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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on November 20, 2024, and February 10, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 3, 11, and 19 are objected to because of the following informalities: “the selected first expert network” should read “the first expert network”, as the claims do not recite a selection of the first expert network until subsequently recited in claims 11 and 19. Claim 3 is further objected to because “is configured process” should read “is configured to process”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an initial weight of the first expert network of the neural network model based on the first word vector matrix”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“wherein the neural network model comprises an expert network layer comprising a first expert network of a first service field”
“obtaining a first word vector matrix through training based on a first training data set of the first service field”
“training the neural network model based on the second training data set to obtain a target neural network model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtaining a second training data set”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the obtaining limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an initial weight of the second expert network based on the second word vector matrix”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f).
The limitations:
“wherein the expert network layer further comprises a second expert network of a second service field”
“obtaining a second word vector matrix through training based on a third training data set of the second service field”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the expert network layer is configured process, through the selected first expert network, the data input into the expert network layer”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining the first training data set based on a first knowledge graph of the first service field”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating at least one first text sequence in the first training data set based on at least one first triplet in the first knowledge graph, wherein three words in the first triplet respectively represent a subject in the first service field, an object in the first service field, and a relationship between the subject and the object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 4.
Step 2B Analysis: See corresponding analysis of claim 4.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 5.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) or are mere instructions to apply an exception (See MPEP 2106.05(f).
The limitations:
“wherein the first word vector matrix is a weight of a hidden layer in a first target word vector generation model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“obtaining the first target word vector generation model by training a word vector generation model using a word other than a target word in the at least one first text sequence as an input of the word vector generation model and using the target word as a target output of the word vector generation model, wherein the target word is a word in the at least one first triplet”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. Additional details that do not apply the exception in a meaningful way and mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the step of determining the initial weight of the first expert network based on the first word vector matrix comprises: using the first word vector matrix as the initial weight of the first expert network”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 1.
Step 2B Analysis: See corresponding analysis of claim 1.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a training method for training a neural network model, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the neural network model is a natural language processing (NLP) model or a speech processing model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an initial weight of a first expert network of a neural network model based on the first word vector matrix, wherein the first expert network is in an expert network layer of a first service field of the neural network model”
“processing the to-be-processed data”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“obtaining a first word vector matrix through training based on a first training data set of a first service field”
“training the neural network model based on the second training data set to obtain a target neural network model”
“using a target neural network model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtaining a second training data set”
“obtaining to-be-processed data”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the obtaining limitations recite the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining an initial weight of the second expert network based on the second word vector matrix”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f).
The limitations:
“wherein the expert network layer further comprises a second expert network of a second service field”
“obtaining a second word vector matrix through training based on a third training data set of the second service field”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“the first expert network is selected based on the data input into the expert network layer”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the expert network layer is configured to process, through the selected first expert network, data input into the expert network layer”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determining the first training data set based on a first knowledge graph of the first service field”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 9.
Step 2B Analysis: See corresponding analysis of claim 9.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generating at least one first text sequence in the first training data set based on at least one first triplet in the first knowledge graph, wherein three words in the first triplet respectively represent a subject in the first service field, an object in the first service field, and a relationship between the subject and the object”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 12.
Step 2B Analysis: See corresponding analysis of claim 12.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 13.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)) or are mere instructions to apply an exception (See MPEP 2106.05(f).
The limitations:
“wherein the first word vector matrix is a weight of a hidden layer in a first target word vector generation model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
The limitations:
“obtaining the first target word vector generation model by training a word vector generation model using a word other than a target word in the at least one first text sequence as an input of the word vector generation model and using the target word as a target output of the word vector generation model, wherein the target word is a word in the at least one first triplet”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way or are “mere instructions to apply”. Additional details that do not apply the exception in a meaningful way and mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the step of determining the initial weight of the first expert network based on the first word vector matrix comprises: using the first word vector matrix as the initial weight of the first expert network”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: See corresponding analysis of claim 9.
