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.
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: “a state observing unit that obtains state data…”, “label data unit that acquires label data..” and “learning unit that…detects the onset of the tinnitus event” in claims 1-12 and 15.
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. Specifically, the “state observing unit”, “label data unit” and “learning unit” have been interpreted to include one or more of logic hardware and a non-transitory computer readable medium storing computer executable code and equivalents therefore, see para. [0005], [0056] of published application US 2024/0416126.
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 Objections
Claims 17-31 are objected to because of the following informalities: Claim 17, line 6 should recite –device configuration data that indicates a-- in order to fix an inadvertent typographical error. Claim 21, lines 6-7 should recite –device configuration data that indicates a-- in order to fix an inadvertent typographical error. Appropriate correction is required. Claims 18-31 directly or indirectly depend from claim 17 and are also objected to for the reasons stated above regarding claim 17.
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 1-12,15 and 17-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-12,15 and 17-31 is/are drawn an apparatus and a method which is/are a statutory category of invention (Step 1: YES).
The claim limitations within independent claims 1 and 17 that set forth or describe the abstract idea is/are: “using the state data and the label data, detects the onset of the tinnitus events of the at least one user and generates device configuration data, wherein the device configuration data indicates a tinnitus therapy for delivery to the at least one user via the stimulation component.” (claim 1) and “using the state data and the label data in a machine-learning model to automatically detect onset of tinnitus events of the at least one user and generate device configuration data indicates a tinnitus therapy for delivery to the at least one user” (claim 17). The reasons that the limitations is/are considered an abstract idea is/are the following: The limitations of , “using the state data and the label data, detects the onset of the tinnitus events of the at least one user and generates device configuration data, wherein the device configuration data indicates a tinnitus therapy for delivery to the at least one user via the stimulation component.” (claim 1) and “using the state data and the label data in a machine-learning model to automatically detect onset of tinnitus events of the at least one user and generate device configuration data indicates a tinnitus therapy for delivery to the at least one user,” (claim 17) is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind, i.e. an observation, evaluation, judgment, opinion, but for the recitation of generic computer components. That is, other than reciting “a machine learning device” (claim 1) and “in a machine-learning model” (claim 17) nothing in the claim element precludes the steps from practically being performed in the mind with the aid of pen and paper. For example but for the recitation of a “a machine learning device” (claim 1) and “in a machine-learning model” (claim 17), “using the state data and the label data, detects the onset of the tinnitus events of the at least one user and generates device configuration data, wherein the device configuration data indicates a tinnitus therapy for delivery to the at least one user via the stimulation component.” (claim 1) and “using the state data and the label data in a machine-learning model to automatically detect onset of tinnitus events of the at least one user and generate device configuration data indicates a tinnitus therapy for delivery to the at least one user” (claim 17) in the context of the claims encompasses the user, with the aid of pen and paper, using state data and label data to detect the onset of tinnitus events and determining device configuration data. Other than reciting the use of generic computer components and a generic machine-learning model, nothing in the elements of the claims precludes the steps from practically being performed in the mind with the aid of pen and paper. Within the specification of the instant application the “machine learning device” and “machine-learning model” are recited as being nothing more than a generic machine-learning model and are disclosed within certain embodiments as being “a classification function/model” and/or a “regression/continuous function/model”, see para. [0075]-[0076] of published application US 2024/0416126. There is nothing to suggest an undue level of complexity in the “using” steps. If a claim limitation, under its broadest reasonable interpretation covers a metal process, i.e. performance of the limitation in the mind, but for the recitation of generic computer components, then it falls with the “Mental Processes” grouping of abstract ideas. Accordingly the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to concepts that have been identified as abstract by the courts, such as: collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016), selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis in SAP America Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed Cir. 2018). Thus, the claim(s) are directed to a judicial exception and fall squarely within the realm of "abstract ideas," which is a patent-ineligible concept (Step 2A: Prong One YES).
