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 .
Response to Amendment
The amendment filed June 18, 2026 has been entered. Claims 1-15 remain pending in the application. Applicant’s amendments to the claims have overcome each and every objection to the drawings, objection to the claims, 101 rejection, and 112 rejection previously set forth in the Non-Final Office Action mailed March 18, 2026. Applicant’s amendments to the claims necessitate new grounds of rejection, as described in the Response to Arguments and 102 and 103 Rejections below.
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 1-12 and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20100257128 A1 (De Vries et al.).
Regarding claim 1, De Vries teaches a hearing estimation system comprising a computerized device and an acoustic output transducer ([0023-0024] “a hearing evaluation device configured to provide a stimulus relating to a hearing evaluation event, an observation registering device configured to register a response related to the hearing evaluation event”; [0134] “a system 44 comprising a sound emitting device 46 configured to emit sounds matching the desired stimuli of the hearing evaluation event.”), wherein the computerized device comprises a program storage storing an executable program comprising instructions and a processor configured to execute the instructions to perform a method ([0123] “a system implemented using a computer, comprising data storage configured for storing data regarding a representation of a hearing ability of a population, and a sound system configured to perform the stimulus of a hearing evaluation event, a mouse or keyboard configured to register the response of the hearing evaluation event and a processor configured to establish a hearing ability model based on the representation of the hearing ability of the population and a observed response. A computer screen may be used to display graphically one or more hearing ability value together with related measure of uncertainty”); comprising the steps of:
receiving a first plurality of first audiograms from a corresponding first plurality of persons ([0028] “a hearing ability model for a person using a representation of a distribution of hearing ability for a population of individuals, includes obtaining information regarding a person's response to a stimulus of a hearing evaluation event”; [0126] “the population data base D.sub.c comprised about 100,000 measured audiograms.”; [0128] “The lines 28 and 30 are determined based on population data, i.e. data from a large group of individuals”; Figs. 4-6);
providing, for each of the first audiograms, a weighting factor and an initial value of said weighting factor (Figs. 4-6 depict initial values recorded for audiograms; [0061-0062] “At this stage an updated estimate of the audiogram is available, providing combined knowledge of the most likely values x.sub.n, and an associated measure of uncertainty .lamda..sub.n.”);
selecting a first frequency, out of a first set of frequencies ([0044] “a hearing threshold audiogram by pure tone audiometry the hearing evaluation event corresponds to a combination of a stimulus characterized by a frequency and power level, and a response, i.e. whether the stimulus is heard.”; [0064] “The set of possible frequencies and power levels defines the set of possible next stimuli”; [0128] “FIG. 4 could be part of the first image displayed when a person is being examined. The lines 28 and 30 delimit the upper and lower boundaries indicating the uncertainty of the hearing loss in dB, the y-axis, at a given frequency, the x-axis”);
controlling the acoustic output transducer to generate an acoustic stimulus at the first frequency and at a sound level determined based on a current estimated audiogram of a specific user ([0134] “a system 44 comprising a sound emitting device 46 configured to emit sounds matching the desired stimuli of the hearing evaluation event.”; [0123] “a sound system configured to perform the stimulus of a hearing evaluation event, a mouse or keyboard configured to register the response of the hearing evaluation event and a processor configured to establish a hearing ability model based on the representation of the hearing ability of the population and a observed response”; [0127] “FIG. 4 schematically illustrates what could be displayed to an operator. In FIG. 4 the informative-audiogram before any hearing evaluation event is illustrated. The filled circles indicate the expected hearing thresholds at the test frequencies, here: 125, 250, 500, 1000, 2000, 4000, 8000 Hz, and connecting line the estimated thresholds at intermediate frequencies, i.e. the current values of the model. The shaded region indicates the current uncertainty about the hearing threshold values. The open circle indicates the best next pure-tone stimulus in the sense that this stimulus will maximize the expected information gain about the thresholds”; [0103-0109] “the i-audiogram provides a very informative picture about the current state of the estimation procedure”; “based on the i-audiogram (and other not-simulated information), the audiologist may choose (and administer) the next pure-tone stimulus s.sub.n+1”);