Step 2B Analysis: See corresponding analysis of claim 9.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to a data processing method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: See corresponding analysis of claim 9.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that do not apply the exception in a meaningful way (See MPEP 2106.05(e)).
The limitations:
“wherein the neural network model is a natural language processing (NLP) model or a speech processing model”
As drafted, is an additional element that does not apply an exception for the abstract ideas in a meaningful way. See MPEP 2106.05(e).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17 is directed to a device for training a neural network model, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine an initial weight of the first expert network of the neural network mode based on the first word vector matrix”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)).
The limitations:
“A device for training a neural network model, comprising: a memory storing executable instructions; and a processor configured to execute the executable instructions to:”
“wherein the neural network model comprises an expert network layer comprising a first expert network of the first service field”
“obtain a first word vector matrix through training based on a first training data set of a first service field”
“train the neural network model based on the second training data set to obtain a target neural network model”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
The limitations:
“obtain a second training data set”
As drafted, are additional elements that amount to no more than insignificant extra-solution activity. See MPEP 2106.05(g).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply” and “insignificant extra-solution activity”. Specifically, the obtaining limitation recites the well-understood, routine, and conventional activity of receiving and transmitting data over a network. MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Mere instructions to apply and insignificant extra-solution activity cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18,
Claim 18 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 18 is directed to a device for training a neural network model, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine an initial weight of the second expert network based on the second word vector matrix”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f).
The limitations:
“wherein the expert network layer further comprises a second expert network of the second service field”
“obtain a second word vector matrix through training based on a third training data set of a second service field”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a device for training a neural network model, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“the first expert network is selected based on the data input into the expert network layer”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the expert network layer is configured to process, through the selected first expert network, data input into the expert network layer”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 20,
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 20 is directed to a device for training a neural network model, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“determine the first training data set based on a first knowledge graph of the first service field”
As drafted, under their broadest reasonable interpretations, cover mental processes, i.e., concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The above limitations in the context of this claim correspond to mental processes, e.g., evaluation and judgement with assistance of pen and paper.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recited additional elements that are mere instructions to apply an exception (See MPEP 2106.05(f)).
The limitations:
“wherein the processor is configured to…”
As drafted, are additional elements that amount to no more than mere instructions to apply an exception for the abstract ideas. See MPEP 2106.05(f).
Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 7-11, and 15-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shazeer et al. (Outrageously Large Neural Networks: The Sparsely-gated mixture-of-experts layer) (“Shazeer”).
Regarding claim 1, Shazeer teaches a training method for training a neural network model (Shazeer Section 1.2 Our Approach “Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE)… All parts of the network are trained jointly by back-propagation.” Shazeer teaches neural network training.), wherein the neural network model comprises an expert network layer comprising a first expert network of a first service field (Shazeer Section 1.1 Conditional Computation “Figure 1: A Mixture of Experts (MoE) layer [an expert network layer] embedded within a recurrent language model.”; Section 2 The Structure of the Mixture-Of-Experts Layer “The Mixture-of-Experts (MoE) layer consists of a set of n “expert networks" E1,··· ,En, [comprising E1., a first expert network] and a “gating network" G whose output is a sparse n-dimensional vector. Figure 1 shows an overview of the MoE module. The experts are themselves neural networks, each with their own parameters.”; Section E Machine Translation “We found that the experts indeed become highly specialized by syntax and/or semantics, as can be seen in Table 9. [Table 9 indicating differing specializations (i.e., a first service field) for each expert]” Shazeer teaches a Mixture of Experts (MoE) layer (expert network) layer comprising a plurality of expert networks, each specializing in a differing service field, as shown in Table 9.), the method comprising: obtaining a first word vector matrix through training based on a first training data set of the first service field (Shazeer Section D “Models are trained [training] once-through over about 100 billion words… For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices]. At each step, the matrix of estimators is taken to be the outer product of those two vectors divided by the mean of either one.”; Section E “Each training batch [a first training data set of the first service field] consisted of a set of sentence pairs containing roughly 16000 words per GPU.”; Section 4 “We define the importance of an expert relative to a batch of training examples” Shazeer teaches obtaining vectors of row-wise and column-wise averages of a matrix of parameters for training, wherein the training comprises words, corresponding to obtaining a first word vector matrix based a first training dataset of words, wherein each expert is trained with a specific batch of training examples.); determining an initial weight of the first expert network of the neural network model based on the first word vector matrix (Shazeer Section 4 Balancing Expert Utilization “. This additional loss encourages all experts to have equal importance. Equation (6) [an initial weight of the first expert network]. While this loss function can ensure equal importance, experts may still receive very different numbers of examples. For example, one expert may receive a few examples with large weights, and another may receive many examples with small weights.”; Section D “Models are trained [training] once-through over about 100 billion words… For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices]” Shazeer teaches assigning importance values to expert networks, in accordance with Equation 6, including word vector matrices during training.); obtaining a second training data set (Shazeer Section E Experimental Details “Each training batch [a plurality of training data sets] consisted of a set of sentence pairs containing roughly 16000 words per GPU” Shazeer teaches a plurality of training batches, wherein each training batch is used for a differing expert network , thus including a second training data set.); training the neural network model based on the second training data set to obtain a target neural network model (Shazeer Section C “Training took 12-16 hours for all models, except for MoE-4, which took 18 hours (since all the expert computation was performed on only 4 of 16 GPUs)… Results: We evaluate our model using perplexity on the hold out data set… Results are reported in Table 7. For each model, we report the test perplexity, the computational budget, the parameter counts, the value of DropProb, and the computational efficiency.” Section E “We use a shared source and target vocabulary of 32K wordpieces… Training was done synchronously on a cluster of up to 64 GPUs as described in section 3. Each training batch [a plurality of training data sets] consisted of a set of sentence pairs containing roughly 16000 words per GPU” Shazeer teaches using a second training dataset including target vocabulary to obtain trained target neural networks, such as those in Table 7.).
Regarding claim 2, Shazeer teaches wherein the expert network layer further comprises a second expert network of a second service field (Shazeer Section 2 The Structure of the Mixture-Of-Experts Layer “The Mixture-of-Experts (MoE) layer consists of a set of n “expert networks" E1,··· ,En, [a second expert network] and a “gating network" G whose output is a sparse n-dimensional vector. Figure 1 shows an overview of the MoE module. The experts are themselves neural networks, each with their own parameters.”; Section E Machine Translation “We found that the experts indeed become highly specialized by syntax and/or semantics, as can be seen in Table 9. [second service field]” Shazeer teaches a set of n expert networks including a second expert network (n=2) with a specialized syntax, as shown in Table 9, wherein each expert comprises its own service field, corresponding to a second service field.), and the method further comprises: obtaining a second word vector matrix through training based on a third training data set of the second service field (Shazeer Section D “Models are trained [training] once-through over about 100 billion words… For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices]. At each step, the matrix of estimators is taken to be the outer product of those two vectors divided by the mean of either one.”; Section E “Each training batch [a third training data set] consisted of a set of sentence pairs containing roughly 16000 words per GPU.”; Section 4 “We define the importance of an expert relative to a batch of training examples”; Section E Machine Translation “We found that the experts indeed become highly specialized by syntax and/or semantics, as can be seen in Table 9. [second service field]” Shazeer teaches obtaining vectors of row-wise and column-wise averages of a matrix of parameters for training, wherein the training comprises words, corresponding to obtaining a second word vector matrix based a third training dataset of words, wherein each expert is trained with a specific batch of training examples with a specific service field, as shown in Table 9.); and determining an initial weight of the second expert network based on the second word vector matrix (Shazeer Section 4 Balancing Expert Utilization “. This additional loss encourages all experts to have equal importance. Equation (6) [initial weight of the second expert network]. While this loss function can ensure equal importance, experts may still receive very different numbers of examples. For example, one expert may receive a few examples with large weights, and another may receive many examples with small weights.”; Section D “For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices].” Shazeer teaches determining importance for a plurality of expert networks corresponding to their initial weights based on word vector matrices.).