Analyzing the claim as a whole for an inventive concept, the claim does not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception. The additionally recited element(s) appended to the abstract idea in claims 1 and 17 include: “a stimulation component configured to deliver stimulation signals to at least one user” (claim 1), “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a state observing unit that obtains state data comprising physiological data representing a current physiological state of the at least one user and environmental data” (claim 1), “a label data unit that acquires label data associated with the onset of tinnitus events” (claim 1), “a learning unit” (claim 1), “obtaining, with a state observing unit, state data indicating a current physiological state of at least one user” (claim 17) and “obtaining, with a label data unit, label data associated with onset of tinnitus events” (claim 17) and “in a machine-learning model” (claim 17). The additional elements of “a stimulation component configured to deliver stimulation signals to at least one user” (claim 1), “a state observing unit that obtains state data comprising physiological data representing a current physiological state of the at least one user and environmental data” (claim 1), “a label data unit that acquires label data associated with the onset of tinnitus events” (claim 1), “obtaining, with a state observing unit, state data indicating a current physiological state of at least one user” (claim 17) and “obtaining, with a label data unit, label data associated with onset of tinnitus events” (claim 17), merely: add insignificant extra-solution activity, reciting “a state observing unit that obtains state data comprising physiological data representing a current physiological state of the at least one user and environmental data” (claim 1), “a label data unit that acquires label data associated with the onset of tinnitus events” (claim 1), “obtaining, with a state observing unit, state data indicating a current physiological state of at least one user” (claim 17) and “obtaining, with a label data unit, label data associated with onset of tinnitus events” (claim 17), is recited at a high level of generality (i.e. as a general means of gathering fetal heart rate data) and is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity). All uses of the recited judicial exception require the pre- solution activity of data gathering. Reciting, “a stimulation component configured to deliver stimulation signals to at least one user” (claim 1), merely add insignificant extra-solution activity, which is merely nominally, insignificantly or tangentially related to the performance of the steps, which is a form of insignificant extra-solution activity. As discussed above with respect to integration of abstract idea into a practical, the additional element of “a state observing unit” (claim 1 and 17), “a label data unit” (claims 1 and 17), “a learning unit” (claim 1), “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a machine-learning model” (claim 17) amount to no more than mere instruction to apply the exception using generic computer components. The “a state observing unit” (claim 1 and 17), “a label data unit” (claims 1 and 17), “a learning unit” (claim 1), “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a machine-learning model” (claim 17) are purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data to enable the abstract process. The specification of the instant application states that “the state observing unit, the label data unit, and the machine-learning model comprise one or more of logic hardware and a non-transitory computer readable medium storing computer executable code within a tinnitus therapy system.”, see para. [0005], [0076] or claim 17 of published application US 2024/0416126, therefore at least the state observing unit, the label data unit, and the machine-learning model/learning unit are nothing more than generic computer components. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”. Further, the addition of “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a machine-learning model” (claim 17) does not preclude the claim from reciting an abstract idea, as the courts have indicated that mere automation of a manual process may not be sufficient to improve functionality, In re Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), and automation of a task can still be performed by a person if their mental capability has been trained to do so. Examiner notes that the claimed invention is still directed to a judicial exception without significantly more, as they are still directed to a concept relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work. The use of “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a machine-learning model” (claim 17) are nonspecific and amount to a drafting technique that places no meaningful limits on the claims. The inclusion of “a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment” (claim 1), “a machine-learning model” (claim 17) is not sufficient to render a claim patent-eligible because not all transformations or machine implementations infuse an ineligible claim with an “inventive concept” - Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 1169 (Fed. Cir. 2019) (citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). Although claims 1 and 17 are tied to a machine-learning model/device its ultimate focus is detecting the onset of tinnitus event and determining device configuration data that is directed towards an abstract idea and one that can be done by mental process. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong Two, NO).
