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 .
This is in response to the application filed on June 03, 2025, claims 1-24 are now pending for examination in the application.
Terminal Disclaimer
The terminal disclaimer filed on June 03, 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Patent No. 11,568,320 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Applicant’s arguments:
In regards to claim 1 on Pages 7, applicant argues “Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter as the claims are allegedly directed to an abstract idea without significantly more. Applicant respectfully disagrees.”
Examiner’s Reply:
The steps of generating, determining, and adjusting can performed within the human mind. A human would be able to iteratively follow these steps along with any needed additional elements while using a computer as a tool (eg determing errors in machine learning models).
Applicant’s arguments:
In regards to claim 1 on Pages 9, applicant argues “Applicant respectfully submits that a human being is not capable of “adjusting, by the error model, the first output based on the error associated with the output to the ML model” or “adjusting, by the error model, the first input based on the error associated with the input to the ML model,” (emphasis added) as recited in claim 1 using their mind or a pen and paper. Therefore, none of the limitations are directed to abstract ideas.”
Examiner’s Reply:
Examiner respectfully disagrees. Adjusting a model is a mental step with mathematical concepts and pairing them with addition elements like machine learning does not integrate the abstract idea into a practical application. The examiner notes that the computer as recited in the claims are being used for error modeling. Therefore, the abstract idea recited in the claims is generally linking it to a computer environment.
Applicant’s arguments:
In regards to claim 1 on Pages 10, applicant argues “Applicant respectfully submits that managing output drift of an ML model that performs a function in an enterprise database system by training an error machine learning model to learn an error in the output of the machine learning model between different versions of the database system and adjusting inputs and outputs to the machine learning model based on the error model is not simply insignificant extra solution activity and/or mere instructions to apply a judicial exception, but features that would couch any alleged judicial exception into a specific solution to a problem in the technological (specifically database systems) arts and would not swallow the judicial exception in any way. See 2019 PEG.”
Examiner’s Reply:
Training an error model using a machine learning model is not a mental process. However, machine learning modeling is a generic computer function which is an additional element. There is nothing in this limitation individually or in combination that is beyond the judicial exception. Appellant’s specification describes concept drift in machine learning which is using well-understood, routine, and conventional. This doesn’t provide practical application resulting in patent eligible subject matter
Applicant’s arguments:
In regards to claim 1 on Pages 10-11, applicant argues the Office action asserts that Fly relates to “training, by a processing device using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system,” as recited in claim 1, citing paragraph [0088] of Fly. Applicant respectfully disagrees. The cited and other relevant portions.
Examiner’s Reply:
Paragraphs 88-89 disclose the merge engine 255 merges offline “baseline” model scores with online “test” scores that are logged by the logging engine 250. In one embodiment, the merge engine 225 uses a combination of cached results for inline anomaly detection and logged results that are comparable in a real time reporting and monitoring solution like the ELK stack. The drift detector 270, herein also referred to as the deviation analysis engine 270, measures the deviation between the offline “baseline” model and the online “test” model, as presented in the merged results generated by the merge engine 255. The drift detector 270 may use a variety of methods for identifying a drift or a deviation in the merged results. A few specific methodologies are detecting deviations are described in reference to FIG. 3 and FIG. 4, however, other methodologies may be used, as would be readily understood by a person of ordinary skill in the art, without departing from the scope of the invention. In order for a model to be trained for determining errors, it would have had to use the test data from the successive versions. Therefore the rejection is maintained.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-24 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim method (claims 1-8), system (claims 9-16), and non-transitory computer-readable medium (claims 17-24) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 9, and 17 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1, 9, and 17 recites the following limitations directed towards a Mental Processes & Mathematical Concepts:
generating test data from successive versions of a database system, the database system comprising a machine learning (ML) model to generate an output corresponding to a function of the database system (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate test data);
in response to the ML model generating a first output based on a first input: when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate model data); and
when the error is associated with the input to the ML model, adjusting, by the error model, the first input based on the error associated with the input to the ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate model data).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 9:
a memory (i.e., as a generic processor/component performing a generic computer function); and
a processing device (i.e., as a generic processor/component performing a generic computer function) operatively coupled to the memory, the processing device to:
training, by a processing device using the test data, an error model
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include
additional elements that are sufficient to amount to significantly more than the judicial
exception. Because the additional elements of the independent claims amount to insignificant
extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 9, and 17 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2, 10, and 18:
Step 2A, prong one of the 2019 PEG:
removing the error model from the latest version of the system (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to remove model data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3, 11, and 19:
Step 2A, prong one of the 2019 PEG:
executing a set of training queries of the ML model on the latest version of the system to generate second test data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generating test data);
retraining the error model based on the second test data to generate an updated error model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to retrain model data); and
deploying the latest version of the database system with the updated error model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to deploy a database system).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4, 12, and 20:
Step 2A, prong one of the 2019 PEG:
wherein generating the training data comprises adding the second test data to the one or more adjusted outputs of the error model accumulated over time (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate training data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5, 13, and 21:
Step 2A, prong one of the 2019 PEG:
wherein the error is associated with the input to the ML model, the method further comprising: outputting the adjusted first input to the ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to determining an error).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6, 14, and 22:
Step 2A, prong one of the 2019 PEG:
generating training data based at least in part on one or more adjusted inputs of the error model accumulated over time (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate training data);
retraining the ML model based on the training data to generate a retrained ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a model); and
deploying a latest version of the database system with the retrained ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to deploy a database system).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7, 15, and 23:
Step 2A, prong one of the 2019 PEG:
wherein the test data is generated using a set of test queries comprising test queries tagged by the database system as relevant to the ML model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate test data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8, 16, and 24:
Step 2A, prong one of the 2019 PEG:
removing the error model from the latest version of the system (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate test data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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) 1-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fly et al. (US Pub. No. 20200082296) in view of Liu et al. (US Pub. No. 20180314735).