measuring a hearing threshold of the specific user at said first frequency using said acoustic output transducer ([0041] “a sequence of N tones (s.sub.1,s.sub.2,K,s.sub.N) is presented at selectable frequency and power levels and the person tested is asked after each presentation if he or she hears the stimulus”; [0127-0128] “The filled circles indicate the expected hearing thresholds at the test frequencies, here: 125, 250, 500, 1000, 2000, 4000, 8000 Hz, and connecting line the estimated thresholds at intermediate frequencies, i.e. the current values of the model … FIG. 4 could be part of the first image displayed when a person is being examined. The lines 28 and 30 delimit the upper and lower boundaries indicating the uncertainty of the hearing loss in dB, the y-axis, at a given frequency, the x-axis”);
performing a first update on each of said weighting factors in dependence on a difference between (i) a value of the respective first audiogram at the first frequency and (ii) the measured hearing threshold of the specific user at the first frequency ([0050-0052] “The determination of a hearing threshold audiogram by pure tone audiometry will be used as an example,”; [0132] “Since the hearing thresholds at different frequencies are correlated and the method is designed to incorporate this correlation, the listening event 36 does not only have effect for the model at the precise frequency at which the event took place, but has relevance to the entire model”); and
determining an updated estimated audiogram for said specific user as a weighted mean of said first plurality of first audiograms based on said updated weighting factors ([0070] “compute mean hearing threshold estimate”; [0101] “the i-audiogram can be updated as shown in FIG. 5. We see that the current mean hearing threshold estimated shifted a bit downwards while the uncertainty about the thresholds decreased.”; [0128] “The population data may be established from a larger pool of data, e.g. by using selection criteria that may characterize the person being tested.”; “establish a proper model for the hearing ability of that person tested”; Figs. 4-6);
wherein the selecting of the first frequency and/or the sound level is performed based on a weighted variance metric so as to iteratively reduce uncertainty of the estimated audiogram and converge to the hearing threshold using fewer measurement iterations ([0056] “the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies”; [0061]; [0076]; [0109] “After a certain number of hearing evaluation events, the i-audiogram might look as shown in FIG. 6, where the threshold uncertainty has been drastically reduced on the basis of the newly obtained observations.”; [0133] “reducing the uncertainty”).
Regarding claim 2, De Vries teaches the hearing estimation system according to claim 1, wherein said method comprises a further step of:
determining, at each of said first set of frequencies, a weighted variance metric for said first plurality of first audiograms, in dependence of said weighting factors, wherein the first frequency is selected out of said first set of frequencies by a determination in dependence on one of the frequencies out of a first frequency range for which the weighted variance metric is largest ([0056] “In this case, the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies. Alternative probabilistic model choices, including a Gaussian process model or polynomial regression model are also possible. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.). Usually, we take a uniform or Gaussian distribution with large variance for p(.theta.).”; [0076] “In this case, the model parameters comprise the set .theta.={.pi..sub.k, .mu..sub.k, .SIGMA..sub.k:k=1,K,K}. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.), which usually, is uniform or Gaussian with large variance for p(.theta.) … Given the database of hearing threshold (population) measurements, it is possible to update our knowledge about the hearing threshold model parameters, in the first instance the model is based on the population data alone, in the following the model is based on the population data and one or more previous measurements”; [0099] “A pure-tone stimulus is a function of a chosen frequency and chosen power level. The set of possible frequencies and power levels defines the set of possible next stimuli. Having access to the full probability distribution p(x|D.sub.n) for the thresholds, makes it possible to select the stimulus s* from the set of all possible stimuli that provides the largest expected information gain (reduction of uncertainty).”).
