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 .
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10-20-2021 and 11-30-2021 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 5-6, 10-14, and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1-20:
Claims 1 and 19 recite:
“from an input layer through a predetermined intermediate layer”
“from a predetermined intermediate layer through an output layer”
Since both claim limitations recite “a predetermined intermediate layer”, it is initially unclear if the predetermined intermediate layers are referring to a same predetermined intermediate layer since a predetermined intermediate layer is present in both the first and second inference models. However, in view of following limitation from claim 1 and 19, “the glue layer connecting the predetermined layer of the first inference model and the predetermined layer of the second inference model”, it is clear the predetermined intermediate layers are referring to different intermediate layers from their respective inference models. Additionally, in view of FIG. 4 of the drawings filed 10-20-2021, it is apparent that both the first inference model and the second inference model have their own input and output layers. Therefore, Applicant is advised to amend the claim limitations as listed:
“from an input layer of the first inference model through a predetermined intermediate layer of the first inference model”
“from a predetermined intermediate layer of the second inference model through an output layer of the second inference model”
Claims 2-18 are rejected for inheriting the deficiencies of claim 1.
Claim 20 is rejected for inheriting the deficiencies of claim 19.
Regarding Claims 5-6:
Claim 5 recites “an autoencoder to reconstruct layers from the input layer through an output layer”. Claim 1 recites an input layer and an output layer referring to both the first and second inference model, making it unclear if the input layer and the output layer from claim 5 is referring to which inference model. The Examiner is interpreting claim 5’s input and output layers to both be referring to the second inference model. Applicant is advised to amend the claim from “an autoencoder to reconstruct layers from the input layer through an output layer” to “an autoencoder to reconstruct layers from the input layer of the second inference model through an output layer of the second inference model”.
Claim 6 is rejected for inheriting the deficiencies of claim 5.
Regarding Claims 10-14 and 16-18:
Claims 10 and 16 recite “a portion of the first inference model from the predetermined intermediate layer through an output layer”. Claims 10 and 16 are unclear in a manner discussed above with respect to claim 1 regarding the predetermined intermediate layer. The Examiner is interpreting the predetermined intermediate layer and output layer to be referring to the layers of the first inference model. Similarly to claim 1, Applicant is advised to amend the claim from “a portion of the first inference model from the predetermined intermediate layer through an output layer” to “a portion of the first inference model from the predetermined intermediate layer of the first inference model through an output layer of the first inference model”.
Claims 11-14 are rejected for inheriting the deficiencies of claim 10.
Claims 17-18 are rejected for inheriting the deficiencies of claim 16.
Regarding Claims 11 and 17:
Claims 11 and 17 recite “using the output layer as an input layer”. Claims 11 and 17 are unclear in a manner discussed above with respect to claim 1 regarding the output layer and input layer. The Examiner is interpreting the output layer and input layer to be referring to the layers of the first inference model. Applicant is advised to amend the claim from “using the output layer as an input layer” to “using the output layer of the first inference model as an input layer of the first inference model”.
Regarding Claims 12-13:
Claim 12 recites “the portion of the first inference model up to the predetermined intermediate layer”. Claim 12 is unclear in a manner discussed above with respect to claim 1. Examiner is interpreting the predetermined intermediate layer to be referring to the predetermined intermediate layer of the first inference model. Similarly to claim 1, Applicant is advised to amend the claim from “the predetermined intermediate layer” to “the predetermined intermediate layer of the first inference model”.
Claim 13 is rejected for inheriting the deficiencies of claim 12.
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 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Claims 19-20 are rejected under 35 U.S.C. § 101 because the claimed invention is non-statutory subject matter. The claims recite:
In claim 19, An inference model, comprising:…
In claim 20, The inference model according to claim 19…
When viewed individually and as a whole, it appears that the claims would be reasonably interpreted by one of ordinary skill as directed to mathematical concepts. As drafted, under their broadest reasonable interpretation (BRI), in view of the specification, the claims are directed to an inference model comprising portions of a first partial inference model and a second partial inference model that is connected by a glue layer. In view of the instant specification, pages 7-8, “The inference models each include a neural network. The inference models each have a multilayer structure, and include an input layer, intermediate layers, and an output layer, for instance. Each layer has a plurality of nodes (not illustrated) corresponding to neurons.” In other words, the claim only recites mathematical concepts (neural networks, neural network layers, neurons) and does not recite a process, machine, manufacture, or composition of matter. Therefore, claims 19-20 are rejected under 35 U.S.C. § 101 as they are not drawn to eligible subject matter.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1, 3-4, 7-9, 15, and 19-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Zheng et al. (US 20180268296), hereinafter Zheng.
