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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicants’ submission filed on 9/10/25 has been entered.
DETAILED ACTION
The instant application having Application No. 17219723 has a total of 40 claims pending in the application, of which claims 1-20 have been cancelled.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 21is a process type claim. Claim 28 is a machine type claim. Claim manufacture type claim. Therefore, claims 21-40 are directed to either a process, machine, manufacture or composition of matter.
As per claim 21,
2A Prong 1:
“Generating … a skeleton architecture including a plurality of … model categories, wherein each category of the plurality of …. Model categories correspond to an intermediate objective used to achieve the final objective” The user mentally or with pencil and paper lays out their model plan.
“generating … a particular … solution based on the skeleton architecture, the particular … solution comprising at least a first … model, a second ... model, and a third ... model organized in a pipeline in which: the second ... model is organized in the pipeline in parallel with the first … model; and the third … model is organized in the pipeline in series with the first … model” The user mentally or with pencil and paper lays out their model plan.
“processing the data by the particular … solution” The user mentally or with pencil and paper uses the skeleton to set up the solution.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computing device, (mere instructions to apply the exception using a generic computer component);
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
“displaying a plurality of options to access, via a platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computing device (mere instructions to apply the exception using a generic computer component)
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
displaying a plurality of options to access, via a platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (MPEP 2106.05(d)(II) indicate that merely receiving and transmitting data is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed output steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 22-27, these claims contain additional generic machine learning and mental steps similar to claim 21, and are rejected for similar reasons.
As per claim 28,
2A Prong 1:
“Generating … a skeleton architecture including a plurality of … model categories, wherein each category of the plurality of …. Model categories correspond to an intermediate objective used to achieve the final objective” The user mentally or with pencil and paper lays out their model plan.
“generating … a particular … solution based on the skeleton architecture, the particular … solution comprising at least a first … model, a second ... model, and a third ... model organized in a pipeline in which: the second ... model is organized in the pipeline in parallel with the first … model; and the third … model is organized in the pipeline in series with the first … model” The user mentally or with pencil and paper lays out their model plan.
“processing the data by the particular … solution” The user mentally or with pencil and paper uses the skeleton to set up the solution.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A server, one or more processors, one or more non-transitory computer readable media, (mere instructions to apply the exception using a generic computer component);
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
“displaying a plurality of options to access, via a platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A server, one or more processors, one or more non-transitory computer readable media, (mere instructions to apply the exception using a generic computer component)
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
displaying a plurality of options to access, via the platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (MPEP 2106.05(d)(II) indicate that merely receiving and transmitting data is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed output steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 29-34, these claims contain additional generic machine learning and mental steps similar to claim 28, and are rejected for similar reasons.
As per claim 35,
2A Prong 1:
“Generating … a skeleton architecture including a plurality of … model categories, wherein each category of the plurality of …. Model categories correspond to an intermediate objective used to achieve the final objective” The user mentally or with pencil and paper lays out their model plan.
“generating … a particular … solution based on the skeleton architecture, the particular … solution comprising at least a first … model, a second ... model, and a third ... model organized in a pipeline in which: the second ... model is organized in the pipeline in parallel with the first … model; and the third … model is organized in the pipeline in series with the first … model” The user mentally or with pencil and paper lays out their model plan.
“processing the data by the particular … solution” The user mentally or with pencil and paper uses the skeleton to set up the solution.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Non-transitory computer readable media, one or more processors, (mere instructions to apply the exception using a generic computer component);
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
“displaying a plurality of options to access, via the platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Non-transitory computer readable media, one or more processors, (mere instructions to apply the exception using a generic computer component)
“Machine learning model”, “machine learning model categories”, “first machine learning model”, “second machine learning model” “third machine learning model”, “artificial intelligence”, “ai-based” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain no additional detail or limitations which make the various machine learning models/categories/ai phrases anything more than generic, off the shelf machine learning.
“receiving … information describing a final objective to be achieved;”, “receiving a selection of the AI-based solution”, “in response to receiving the selection, providing access to the particular … solution via the platform”, “receiving data associated with the objective” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed receiving steps are well-understood, routine, conventional activity is supported under Berkheimer).
displaying a plurality of options to access, via the platform, a plurality of corresponding … solutions including the particular … solution”, “provide an output of the particular … solution based on processing the data using the particular … solution” (MPEP 2106.05(d)(II) indicate that merely receiving and transmitting data is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed output steps are well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 36-40, these claims contain additional generic machine learning and mental steps similar to claim 28, and are rejected for similar reasons.