Regarding claim 3, Shazeer teaches wherein the expert network layer is configured process, through the selected first expert network, the data input into the expert network layer (Shazeer Section 1.2 “Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a number of experts, each a simple feed-forward neural network, and a trainable gating network which selects a sparse combination of the experts to process [process data input to the expert network layer] each input (see Figure 1).” Shazeer teaches processing data input into the Mixture of Experts (expert network) Layer, as shown in Figure 1.).
Regarding claim 7, Shazeer teaches wherein the step of determining the initial weight of the first expert network based on the first word vector matrix comprises: using the first word vector matrix as the initial weight of the first expert network (Shazeer Section 4 Balancing Expert Utilization “We define the importance of an expert [weight of expert network] relative to a batch of training examples to be the batchwise sum of the gate values for that expert.; Section D “For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices].”; Section A “To accomplish this, we initialize the matrices Wg and Wnoise to all zeros, which yields no signal and some noise. Experiments: We trained a set of models with identical architecture…, using different values of wimportance and wload. We trained each model for 10 epochs, then measured perplexity on the test set” Shazeer teaches using word vector matrices as parameters (weights) during training of the expert networks including their respective weight matrices.).
Regarding claim 8, Shazeer teaches wherein the neural network model is a natural language processing (NLP) model or a speech processing model (Shazeer Section 1.2 “Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE).”; Section 3.1 “It is our goal to train a trillion parameter model on a trillion-word corpus… Taking Advantage of Convolutionality: In our language models, we apply the same MoE to each time step of the previous layer.” Shazeer teaches language models trained with word datasets, corresponding to a natural language processing (NLP) model.).
Regarding claim 9, it is the data processing method of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Shazeer teaches obtaining to-be-processed data (Shazeer Section C “Results: We evaluate our model using perplexity on the holdout dataset [to-be-processed data].. Results are reported in Table7. For each model, we report the test perplexity, the computational budget, the parameter counts, the value of Drop Prob, and the computational efficiency.”); and processing the to-be-processed data by using a target neural network model (Shazeer Section C “Training: The models were trained on a cluster of 16K40 GPUs using the synchronous method described in Section3. Each batch consisted of a set of sentences totaling roughly 300,000 words. In the interest of time, we limited training to 10epochs, (27,000steps)… Results: We evaluate our model using perplexity on the holdout dataset.. Results are reported in Table7. For each model, we report the test perplexity, the computational budget, the parameter counts, the value of Drop Prob, and the computational efficiency.” Shazeer teaches evaluating the trained expert networks using a holdout dataset, corresponding to processing to-be-processed data using the target neural network (the trained expert networks), as shown by the results in Table 7.).
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 11, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 3. Further, Shazeer teaches the first expert network is selected based on the data input into the expert network layer (Shazeer Section 1.2 “Our approach to conditional computation is to introduce a new type of general purpose neural network component: a Sparsely-Gated Mixture-of-Experts Layer (MoE). The MoE consists of a number of experts, each a simple feed-forward neural network, and a trainable gating network which selects a sparse combination of the experts to process each input [selected based on the data input into] (see Figure 1).”; Section 3.1 “If the gating network chooses k out of n experts for each example, then for a batch of b examples, each expert receives a much smaller batch of approximately kb n b examples.” Shazeer teaches a gating network which selects an expert network based on the input into the Mixture of Experts Layer (expert network layer), as shown in Figure 1.).
Regarding claim 15, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 7.
Regarding claim 16, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 8.
