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
The instant application having Application No. 18584259 has a total of 20 claims pending in the application.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5 of U.S. Patent No.11928572 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of the instant claims can be met by claims from the associated Patent as shown below.
As per claim 21,
Instant Application
11928572 B2
Examiners Comment
A method, comprising:
Claim 1: A method comprising:
receiving, by one or more processors, a request including information associated with an objective
Claim 1: receiving information associated with a requested operator;
Here the operator is the objective/goal of the user.
generating, by the one or more processors executing a first machine learning model, an architecture of an artificial intelligence-based solution to address the objective
Claim 1: generating, by a processing device executing a first machine learning model, a skeleton architecture of an artificial intelligence (AI)-based solution to the operator based on the information
“generating, by the one or more processors, the artificial intelligence-based solution based on the architecture by:
Claim 1: generating the AI-based solution to the requested operator, based on the skeleton architecture, wherein the generating of the AI-based solution comprises: identifying a second machine learning model in a first database within the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution, identifying a third machine learning model in a second database external to the marketplace platform, wherein the third machine learning model is a second portion of the AI-based solution,
Identifying a second machine learning model in a first database, wherein the second machine learning model is a first portion of the artificial intelligence based solution and is available via a marketplace
Claim 1: generating the AI-based solution to the requested operator, based on the skeleton architecture, wherein the generating of the AI-based solution comprises: identifying a second machine learning model in a first database within the marketplace platform,
Identifying a third machine learning model in a second database, wherein the third machine learning model is a second portion of the artificial intelligence based solution and is unavailable via the marketplace
Claim 1: generating the AI-based solution to the requested operator, based on the skeleton architecture, wherein the generating of the AI-based solution comprises: identifying a second machine learning model in a first database within the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution, identifying a third machine learning model in a second database external to the marketplace platform, wherein the third machine learning model is a second portion of the AI-based solution
It would be obvious to one of ordinary skill in the art at the time of filing that a system that makes machine learning models can make new machine learning models as needed to fit the user’s needs if a pre-made model is not available, as this would allow the solution to be met with new data as needed by the user.
Determining that a third portion of the artificial intelligence-based solution does not exist in the first database and the second database
The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art at the time of filing that an algorithm that fits the current needs may not be stored in readily available databases, as there are new algorithms and new needs made every day for machine learning algorithms and existing algorithms may not fulfill the current needs of a user.
In response to the determining, providing a request for generation of a new third machine learning model corresponding to the third portion to users of the marketplace, third-party developers, or both
The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art at the time of filing that a new algorithm could be trained in order to fulfil the requirements of the user by an AI based solution system.
Receiving the new third machine learning model in response to providing the request, wherein the new third machine learning model is the third portion of artificial intelligence-based solution
The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art at the time of filing that a new algorithm could be trained in order to fulfil the requirements of the user by an AI based solution system.
Generating the artificial intelligence-based solution by combining: the second machine learning model, and the new third machine learning model”
Claim 1: generating the AI-based solution to the requested operator, based on the skeleton architecture, wherein the generating of the AI-based solution comprises: identifying a second machine learning model in a first database within the marketplace platform, wherein the second machine learning model is a first portion of the AI-based solution, identifying a third machine learning model in a second database external to the marketplace platform, wherein the third machine learning model is a second portion of the AI-based solution
Enabling, by the one or more processors, access to the artificial intelligence based solution via the marketplace
Claim 1: “Displaying an option to access the AI based solution in a marketplace platform”
As can be shown above, each of the limitations of the instant claims can be met by various claims of the 572 patent , and therefore the claims are rejected under non-statutory obvious type double patenting.
As per claims 2-20, these claims are rejected for similar reasons over claims 1-5 of the 572 as shown above.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1, 8, and 14, this claim calls for “identifying a third machine learning model in a second database, wherein the third machine learning model is a second portion of the artificial intelligence based solution, determining that a third portion of the artificial intelligence based solution does not exist in the first database and the second database, in response to the determining, providing a request for generation of new third machine learning model corresponding to the third portion….” However these limitations are not supported by the specification.