Regarding claim 3, De Vries teaches the hearing estimation system according to claim 1, wherein said method comprises an iteration of the following steps:
determining, at several frequencies out of said first set of frequencies, a weighted variance metric for said first plurality of first audiograms in dependence on said weighting factors, according to their most recent update, respectively ([0056] “In this case, the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies. Alternative probabilistic model choices, including a Gaussian process model or polynomial regression model are also possible. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.). Usually, we take a uniform or Gaussian distribution with large variance for p(.theta.).”; [0076] “In this case, the model parameters comprise the set .theta.={.pi..sub.k, .mu..sub.k, .SIGMA..sub.k:k=1,K,K}. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.), which usually, is uniform or Gaussian with large variance for p(.theta.) … Given the database of hearing threshold (population) measurements, it is possible to update our knowledge about the hearing threshold model parameters, in the first instance the model is based on the population data alone, in the following the model is based on the population data and one or more previous measurements”);
determining a distinguished frequency out of said several frequencies, in dependence on the frequency out of a second frequency range for which said weighted variance metric is largest ([0099] “A pure-tone stimulus is a function of a chosen frequency and chosen power level. The set of possible frequencies and power levels defines the set of possible next stimuli. Having access to the full probability distribution p(x|D.sub.n) for the thresholds, makes it possible to select the stimulus s* from the set of all possible stimuli that provides the largest expected information gain (reduction of uncertainty).”);
measuring a hearing threshold of said specific user at said distinguished frequency ([0103] “we have available a hearing threshold estimates {circumflex over (x)}.sub.n, uncertainty measures .lamda..sub.n and the best next stimulus s*.sub.n+1.”; [0128] “The population data may be established from a larger pool of data, e.g. by using selection criteria that may characterize the person being tested.”; “establish a proper model for the hearing ability of that person tested”; Figs. 4-6);
performing an update on each of said weighting factor corresponding to said first plurality of first audiograms also in dependence on the difference between the value of the respective first audiogram and said measured hearing threshold of said specific user at said distinguished frequency, respectively ([0103] “In a regular audiogram, hearing loss (in dB HL) is displayed on the ordinate axis versus frequency (in Hz) on the abscissa. In contrast, the i-audiogram displays, after the n-th stimulus-response event, the current best hearing threshold estimate {circumflex over (x)}.sub.n (32 in FIG. 4), the current uncertainty about the thresholds .lamda..sub.n (28/30 in FIG. 4, also indicated by the shaded region), and the best next stimulus s*.sub.n+1 (36 in FIG. 4). Note that the i-audiogram is updated after each response of the person tested.”);
and, after finishing said iteration ([0109] “estimation updates in the next iteration of the REPEAT loop”), further comprising the step of
determining the estimated audiogram for said specific user as a weighted mean of said first plurality of first audiograms, using said weighting factors according to their most recent update, respectively, for said weighted mean ([0109] “On the basis of this new information, the i-audiogram can be updated as shown in FIG. 5. We see that the current mean hearing threshold estimated shifted a bit downwards while the uncertainty about the thresholds decreased. Also, a new best next stimulus is indicated by the circle in FIG. 5. After a certain number of hearing evaluation events, the i-audiogram might look as shown in FIG. 6, where the threshold uncertainty has been drastically reduced on the basis of the newly obtained observations.”).
Regarding claim 4, De Vries teaches the hearing estimation system according to claim 3, wherein the iteration is finished after completion of a given number of iteration runs or when the weighted variance metric for said first plurality of first audiograms or a variance for said first plurality of first audiograms falls below a given first threshold ([0062] “From the updated estimate of the audiogram uncertainty, .lamda..sub.n, a decision is established whether the uncertainty is satisfactory, in which case the audiogram is considered the final value and the test is completed, or whether a next hearing evaluation event must be carried out”; [0109]).
Regarding claim 5, De Vries teaches the hearing estimation system according to claim 3, wherein, after each iteration run,
-the estimated audiogram for said specific user is determined as a weighted mean of said first plurality of first audiograms, using said weighting factors according to their most recent update, respectively, for said weighted mean ([0109] “The event index n is incremented by 1 and consequently, d.sub.n.rarw.d.sub.n+1 in order to prepare for the estimation updates in the next iteration of the REPEAT loop. Assume now that the audiologist selected for s.sub.n+1 where the `+`-sign is positioned in FIG. 4. Assume that the response of the person tested is `no` (did not hear the stimulus). On the basis of this new information, the i-audiogram can be updated as shown in FIG. 5. We see that the current mean hearing threshold estimated shifted a bit downwards while the uncertainty about the thresholds decreased.”),
-the estimated audiogram is visualized using said graphical user interface ([0127] “FIG. 4 schematically illustrates what could be displayed to an operator”), and
-said visualized estimated audiogram is presented to a hearing care professional for decision about stopping the iteration ([0047] “The uncertainty relates to the model and provides an indication to the operator, e.g. an audiologist, how certain, or uncertain, the model is. Based on this uncertainty the operator may decide if more observations are needed or if the model is sufficient.”; [0122] “the estimated hearing ability value is displayed graphically together with a measure of uncertainty relating to the hearing ability value giving the operator an overview of the progress of the test”).