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 21-24, 26, 28-31, 33, 35-38, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Smith et al (US 20200125482 A1).
As per claims 21, 28, and 35, Dirac discloses, “A method comprising: receiving by a computing device” (Pg.28, particularly paragraph 0171; EN: this denotes the hardware of the system). “information describing a final objective to be achieved” (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 a machine learning model executed by the computing device, a skeleton architecture of a solution to the operator based on the information” (Pg.2, particularly paragraph 0043; EN: this denotes recipes, which are set ups of machine learning model combinations that can be used by users and filled in as needed. Here the machine learning model building the set of models is the machine learning service, which learns over time how best to assemble these models). “The skeleton architecture including a plurality of machine learning model categories wherein each category of the plurality of machine learning model categories corresponds to an intermediate objective used to achieve the final objective” (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).
“Generating, by the machine learning model, a particular artificial intelligence based solution based on the skeleton architecture, the particular artificial intelligence based solution comprising at least a first machine learning model, a second machine learning model, and a third machine learning model organized in a pipeline in which” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“displaying a plurality of options to access, via the platform, a plurality of corresponding artificial intelligence based solutions including the particular artificial intelligence based solution” (Pg.3, particularly paragraph 0043; NE: this denotes the ability to access and look at various recipes of the system).
“receiving a selection of the AI-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).
”in response to receiving the selection, providing access to the particular AI-based solution via the platform” (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).
“receiving data associated with the objective” (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).
“processing the data by the particular AI-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).
“producing an output of the articular AI-based solution based on processing the data using the particular AI-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, “the second machine learning model is organized in the pipeline in parallel with the first machine learning model; and the third machine learning model is organized in the pipeline in series with the first machine learning model”
Smith discloses, “the second machine learning model is organized in the pipeline in parallel with the first machine learning model; and the third machine learning model is organized in the pipeline in series with the first machine learning model” (pg.4, particularly paragraph 0053; EN: this denotes that machine learning models can be placed in series or in parallel with each other as needed).
Dirac and Smith are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Smith in order to allow algorithms to be placed in parallel and series as needed.
The motivation for doing so would be to allow “neural networks of different types or the same type may be linked together into a sequential or parallel series of networks, where subsequent neural networks accept as input the output of one or more preceding neural networks” (Smith, Pg.4, paragraph 0053) or in the case of Dirac, allow the system to make whatever combination of models needed for the recipe of the Dirac reference.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Smith in order to allow algorithms to be placed in parallel and series as needed.
As per claims 22, 29, and 36, Dirac discloses, “wherein the first machine learning model corresponds to a first category of machine learning models; the second machine learning model corresponds to a second category of machine learning models; and the third machine learning model corresponds to a third category of machine learning models” (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 23, 30, and 37, Dirac discloses, “wherein the first category, the second category, and the third category each comprise one of an artificial neural network, a decision tree, a support vector machine, a regression analysis, or a Bayesian network” (Pg.5, particularly paragraph 0053; EN: this denotes various potential models, including a neural network, random forest (i.e. decision trees). Pg.4, particularly paragraph 0047; EN; This denotes the use of regression, all of which can be used for the various models in the recipes).
As per claims 24, 31, and 38, Dirac discloses, “determining that the third machine learning model does not exist” (Pg.12, particularly paragraphs 091-0092; EN: this denotes checking within the system for existing models and the like, and using them if they are available).
“sending, to an AI platform, a request to create the third machine learning model, wherein the AI platform uses artificial intelligence to generate the third machine learning model;” (Pg.12, particularly paragraphs 091-0092; EN: this denotes checking within the system for existing models and the like, and using them if they are available).
“receiving, from the AI platform, the third machine learning model” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
“Generating the AI based solution by combining, based on the skeleton architecture, the third machine learning model with the first machine learning model and the second machine learning model” (pg.13, particularly paragraph 0097; EN: this denotes recipes containing multiple models including models that connect to one another).
As per claims 26, 33, and 40, Dirac discloses, “receiving an AI pipeline training job that packages AI pipeline training code and dependences…” (Pg.5, particularly paragraph 0051-0053; EN: this denotes the job processing and such of the AI recipes).
“training the particular artificial intelligence based solution using the AI pipeline training job” (pg.7-8, particularly paragraph 0066; EN: this denotes the training of the algorithms in the recipes).
Smith discloses, “docker image” (pg.6-7, particularly 0073-0075; EN: this denotes the use of Docker images for handling cod).
Dirac and Smith are analogous art because both involve machine learning.