Claims 1 and 17 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. e.g., all elements are directed to pre-solution activity, carried out using well-understood routine, conventional activities previously known to the industry and amount to elements that have been recognized as well- understood, routine and conventional activity in particular fields, e.g. receiving or transmitting data over a network, Symantec, see MPEP 2106.05(d)(II), MPEP 2106.05(g) or purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data to enable the abstract process (as stated above, the specification of the instant application states that “the state observing unit, the label data unit, and the machine-learning model comprise one or more of logic hardware and a non-transitory computer readable medium storing computer executable code within a tinnitus therapy system.”, see para. [0005], [0076] or claim 17 of published application US 2024/0416126), see MPEP 2106.05(f), the additional elements do not amount to significantly more than the above-identified judicial exception(s). Further, the use of a stimulation component to deliver stimulation signals to a user is well-understood routine, conventional activities previously known to the industry and amount to elements that have been recognized as well- understood, routine and conventional activity, see paragraphs recited within the 103 rejection below utilizing US 2023/0255514 to Arndt et al. Similarly, when considered as an ordered combination, the additional components/steps of the claim(s) add nothing that is not already present when the steps are considered separately (Step 2B: NO). The claims are not patent eligible.
Claim(s) 2-12,15 and 18-31 depend directly or indirectly from claim(s) 1 and 17. Therefore, the dependent claims rely upon the same abstract idea as the independent claim(s), as set forth above. Additionally, the dependent claims do nothing more than further limiting the abstract idea while failing to qualify as "significantly more", and the specificity of an abstract idea does not make it any "less abstract" as it is still directed to concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work subject matter. Therefore, the dependent claim(s) are also not patent eligible for the reasons discussed above. Claim(s) 2-12, 15 and 18-31 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitations regarding the abstract idea and/or data that is received, which is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity. All uses of the recited judicial exception require the pre-solution activity of data gathering.
The instantly rejected claim(s) are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. In the interest of advancing prosecution, the examiner suggests: providing evidence, for example, delineating how the abstract idea and/or additional elements appended to the abstract idea results in an improvement to the technology/technical field, which can show eligibility and/or adding a practical application of the claimed method outside of the computer (e.g. treating a patient). See MPEP § 716.01(c) for examples of providing evidence supported by an appropriate affidavit or declaration. For additional guidance, applicant is directed generally to MPEP §2106.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 17, 21-22, 24-26 and 28 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2023/0255514 to Arndt et al. (Arndt).
In reference to at least claim 17
Arndt discloses a method for treating tinnitus events using machine learning, comprising: obtaining, with a state observing unit, state data indicating a current physiological state of at least one user (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]); obtaining, with a label data unit, label data associated with onset of tinnitus events (e.g. “historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus,”, para. [0129]); and using the state data and the label data in a machine-learning model to automatically detect onset of tinnitus events of the at least one user (e.g. “It will be appreciated that the machine learning algorithm, the regression model, the AI, or the neural network algorithm would correlate/learn from the aforesaid information and would be able to predict future tinnitus attacks.”, para. [0129]) and generate device configuration data indicates a tinnitus therapy for delivery to the at least one user (e.g. “ suggest the guidance and the treatment for the tinnitus.”, para. [0137], “suggesting treatment for the tinnitus based on the patient data; and [0144] generating energy based on the suggested treatment to transmit the energy to the patient through bone conductivity for treating the tinnitus.”, para. [0139]), wherein the state observing unit, the label data unit, and the machine-learning model comprise one or more of logic hardware and a non-transitory computer readable medium storing computer executable code (e.g.120, 104 and 108, Fig. 1, para. [0146]; “Machine Learning or a neural network algorithm will be understood by a person skilled in the art of computer system design.”, para. [0129], “In an embodiment, the sensor unit is optionally a smartphone, a tablet, a desktop, a personal computer, an electronic notebook, or a smart watch. In an embodiment, the sensor unit may comprise a software application”, para. [0131]) within a tinnitus therapy system (e.g. system 100).
In reference to at least claim 21
Arndt further discloses receiving operating state data representing a current operating state of the tinnitus therapy apparatus (e.g. “treatment provided for the auditory dysfunctions/tinnitus”, para. [0129]), and further using the operating state data in the machine-learning model to automatically detect the onset of tinnitus events of the at least one user (e.g. “It will be appreciated that the machine learning algorithm, the regression model, the AI, or the neural network algorithm would correlate/learn from the aforesaid information and would be able to predict future tinnitus attacks.”, para. [0129]) and generate the device configuration data indicates a tinnitus therapy for delivery to the at least one user (e.g. “the first processor may employ at least one of the machine learning algorithm, the regression model, artificial intelligence (AI), or the neural network algorithm to determine a treatment option for the tinnitus based on the patient data. Such regression model, artificial intelligence (AI), Machine Learning or a neural network algorithm will be understood by a person skilled in the art of computer system design. It will be appreciated in embodiments of the present disclosure that the machine learning algorithm, the regression model, the AI or the neural network are trained with (i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus, and (iv) the subjective information pertaining to the health condition of said patient.”, para. [0129]).