Regarding claim 6, De Vries teaches the hearing estimation system according to claim 1, wherein for each weighting factor, the initial value is chosen in dependence on a demographic similarity of the specific user with a person corresponding to the first audiogram for which the weighting factor is being provided ([0113] “Effectively, this means that the individual responses in the population data are weighted according to their relevance for estimating the thresholds of the person tested.”; [0118] “An embodiment may include parameters known to correlate to hearing loss, without explicitly being related to a test of hearing loss. These parameters may include age, gender and medical status and history of the person tested, or a combination thereof. The parameters may either be used as model parameters, or for defining subsets of the population, matching the person tested better”; [0128]).
Regarding claim 7, De Vries teaches the hearing estimation system according to claim 1,
wherein the first plurality of first audiograms is chosen as a subset out of a multiplicity of
first audiograms from a corresponding multiplicity of persons in dependence on a demographic similarity of the specific user with a person from said multiplicity corresponding to a respective first audiogram ([0045] “establishing a hearing ability model representing the hearing ability of the person tested, based on the observation of a response related to the hearing evaluation event and the representation of a population 18. Further the step 12 may include providing previously recorded data relating to the person tested, e.g. previously observed responses to hearing evaluation events, age and/or gender etc.”; [0128]).
Regarding claim 8, De Vries teaches the hearing estimation system according to claim 1,
wherein a starting sound pressure level for measuring the hearing threshold of said specific user at a certain frequency is chosen in dependence on the estimated audiogram and/or the weighted variance metric at said certain frequency, using the weighting factors according to their most recent update, respectively ([0128] “The lines 28 and 30 delimit the upper and lower boundaries indicating the uncertainty of the hearing loss in dB, the y-axis, at a given frequency, the x-axis. The lines 28 and 30 are determined based on population data, i.e. data from a large group of individuals. The population data may be established from a larger pool of data, e.g. by using selection criteria that may characterize the person being tested. Such criteria may for instance be age, gender, occupation, medical history”).
Regarding claim 9, De Vries teaches the hearing estimation system according to claim 1,
wherein a continuous fit is performed on the weighted variance metric for all frequencies in an interval spanned by said first set of frequencies ([0054] “The present embodiments are based on the availability of a data set of hearing abilities for a group of persons, with a certain similarity to the person tested. From this data set a representation of the hearing abilities for a population (in its statistical sense i.e. a defined group of individuals) is provided,--either by looking up values in a database comprising the dataset, or by establishing a mathematical model of the hearing ability of the population (in the following "a population model"). In the case where a mathematical model is established, this may be done by any appropriate regression method, and the mathematical population model may be either nonparametric, such as a neural network, or the model may be parametric”; [0055-0056]; Figs. 4-6), and
wherein the first frequency is determined from said continuous fit ([0056] “the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies”).
Regarding claim 10, De Vries teaches the hearing estimation system according to claim 9,
wherein in order to obtain the value of a first audiogram at the first frequency, an interpolation and/or the continuous fit is performed on basis of said first audiogram's values at the frequencies of said first set of frequencies (Figs. 4-6; [0054-0056] “The following example will relate to a probabilistic model for hearing thresholds p(x|.theta.) where x refers to the hearing thresholds and .theta. to the model parameters”).