These limitations require that a third machine learning model be found in a second database, and be the second portion of the AI based solution, then determining a third portion of the AI based solution does NOT exist in the first and second database, and in response make a new third machine learning algorithm. This means that the claim calls for finding the second portion of the AI solution in the second database, then looking for a third portion of the AI solution, not finding it, but then recreating the THIRD machine learning algorithm which is actually the model for the second portion. Essentially, the claim requires that a third portion be missing, and the system to then create a new version of the second portion of the proposed AI solution to fit into the third portion slot. There is no support in the specification for finding a second portion of the AI solution, looking for a third portion of the AI solution, not finding it, then recreating a new version of the second portion to then use in the third portion. Since the specification fails to support this limitation, these limitations are found to be new matter and therefore rejected under U.S.C. 112(a).
As per claims 2-7, 9-13, and 15-20, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7, 13, and 19-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 7, 13, and 19, these claims call for “estimating, based on past knowledge of the …. New third machine learning model, a benchmark for the artificial intelligence based solution.” This is confusing because the new third machine learning model is created in claim 1, and is designated explicitly as a “new third machine learning model.” How can the system have “past knowledge” of a newly created machine learning model? This seems to be mutually exclusive with the model being new, and this causes the claim to be confusing, and therefore rejected for failing to particularly point out and claim the intended invention.
As per claim 20, this claim is rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
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.
Claims 1-6, 8-12, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Fritchman et al (US 10198399 B1) and Janiszewski (US 20010025267 A1).
As per claims 1, 8, and 14, “A method comprising: receiving, by one or more processors” (Pg.28, particularly paragraph 0171; EN: this denotes the hardware of the system). “a request including information associated with an objective” (Pg.2-3, particularly paragraphs 0041-0042; EN: this denotes the system to receive input/output from the user for them to designate their goals and requests for the system).
“generating, by the one or more processors executing a first machine learning model” (Pg.2, particularly paragraph 0041; EN: this denotes the system learning the best practices for machine learning models over time by interaction with its system. The machine learning service is the first machine learning model). “an architecture of an artificial intelligence-based solution to address the objective” (pg.3, particularly paragraph 0043; EN: this denotes setting up recipes and other entities related to machine learning for machine learning goals of users of the system).
“generating, by the one or more processors the artificial intelligence-based solution based on the architecture by:” (pg.3, particularly paragraph 0043; EN: this denotes setting up recipes and other entities related to machine learning for machine learning goals of users of the system).
“identifying a second machine learning model in a first database” (pg.9, particularly paragraph 0077; EN: this denotes creating and storing machine learning models). “wherein the second machine learning model is a first portion of the artificial intelligence-based solution…” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“Identifying a third machine learning model in a second database” (pg.2, particularly paragraph 0042; EN: this denotes private models stored separately and not being publicly available). “Wherein the machine learning model is a second portion of the artificial intelligence solution” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another). “and is unavailable…” (pg.2, particularly paragraph 0042; EN: this denotes private models stored separately and not being publicly available).
“determining that a third portion of the artificial intelligence model does not exist in the first database and the second database” (Pg.12, particularly paragraph 0092; EN: this denotes the system looking to see if there are instances of an entity type available for the user to use in the repository).
“In response to the determining, providing … generation of a new third machine learning model…” (Pg.12, particularly paragraph 0092; EN: this denotes the system looking to see if there are instances of an entity type available for the user to use in the repository, and creating a new one if it is not available).
“receiving the new third machine learning model…” (Pg.12, particularly paragraph 0092; EN: this denotes the system looking to see if there are instances of an entity type available for the user to use in the repository, and creating a new one if it is not available). “Wherein the new third machine learning model is the third portion of the artificial intelligence based solution” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“generating the artificial intelligence based solution by combining” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“the second machine learning model and” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“the new third machine learning model” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“enabling, by the one or more processors, access to the artificial intelligence based solution…” (Pg.3, particularly paragraph 0043; NE: this denotes the ability to access and look at various recipes of the system. It further denotes the user being able to execute and use these recipes).