In reference to at least claim 22
Arndt further discloses automatically generating the label data based on historical tinnitus event reporting data representing a history of prior tinnitus events experienced by the at least one user (e.g. “(i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]).
In reference to at least claim 24
Arndt further discloses wherein automatically generating the historical tinnitus event reporting data comprises: automatically generating the historical tinnitus event reporting data based on prior real- time tinnitus event reporting data (e.g. “(i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]; “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
In reference to at least claim 25
Arndt further discloses wherein the prior real-time tinnitus event reporting data comprises data representing prior real-time subjective feedback of the at least one user in relation to one or more of the onset of a prior tinnitus event (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
In reference to at least claim 26
Arndt further discloses wherein the real-time subjective feedback comprises a subjective grading of a severity of at least one prior tinnitus event (e.g. “based on his/her subjective assessment of the prevailing severity of tinnitus he/she is experiencing”, para. [0138]).
In reference to at least claim 28
Arndt further discloses automatically generating the historical tinnitus event reporting data based on the prior real-time tinnitus event reporting data and retrospective tinnitus event reporting data (e.g. “ It will be appreciated in embodiments of the present disclosure that the machine learning algorithm, the regression model, the AI or the neural network are trained with…(ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus,”, para. [0129]).
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(s) 1-11, 15, 18-20,23,27 and 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0255514 to Arndt et al. (Arndt) in view of US 2020/0268260 to Tran (Tran).
In reference to at least claim 1
Arndt discloses a tinnitus therapy apparatus comprising: a stimulation component configured to deliver stimulation signals to at least one user (e.g. “In an embodiment, the energy transmitting device transmits energy based on the suggested treatment for treating tinnitus in the daytime without affecting the hearing of the patient, the environment of the patient, and the self-esteem of the patient.”, para. [0132]; “In an embodiment, the energy transmitting device may convert the energy into a mechanical vibratory stimulus and transmits the mechanical vibratory stimulus through bone structures of the patient for treating the tinnitus.”, para. [0133]); and a machine learning device that detects onset of tinnitus events of the least one user with respect to an external sound environment (e.g. “It will be appreciated that the machine learning algorithm, the regression model, the AI, or the neural network algorithm would correlate/learn from the aforesaid information and would be able to predict future tinnitus attacks.”, para. [0129]), wherein the machine learning device includes: a state observing unit that obtains state data comprising physiological data representing a current physiological state of the at least one user (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]) and environmental data, a label data unit that acquires label data associated with the onset of tinnitus events (e.g. “historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus,”, para. [0129]), and a learning unit that, by using the state data and the label data, detects the onset of the tinnitus events of the at least one user (e.g. “It will be appreciated that the machine learning algorithm, the regression model, the AI, or the neural network algorithm would correlate/learn from the aforesaid information and would be able to predict future tinnitus attacks.”, para. [0129]), and generates device configuration data (e.g. “ suggest the guidance and the treatment for the tinnitus.”, para. [0137], “suggesting treatment for the tinnitus based on the patient data; and [0144] generating energy based on the suggested treatment to transmit the energy to the patient through bone conductivity for treating the tinnitus.”, para. [0139]), wherein the device configuration data indicates a tinnitus therapy for delivery to the at least one user via the stimulation component (e.g. “ suggest the guidance and the treatment for the tinnitus.”, para. [0137], “suggesting treatment for the tinnitus based on the patient data; and [0144] generating energy based on the suggested treatment to transmit the energy to the patient through bone conductivity for treating the tinnitus.”, para. [0139]).
Arndt discloses utilizing state data that comprises physiological data (e.g. (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]). However, Arndt does not explicitly disclose the state data including environmental data.