Regarding Claim 1:
Zheng discloses:
A method for generating a third inference model using a first inference model and a second inference model,
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Zheng, [0019], “FIG. 3c is another schematic diagram of a process of merging a sub-network b1 and a sub-network b2 and a process of performing optimization training on a network obtained by means of merging connection according to an embodiment of this application;”
In para. 19, Zheng discloses a process of merging sub-network b1 and sub-network b2 to create a merged network, further depicted in FIG. 3c. The merged network contains both sub-network b1 and sub-network b2 [generating a third inference model using a first inference model and a second inference model].
when a type of output data that is output from the first inference model is the same type of input data that is input to the second inference model,
Zheng, [0078]-[0079], “S212: Perform merging connection on the seed network and the to-be-merged object network in a fully connected manner… The fully connected manner means that for pre-data A and target data B, it is set B=W×A. W is a weight matrix, and × indicates a matrix multiplication…the seed network is A and the to-be-merged object network is B. Each neuron used as an output in the seed network is mapped to a neuron used as an input in the to-be-merged object network…that is, a mapping relationship between each neuron used as an output in the seed network and each neuron used as an input in the to-be-merged object network is established”
In paras. 78-79, Zheng discloses that the data that is output by the seed network is used as input in the to-be-merged object network. In other words, network A’s output data [a type of output data that is output from the first inference model] is used as input data into network B [is the same type of input data that is input to the second inference model].
the first inference model and the second inference model each being trained and having a multilayer structure, the method comprising:
Zheng, [0005], “the processing circuitry performs optimization training on the first sub-network by using the reference dataset”
[0006], “the processing circuitry performs optimization training on the second sub-network by using the reference dataset.”
In paras. 5 and 6, Zheng discloses training the first and second sub-networks [the first inference model and the second inference model each being trained]. Depicted above in FIG. 3c, sub-network b1 and sub-network b2 each have an input layer, an output layer, and structure in-between [having a multi-layer structure].
preparing a first partial inference model that includes a portion of the first inference model from an input layer through a predetermined layer
Zheng, [0010], “the processing circuitry selects one of the first sub-network and the second sub-network as a seed network, and the other one of the first sub-network and the second sub-network as a to-be-merged network. Then, the processing circuitry removes the second input layer structure and the first output layer structure between the seed network and the to-be-merged network, and merges the seed network with the to-be-merged network to form a grown seed network…the processing circuitry adds an intermediate hidden layer structure between the seed network and the to-be-merged network when the merging fails and merges the seed network and the to-be-merged network using the intermediate hidden layer.”
Zheng discloses removing the first output layer structure from the first sub-network to merge. In other words, the processing circuitry processes the first sub-network [preparing a first partial inference model] which contains layers from the input layer through to a hidden layer [includes a portion of the first inference model from an input layer through a predetermined layer].
preparing a second partial inference model that includes a portion of the second inference model from a predetermined layer through an output layer
As cited above in para. 10, Zheng discloses processing the second sub-network [preparing a second partial inference model] by removing the input layer structure from the second sub-network [includes a portion of the second inference model from a predetermined layer through an output layer].
generating the third inference model by disposing a glue layer between the first partial inference model and the second partial inference model,
As cited above in para. 10 and FIG. 3c, Zheng discloses forming the grown seed network [generating the third inference model] by using an intermediate hidden layer [by disposing a glue layer] between the first sub-network and the second sub-network [between the first partial inference model and the second partial inference model].
the glue layer connecting the predetermined intermediate layer of the first inference model and the predetermined intermediate layer of the second inference model
As depicted above in FIG. 3c, the intermediate hidden layer connects [the glue layer connecting] the network main structure of sub-network b1 [the predetermined intermediate layer of the first inference model] with the network main structure of sub-network b2 [the predetermined intermediate layer of the second inference model].
