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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/31/2026 has been entered.
Status of the Claims
Claims 1, 5, 19, and 30-34 have been amended. Claim 35 is new. Claims 1, 5-12, 16-17, 19, 22, 24-25, and 28-35 are currently pending and have been considered by the Examiner.
Claim Objections
Claims 19 and 30 are objected to because of the following informalities: In claim 19 on page 5, line 7, “included” should recite “specified”. In claim 30 on page 8, line 6, “included” should recite “specified”. Appropriate correction is required.
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, 5-12, 16-17, 19, 22, 24-25, and 28-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 5-12, 16-17, 22, 25, 28, 31, 33-35 recite a method. Claims 19, 24, 29, 32 recite a hyperparameter tuning device comprising a processor (a product). Claim 30 recites a hyperparameter tuning system comprising a processor (a system). Each of a method, a product, and a system falls within one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Providing, by the user
Receiving, by the hyperparameter tuning [module]
Selecting, by the hyperparameter tuning [module]
Providing, by the hyperparameter tuning [module]
Receiving, by the user
Providing, by the user between people” includes a teacher teaching a student. The student may provide a request to the teacher.
Receiving, by the hyperparameter tuning [module]
Selecting, by the hyperparameter tuning [module]
Providing, by the hyperparameter tuning [module]
The user
Step 2A Prong 2 and Step 2B: One or more processors amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Generating, by a user program, a first hyperparameter obtaining request for a first hyperparameter according to a hyperparameter obtaining code written in the user program written by using a machine learning library for training a machine learning model, … the plurality of candidates being written in the hyperparameter obtaining code in the user program amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h).
A hyperparameter tuning program amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Generating, by the user program, a second hyperparameter obtaining request for a second hyperparameter, based on the selected first candidate for the first hyperparameter, according to the hyperparameter obtaining code amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h).
Training, by the user program, the machine learning model by applying a set of the provided first hyperparameter and the provided second hyperparameter amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
During execution of the user program and after receiving the selected first candidate, the user program [performs operations] amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
[The user program] executes, according to a result of the evaluation, a code portion corresponding to the selected first candidate from among a plurality of code portions respectively corresponding to the plurality of candidates, wherein the code portion specifies the type of the second hyperparameter specific to the selected first candidate, wherein the execution of the code portion causes the user program to generate the second hyperparameter obtaining request to be provided to the hyperparameter tuning program, and wherein the conditional branch and the plurality of code portions are written in the user program amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions as disclosed in combination with a mere field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea. The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed in combination with a mere field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The second hyperparameter obtaining request [requests] a hyperparameter specific to the type of the machine learning model is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may receive a second request from the student.
Step 2A Prong 2 and Step 2B: The first hyperparameter is a type of the machine learning model amounts to a field of use and technological environment under MPEP 2106.05(h).
The execution of the code portion by the user program amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Setting a hyperparameter that defines a structure of the machine learning model is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Setting a hyperparameter that defines a training process of the machine learning model is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
A human mind can reasonably set a hyperparameter that defines a structure of a machine learning model and a hyperparameter that defines a training process of the machine learning model by recording them on paper. Specification paragraph [0010], lines 13-18 and Fig. 1 discloses setting a hyperparameter that defines a number of layers to 10 layers, and setting a hyperparameter that defines a learning rate to 0.001. Fig. 1 itself shows that one can set these hyperparameters by recording them on paper.
Step 2A Prong 2 and Step 2B: The hyperparameter obtaining code is modularized by sets of code amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. The providing of the second hyperparameter provides a hyperparameter is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may provide the second hyperparameter to the student.
Selecting a hyperparameter based on a predetermined hyperparameter selection algorithm is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. A human mind can reasonably select a hyperparameter based on the result of applying an algorithm.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional element which, alone or in combination, do not integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated.