However, Dirac fails to explicitly disclose, “available via a marketplace”, “via the marketplace”, “Providing a request for generation of a new third machine learning model corresponding to the third portion to users of the marketplace, third party developers, or both” , “receiving the new third machine learning model in response to providing the request”
Fritchman discloses, “available via a marketplace”, “via the marketplace” (C26, particularly L30-48; EN: this denotes the displaying of machine learning models including price and other data about the available models for use/sale).
Janiszewski discloses, “in response to the determining, providing a request for generation of a new third machine learning model corresponding to the third portion to users of the marketplace, third party developers, or both” (Pg.1, particularly paragraph 0008; EN: This denotes allowing a user to contract out software development, such as the software development of the AI models in the Dirac reference, to others as needed).
“receiving the new third machine learning model in response to providing the request” (Pg.5, particularly paragraph 0049; EN: this denotes the system monitoring for the contract to be completed, and the contractor accepting the final product from the winning bidder).
Dirac and Fritchman are analogous art because both involve machine learning services.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning services to combine the work of Dirac and Fritchman in order to have a marketplace to display information about available models.
The motivation for doing so would be to display “meta-data associated with a secure ML model [such as] price, classification accuracy, training level, age, names or descriptions of the type of classifications or answers, or the like” (Fritchman, C17, L19-30) or in the case of Dirac, allow the system to display models and their prices for purchase/use by the users of their system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning services to combine the work of Dirac and Fritchman in order to have a marketplace to display information about available models.
Janiszewski and Dirac modified by Fritchman are analogous art because both involve software development and sales.
Before the effective filing date it would have been obvious to one skilled in the art of software development and sales to combine the work of Janiszewski and Dirac modified by Fritchman in order to allow developers to create models for other users on request.
The motivation for doing so would be to “ensure[] high quality and reliable software product delivered in a timely manner at a reasonable cost to the contractor” (Janiszewski, Pg.1, paragraph 0008) or in the case of Dirac, allow the users of the Dirac system to be contracted and paid for models they create that will be used by others.
Therefore before the effective filing date it would have been obvious to one skilled in the art of software development and sales to combine the work of Janiszewski and Dirac modified by Fritchman in order to allow developers to create models for other users on request.
As per claim 2, Dirac discloses, “wherein the information associated with the objective is in a natural language” (Fig.15 and associated paragraphs; EN: this denotes being able to identify the problems with search terms).
As per claims 3, 9, and 15, Dirac discloses, “generating, using the second machine learning model and based on the natural language, a plurality of computer recognizable commands” (Pg.13, particularly paragraph 0097; EN: this denotes using the system to enact recipes they have chosen using the machine learning service (i.e. plurality of computer recognizable commands).
“generating the architecture of the artificial intelligence-based solution based on the plurality of computer recognizable commands” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
As per claims 4, 10, and 16, Dirac discloses, “the architecture comprise a plurality of machine learning model categories” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models which feed into one another. Each individual model will perform different actions and feeding one into the other will make these intermediate objectives).
“each category of the plurality of machine learning model categories corresponds to an intermediate objective of the artificial intelligence based solution” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models which feed into one another. Each individual model will perform different actions and feeding one into the other will make these intermediate objectives).
As per claims 5, 11, and 17, Dirac discloses, “determining that the request identifies a process” (Pg.12-13, particularly paragraph 0094; EN: this denotes identifying different machine learning goals (i.e. processes) for the user).
“determining a modification to the process based on the information” (Pg.12-13, particularly paragraph 0094; EN: this denotes the system identifying best practices for the requested model and modifying them to be as efficient/effective as possible).
“generating a modified process based on the modification” (Pg.12-13, particularly paragraph 0094; EN: this denotes the system identifying best practices for the requested model and modifying them to be as efficient/effective as possible).
As per claims 6, 12, and 18, Fritchman discloses, “the marketplace comprises a platform to access one or more machine learning models” (C26, particularly L30-48; EN: this denotes the displaying of machine learning models including price and other data about the available models for use/sale).
However, Fritchman fails to explicitly disclose, “access one or more machine learning models under a licensing agreement.”