Regarding Claim 3:
As discussed above, Zheng teaches [the] method according to claim 1, and Zheng further discloses:
wherein the glue layer disposed in generating the third inference model is untrained
Zheng, [0087], “In an embodiment of this application, the intermediate hidden layer is used for adapting an output of a previous sub-network to an input of a next sub-network. For example, in FIG. 3b, if the format of an output of the sub-network b1 does not match the format of an input of the sub-network b2, the output of the sub-network b1 may be adjusted by means of processing of the intermediate hidden layer, so that the form of the output of the adjusted sub-network b1 matches the form of the input of the sub-network b2.”
As discussed above in claim 1, Zheng discloses the intermediate hidden layer [glue layer] disposed between the first and second sub-networks, creating a seed grown network [the third inference model]. Para. 87 specifies that the intermediate hidden layer is used to adapt the output from the first sub-network to the second sub-network, and this is performed by processing the intermediate hidden layer. In other words, the intermediate hidden layer is interpreted as untrained because it needs to be processed/trained before it matches the output accordingly.
Regarding Claim 4:
As discussed above, Zheng teaches [the] method according to claim 1, and Zheng further discloses:
further comprising: determining the predetermined intermediate layer of the first inference model before preparing the first partial inference model
Zheng, [0042], For example, assuming that the data processing procedure of the original network model is represented by four data processing steps in total: “step a1→step a2→ step a3→step a4”, step a1 is equivalent to a sub-network b1 at a first layer, a main structure of the sub-network b1 is determined by step a1, and an input layer and an output layer of the sub-network b1 is determined by input/output data extracted from step a1; similarly, step a2 is equivalent to a sub-network b2 at a second layer, a main structure of the sub-network b2 is determined by step a2, and an input layer and an output layer of the sub-network b2 is determined by input/output data extracted from step a2.”
Zheng discloses building the sub-networks in para. 42. In the disclosed example at step a1, the main structure of sub-network b1 is determined first [determining the predetermined intermediate layer of the first inference model]. Then based on step a1, the input layer and the output layer of sub-network b1 is determined [before preparing the first inference model].
wherein the predetermined intermediate layer of the first inference model determined in the determining is an intermediate layer that expresses a principal component of input data that is input to the first inference model, out of a plurality of intermediate layers included in the first inference model
Zheng, [0042], “The idea of hierarchical building is: each data processing step of the original network model may be performed by a sub-network having an equivalent function, and therefore, one data processing step may correspond to a network main structure of one sub-network.”
Zheng discloses that the main structure of the sub-networks chosen because they correspond to specific data processing steps [the predetermined intermediate layer of the first inference model determined in the determining is an intermediate layer that expresses a principal component of input data that is input to the first inference model, out of a plurality of intermediate layers included in the first inference model].
Regarding Claim 7:
As discussed above, Zheng teaches [the] method according to claim 1, and Zheng further discloses:
wherein the glue layer includes one of or a combination of two or more of:
a convolution layer that converts output data that is output from the first partial inference model into input data that is input to the second partial model
a pooling layer
a fully connected layer
Zheng, “S213: Add an intermediate hidden layer between the seed network and the to-be-merged object network, and perform merging connection on the seed network and the to-be-merged object network in a fully connected manner by using the intermediate hidden layer.”
Zheng discloses that the intermediate hidden layer [the glue layer] is fully connected between the seed network and the to-be-merged network [a fully connected layer].
Regarding Claim 8:
As discussed above, Zheng teaches [the] method according to claim 1, and Zheng further discloses:
further comprising: training the third inference model
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Zheng depicts training the merged model [the third inference model] in FIG. 3c.
Regarding Claim 9:
As discussed above, Zheng teaches [the] method according to claim 8, and Zheng further discloses:
wherein the training includes training the glue layer using a training data set according to input and output of the glue layer
Zheng, [0019], “FIG. 3c is another schematic diagram of a process of merging a sub-network b1 and a sub-network b2 and a process of performing optimization training on a network obtained by means of merging connection according to an embodiment of this application”
[0087], “For example, in FIG. 3b, if the format of an output of the sub-network b1 does not match the format of an input of the sub-network b2, the output of the sub-network b1 may be adjusted by means of processing of the intermediate hidden layer, so that the form of the output of the adjusted sub-network b1 matches the form of the input of the sub-network b2.”
