DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11 - 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to claim 11, which include a data storage device with instructions to perform the method steps of “iteratively performing trials at locations in the sensory field… by providing a particular intensity of a stimulus to the user” and “receiving a result comprising an indication from a user”, it is unclear what claim elements are “providing a particular intensity of stimulus to the user” and “receiving a result comprising an indication from a user”. Further details that more explicitly define the relationship between the “sensory device” and the method steps performed are required. Claims 12 - 20 are rejected by virtue of dependence on claims 1 11.
In regard to claims 5 & 15, lines 1 - 2 recite, “the transformation of the psychometric function can be based on at least one of…” However, the phrase “can be” makes the metes and bounds of the claim limitation unclear because the details are not positively claimed. Examiner suggests amending “can be based on…” to “is based on…” to positively claim basis for the transformation of the psychometric function.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
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 1 - 5, 7 - 15, & 17 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Benner (US 9883793 B2 - Cited by Applicant) in view of Rubenstein (RUBINSTEIN, NIKKI J., et al., "Incorporating spatial models in visual field test 26 procedures", Translational vision science & technology. 2016 Mar 1;5(2). 27. - Cited by Applicant)
In regard to claim 1 and 11, Benner discloses a computer-implemented method and system for sensory testing across a visual field of a user that includes one or more processors and for implementing computer executable instructions stored in the memory or data storage (Column 3, lines 5 - 19) that include initializing probability matrices based on knowledge of a sensory field of a user (FIG. 1) where the processor generates a probabilistic model representing a visual ability of a user across a visual field at discrete measurement locations (FIG. 1, component 110; Column 3, lines 5 - 19) based on data from the user (Column 3, lines 10 - 19). Benner further discloses that testing is iteratively performed at different locations of the visual field (FIG. 6A) where the testing is adapted in real time based on the output of a probabilistic model that determines the confidence level of the measurements and the user’s response to the testing stimuli (Column 7, lines 47 - 65). Each trial or round of testing includes receiving a result from a user in response to testing stimulus (Column 7, lines 47 - 65; see “patient’s responses) which is incorporated into iteratively testing different locations in the field of vision. The result from a user is input using a patient response device connected to the perimeter (FIG. 7, component 720; Column 8, lines 22 - 37).
While Benner discloses a computer-implemented method and system for visual testing of a user using a perimeter such as a Humphrey Field Analyzer that includes a probabilistic model (FIG. 1) generated from user data, connection strength between visual field locations, and noise estimates (Column 3, line 57 - 67), they do not specify that each trial includes transforming a set of psychometric functions using user input, updating the probability matrices by multiplying them by the set of transformed psychometric functions, and generating updated probability distributions of the sensory field for locations in the sensory field using the updated probability matrices. While Benner further discloses that the testing protocol is adapted based on results from a user, they also do not specify a termination criteria for the testing. Additionally, Benner does not include details on determining statistical measures that describe the updated probability distributions or outputting the statistical measures as estimates of the sensory field.
However, Rubinstein teaches an algorithm, Spatially Weighted Likelihoods in Zippy Estimation by Sequential Testing (SWELZ), that can be incorporated into the analysis of perimetry testing such as a testing using a Humphrey field analyzer (Page 6, Section: Input Visual Fields). SWELZ includes assigning a probability mass function (PMF) to each location in the visual field, forming a probability matrix. Stimuli is presented to a user in a specific testing location of the visual field where after each trial where a stimuli is presented, a new probability mass function is generated for the visual field location by multiplying the current probability mass function by a likelihood function that represents the result from the user or the probability that the observer sees the stimulus to generate a updated probability matrix (FIG. 2; Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing). Testing continues until a termination criteria is met, where the test terminates when the standard deviation of the PMF at each location is < 1.5 dB (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing). Rubinstein further teaches that the final output of the system is the determined estimate of visual field for each location where the probability is the mean of the final PMF for that location (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing). Examiner notes that mean or average of a PMF comprises a statistical measure.
It would have been obvious to one of ordinary skill in the art to have modified the computer-implemented method and system disclosed by Benner with the teachings of Rubinstein that include the use of an algorithm for analyzing perimetry results because Benner is already concerned with generating a probabilistic model for analyzing a user’s visual field where the probabilistic model represents an expected measure of visual ability (Column 2, lines 46 - 61) and Rubinstein further teaches the use of a probabilistic model for estimating a user’s visual field or ability such that modifying the method and system of Benner with the teaching of Rubinstein would be considered simple substitution of one known element, in this case the probabilistic model disclosed by Benner, for another, in this case the probabilistic model taught by Rubinstein, to obtain the predictable results of assessing a user’s visual field.
In regard to claims 2 and 12, Benner as modified discloses the invention of claim 1. Rubinstein further teaches that the location and intensity of the light stimuli in a given iteration is based on the testing location’s PMF (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing).