Regarding claim 11, De Vries teaches the hearing estimation system according claim 1, wherein said method comprises the further steps of:
-providing a second plurality of second audiograms from a corresponding second plurality of persons, wherein each person out of the first plurality is also comprised in the second plurality of persons ([0116] “forming the basis of selection of sub-groups of the population, with a higher internal similarity, and thus a lower estimated uncertainty.”);
-providing, for each of the second audiograms, a value of a weighting factor ([0061-0062] “At this stage an updated estimate of the audiogram is available, providing combined knowledge of the most likely values x.sub.n, and an associated measure of uncertainty .lamda..sub.n.”);
-determining, at each of a second set of frequencies, a weighted variance metric for said second plurality of second audiograms, in dependence of said weighting factors, wherein the second set of frequencies is a subset said first set of frequencies ([0044] “a hearing threshold audiogram by pure tone audiometry the hearing evaluation event corresponds to a combination of a stimulus characterized by a frequency and power level, and a response, i.e. whether the stimulus is heard.”; [0064] “The set of possible frequencies and power levels defines the set of possible next stimuli”; [0128] “FIG. 4 could be part of the first image displayed when a person is being examined. The lines 28 and 30 delimit the upper and lower boundaries indicating the uncertainty of the hearing loss in dB, the y-axis, at a given frequency, the x-axis”); and
-determining said first frequency and/or said distinguished frequency for the first plurality of first audiograms and/or performing said first update on the weighting factors corresponding to the first audiograms also based on the weighted variance metric for said second plurality of second audiograms ([0054-0056]; [0099] “A pure-tone stimulus is a function of a chosen frequency and chosen power level. The set of possible frequencies and power levels defines the set of possible next stimuli. Having access to the full probability distribution p(x|D.sub.n) for the thresholds, makes it possible to select the stimulus s* from the set of all possible stimuli that provides the largest expected information gain (reduction of uncertainty).”);.
Regarding claim 12, De Vries teaches the hearing estimation system according to claim 11,
wherein persons from the first and second pluralities are the same ([0115] “A further related hearing loss ability value may be historical hearing ability values for the same person”), and
wherein for each person out of said first and second pluralities, respectively, the corresponding first and second audiogram are representing the hearing at either of two ears ([0115] “For several types of hearing losses a correlation between left and right ear hearing ability will also mean that the use of binaural information, i.e. any information relating to the hearing ability of the other ear of the person tested, will be beneficial.”).
Regarding claim 14, De Vries teaches a non-transitory computer readable medium carrying instructions which, when executed by a computer ([0135] “system 44 includes a computer-readable medium having a set of stored instructions”), cause the following method steps to be performed:
receiving a first plurality of first audiograms from a corresponding first plurality of persons ([0028] “a hearing ability model for a person using a representation of a distribution of hearing ability for a population of individuals, includes obtaining information regarding a person's response to a stimulus of a hearing evaluation event”; [0126] “the population data base D.sub.c comprised about 100,000 measured audiograms.”; [0128] “The lines 28 and 30 are determined based on population data, i.e. data from a large group of individuals”; Figs. 4-6);
providing, for each of the first audiograms, a weighting factor and an initial value of said weighting factor (Figs. 4-6 depict initial values recorded for audiograms; [0061-0062] “At this stage an updated estimate of the audiogram is available, providing combined knowledge of the most likely values x.sub.n, and an associated measure of uncertainty .lamda..sub.n.”);
selecting a first frequency, out of a first set of frequencies ([0044] “a hearing threshold audiogram by pure tone audiometry the hearing evaluation event corresponds to a combination of a stimulus characterized by a frequency and power level, and a response, i.e. whether the stimulus is heard.”; [0064] “The set of possible frequencies and power levels defines the set of possible next stimuli”; [0128] “FIG. 4 could be part of the first image displayed when a person is being examined. The lines 28 and 30 delimit the upper and lower boundaries indicating the uncertainty of the hearing loss in dB, the y-axis, at a given frequency, the x-axis”);
