given in the treatment of claim 13.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (MEMS ultrasonic transducers for safe, low-power and portable eye-blinking monitoring, 2022, Microsystems & Nanoengineering, Vol. 8, No. 63, Pages 1-14) in view of Asaduzzaman et al. (An Eye Blinking Password Based Liveness Monitoring System to Improve the Detection of Video Spoofing, October 2015, International Journal of Mechatronics, Electrical and Computer Technology, Vol. 5, No. 17, Pages 2398-2407), hereinafter “Asaduzzaman”.
Claim 14 is met by the combination of Sun and Asaduzzaman, wherein
Sun teaches:
The apparatus of claim 12, wherein the UI component is further configured to
Sun does not explicitly disclose the following; however, Asaduzzaman discloses:
generate the at least one of the data set or instruction that facilitates user authentication of the user of the wearable device based at least in part on the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user of the wearable device (See Fig. 3 and the Abstract: “An eye blinking password system using ultrasonic range sensing module is developed for liveliness detection of the users.”).
Sun and Asaduzzaman together disclose the limitations of claim 14. Asaduzzaman is directed to a similar field of art (eye blink detection and biometrics using an ultrasonic module). Therefore, Sun and Asaduzzaman are combinable. Modifying the system and method of Sun by adding the capability of “[generating] the at least one of the data set or instruction that facilitates user authentication of the user of the wearable device based at least in part on the determination of the at least one of the eye blink, the predetermined set of eye blinks, or the predetermined blinking pattern of the user”, as disclosed by Asaduzzaman, would yield the expected and predictable result of improved security of the smartglasses or VR headset. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Sun and Asaduzzaman in this way.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN S LEE whose telephone number is (571)272-1981. The examiner can normally be reached 11:30 AM - 7:30 PM.
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/Jonathan S Lee/Primary Examiner, Art Unit 2677