Before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Smith in order to allow algorithms to use docker images.
The motivation for doing so would be to allow “a Docker image for executing a code example … specify[ing] a particular operating system image that includes a general execution environment for a programming language such as python” (Smith, Pg.6, paragraph 0073) or “a set of instructions for an orchestration platform such as Kubernetes” (Smith, pg.6-7, paragraph 0075) or in the case of Dirac, allow the system to use the format for holding and executing code as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of machine learning to combine the work of Dirac and Smith in order to allow algorithms to use docker images.
Claim Rejections - 35 USC § 103
Claims 25, 32, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Smith et al (US 20200125482 A1) as seen in claim 21, and further in view of Proidl et al (Us 20080195386 A1) and Sugiura et al (US 10062384 B1).
As per claims 25, 32, and 39, Dirac fails to explicitly disclose, “a source separation model to separate, in a movie, video from audio”, “a speaker diarization model to identify speakers in the movie and output speaker audio corresponding to individual speakers in the movie”, “a speech recognition model to convert … speaker audio to text”, “a machine translation model to translate the text from one language to another language”
Proidl discloses, “a source separation model to separate in a movie, video from audio” (Pg.2, particularly paragraph 0036; EN: this denotes performing source separation to break up the voices in speech).
“a machine translation model to translate the text from one language to another language” (pg.2, particularly paragraph 0033; EN: this denotes converting text from one language to another).
“A speech synthesis model to convert the text in another language to second audio” (pg.2, particularly paragraph 0033; EN: this denotes synthesizing speech from the text).
“a channel merging model to merge the second audio with the video” (pg.2, particularly paragraph 0033; EN: this denotes merging it with the video and playing the second audio).
Sugiura discloses, “a speaker diarization model to identify speakers in the movie and output speaker audio corresponding to individual speakers in the movie” (C4, particularly L18-28; EN: this denotes doing speaker diarization to identify who is speaking).
“a speech recognition model to convert … speaker audio to text” (C3, particularly L57-68; C4, L1-18; EN: this denotes doing speech recognition to convert audio to speech).
Dirac and Proidl are analogous art because both involve video and audio processing.
Before the effective filing date it would have been obvious to one skilled in the art of audio/video processing to combine the work of Dirac and Proidl in order to allow algorithms to perform the various aspects of video processing.
The motivation for doing so would be to allow “ the multimedia signal comprising information relating to video and speech and further comprises textual information corresponding to speech” in order to “perform[] automatic dubbing on a multimedia signal, such as a TV or a DVD signal” (Proidl, Abstract) or in the case of Dirac, allow the system to take up video/audio processing aspects of Proidl via machine learning service as needed by their clients.
Therefore before the effective filing date it would have been obvious to one skilled in the art of audio/video processing to combine the work of Dirac and Proidl in order to allow algorithms to perform the various aspects of video processing.
Sugiura and Dirac modified by Proidl are analogous art because both involve video and audio processing.
Before the effective filing date it would have been obvious to one skilled in the art of audio/video processing to combine the work of Sugiura and Dirac modified by Proidl in order to allow algorithms to perform the various aspects of video processing.
The motivation for doing so would be to allow “speech recognition engine for converting audio data in the video into a text of utterances” (Sugiura, C3 L58-68; C4, L1-18) and for using speaker diarization to “identify merely when the same speaker is speaking” (Sugiura, C4 L18-28) or in the case of Sugiura and Dirac modified by Proidl, allow the system to translate the speech into text form when text is not available for the Proidl system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of audio/video processing to combine the work of Sugiura and Dirac modified by Proidl in order to allow algorithms to perform the various aspects of video processing.
Claim Rejections - 35 USC § 103
Claims 27 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al (US 20150379072 A1) in view of Smith et al (US 20200125482 A1) as seen in claim 21, and further in view of Gangadharaiah et al (US 10860629 B1).
As per claims 27 and 34, Dirac discloses, “wherein the information describing the final objective to be achieved is received in a … text format” (Pg.5, particularly paragraph 0053; EN: this denotes being able to receive input as text).
However, Dirac fails to explicitly disclose “in a conversation with a chat bot in which the conversation is either in : audible speech format or a text format.”
Gangadharaiah discloses, “in a conversation with a chat bot in which the conversation is either in : audible speech format or a text format” (C3, L63-68; C4, L1-43; EN: this denotes using a chatbot as an agent to deal with interaction with a system).
Dirac and Gangadhariah 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 Gangadhariah in order to allow a chatbot to deal with users.