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Arndt to include utilizing state data including environmental data, as taught by Tran, within the machine learning device in order to adjust amplifier controls to optimize hearing based on the identified environment maximizing speech intelligibility and listening comfort (‘260, para. [0004], [0166]).
In reference to at least claim 2
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the learning unit receives operating state data representing a current operating state of the tinnitus therapy apparatus (e.g. “treatment provided for the auditory dysfunctions/tinnitus”, para. [0129]), and wherein the learning unit generates the device configuration data further based on the operating state data (e.g. “the first processor may employ at least one of the machine learning algorithm, the regression model, artificial intelligence (AI), or the neural network algorithm to determine a treatment option for the tinnitus based on the patient data. Such regression model, artificial intelligence (AI), Machine Learning or a neural network algorithm will be understood by a person skilled in the art of computer system design. It will be appreciated in embodiments of the present disclosure that the machine learning algorithm, the regression model, the AI or the neural network are trained with (i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus, and (iv) the subjective information pertaining to the health condition of said patient.”, para. [0129]).
In reference to at least claim 3
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the label data is automatically generated based on historical tinnitus event reporting data representing a history of prior tinnitus events experienced by the at least one user (e.g. “ (i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]).
In reference to at least claim 4
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the label data is automatically generated based on historical tinnitus event reporting data representing a history of prior tinnitus events experienced by the at least one user (e.g. “ (i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]) and utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]). Tran further discloses utilizing data representing user preferences (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]). It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Arndt to include utilizing a personal preference, as taught by Tran, such as user preferences for treatment of prior tinnitus events experienced by the at least one user in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 5
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the historical tinnitus event reporting data is generated based on prior real-time tinnitus event reporting data (e.g. “(i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]; “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
In reference to at least claim 6
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the prior real-time tinnitus event reporting data comprises data representing prior real-time subjective feedback of the at least one user in relation to one or more of the onset of a prior tinnitus event (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
In reference to at least claim 7
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the real-time subjective feedback comprises a subjective grading of a severity of at least one prior tinnitus event (e.g. “based on his/her subjective assessment of the prevailing severity of tinnitus he/she is experiencing”, para. [0138]).
In reference to at least claim 8
Arndt modified by Tran renders obvious an apparatus according to claim 5. Arndt further discloses wherein the historical tinnitus event reporting data is generated based on prior real-time tinnitus event reporting data (e.g. “(i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]; “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]). Tran further discloses utilizing data representing prior real-time subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]). It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Arndt to include utilizing a personal preference, as taught by Tran, such as real-time subjective preferences of the at least one user in relation to a preferred therapy in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 9
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses wherein the historical tinnitus event reporting data is generated based on the prior real-time tinnitus event reporting data and retrospective tinnitus event reporting data (e.g. “ It will be appreciated in embodiments of the present disclosure that the machine learning algorithm, the regression model, the AI or the neural network are trained with…(ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients, (iii) treatment provided for the auditory dysfunctions/tinnitus,”, para. [0129]).
In reference to at least claim 10
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]). Tran further discloses utilizing data representing prior retrospective subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]). It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Arndt to include utilizing a direct feedback from the patient a user preference, as taught by Tran, such as prior retrospective subjective preferences of the at least one user in relation to a tinnitus therapy selection made in response to a prior tinnitus event in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 11
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses the label data being a subjective grading of a severity of a tinnitus event experience by the at least one user (e.g. “based on his/her subjective assessment of the prevailing severity of tinnitus he/she is experiencing”, para. [0138]) and utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]). Tran further discloses utilizing data representing prior retrospective subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]). It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Arndt to include utilizing a personal preference, as taught by Tran, such as a preferred tinnitus therapy in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 15
Arndt modified by Tran renders obvious an apparatus according to claim 1. Tran further discloses wherein the environmental data includes sound signals captured from an ambient environment of the at least one user or environmental classification data generated from sound signals captured from the ambient environment of the at least one user (e.g.” In another embodiment, a learning machine automatically selects an optimal set of hearing aid parameters based on ambient sound and other confirmatory data., para. [0029], [0077], [0164]).
