Patent Application 18525739 - FITTING SYSTEM AND METHOD OF FITTING A HEARING - Rejection

Patent Application 18525739 - FITTING SYSTEM AND METHOD OF FITTING A HEARING

Title: FITTING SYSTEM, AND METHOD OF FITTING A HEARING DEVICE

Application Information

• Invention Title: FITTING SYSTEM, AND METHOD OF FITTING A HEARING DEVICE
• Application Number: 18525739
• Submission Date: 2025-05-14T00:00:00.000Z
• Effective Filing Date: 2023-11-30T00:00:00.000Z
• Filing Date: 2023-11-30T00:00:00.000Z
• National Class: 705
• National Sub-Class: 002000
• Examiner Employee Number: 92150
• Art Unit: 3682
• Tech Center: 3600

Rejection Summary

• 102 Rejections: 0
• 103 Rejections: 1

Cited Patents

The following patents were cited in the rejection:

• US 0321208🔗

Office Action Text

    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 .
DETAILED ACTION
Status of the Application
Claims 1-25 are currently pending in this case and have been examined and addressed below.  This communication is a Non-Final Rejection in response to the Claims filed on 11/30/2023.
	
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 1-25 are rejected because the claimed invention is directed to an abstract idea without significantly more.  
Step 1
Claims 1-15 and 23-25 fall within the statutory category of an apparatus or system.  Claims 16-22 fall within the statutory category of a process.
Step 2A, Prong One
As per Claims 1 and 16, the limitations of determine intermediate gain values based on the input associated with the individual user hearing characteristic, determine the intermediate gain values, determine a statistical value based on the intermediate gain values, and determine a gain value range based on the statistical value, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components.  The steps of determining intermediate gain values based on individual user hearing characteristic, determining intermediate gain values, determining a statistical value based on the intermediate gain values, and determining a gain value range based on the statistical value are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.  
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional elements – an input interface, a processing unit, and an output interface.  The system in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component.  Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also recite an ensemble of at least two fitting models embodied as a neural network for determining intermediate gain values which is part of the abstract idea. The use of fitting models embodied as a neural network is a mathematical algorithm being applied on a general purpose computer to perform the abstract idea, which as per MPEP 2106.05(f)(2), amounts to mere instructions to apply the exception. The claims also recite the additional elements of obtaining an input associated with an individual user hearing characteristic and providing the determined gain value which amount to insignificant extra-solution activity, as in MPEP 2106.05(g), because the step of obtaining an input associated with an individual user hearing characteristic are mere data gathering and the step of providing the determined gain value is mere data outputting in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).  Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.  As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a computing device to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component.  The system including an input interface, a processing unit, and an output interface are recited at a high level of generality and are recited as generic computer components by reciting the input interface as an input transducer (Specification [0033]), a processing unit which is a processor, etc. (Specification, [0037]), and the output interface as an output transducer coupled to the processing unit (Specification [0038]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception.  Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of obtaining an input associated with an individual user hearing characteristic and providing the determined gain value which are both elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as presenting offers and gathering statistics, which have been found to be well-understood, routine and conventional computer functions by the Court (MPEP 2106.05(d)(II)(i)  Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added) and (iv) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).  Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.  There is no indication that the combination of elements improves the functioning of the computer or improves another technology.  The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims
Dependent Claims 2-15, 17-22, and 23-25 add further limitations which are also directed to an abstract idea as described below.  
Claims 2-4 provide further description of the processing unit as being “a part of” a fitting instrument, a computer, or a hearing device.  However, claim language does not positively recite the fitting instrument, computer, or hearing device and does not provide for how the processing unit is “part of” these devices.  Therefore, this is merely descriptive and does not provide any functional limitations beyond the abstract idea and the claims are therefore directed to the same abstract idea as the independent claims. 
Claim 5 further specifies and limits the neural network of the independent claim and is therefore directed to the same abstract idea. 
Claims 6 and 22 include determining an individual gain value based on a mean value of the intermediate gain values which is a mental process for the same reasons as the independent claims. 
Claims 7 and 21 include applying the individual gain value to a hearing device which amounts to insignificant extra-solution activity as mere data outputting because the claims does not provide any specific details about how this is applied to the device which would amount to more than outputting the data.  This is found to be well-understood, routine, and conventional activity in the field similar to receiving or transmitting data over a network, as per MPEP 2106.05(d)(II), which has been found by the courts to be well-understood, and routine computer functions. 
Claim 8 includes applying a set of parameters to the ensemble of the fitting models  which is a mathematical calculation and thus falls into the abstract idea of mathematical concepts. The claim also includes retrieving a set of parameters of a machine learning algorithm form a remote physical location which is mere data gathering that is insignificant extra-solution activity for the same reasons as the independent claims. 
Claim 9 includes the use of a statistical algorithm to determine the statistical value which is a mathematical calculation and falls in the abstract idea of mathematical concepts.
Claim 10 includes determining the gain value range based on the statistical value which is directed to a mental process for the same reasons as the independent claims.
Claims 11-13 and 17 further specifies and limits the input of the independent claim and is therefore directed to the same abstract idea. 
Claims 14 and 18 includes determining a statistical value by determining a mean value and/or a standard deviation of the intermediate gain values which is directed to a mental process for the same reasons as the independent claims. This can also fall into the abstract grouping of mathematical concepts since determining a mean value and determining a standard deviation are mathematical calculations.
Claims 15 and 19 further specifies and limits the user hearing characteristic and is therefore directed to the same abstract idea. 
Claim 20 includes determining an individual gain value based on the gain value range which is directed to a mental process for the same reasons as the independent claims.
Claims 23-25 include limitations similar to those in Claim 1 and are directed to the same abstract idea.
Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.

Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA  35 U.S.C. 102 and 103 (or as subject to pre-AIA  35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA  to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.  
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Harianawala et al. (WO 2023/028122 A1), hereinafter Harianawala, in view of Alamdari et al. (Nasim Alamdari, Edward Lobarinas, Nasser Kehtarnavaz; Personalization of Hearing Aid Compression by Human-in-the-Loop Deep Reinforcement Learning; 19 Nov 2020; IEEE Access, Vol. 8; Pages 203503 203515), hereinafter Alamdari, in view of van de Laar, et al. (Thijs van de Laar and Bert de Vries; A Probabilistic Modeling Approach to Hearing Loss Compensation; 6 Sep 2016; IEEE Transactions on Audio, Speech and Language Processing), hereinafter van de Laar.
As per Claims 1 and 16, Harianawala discloses an electronic system for determining a gain value range, the system comprising:
an input interface configured to obtain an input associated with an individual user hearing characteristic ([0066] interface of record generation unit to input clinician interpretation data for user, [0139] interfaces to obtain input from users to provide information to the system, see [0044-0046] audiogram data obtained, converted and extracted and input to system); 
a processing unit (see Fig. 1 processors 112C, [0035]) configured to: 
each of the at least two fitting models is embodied as a neural network ([0078] initial fitting model implemented as an artificial neural network, [0083] post-fitting adjustment model is an artificial neural network, [0089] fitting support model implemented as artificial neural network, [0144] ML model implemented as artificial neural network); and 
an output interface configured to provide the determined gain value range ([0114] post-fitting adjustments are included in the adjust record, [0119-0120] adjustment record which includes the fitting adjustments, i.e. gain value range are requested and displayed).
Harianawala may not explicitly disclose the following which is taught by Alamdari: determine intermediate gain values based on the input associated with the individual user hearing characteristic (Page 203505 Personalized Fitting Protocol teaches using a user’s audiogram to compute reference parameters including gain change), wherein the processing unit comprises an ensemble of at least two fitting models configured to determine the intermediate gain values (Page 203504 Col. 1, second paragraph combination of convolutional neural network and bidirectional long short-term memory recurrent neural network used to model parameters);
determine a gain value range based on the statistical value (Page 203505 Fig. 2 shows the gain range for each frequency including max and min values, Col. 1 A. Personalized Fitting Protocol teaches gains determined based on group averages).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of at least two fitting models to determine intermediate gain values and a gain value range from Alamdari with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala in order to personalize hearing aid settings to achieve improved hearing perception for a hearing aid user (Alamdari Page 203503 Abstract).
Harianawala and Alamdari may not explicitly disclose the following which is taught by van de Laar: determine a statistical value based on the intermediate gain values (Page 9 IV. Simulations teaches mean values or standard deviation values were used to model the gain values).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of using gain values to determine a statistical value from van de Laar with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala and Alamdari in order to provide a satisfying sound experience for hearing aid users by tuning algorithms to personalize the tuning/adjustment for the patient’s specific impairment (van de Laar Page one Abstract).
As per Claim 2, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the processing unit is a part of a fitting instrument configured for fitting a hearing device (see Fig. 1 processors 112C, [0035] processors distributed among devices including the processor in the computing system 106, [0061] device can be a programming device to communicate with the hearing instrument).
As per Claim 3, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the processing unit is a part of a computer configured for fitting a hearing device (see Fig. 1 processors 112C, [0035] processors distributed among devices including the processor in the computing system 106).
As per Claim 4, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the processing unit is a part of a hearing device (see Fig. 1 processors 112A/B, 114, [0035] processors distributed among devices including the hearing instrument, [0036] processors work as a system including processor located in the hearing instrument).