Step 2A Prong 2 and Step 2B: The predetermined hyperparameter selection algorithm is based on Bayesian optimization amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 9 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated. The predetermined hyperparameter selection algorithm is based on a random search is a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper. A human mind can reasonably perform a random search of hyperparameters by selecting hyperparameter values at random from a list of available values.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional element which, alone or in combination, do not integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 10 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: Obtaining an evaluation result of the user program to which the set of the provided first hyperparameter and the provided second hyperparameter are applied amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 11 incorporates the rejection of claim 10.
Step 2A Prong 1: The abstract ideas of claim 10 are incorporated.
Step 2A Prong 2 and Step 2B: The evaluation result of the user program includes accuracy of the machine learning model amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 12 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Repeating the receiving of the second hyperparameter obtaining request and the providing of the second hyperparameter until a termination condition is satisfied is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may receive the second request from the student, and the teacher may provide the second hyperparameter to the student.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional element which, alone or in combination, do not integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 16 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: Generating a computer program using the hyperparameter tuning method as claimed in claim 1 amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 17 incorporates the rejection of claim 16.
Step 2A Prong 1: The abstract ideas of claim 16 are incorporated.
Step 2A Prong 2 and Step 2B: The computer program is a machine learning model amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 19 recites device which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a hyperparameter tuning device comprising one or more processors amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 22 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Examiner treats a “trial” as any period of time. The receiving the first hyperparameter obtaining request of the first hyperparameter, the providing the first hyperparameter, the receiving the second hyperparameter obtaining request of the second hyperparameter, and the providing the second hyperparameter being performed in a same trial is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may receive the first request, provide the first hyperparameter, receive the second request, and provide the second hyperparameter in a same trial or period of time.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional element which, alone or in combination, do not integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas. The claim is not patent eligible.
Claim 24 recites a device which implements the same features as the method of claim 22 and is therefore rejected for at least the same reasons. The claim is not patent eligible.
Claim 25 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The set of the provided first hyperparameter and the provided second hyperparameter are applied as at least a part of hyperparameters for a same trial of training of the machine learning model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 28 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The type of the second hyperparameter and the type of the provided first hyperparameter are a number of layer nodes and a number of layers, respectively, amounts to a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 29 recites a device which implements the same features as the method of claim 28 and is therefore rejected for at least the same reasons. The claim is not patent eligible.
Claim 30 recites a system which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, one or more memories and one or more processors amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 31 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Defining at least a first type and a second type for the second hyperparameter corresponding respectively to the first candidate and a second candidate for the first hyperparameter and a range of a value for each of the first type and the second type for the second hyperparameter is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: A hyperparameter tuning program for the hyperparameter tuning method being executed by the one or more processors amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The hyperparameter obtaining code amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 32 incorporates the rejection of claim 19.
Step 2A Prong 1: The abstract ideas of claim 19 are incorporated. Receiving of the first hyperparameter obtaining request and the second hyperparameter obtaining request is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may receive the first request and the second request from the student.
Providing the first hyperparameter and the second hyperparameter is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may provide the first hyperparameter and the second hyperparameter to the student.
Defining at least a first type and a second type for the second hyperparameter corresponding respectively to the first candidate and a second candidate for the first hyperparameter and a range of a value for each of the first type and the second type for the second hyperparameter is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The one or more processors are configured to execute the hyperparameter tuning program to perform operations amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 33 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: Providing, to the user
Receiving, from the user
Step 2A Prong 2 and Step 2B: The user program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The hyperparameter obtaining code written in the user program for training the machine learning model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 34 incorporates the rejection of claim 33.
Step 2A Prong 1: The abstract ideas of claim 33 are incorporated. According to the result of the evaluation of the branch condition of the conditional branch of the hyperparameter obtaining code, the user
Step 2A Prong 2 and Step 2B: The hyperparameter obtaining code amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h).
The user program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 35 incorporates the rejection of claim 34.
Step 2A Prong 1: The abstract ideas of claim 34 are incorporated.
Step 2A Prong 2 and Step 2B: The first hyperparameter is a type of the machine learning model is a field of use and technological environment under MPEP 2106.05(h).