In reference to at least claim 18
Arndt discloses a method according to claim 17. Arndt further discloses utilizing state data that comprises physiological data (e.g. (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]). However, Arndt does not explicitly disclose the state data representing an ambient sound environment of the at least one user.
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]). Tran further discloses the environment including an ambient sound environment (e.g.” In another embodiment, a learning machine automatically selects an optimal set of hearing aid parameters based on ambient sound and other confirmatory data., para. [0029], [0077], [0164]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing state data including an ambient sound environment, as taught by Tran, within the machine learning model in order to adjust amplifier controls to optimize hearing based on the identified environment maximizing speech intelligibility and listening comfort (‘260, para. [0004], [0166]).
In reference to at least claim 19
Arndt modified by Tran renders obvious a method according to claim 18. Tran further discloses obtaining sound signals captured from an ambient environment of the at least one user (e.g.” In another embodiment, a learning machine automatically selects an optimal set of hearing aid parameters based on ambient sound and other confirmatory data., para. [0029], [0077], [0164]).
In reference to at least claim 20
Arndt modified by Tran renders obvious a method according to claim 18. Tran further discloses obtaining environmental classification data generated from sound signals captured from an ambient environment of the at least one user (e.g.” to identify an aural environment (such as party, movie, office, or home environment)”, para. [0004], “and then repeats the test in different ambient environments such as restaurant, office, home, theater, party, concert, among others and records the best audio responses in view of the “noise”.”, para. [0165]).
In reference to at least claim 23
Arndt discloses a method according to claim 22. Arndt further discloses wherein the label data is automatically generated based on historical tinnitus event reporting data representing a history of prior tinnitus events experienced by the at least one user (e.g. “ (i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]) and utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]). Tran further discloses utilizing data representing user preferences (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]). It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing a personal preference, as taught by Tran, such as user preferences for treatment of prior tinnitus events experienced by the at least one user in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 27
Arndt discloses a method according to claim 24. Arndt further discloses wherein the historical tinnitus event reporting data is generated based on prior real-time tinnitus event reporting data (e.g. “(i) historical patient data of a plurality of patients, (ii) auditory dysfunctions/tinnitus associated with the historical patient data of the plurality of patients,”, para. [0129]; “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]). Tran further discloses utilizing data representing prior real-time subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing a personal preference, as taught by Tran, such as real-time subjective preferences of the at least one user in relation to a preferred therapy in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 29
Arndt discloses a method according to claim 28. Arndt further discloses utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]). Tran further discloses utilizing data representing prior retrospective subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing a direct feedback from the patient a user preference, as taught by Tran, such as prior retrospective subjective preferences of the at least one user in relation to a tinnitus therapy selection made in response to a prior tinnitus event in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
In reference to at least claim 30
Arndt discloses a method according to 17. Arndt further discloses the label data being a subjective grading of a severity of a tinnitus event experience by the at least one user (e.g. “based on his/her subjective assessment of the prevailing severity of tinnitus he/she is experiencing”, para. [0138]) and utilizing feedback from the patient within the training model (e.g. “Training for the machine learning algorithm, the regression model, the AI, or the neural network algorithm may also be provided by a direct feedback from said patient using the interface”, para. [0138], [0128]).
Tran, in the same field of endeavor of hearing systems, discloses utilizing a learning machine that detects an acoustic environment and adjust amplification and gain control based on the particular acoustic environment (e.g. “based on the detected environment, the learning machine can adjust amplification and gain control. It can optimize the gain at each or every frequency of the amplifier in a particular acoustic environment. The variable processing factor is can be the amplifier gain at each or every frequency”, para. [0166], [0168]) to maximize speech intelligibility or listening comfort (e.g. “an amplifier with gain and amplitude controls for each hearing frequency; and a learning machine (such as a neural network) to identify an aural environment (such as party, movie, office, or home environment) and adjusting amplifier controls to optimize hearing based on the identified aural environment.”, para. [0004], “maximizing speech intelligibility or listening comfort.”, para. [0166]). Tran further discloses utilizing data representing prior retrospective subjective preferences of the at least one user (e.g. “as well as store preference information”, para. [0158], “the personal preference and needs of each user”, para. [0166]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing a personal preference, as taught by Tran, such as a preferred tinnitus therapy in order to improve listening comfort, speech intelligibility and/or sound quality by providing parameters that are optimized for each user based on their personal preference and needs (‘260, para. [0166]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0255514 to Arndt et al. (Arndt) in view of US 2020/0268260 to Tran (Tran) as applied to claim 1 further in view of US 2022/0210586 to Reinhart et al. (Reinhart).