Regarding claim 17, it is the device embodiment of claim 1 with similar limitations to claim 1 and is rejected using the same reasoning found above in the rejection of claim 1. Further, Shazeer teaches a device for training a neural network model, comprising: a memory storing executable instructions; and a processor configured to execute the executable instructions (Shazeer Section D “Models are trained on a cluster of 32 Tesla K40 GPUs, except for the last two models, which are trained on clusters of 64 and 128 GPUs so as to have enough memory for all the parameters. For all models, training batch sizes are approximately 2.5 million words. Models are trained once-through over about 100billion words.” Shazeer teaches training with GPU clusters including memory, corresponding to the device for training comprising processing hardware.)
Regarding claim 18, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 2.
Regarding claim 19, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Shazeer for the same reasons disclosed above in the rejection of claim 11.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-6, 12-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shazeer et al. (Outrageously Large Neural Networks: The Sparsely-gated mixture-of-experts layer) (“Shazeer”) in view of Jiao et al. (U.S. Patent Publication No. 2022/0067030) (“Jiao”).
Regarding claim 4, Shazeer teaches the training method according to claim 1, as discussed above in the rejection of claim 1, but fails to teach further comprising: determining the first training data set based on a first knowledge graph of the first service field.
However, Jiao teaches determining the first training data set based on a first knowledge graph of the first service field (Jiao [0028] “Knowledge Graphs (KGs) are a way of structuring information in graph form, by representing entities (e.g., people, places, objects) as nodes, and relationships between entities (e.g., being married to, being located in) as edges. Facts are typically represented as “SPO” triplets: (Subject, Predicate, Object) or (s, p, o). Two nodes connected by a relationship form a fact. For instance, (Mona Lisa, painted by, Da Vinci) is a triplet”; [0063] “In aspects of the present disclosure, the training/test data is compiled from public knowledge graphs” Jiao teaches determining training data sets from knowledge graphs which contains triplets, corresponding to first knowledge graph of a first service field, such as a triplet.).
Shazeer and Jiao are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to neural network training. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shazeer with the above teachings of Jiao. Doing so can greatly improve the quality of content recommendation systems such search queries and online advertising searches (Jiao [0030] “Knowledge graphs, such as KG 100, can greatly improve the quality of content recommendation systems such search queries and online advertising searches.”).
Regarding claim 5, Shazeer in view of Jiao teaches wherein the step of determining the first training data set comprises: generating at least one first text sequence in the first training data set based on at least one first triplet in the first knowledge graph (Jiao [0028] “Knowledge Graphs (KGs) are a way of structuring information in graph form, by representing entities (e.g., people, places, objects) as nodes, and relationships between entities (e.g., being married to, being located in) as edges. Facts are typically represented as “SPO” triplets: (Subject, Predicate, Object) or (s, p, o). Two nodes connected by a relationship form a fact. For instance, (Mona Lisa, painted by, Da Vinci) is a triplet. [text sequence based on a first triplet]”; [0063] “In aspects of the present disclosure, the training/test data is compiled from public knowledge graphs ” Jiao teaches triplets of a knowledge graph comprising text sequences, such as “Mona Lisa”.), wherein three words in the first triplet respectively represent a subject in the first service field, an object in the first service field, and a relationship between the subject and the object (Jiao [0030] “Each pair of nodes connected by an edge is considered a triplet or a fact comprising a subject, predicate (e.g., relationship), and object (s, p, o). For example, node 102, edge 124, node 126 is a triplet—the Mona Lisa (e.g., subject) was painted by (e.g., predicate) Da Vinci (e.g., object).” Jiao teaches a triplet representing subject, predicate, object, corresponding subject, object, relationship of a first service field.).
Shazeer and Jiao are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to neural network training. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shazeer with the teachings of Jiao. Doing so can greatly improve the quality of content recommendation systems such search queries and online advertising searches (Jiao [0030] “Knowledge graphs, such as KG 100, can greatly improve the quality of content recommendation systems such search queries and online advertising searches.”).