controlling the acoustic output transducer to generate an acoustic stimulus at the first frequency and at a sound level determined based on a current estimated audiogram of a specific user ([0123] “a sound system configured to perform the stimulus of a hearing evaluation event, a mouse or keyboard configured to register the response of the hearing evaluation event and a processor configured to establish a hearing ability model based on the representation of the hearing ability of the population and a observed response”; [0134] “a system 44 comprising a sound emitting device 46 configured to emit sounds matching the desired stimuli of the hearing evaluation event.”; [0127] “FIG. 4 schematically illustrates what could be displayed to an operator. In FIG. 4 the informative-audiogram before any hearing evaluation event is illustrated. The filled circles indicate the expected hearing thresholds at the test frequencies, here: 125, 250, 500, 1000, 2000, 4000, 8000 Hz, and connecting line the estimated thresholds at intermediate frequencies, i.e. the current values of the model. The shaded region indicates the current uncertainty about the hearing threshold values. The open circle indicates the best next pure-tone stimulus in the sense that this stimulus will maximize the expected information gain about the thresholds”; [0103-0109] “The i-audiogram provides a very informative picture about the current state of the estimation procedure”; “based on the i-audiogram (and other not-simulated information), the audiologist may choose (and administer) the next pure-tone stimulus s.sub.n+1”);
using an electro-acoustical transducer at least operationally connected to the computer to measure a hearing threshold of a specific user at said first frequency ([0023-0024] “a hearing evaluation device configured to provide a stimulus relating to a hearing evaluation event, an observation registering device configured to register a response related to the hearing evaluation event”; [0134] “a system 44 comprising a sound emitting device 46 configured to emit sounds matching the desired stimuli of the hearing evaluation event.”);
performing a first update on each of said weighting factors in dependence on a difference between (i) a value of the respective first audiogram at the first frequency and (ii) the measured hearing threshold of the specific user at the first frequency ([0050-0052] “The determination of a hearing threshold audiogram by pure tone audiometry will be used as an example,”; [0132] “Since the hearing thresholds at different frequencies are correlated and the method is designed to incorporate this correlation, the listening event 36 does not only have effect for the model at the precise frequency at which the event took place, but has relevance to the entire model”); and
determining an updated estimated audiogram for said specific user as a weighted mean of said first plurality of first audiograms based on said updated weighting factors ([0070] “compute mean hearing threshold estimate”; [0101] “the i-audiogram can be updated as shown in FIG. 5. We see that the current mean hearing threshold estimated shifted a bit downwards while the uncertainty about the thresholds decreased.”; [0128] “The population data may be established from a larger pool of data, e.g. by using selection criteria that may characterize the person being tested.”; “establish a proper model for the hearing ability of that person tested”; Figs. 4-6);
wherein the selecting of the first frequency and/or sound level is performed based on a weighted variance metric so as to iteratively reduce uncertainty of the estimated audiogram and converge to the hearing threshold using fewer measurement iterations ([0056] “the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies”; [0061]; [0076]; [0109] “After a certain number of hearing evaluation events, the i-audiogram might look as shown in FIG. 6, where the threshold uncertainty has been drastically reduced on the basis of the newly obtained observations.”; [0133] “reducing the uncertainty”).
Regarding claim 15, De Vries teaches the non-transitory computer readable medium according to claim 14 causing the following further method step to be performed:
determining, at each of said first set of frequencies, a weighted variance metric for said first plurality of first audiograms, in dependence of said weighting factors, wherein the first frequency is selected out of said first set of frequencies by a determination in dependence on one of the frequencies out of a first frequency range for which the weighted variance metric is largest ([0056] “In this case, the model parameters consist of the set .theta.={.pi..sub.k,.mu..sub.k,.SIGMA..sub.k:k=1,K,K}, where .pi. is a scaling factor, and .mu. and .SIGMA. correspond to mean value and covariance matrix where the subscripts are indices for the tested frequencies. Alternative probabilistic model choices, including a Gaussian process model or polynomial regression model are also possible. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.). Usually, we take a uniform or Gaussian distribution with large variance for p(.theta.).”; [0076] “In this case, the model parameters comprise the set .theta.={.pi..sub.k, .mu..sub.k, .SIGMA..sub.k:k=1,K,K}. Prior to any experiments, our state of knowledge about proper values for the hearing threshold model parameters is represented by a distribution p(.theta.), which usually, is uniform or Gaussian with large variance for p(.theta.) … Given the database of hearing threshold (population) measurements, it is possible to update our knowledge about the hearing threshold model parameters, in the first instance the model is based on the population data alone, in the following the model is based on the population data and one or more previous measurements”; [0099] “A pure-tone stimulus is a function of a chosen frequency and chosen power level. The set of possible frequencies and power levels defines the set of possible next stimuli. Having access to the full probability distribution p(x|D.sub.n) for the thresholds, makes it possible to select the stimulus s* from the set of all possible stimuli that provides the largest expected information gain (reduction of uncertainty).”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US 20100257128 A1 (De Vries et al.) in view of US 20200268260 A1 (Tran, Bao).