The Examiner takes Official Notice that it would be obvious to one of ordinary skill in the art of selling software that purchasing or otherwise using marketplace software would be done with a licensing agreement, as owners of software typically wish to control how their software is used by purchasers of their software in order to establish how their software can be used by others and the use of a licensing agreement defines the terms of how purchased software can be used.
As per claims 7, 13, and 19, Dirac discloses, “estimating, based on past knowledge of the second machine learning model and the new third machine learning model, a benchmark for the artificial intelligence based solution” (Pg.13, particularly paragraph 0095; EN: this denotes the system monitoring performance and other metrics of the machine learning operations being performed by the system, including ratings/rankings (i.e. benchmarks) of the models).
“displaying the benchmark with the option to access the artificial intelligence-based solution…” (Pg. 13, particularly paragraph 0096; EN: this denotes allowing access to the information).
Fritchman discloses, “in the marketplace” (C26, particularly L30-48; EN: this denotes the displaying of machine learning models including price and other data about the available models for use/sale).
As per claim 20, Dirac disclose, “the third machine learning model” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models which feed into one another. Each individual model will perform different actions and feeding one into the other will make these intermediate objectives).
Janiszewski discloses, “estimating a cost to build the … model;” (pg.4, particularly paragraph 0044; EN: this denotes the cost estimates for developing the software).
“estimating a time to build the … model” (Pg.2, particularly paragraph 0025; EN: this denotes estimates of time to completion of the project).
Response to Arguments
In pg.10, the Applicant argues in regards to the double patenting rejection,
Claims 1-20 were rejected on the ground of nonstatutory double patenting over claims 1- 5 of U.S. Patent No. 11,928,572. Applicant respectfully notes that the amended independent claims 1, 8, and 14 now include limitations not present in the claims of the '572 patent. Specifically, the '572 patent does not claim: determining that a third portion of the AI-based solution does not exist in the first database and the second database; in response to the determining, providing a request for generation of a new third machine learning model corresponding to the third portion; receiving the new third machine learning model in response to providing the request; or generating the Al-based solution by combining the second machine learning model and the new third machine learning model. Therefore, Applicant asks for reconsideration of the Non-Statutory Double Patenting rejection. Should the Examiner maintain the double patenting rejection notwithstanding the amendments, Applicant is willing to file a terminal disclaimer upon indication that the claims are otherwise allowable.
In response, the Examiner maintains the rejection as shown above. Merely noting that an algorithm may not exist or be readily available for us in a system designed to create and supply machine learning algorithms would be an obvious variation of Patent No. 11928572, as new needs for models occur at all times, and the ability to train and create new models as needed by the system. Since this is an obvious variation of Patent No. 11928572, this remains an obvious type double patenting rejection as shown above.
In pg.11, the Applicant argues in regards to the rejection of the independent claims,
Critically, at most Dirac teaches that models may be "private" and stored separately from public models (Dirac, I 0042)-this is a teaching of unavailability, not nonexistence. A private model in Dirac exists in the system; it is simply access-restricted. The amended claims require something fundamentally different: a determination that no suitable model exists at all in either the first or second database, which then triggers the generation request. Dirac's system never performs this nonexistence determination across multiple databases, never responsively solicits third-party creation of an entirely new model to fill a gap in a multi-model AI solution, and never receives such a model back for integration.
In response, the Examiner maintains the rejection as shown above. The Dirac reference clearly denotes checking the repository for an appropriate model, and creating a new one if it is not available. Since the repository contains both the private models and the public models, this would meet the broadest reasonable interpretation of checking for existence of a particular model in the system. Therefore the rejection is maintained as shown above.
Furthermore, the Dirac reference explicitly discloses the ability to create, query attributes, read, update/modify, search or delete instances of models in the system (Dirac, Pg.4, paragraph 0046). Anything deleted would no longer be available, and searching for something and not finding it would be an example of finding that something does not exist. Merely checking to see if something is available is clearly denoted in the Dirac reference, and therefore the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1-20 have been considered but are either conclusory, arguments that secondary references do not meet limitations met by other references, or moot in view of the new ground(s) of rejection, and therefore the rejections are maintained as shown above.
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 BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 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, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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