Regarding claim 6, Shazeer in view of Jiao teaches wherein the first word vector matrix is a weight of a hidden layer in a first target word vector generation model (Shazeer Section 3.2 Network Bandwidth “In our experiments, we use experts with one hidden layer [a hidden layer] containing thousands of RELU-activated units. Since the weight matrices in the expert have sizes input_size×hidden_size and hidden_size×output_size, the ratio of computation to input and output is equal to the size of the hidden layer. Conveniently, we can increase computational efficiency simply by using a larger hidden layer, or more hidden layers.”; Section D “For a matrix of parameters, instead of maintaining a full matrix of second-moment estimators, we maintain vectors of row-wise and column-wise averages of that matrix [word vector matrices].” Shazeer teaches hidden layers of the expert networks.), and wherein the step of obtaining the first word vector matrix through training comprises: obtaining the first target word vector generation model by training a word vector generation model using a word other than a target word in the at least one first text sequence as an input of the word vector generation model (Shazeer Section E “Similar to GNMT, to effectively deal with rare words, we used sub word units (also known as “wordpieces") (Schuster & Nakajima, 2012) for inputs and outputs in our system. We use a shared source and target vocabulary of 32K wordpieces. We also used the same beam search technique as proposed in (Wu et al., 2016).” Shazeer teaches using sub words as input corresponding to using a word other than a target word as input.) and using the target word as a target output of the word vector generation model (Shazeer Section G “The attention mechanism described in GNMT (Wu et al., 2016) involves a learned “Attention Function" A(xi,yj) which takes a “source vector" xi and a “target vector" [target output] yj, and must be computed for every source time step i and target time step j.” Shazeer teaches the target vector, corresponding to target output of the word vector generation model.),
Shazeer fails to teach wherein the target word is a word in the at least one first triplet.
However, Jiao teaches wherein the target word is a word in the at least one first triplet (Jiao [0028] “Knowledge Graphs (KGs) are a way of structuring information in graph form, by representing entities (e.g., people, places, objects) as nodes, and relationships between entities (e.g., being married to, being located in) as edges. Facts are typically represented as “SPO” triplets: (Subject, Predicate, Object) or (s, p, o). Two nodes connected by a relationship form a fact. For instance, (Mona Lisa, painted by, Da Vinci) is a triplet. [at least one first triplet]”; [0040] “FIG. 3 illustrates a context-independent transformer model 300 that may be used to predict a link (e.g., a target node [target word]) in a knowledge graph, such as knowledge graph 100.” Jiao teaches target nodes in a knowledge graph including triplets including words.).
Shazeer and Jiao are both considered to be analogous to the claimed invention because they are in the same field of artificial intelligence and more specifically applied to neural network training. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Shazeer with the above teachings of Jiao. Doing so can greatly improve the quality of content recommendation systems such search queries and online advertising searches (Jiao [0030] “Knowledge graphs, such as KG 100, can greatly improve the quality of content recommendation systems such search queries and online advertising searches.”).
Regarding claim 12, the rejection of claim 9 is incorporated herein. Further, the limitations in this claim are taught by Shazeer in view of Jiao for the same reasons disclosed above in the rejection of claim 4.
Regarding claim 13, the rejection of claim 12 is incorporated herein. Further, the limitations in this claim are taught by Shazeer in view of Jiao for the same reasons disclosed above in the rejection of claim 5.
Regarding claim 14, the rejection of claim 13 is incorporated herein. Further, the limitations in this claim are taught by Shazeer in view of Jiao for the same reasons disclosed above in the rejection of claim 6.
Regarding claim 20, the rejection of claim 17 is incorporated herein. Further, the limitations in this claim are taught by Shazeer in view of Jiao for the same reasons disclosed above in the rejection of claim 4.
Conclusion
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/KURT NICHOLAS PRESSLY/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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