Regarding claim 13, De Vries teaches the hearing estimation system according to claim 11,
wherein each of the second audiograms is measured in the absence of a hearing aid at the respective ear of the corresponding person out of the second plurality (Figs. 4-6; [0101] “i-audiogram is updated after each response of the person tested.”; [0115]).
De Vries does not teach wherein each of the first audiograms is measured as an in-situ audiogram in the presence of a hearing aid at the respective ear of the corresponding person out of the first plurality.
However,
Tran teaches wherein each of the first audiograms is measured as an in-situ audiogram in the presence of a hearing aid at the respective ear of the corresponding person out of the first plurality ([0163] “A representative audiogram is created for each set of audiograms. A hearing enhancement fitting is computed from each representative audiogram. A hearing aid device is programmed with one or more hearing enhancement fittings computed from each representative audiogram.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the system taught by De Vries to include measuring audiograms in the presence of a hearing aid. One would have been motivated to make this modification because determining the threshold of hearing in each frequency band enables adjustments to be determined to compensate for the individual’s loss of hearing, as suggested by Tran [0163].
Response to Arguments
Applicant's arguments filed June 18, 2026 have been fully considered but they are not persuasive. With respect to the 102 Rejections in the Non-Final Office Action (See Pages 12-14 of Applicant’s Response “Claim Rejections – 35 U.S.C. § 102”), Applicant argues that De Vries does not disclose controlling an acoustic output transducer based on a current estimated audiogram, the claimed uncertainty-driven convergence process, or the claimed integrated feedback update mechanism.
With respect to the 103 Rejections in the Non-Final Office Action (See Pages 14-15 of Applicant’s Response “Claim Rejections – 35 U.S.C. § 103”), Applicant argues that the cited references do not teach or suggest the claimed closed-loop adaptive control. Applicant also states that the office action lacks a sufficient motivation to combine the references to arrive at the claimed invention of claim 1, and the claimed combination achieves technical advantages not taught or suggested by the prior art.
There are new grounds of claim rejections that were necessitated by the claim amendments.
MPEP § 2111 discusses proper claim interpretation, including giving claims their broadest reasonable interpretation in light of the specification during examination. Under broadest reasonable interpretation (BRI), the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim.
Under BRI, the amended claim limitation “controlling the acoustic output transducer to generate an acoustic stimulus at the first frequency and at a sound level determined based on a current estimated audiogram of a specific user” may be interpreted to mean that a current estimated audiogram of a specific user contributes in some way to the properties of the acoustic stimulus. De Vries describes that a sound emitting device is configured to emit stimulus sounds matching the desired stimuli of the hearing evaluation event based on an audiogram about the current state of the estimation procedure [0103-0105], which reads on the claim language of claims 1 and 14 under BRI. Additionally, under BRI, the performing of selecting the first frequency and/or sound level is “based on” a weighted variance metric to reduce uncertainty and converge the hearing threshold. This is taught by De Vries because the model described in [0056] takes into account variance values, which informs the frequencies, and [0109] and [0132-0133] describe using different thresholds correlating to different frequencies and reducing the threshold and uncertainty using the audiogram and the model. Therefore, the uncertainty-driven convergence process and integrated feedback update mechanism as written are taught by De Vries under BRI. It would be obvious for one to incorporate a hearing aid such as the one taught by Tran into the personalized audiogram analysis of De Vries because the hearing aid can be used to compensate for hearing loss in the user, as suggested by Tran [0163]. Tran teaches analogous audiogram analysis used to adjust hearing aid parameters to improve hearing, and therefore the combination of Tran and De Vries regarding claim 13 would be obvious for one of ordinary skill in the art before the effective filing date of the invention.
Claims 2-13 and 15 are rejected because the rejections of claims 1 and 14 are proper and the prior art teaches or suggests all the features of these claims for the reasons described in the 102 and 103 Rejections.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EVELYN GRACE PARK/Examiner, Art Unit 3791 /TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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