The first candidate is a first type of machine learning model and the second candidate is a second type of machine learning model different from the first type of machine learning model is a field of use and technological environment under MPEP 2106.05(h).
The type of the second hyperparameter specific to the first candidate is different from the type of the third hyperparameter specific to the second candidate is a field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Examiner’s Note
No prior art rejection has been provided for pending claims 1, 19, and 30. The features of a hyperparameter tuning method executed by one or more processors, comprising: generating, by a user program, a first hyperparameter obtaining request for a first hyperparameter according to a hyperparameter obtaining code written in the user program written by using a machine learning library for training a machine learning model, and providing, by the user program, the generated first hyperparameter obtaining request to a hyperparameter tuning program, the first hyperparameter obtaining request specifying a plurality of candidates for the first hyperparameter, the plurality of candidates being written in the hyperparameter obtaining code in the user program; receiving, by the hyperparameter tuning program, from the user program, the first hyperparameter obtaining request; selecting, by the hyperparameter tuning program, a first candidate from among the plurality of candidates specified in the first hyperparameter obtaining request received from the user program, as the first hyperparameter to be provided to the user program; providing, by the hyperparameter tuning program, to the user program, the selected first candidate as the first hyperparameter; receiving, by the user program, the selected first candidate from the hyperparameter tuning program; generating, by the user program, a second hyperparameter obtaining request for a second hyperparameter, based on the selected first candidate for the first hyperparameter, according to the hyperparameter obtaining code, and providing, by the user program, the generated second hyperparameter obtaining request to the hyperparameter tuning program, a type of the second hyperparameter specified in the second hyperparameter obtaining request being different from a type of the first hyperparameter and being specific to the selected first candidate; receiving, by the hyperparameter tuning program, from the user program, the second hyperparameter obtaining request; selecting, by the hyperparameter tuning program, a value for the second hyperparameter based on the received second hyperparameter obtaining request; providing, by the hyperparameter tuning program, the selected value as the second hyperparameter specific to the selected first candidate to the user program; and training, by the user program, the machine learning model by applying a set of the provided first hyperparameter and the provided second hyperparameter, wherein, during execution of the user program and after receiving the selected first candidate, the user program evaluates, based on the selected first candidate, a branch condition of a conditional branch of the hyperparameter obtaining code, and executes, according to a result of the evaluation, a code portion corresponding to the selected first candidate from among a plurality of code portions respectively corresponding to the plurality of candidates, wherein the code portion specifies the type of the second hyperparameter specific to the selected first candidate, and the execution of the code portion causes the user program to generate the second hyperparameter obtaining request to be provided to the hyperparameter tuning program, and wherein the conditional branch and the plurality of code portions are written in the user program, when taken in the context of the claim as a whole, were not uncovered in the prior art of record.
Response to Arguments
Below are Examiner’s responses to Applicant’s arguments filed 08/31/2026.
Applicant’s Argument Under 35 U.S.C. 101 – Step 2A Prong 1: On page 1, the Applicant submits that the Examiner’s rejection improperly introduces human actors that are nowhere recited in the claims. The Applicant argues that claim 1 recites interactions between software components – specifically, a user program and a hyperparameter tuning program. The Applicant argues that the recited operations—evaluating a branch condition at runtime, executing a candidate-corresponding code portion, and generating a request through that execution—are specific software control flow operations rather than the organization of human activity or a mental process.
Examiner’s Response: The applicant’s arguments have been fully considered but they are not persuasive. Examiner respectfully disagrees with the Applicant that the Examiner’s rejection improperly introduces human actors. The claimed interaction between a user program and a hyperparameter tuning device is analogous to an interaction between a student and a teacher. MPEP 2106.04(a)(2), subsection II, third bullet point states that teaching is an example of a method of organizing human activity. In analysis of claim 1, under Step 2A Prong 1, the claim feature of a “user” is analogous to a student and the claim feature of a “hyperparameter tuning” module is analogous to a teacher in a scenario of a teacher teaching a student. The claim elements of programs are evaluated in Step 2A Prong 2.