In reference to at least claim 12
Arndt modified by Tran renders obvious an apparatus according to claim 1. Arndt further discloses the state data comprising physiological data representing a current physiological state of the at least one user (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]).
However, Arndt does not explicitly disclose the physiological data includes data representing at least one of a heart rate or a heart rate variability of the at least one user, a skin conductance of the at least one user, or a neural activity of the at least one user.
Reinhart, in the same field of endeavor of tinnitus therapy, discloses utilizing physiological data such as heart and heart rate variability within a machine learning algorithm to detect one or more of absence presence and severity of tinnitus (e.g. “to produce heart rate variability data using the PPG signal, and detect one or more of absence, presence, and severity of tinnitus of the wearer using the heart rate variability data”, para. [0046], [0107]). Reinhart further discloses monitoring neural activity and utilizing the monitored neural activity within a machine learning algorithm to detect one or more of absence presence and severity of tinnitus (e.g. Figs. 8A-8B, “EEG sensor signals can be used by the controller 120 of the hearing device 100 to detect one or more of presence, absence, and severity of tinnitus of the device wearer using EEG sensor signals, and to adjust a tinnitus masking sound produced by the hearing device 100 to mitigate the wearer's tinnitus using EEG sensor signals.”, para. [0099]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the apparatus of Arndt modified by Tran to include utilizing physiological data that includes heart rate, heart rate variability and/or neural activity within the machine learning device, as taught by Reinhart, to refine the machine learning device based on the current state of the user requiring fewer manual adjustments improving satisfaction among users (‘586, para. [0125]).
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0255514 to Arndt et al. (Arndt) in view of US 2022/0210586 to Reinhart et al. (Reinhart).
In reference to at least claim 31
Arndt discloses a method according to claim 17. Arndt further discloses the state data comprising physiological data representing a current physiological state of the at least one user (e.g.” collect patient data associated with said health condition associated with said auditory dysfunction of said patient and to suggest the suggested treatment for said tinnitus to said patient based on said collected patient data;”, para. [0044]).
However, Arndt does not explicitly disclose obtaining physiological data representing at least one of a heart rate or a heart rate variability of the at least one user, a skin conductance of the at least one user, or a neural activity of the at least one user.
Reinhart, in the same field of endeavor of tinnitus therapy, discloses utilizing physiological data such as heart and heart rate variability within a machine learning algorithm to detect one or more of absence presence and severity of tinnitus (e.g. “to produce heart rate variability data using the PPG signal, and detect one or more of absence, presence, and severity of tinnitus of the wearer using the heart rate variability data”, para. [0046], [0107]). Reinhart further discloses monitoring neural activity and utilizing the monitored neural activity within a machine learning algorithm to detect one or more of absence presence and severity of tinnitus (e.g. Figs. 8A-8B, “EEG sensor signals can be used by the controller 120 of the hearing device 100 to detect one or more of presence, absence, and severity of tinnitus of the device wearer using EEG sensor signals, and to adjust a tinnitus masking sound produced by the hearing device 100 to mitigate the wearer's tinnitus using EEG sensor signals.”, para. [0099]).
It would have been obvious to one having ordinary skill in the art to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arndt to include utilizing physiological data that includes heart rate, heart rate variability and/or neural activity within the machine learning device, as taught by Reinhart, to refine the machine learning device based on the current state of the user requiring fewer manual adjustments improving satisfaction among users (‘586, para. [0125]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0110745 to Leroux et al. which discloses implantable tinnitus therapy which discloses using a machine learning engine to control tinnitus therapy provided to a user. US 2023/0329912 to Von Brasch et al. which discloses a new tinnitus management technique that utilizes physiological features or ambient environment within a machine learning system to determine likelihood of a tinnitus event.
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