As per Claim 5, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala may not explicitly disclose the following which is taught by Alamdari: the neural network embodying each of the fitting models comprises a deep neural network (Page 203514 Col. 1 Conclusion and Future work teaches fitting practice personalizes the settings via a deep reinforcement learning framework).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of a deep neural network to determine intermediate gain values and a gain value range from Alamdari with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala in order to personalize hearing aid settings to achieve improved hearing perception for a hearing aid user (Alamdari Page 203503 Abstract).
As per Claims 6 and 22, Harianawala, Alamdari, and van de Laar discloses the limitations of Claims 1 and 16. Harianawala and Alamdari may not explicitly disclose the following which is taught by van de Laar: the processing unit is configured to determine an individual gain value based on a mean value of the intermediate gain values (Page 9 IV. Simulations teaches mean values or standard deviation values were used to model the gain values).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of using a mean of gain values to determine a gain value from van de Laar with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala and Alamdari in order to provide a satisfying sound experience for hearing aid users by tuning algorithms to personalize the tuning/adjustment for the patient’s specific impairment (van de Laar Page one Abstract).
As per Claims 7 and 21, Harianawala, Alamdari, and van de Laar discloses the limitations of Claims 1 and 20. Harianawala also teaches the processing unit is configured to apply the individual gain value to a hearing device ([0061] UMS provides setting values to a programming device which communicates the settings to the hearing instrument to configure the hearing instrument).
As per Claim 8, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the system is configured to retrieve a set of parameters of a machine learning algorithm from a remote physical location ([0051] training data obtained from the UMS based on profiles of other users of the plurality of hearing instruments, [0107] UMS data may be stored in a system remote from the hearing instruments), and 
to apply the set of parameters to the ensemble of the fitting models prior to receiving the input associated with the individual user hearing characteristic ([0051] UMS trains the machine learning model to generate a model which is then applied to the current user profile data to generate an output).
As per Claim 9, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala and Alamdari may not explicitly disclose the following which is taught by van de Laar: the processing unit is configured to determine the statistical value using a statistical algorithm (Page 10, IV. Simulations A. signal processing teaches the statistical values of mean and standard deviation are determined by modelling a gaussian function). 
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determine a statistical value from van de Laar with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala and Alamdari in order to provide a satisfying sound experience for hearing aid users by tuning algorithms to personalize the tuning/adjustment for the patient’s specific impairment (van de Laar Page one Abstract).
As per Claim 10, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala may not explicitly disclose the following which is taught by Alamdari: the processing unit is configured to determine the gain value range based on the statistical value using a gain value algorithm (Page 203505 Fig. 2 shows the determined gain value ranges, Col. 1, Personalized Fitting Protocols determining the DRL framework including gain change ranges using the ratios generated by the algorithm applied to compression gains).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining a gain value range from Alamdari with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala in order to personalize hearing aid settings to achieve improved hearing perception for a hearing aid user (Alamdari Page 203503 Abstract).
As per Claim 11, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the input comprises an individual user dataset ([0046] hearing data includes audiogram data; [0047] audiometric data used to provide initial fitting settings, [0075] input dataset includes audiological information, [0144] user profile is input to ML model).
As per Claim 12, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the input comprises a particular hearing device dataset ([0075] user profile data includes device information of the hearing instrument used, [0097] user profile includes device information).
As per Claim 13, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala also teaches the input comprises an audiogram ([0046] hearing data includes audiogram data; [0047] audiometric data used to provide initial fitting settings, [0075] input dataset includes audiological information). 