With respect to claim 1, Fly et al. teaches a method comprising:
generating test data from successive versions of a database system, the database system comprising a machine learning (ML) model to generate an output corresponding to a function of the database system (Paragraph 112 teaches online “test” model that is generated by the test results engine 1115 and Paragraph 115 detecting performance degradation (caused by drift) of an online scoring system in a machine learning/classification system);
training, by a processing device using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system (Paragraph 88 discloses error for numeric predictors classification errors for classifiers recall and precision for IR models, etc.). Fly et al. does not disclose when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model.
However, Liu et al. teaches in response to the ML model generating a first output based on a first input:
when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost); and
when the error is associated with the input to the ML model, adjusting, by the error model, the first input based on the error associated with the input to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost). Liu et al. does not disclose training, by a processing device using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Fly et al. with Liu et al. This would have facilitated applying machine learning and to train models and detect real-time drift which would have prevented performance degradation which would have improved deployment. See Liu et al. Paragraph(s) 4-23.
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 1. With respect to claim 2, Fly et al. teaches the method of claim 1, further comprising:
removing the error model from the latest version of the system (Paragraph 64 discloses The database(s) 125 may include databases for storing data, storing features, storing outcomes (training sets), and storing models. Other databases may be added or subtracted, as would be readily understood by a person of ordinary skill in the art, without departing from the scope of the invention,” See Paragraph 64).
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 2. With respect to claim 3, Fly et al. teaches the method of claim 2, further comprising:
executing a set of training queries of the ML model on the latest version of the system to generate second test data (Paragraph 62 discloses offline training system 130 trains a machine learning model from "offline" training data);
retraining the error model based on the second test data to generate an updated error model (Paragraph 62 discloses offline training system 130 trains a machine learning model from "offline" training data); and
deploying the latest version of the database system with the updated error model (Paragraph 146 discloses clients 33 or servers 32 (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31).
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 3. With respect to claim 4, Fly et al. teaches the method of claim 3, wherein generating the training data comprises adding the second test data to the one or more adjusted outputs of the error model accumulated over time (Paragraph 62 discloses offline training system 130 trains a machine learning model from "offline" training data).
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 1. With respect to claim 5, Liu et al. teaches the method of claim 1, wherein the error is associated with the input to the ML model, the method further comprising: outputting the adjusted first input to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost). The motivation to combine statement previously provided in the rejection of dependent claim 1 provided above, combining the Fly et al. reference and the Liu et al. reference is applicable to dependent claim 5.
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 2. With respect to claim 6, Fly et al. teaches the method of claim 2, further comprising:
generating training data based at least in part on one or more adjusted inputs of the error model accumulated over time (Paragraph 62 discloses offline training system 130 trains a machine learning model from "offline" training data);
retraining the ML model based on the training data to generate a retrained ML model (Paragraph 62 discloses offline training system 130 trains a machine learning model from "offline" training data); and
deploying a latest version of the database system with the retrained ML model (Paragraph 146 discloses clients 33 or servers 32 (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31).
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 1. With respect to claim 7, Fly et al. teaches the method of claim 1, wherein the test data is generated using a set of test queries comprising test queries tagged by the database system as relevant to the ML model (Paragraph 94 discloses relevance engine 315 identifies and/or flags features and/or feature values that are unlikely to have any relevance on prediction scores).
The Fly et al. reference as modified by Liu et al. teaches all the limitations of claim 1. With respect to claim 8, Fly et al. teaches the method of claim 1, wherein the function comprises one of: a query execution engine, a query optimizer, or a resource predictor (Paragraph 88 discloses error for numeric predictors classification errors for classifiers recall and precision for IR models, etc.).