In para. 19 and FIG. 3c, Zheng discloses/depicts training the merged network. Para. 87 further specifies that the intermediate hidden layer is adjusted/trained to use the output of sub-network b1 [using a training data set according to input] and the input of sub-network b2 [output of the glue layer].
Regarding Claim 15:
As discussed above, Zheng teaches [the] method according to claim 8, and Zhen further discloses:
wherein the training includes training a connected model using a training data set according to input and output of the connected model, the connected model being obtained by connecting the glue layer and the second partial inference model
Zheng, [0019], “FIG. 3c is another schematic diagram of a process of merging a sub-network b1 and a sub-network b2 and a process of performing optimization training on a network obtained by means of merging connection according to an embodiment of this application”
[0087], “For example, in FIG. 3b, if the format of an output of the sub-network b1 does not match the format of an input of the sub-network b2, the output of the sub-network b1 may be adjusted by means of processing of the intermediate hidden layer, so that the form of the output of the adjusted sub-network b1 matches the form of the input of the sub-network b2.”
[0044], “at least one group of input/output data corresponding to step a2 is used for performing optimization training on the sub-network b2.”
In para. 19 and FIG. 3c, Zheng discloses/depicts training the merged network which contains the intermediate hidden layer connected to the second sub-network [the connected model being obtained by connecting the glue layer and the second partial inference model]. Para. 87 further specifies that the intermediate hidden layer is adjusted/trained to use the output of sub-network b1 [the training includes training a connected model using a training data set according to input…of the connected model], and para. 44 further specifies the sub-network b2’s output is used to train the network [the output of the connected model].
Regarding Claim 19:
Zheng discloses:
An inference model, comprising: a first partial inference model that is a portion of a first inference model that has been trained; a second partial inference model that is a portion of a second inference model that has been trained;
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Zheng, [0010], “the processing circuitry selects one of the first sub-network and the second sub-network as a seed network, and the other one of the first sub-network and the second sub-network as a to-be-merged network. Then, the processing circuitry removes the second input layer structure and the first output layer structure between the seed network and the to-be-merged network, and merges the seed network with the to-be-merged network to form a grown seed network…the processing circuitry adds an intermediate hidden layer structure between the seed network and the to-be-merged network when the merging fails and merges the seed network and the to-be-merged network using the intermediate hidden layer.”
[0005], “the processing circuitry performs optimization training on the first sub-network by using the reference dataset”
[0006], “the processing circuitry performs optimization training on the second sub-network by using the reference dataset.”
In para. 10 and FIG. 3c, Zheng discloses/depicts a grown seed network [An inference model]. The grown seed network is obtained by merging a first sub-network that had its output layer structure removed [a first partial inference model that is a portion of a first inference model] with a second sub-network that had its input layer structure removed [a second partial inference model that is a portion of a second inference model]. Paras. 5 and 6 respectively disclose that the first and second sub-networks are trained [that has been trained].
a glue layer disposed between the first partial inference model and the second partial inference model,
As cited above in para. 10 and FIG. 3c, Zheng discloses forming the grown seed network by using an intermediate hidden layer [a glue layer disposed] between the first sub-network and the second sub-network [between the first partial inference model and the second partial inference model].
the glue layer being untrained,
Zheng, [0087], “In an embodiment of this application, the intermediate hidden layer is used for adapting an output of a previous sub-network to an input of a next sub-network. For example, in FIG. 3b, if the format of an output of the sub-network b1 does not match the format of an input of the sub-network b2, the output of the sub-network b1 may be adjusted by means of processing of the intermediate hidden layer, so that the form of the output of the adjusted sub-network b1 matches the form of the input of the sub-network b2.”