As per Claim 14, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 1. Harianawala and Alamdari may not explicitly disclose the following which is taught by van de Laar:  processing unit is configured to determining the statistical value by determining a mean value and/or a standard deviation of the intermediate gain values (Page 9 IV. Simulations teaches mean values or standard deviation values were used to model the gain values).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determine a mean or standard deviation value from van de Laar with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala and Alamdari in order to provide a satisfying sound experience for hearing aid users by tuning algorithms to personalize the tuning/adjustment for the patient’s specific impairment (van de Laar Page one Abstract).
As per Claims 15 and 19, Harianawala, Alamdari, and van de Laar discloses the limitations of Claims 1 and 16. Harianawala also teaches the individual user hearing characteristic corresponds to a user's hearing ability in a particular frequency band ([0046] hearing data includes audiometric data and hearing ability such as uncomfortable loudness levels at each of the frequencies tested).
Harianawala may not explicitly disclose the following which is taught by Alamdari: wherein the gain value range corresponds to the particular frequency band (Page 203505 Fig. 2 shows the gain ranges for each different corresponding frequency band).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining gain value ranges corresponding to frequency bands from Alamdari with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala in order to personalize hearing aid settings to achieve improved hearing perception for a hearing aid user (Alamdari Page 203503 Abstract).
As per Claim 17, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 16. Harianawala also teaches the input comprises an individual user dataset, a particular hearing device data set, an audiogram, or any combination of the foregoing  ([0046] hearing data includes audiogram data; [0047] audiometric data used to provide initial fitting settings, [0075] input dataset includes audiological information, [0144] user profile is input to ML model).
As per Claim 18, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 16. Harianawala and Alamdari may not explicitly disclose the following which is taught by van de Laar:  determining the statistical value comprises determining a mean value and/or a standard deviation of the intermediate gain values (Page 9 IV. Simulations teaches mean values or standard deviation values were used to model the gain values).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining a mean or standard deviation value from van de Laar with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala and Alamdari in order to provide a satisfying sound experience for hearing aid users by tuning algorithms to personalize the tuning/adjustment for the patient’s specific impairment (van de Laar Page one Abstract).
As per Claim 20, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 16. Harianawala may not explicitly disclose the following which is taught by Alamdari:  determining an individual gain value based on the gain value range (Page 203505 see Fig. 2 where the gain is shown between the maximum and minimum levels of the gain range).
It would have been obvious to a person of ordinary skill in the art before the filing of the current invention to combine the known concept of determining a gain value based on a gain value range from Alamdari with the known system of obtaining audiological data to analyze and output a determined gain value from Harianawala in order to personalize hearing aid settings to achieve improved hearing perception for a hearing aid user (Alamdari Page 203503 Abstract).
As per Claim 23, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 15. Harianawala also teaches each of the at least two fitting models is embodied as a neural network ([0078] initial fitting model implemented as an artificial neural network, [0083] post-fitting adjustment model is an artificial neural network, [0089] fitting support model implemented as artificial neural network, [0144] ML model implemented as artificial neural network). (see claim 1)
As per Claim 24, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 15. Harianawala also teaches the intermediate gain values comprise at least two intermediate gain values determined using respective ones of the at least two fitting models ([0053] initial fitting model generates initial fitting suggestions, which are indicated as more than one suggestion). 
As per Claim 25, Harianawala, Alamdari, and van de Laar discloses the limitations of Claim 15. Harianawala also teaches multiple ones of the intermediate gain values are determined by one of the at least two fitting models ([0053] initial fitting model generates initial fitting suggestions, which are indicated as more than one suggestion). 

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. 
Cumming (US 2021/0321208 A1) teaches using artificial intelligence to input audiogram data and output fitting data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.


/EVANGELINE BARR/Primary Examiner, Art Unit 3682