With respect to claim 9, Fly et al. teaches a system comprising:
a memory (Paragraph 136 discloses memory); and
a processing device (Paragraph 136 discloses processing units (CPU)) operatively coupled to the memory, the processing device to:
generate test data from successive versions of a database system, the database system comprising a machine learning (ML) model to generate an output corresponding to a function of the database system (Paragraph 112 teaches online “test” model that is generated by the test results engine 1115 and Paragraph 115 detecting performance degradation (caused by drift) of an online scoring system in a machine learning/classification system);
train, using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system (Paragraph 88 discloses error for numeric predictors classification errors for classifiers recall and precision for IR models, etc.). Fly et al. does not disclose when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model.
However, Liu et al. teaches in response to the ML model generate a first output based on a first input:
when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost); and
when the error is associated with the input to the ML model, adjusting, by the error model, the first input based on the error associated with the input to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost). Liu et al. does not disclose training, by a processing device using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Fly et al. with Liu et al. This would have facilitated applying machine learning and to train models and detect real-time drift which would have prevented performance degradation which would have improved deployment. See Liu et al. Paragraph(s) 4-23.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 2, because claim 10 is substantially equivalent to claim 2.
With respect to claim 11, it is rejected on grounds corresponding to above rejected claim 3, because claim 11 is substantially equivalent to claim 3.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 4, because claim 12 is substantially equivalent to claim 4.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 5, because claim 13 is substantially equivalent to claim 5.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 6, because claim 14 is substantially equivalent to claim 6.
With respect to claim 15, it is rejected on grounds corresponding to above rejected claim 7, because claim 15 is substantially equivalent to claim 7.
With respect to claim 16, it is rejected on grounds corresponding to above rejected claim 8, because claim 16 is substantially equivalent to claim 8.
With respect to claim 17, Fly et al. teaches a non-transitory computer-readable medium having instructions stored thereon which, when executed by a processing device, cause the processing device to:
generate test data from successive versions of a database system, the database system comprising a machine learning (ML) model to generate an output corresponding to a function of the database system (Paragraph 112 teaches online “test” model that is generated by the test results engine 1115 and Paragraph 115 detecting performance degradation (caused by drift) of an online scoring system in a machine learning/classification system);
train, using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system (Paragraph 88 discloses error for numeric predictors classification errors for classifiers recall and precision for IR models, etc.). Fly et al. does not disclose when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model.
However, Liu et al. teaches in response to the ML model generate a first output based on a first input:
when the error is associated with the output to the ML model, adjusting, by the error model, the first output based on the error associated with the output to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost); and
when the error is associated with the input to the ML model, adjusting, by the error model, the first input based on the error associated with the input to the ML model (Paragraph 84 discloses system resources are better utilized and managed, and resource utilization is improved as query concurrency levels are dynamically adjusted based on query cost). Liu et al. does not disclose training, by a processing device using the test data, an error model to determine an error associated with the output of or an input to the ML model between the successive versions of the database system.
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Fly et al. with Liu et al. This would have facilitated applying machine learning and to train models and detect real-time drift which would have prevented performance degradation which would have improved deployment. See Liu et al. Paragraph(s) 4-23.
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 2, because claim 18 is substantially equivalent to claim 2.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 3, because claim 19 is substantially equivalent to claim 3.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 4, because claim 20 is substantially equivalent to claim 4.
With respect to claim 21, it is rejected on grounds corresponding to above rejected claim 5, because claim 21 is substantially equivalent to claim 5.
With respect to claim 22, it is rejected on grounds corresponding to above rejected claim 6, because claim 22 is substantially equivalent to claim 6.
With respect to claim 23, it is rejected on grounds corresponding to above rejected claim 7, because claim 23 is substantially equivalent to claim 7.
With respect to claim 24, it is rejected on grounds corresponding to above rejected claim 8, because claim 24 is substantially equivalent to claim 8.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-PUB 20200151619 is directed to SYSTEMS AND METHODS FOR DETERMINING MACHINE LEARNING TRAINING APPROACHES BASED ON IDENTIFIED IMPACTS OF ONE OR MORE TYPES OF CONCEPT DRIFT: [0031] the impact of data drift in term of patterns in performance metrics provides valuable insight into how to efficiently and quickly address such drift through the training process. By feeding the results of the various performance analyses within production model performance analyzer 214 into a prediction pattern assessment module 236, embodiments of the technology of the present disclosure is capable of determining the impact of the drift. The prediction pattern assessment module 236 analyzes the pattern of the performance metrics. For example, a user may extract a performance metric values sequence from the results of 214, such as precision with a specific length that are above a desired threshold, and using such extracted sequence as a baseline.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
/N.E.A/Examiner, Art Unit 2154