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, all of which are ready for examination by the examiner.
I. REJECTIONS NOT BASED ON PRIOR ART
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.
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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.
Generating the artificial intelligence based solution based at least in part on a combination of: the second machine learning model, and the 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.
II. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
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).
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 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).
“generating the artificial intelligence based solution based at least in part on a combination of: (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 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.”
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).
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.
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.
Claim Rejections - 35 USC § 103
Claims 7, 13, and 19-20 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 further in view of Janiszewski (US 20010025267 A1).
As per claims 7, 13, and 19, Dirac discloses, “determining that the third machine learning model is unavailable …” (Pg.16, particularly paragraph 0109-0110; EN: this denotes being able to search for pre-made models or add new models as needed).
“send a model request to create the third machine learning model…” (Pg.16, particularly paragraph 0109-0110; EN: this denotes being able to search for pre-made models or add new models as needed).
Fritchman discloses, “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).
However, Dirac and Fritchman fail to explicitly disclose, “sending a model request to create the … model to users of the marketplace, third party developers, or both.”
Janiszewski discloses, “sending a model request to create the … model 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).
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 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).
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
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