In regard to claims 3 and 13, Benner as modified discloses the invention of claim 1. Rubinstein further teaches that the psychometric functions map sensory stimuli to probabilities that a user will see the stimulus by incorporating the likelihood function into the PMF for each location of the visual field (FIG. 2, “likelihood function”; Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing).
In regard to claims 4 and 14, Benner as modified discloses the invention of claim 1. Rubinstein further teaches that transformation of the psychometric function or PMF includes translation and contraction (FIG. 2, see “prior PMF” and “posterior PMF”) when the PMF is transformed by multiplying the current PMF by a likelihood function (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing).
In regard to claims 5 and 15, Benner as modified discloses the invention of claim 4. Rubinstein further teaches that transformation of the psychometric function or PMF is based on whether the psychometric function is associated with the particular location in the particular iteration or associated with other locations in the sensory field where the PMF of each location that shares a border with the particular location being assessed in the associated particular iteration is updated by a modified likelihood function based on the edge weights of the associated locations and intensity value presented at the particular location being assessed in the associated particular iteration (FIG. 2; Page 3, Section: Test Procedures: Spatially Weighted Likelihoods in ZEST).
In regard to claims 7 and 17, Benner as modified discloses the invention of claim 1. Benner discloses that the sensory field being assessed is a visual field (Benner, Column 2, lines 46 - 61).
In regard to claims 8 and 18, Benner as modified discloses the invention of claim 1. Rubenstein further teaches that testing continues until a termination criteria is met, where the test terminates when the standard deviation of the PMF at each location is < 1.5 dB (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing). Examiner notes that determining the standard deviation of the PMF would be considered statistics performed on the updated probability distribution of the sensory field estimate at such location.
In regard to claims 9 and 19, Benner as modified discloses the invention of claim 8. Rubenstein further teaches that the statistics are compared to a predetermined threshold value of 1.5 dB (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing).
In regard to claims 10 and 20, Benner as modified discloses the invention of claim 1. Rubenstein teaches that multiple algorithms can be utilized to assess visual field including ZEST and other algorithms such as SWeLZ that extend the ZEST procedure to include information actors the visual field using a spatial graph to define relationships between visual field locations (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing; Page 3, Section: Test Procedures: Spatially Weighted Likelihoods in ZEST) where the ZEST algorithm comprises a probability matrix that is updated each iteration with regard to the particular testing location (Page 3, Section: Test Procedures: Zippy Estimation by Sequential Testing ) and the SWeLZ algorithm comprises a probability matrix that is updated each iteration with regard to the probability values associated with the particular testing location and related other locations that share an edge with the tested location (Page 3, Section: Test Procedures: Spatially Weighted Likelihoods in ZEST).
Claims 6 & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Benner (US 9883793 B2 - Cited by Applicant) in view of Rubenstein (RUBINSTEIN, NIKKI J., et al., "Incorporating spatial models in visual field test 26 procedures", Translational vision science & technology. 2016 Mar 1;5(2). 27. - Cited by Applicant) as applied to claim 1 above, and further in view of Gong (GONG, YUXIN, et al., "Trail-Traced Threshold Test (T4) With a Weighted Binomial Distribution for a Psychophysical Test", IEEE J Biomed Health Inform. 2021;25: 2787-2800. doi:10.1109/JBHI.2021.3057437. - Cited by Applicant).
In regard to claims 6 and 16, Benner as modified discloses the invention of claim 1. While Benner discloses a computer-implemented method and system for sensory testing across a visual field of a user and Rubinstein further teaches the use and extension of Zippy Estimation by Sequential Testing (ZEST) algorithms by incorporating spatially weighted likelihoods (SWeLZ), neither Benner nor Rubinstein specify normalizing values in the probability matrix to construct probability distributions of each location.
However, Gong teaches that a ZEST algorithm can include a normalization step after each multiplication step to make the sum of the probabilities of each location equal to 1, such that after the normalization step, a new PDF is obtained (Page 2790, Section: III. Method: A. Zippy Estimation of Sequential Testing).
It would have been obvious to one of ordinary skill in the art to have modified the method and system disclosed by Benner as modified by Rubinstein, which includes evaluating a user’s visual field using a ZEST algorithm, with the teaching that a ZEST algorithm can include a normalization step for normalizing values in the probability matrix because it would be considered combining prior art elements according to known methods to yield the predictable result of evaluating a user’s visual field using a ZEST algorithm.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIENNA CHRISTINE PYLE whose telephone number is (703)756-5798. The examiner can normally be reached 8 am - 5:30 pm M - T; Off first Fridays; 8 am - 4 pm second Fridays.
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/ERIC F WINAKUR/Primary Examiner, Art Unit 3791
/S.C.P./Examiner, Art Unit 3791
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