As cited above in paras. 5-6, Zheng discloses that the first and second sub-networks are trained. Para. 12 further specifies that the training occurs before the merged network is formed. Following FIG. 3c, the merged network includes the first sub-network, intermediate hidden layer, and second sub-network. Para. 87 specifies that the intermediate hidden layer is used to adapt the output from the first sub-network to the second sub-network, and this is performed by processing the intermediate hidden layer. In other words, the intermediate hidden layer is interpreted as untrained because it needs to be processed/trained before it matches the output accordingly.
wherein the first partial inference model includes a portion of the first inference model from an input layer through a predetermined layer
As cited above in para. 10, Zheng discloses removing the first output layer structure from the first sub-network to merge. In other words, the processing circuitry processes the first sub-network [first partial inference model] which contains layers from the input layer through to a hidden layer [includes a portion of the first inference model from an input layer through a predetermined layer].
the second partial inference model includes a portion of the second inference model from a predetermined intermediate layer through an output layer
As cited above in para. 10, Zheng discloses processing the second sub-network [second partial inference model] by removing the input layer structure from the second sub-network [includes a portion of the second inference model from a predetermined layer through an output layer].
the glue layer connects the predetermined intermediate layer included in the first partial inference model to the predetermined layer included in the second partial inference model
As depicted above in FIG. 3c, the intermediate hidden layer connects [the glue layer connecting] the network main structure of sub-network b1 [the predetermined intermediate layer of the first inference model] with the network main structure of sub-network b2 [the predetermined intermediate layer of the second inference model].
Regarding Claim 20:
As discussed above, Zheng teaches [the] inference model according to claim 19, and further discloses:
wherein a type of data that is output from the first partial inference model and input to the glue layer is the same as a type of data that is output from the glue layer and input to the second partial inference model
Zheng, [0078], “S212: Perform merging connection on the seed network and the to-be-merged object network in a fully connected manner…”
[0079], “The fully connected manner means that for pre-data A and target data B, it is set B=W×A. W is a weight matrix, and × indicates a matrix multiplication…the seed network is A and the to-be-merged object network is B. Each neuron used as an output in the seed network is mapped to a neuron used as an input in the to-be-merged object network…that is, a mapping relationship between each neuron used as an output in the seed network and each neuron used as an input in the to-be-merged object network is established”
[0080], “S213: Add an intermediate hidden layer between the seed network and the to-be-merged object network, and perform merging connection on the seed network and the to-be-merged object network in a fully connected manner by using the intermediate hidden layer.”
In paras. 78-79, Zheng discloses that the data that is output by the seed network is fully connected and to be used as input in the to-be-merged object network. Para. 80 further specifies the fully connected merging can be done through the intermediate hidden layer between the seed network [a type of output data that is output from the first partial inference model and input to the glue layer] and the to-be-merged network [is the same type of input data that is output from the glue layer and input to the second partial inference model.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng in view of Ning et al. (US 20160132783), hereinafter Ning.
Regarding Claim 2:
As discussed above, Zheng teaches [the] method according to claim 1, and but does not explicitly disclose:
wherein a domain that is a group of objects when the first inference model is inferred is different from a domain that is a group of objects when the second inference model is inferred
However, in the same field, analogous art Ning teaches:
wherein a domain that is a group of objects when the first inference model is inferred is different from a domain that is a group of objects when the second inference model is inferred
Ning, [0028], “When a user creates a user account with the system of the source domain 300, the system of the source domain 300 builds a first user model”
[0029], “the user may create an account with the system of the target domain 310, resulting in the building of a second user model 330”
Ning discloses a first user model [when the first inference model is inferred] in a source domain [a domain that is a group of objects] that is different from the target domain for the second user model [is different from a domain that is a group of objects when the second inference model is inferred].
Zheng, Ning, and the instant application are analogous art because they are all directed to combining machine learning models.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Zheng with Ning in order to allow for domain adaptation and improve prediction accuracy in different domains. “Given the scenario that the user model 320 is more developed and accurate than user model 330, for example, due to the user's habits, more active behavior patterns, longer history, preferences, etc., user model 330 may be improved by adapting user model 320 to user model 330 and merging the adapted model with user model 330 to build a final improved user model for the user in target domain 310.” (Ning, [0041]). Ning discloses that merging models with multiple domains provides for more accurate predictions.
Conclusion
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/STEVEN PHUNG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125