In Step 2A Prong 1, “providing, by the hyperparameter tuning [module], to the user…, the selected first candidate as the first hyperparameter” is a method of organizing human activity. The sub-grouping “managing personal behavior or relationships or interactions between people” includes a teacher teaching a student. The teacher may provide the first hyperparameter to the student.
The claimed features of a user evaluating a conditional branch based on the selected first candidate is an evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. In an example based on specification paragraphs [0010], [0041], and Fig. 1, a person can reasonably evaluate conditional branches such as “IF the type of machine learning model is a neural network”. This essentially means determining whether the selected first candidate matches a specific type of machine learning model.
The claimed features in (2) of executing a user program, and all claimed features in (3) are not judicial exceptions.
Applicant’s Argument Under 35 U.S.C. 101 – Step 2A Prong 2 and Step 2B: On page 1, the Applicant argues that by coding candidate-specific code portions within the user program, evaluating a branch condition upon receiving the first candidate, and executing a candidate-specific code portion to specify the downstream hyperparameter type and generate the subsequent request, the claimed architecture addresses a technical problem associated with the maintainability and flexibility of hyperparameter tuning software. On pages 1-2, the Applicant argues this specific implementation improves program maintainability and permits appropriate hyperparameters to be requested according to sequentially selected hyperparameters, and is consistent with Enfish and emphasized in the USPTO Memo.
On pages 3-4, the Applicant argues that a specific ordered combination supplies significantly more than any alleged abstract idea.
Examiner’s Response: The applicant’s arguments have been fully considered but they are not persuasive. Applicant argues that the limitations of claim 1 in lines 20-21 which recites “generating, by the user program, a second hyperparameter obtaining request for a second hyperparameter” and the limitations recited on page 2, lines 11-22 integrate the abstract ideas into a practical application. Examiner respectfully disagrees.
First evaluating a branch condition upon receiving the first candidate is an evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. It is noted that the judicial exception alone cannot provide the improvement, and that an improvement in the abstract idea itself is not an improvement in technology. See MPEP 2106.05(a) and (a)(II).
In Step 2A Prong 2, the limitation of during execution of the user program and after receiving the selected first candidate, the user program [performs operations] amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The limitation of the user program executing, “according to a result of the evaluation, a code portion corresponding to the selected first candidate from among a plurality of code portions respectively corresponding to the plurality of candidates, wherein the code portion specifies the type of the second hyperparameter specific to the selected first candidate, wherein the execution of the code portion causes the user program to generate the second hyperparameter obtaining request to be provided to the hyperparameter tuning program, and wherein the conditional branch and the plurality of code portions are written in the user program” amounts to mere instructions for applying the abstract ideas on a generic computer under MPEP 2106.05(f) and a field of use and technological environment under MPEP 2106.05(h).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are generic computer functions as disclosed in combination with a mere field of use that are implemented to perform the abstract ideas disclosed above.
The Examiner respectfully disagrees that the claimed architecture solves a technical problem such as maintaining hyperparameter tuning software. Evaluating a branch condition of a conditional branch is an evaluation mental process, and this cannot provide a technical improvement as explained in the Examiner’s response above. The user program executes code portions according to a result of the evaluation which is used to generate the second hyperparameter obtaining request. This amounts to generic computer instructions under MPEP 2106.05(f). The user program comprising the conditional branch and the plurality of code portions are more generic computer instructions.
The claims in Enfish were eligible due to the unique and improved self-referential table for a computer database. The Enfish invention is not equivalent to the claimed inventions of pending claim 1. With respect to arguments citing the USPTO Memo, the alleged improvement appears to be an improvement to a mental process itself, which is implemented in combination with generic instructions for applying the abstract idea on a generic computer.
The Applicant’s arguments under Step 2B are not persuasive for the same reasons given in response to the arguments under Step 2A. The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are generic computer functions as disclosed in combination with a mere field of use that are implemented to perform the abstract ideas disclosed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 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, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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