The motivation for doing so would be to allow “an agent to perform actions associated with the task, such as cancelling an order, inputting/updating user information” (Gangadhariah C3, L63-68; C4, L1-15) or in the case of Dirac, allow a chatbot to do the interaction with the usage of the 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 Gangadhariah in order to allow a chatbot to deal with users.
Response to Arguments
Responses below are the same responses given in the Advisory action mailed 10/1/2025. No new arguments or amendments have been filed after the amendments and arguments filed after final by applicant on 9/10/2025.
In pg.9-10, the Applicant argues in regards to the rejection under U.S.C. 101,
Recent guidance from the Patent Office reinforces that the claims are not directed toward an abstract idea. The August 4, 2025 memorandum states that a "mental process" is limited to activity that "can be performed in the human mind, or by a human using a pen and paper," and cautions Examiners "not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." The pending claims exceed that boundary at every step. The pending claims generate multiple machine learning models for an AI solution, and the process data through the AI solution. How can a human mind generate multiple machine learning models with a pen and paper, and then process data through them? According to the memorandum, it is the Examiner's burden to show that this is an activity that is practically performed by a human. Because none of the recited steps "can practically be performed in the human mind," the claims do not fall within the memorandum's mental process or organizing human activity grouping. Instead, the claims provide "a particular solution to a problem" by improving browser functionality, as described below.
In response, the Examiner maintains the rejection as shown in the previous office action. The Applicant claims generic machine learning models with no additional detail or limitations beyond a generic, off the shelf machine learning model. Merely placing an abstract idea on a generic computer or generic machine learning model does not cause the claim to be significantly more than the abstract idea. Therefore the rejection is maintained as shown in the previous office action.
In pg.10, the Applicant further argues in regards to the 101 rejection,
Second, the claims integrate the alleged abstract idea into a practical application under Step 2A (Prong II). The claims are patent eligible because they provide a technical improvement to AI systems. In particular, the recited concepts improve the AI operations by generating a skeleton architecture using a machine learning model.
In response, the Examiner maintains the rejection as shown in the previous office action. The Applicant claims supposed “technical improvements to AI” but merely state a general statement that generating a skeleton using a machine learning model somehow is a technical improvement. These arguments are conclusory, and therefore the rejection is maintained as shown in the previous office action.
In pg.11-12, the Applicant argues in regards to the 103 rejection of the independent claims after citing paragraph 0098 of the Dirac reference,
Therefore, Dirac TEACHES AWAY from the claimed machine learning-generated skeleton architecture.
In response, the Examiner maintains the rejection as shown in the previous office action. The Dirac reference discloses a system that learns over time how to create more effective machine learning models and assemble those models (see Dirac, Paragraph 0097). Merely because Dirac also allows users to create initial recipes and machine learning models and add them to the system does not stop the system from being able to learn over time how to most effectively execute these models and optimize those models for users. Since this meets the broadest reasonable interpretation of a machine learning model, the rejection is maintained as shown in the previous office action.
In pg.12, the Applicant further argues in regards to the rejection under U.S.C. 103,
Furthermore, we explicitly claim a first machine learning model in a parallel arrangement, and a second machine learning model in a series arrangement. This POSITIVELY claimed limitation is ENTIRELY MISSING FROM THE PRIOR ART.
In response, the Examiner maintains the rejection as shown in the previous office action. The Smith reference explicitly discloses that its known in the art to assemble machine learning models, in this case neural networks, in series or in parallel. As this has been combined with the Dirac reference, clearly are analogous art, and a motivation statement was provided the rejection is proper under U.S.C. and therefore the rejection is maintained as shown in the previous office action.
In pg.12, the Applicant further argues in regards to the rejection under U.S.C. 103,
Even furthermore, we explicitly claim that the machine learning categories correspond to intermediate objectives that contribute to a final objective. This POSITIVELY claimed limitation is ENTIRELY MISSING FROM THE PRIOR ART as well.
In response, the Examiner maintains the rejection as shown above. As stated in the rejection, the Dirac reference explicitly discloses using multiple different models together, each of which will perform different actions and contribute to the final objective. This meets the broadest reasonable interpretation of the claims, and Applicant has not offered any actual arguments as to why the Dirac reference may not meet these limitations. Therefore the rejection is maintained as shown in the previous office action.
Applicant's remaining arguments with respect to claims 21-40 have been considered but are either repetitions of the above arguments or conclusory statements, and therefore the rejections are maintained as shown above for similar reasons.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BEN M RIFKIN/Primary Examiner, Art Unit 2123
Prosecution Insights