Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
The following action is in response to the communication(s) received on 01/23/2026.
As of the claims filed 01/23/2026:
Claims 1, 8, 14, and 15 have been amended.
Claims 2, 9, and 18-20 have been canceled.
Claims 1, 3-8, and 10-17 are now pending.
Claims 1, 8, 14, and 15 are independent claims.
Response to Arguments
Applicant’s arguments filed 01/23/2026 have been fully considered, but are not fully persuasive.
With respect to the interpretation 35 USC § 112(f):
The amendments have withdrawn the claim language which required this interpretation for all but one limitation in claim 8 (“a first output data generation unit…”); thus, 35 USC § 112(f) remains invoked for claim 8 and withdrawn for the other claims.
With respect to the rejection under 35 USC § 112:
The amendments have overcome the indefinite language regarding “output data”. Thus, the rejection has been withdrawn.
With respect to the rejection under 35 USC § 101:
Applicant asserts that claim 1, as a whole, sets forth an improvement to machine learning techniques for additional learning of decision trees and necessarily rooted in computing activity with the embedded control device which provide the improvement (p.13 ¶1,2). Examiner respectfully submits that, in order for the improvement to be directed to the machine learning techniques, there must be details on the training of the machine-learned model which either suggest that it is impractical to perform in the human mind or an improvement to the machine learning training technology. As currently recited, the improvements are not towards the workings of an embedded control device, but rather towards constructing the decision tree in continuous learning (“generating updated output data… based on the output data and the actual data”). A decision tree is an algorithm, which is an abstract idea, and the embedded control device remains merely generally linked to the abstract idea.
Applicant further asserts that the improvement is a technical solution to a technical problem, since it involves a small computational cost and performs online additional learning under limited hardware resources (p.14 ¶1). This is unpersuasive; similar to above, the improvement is reflected on the algorithm of decision trees in the context of online learning, in which decision trees are not a technology.
Applicant further asserts that the improvements to the machine learning in the claims is performing additional learning in an environment with limited hardware resources (p.15 ¶2). Examiner respectfully submits that the improvement to the abstract idea enables the limited hardware resources to perform the abstract ideas. In order for the improvement to be directed to the technology, the ways that the specific technology of hardware resources are being improved upon should be recited.
Applicant further asserts that the improvement is recited in the claims (p.15 last ¶). Examiner respectfully disagrees, as the claims merely recite performing abstract ideas (“generates…output data”; “updating the output data”) that are generally linked to the computing resources performing the abstract ideas (“using a…decision tree stored in the memory”; “store…the learned decision tree for which the additional learning processing is performed”).
Applicant further asserts that the improvement is not to the abstract idea and is different from Alice Corp (p.16). However, Applicant does not elaborate to how this is the case. As explained above, “additional learning processing” is merely an abstract idea, and the crux of the invention is the improvement to the decision tree algorithm in continuous learning, which is an abstract idea.
Applicant further asserts that the additional elements are significantly more than the abstract idea because the claim includes a specific technique for improving machine learning techniques for additional learning of decision trees (p.18). Similarly to above, “updating the output data” is merely an abstract idea; this is not an additional element to consider whether it provides significantly more than the abstract idea itself. i.e., it is merely an improvement to an abstract idea. Thus, the claims remain ineligible.
With respect to the rejection under 35 USC § 102:
Applicant asserts that Zhang does not teach “such that a change in structure of the learned decision tree or a change in a branch condition does not occur”, as it only teaches rebuilding the sub-tree when the best split attribute has changed. Examiner respectfully submits that the claim merely recites that a change in a branch condition does not occur; when Zhang rebuilds the sub-tree¸ it does not rebuild the rest of the tree, which correspond to a branch condition which does not change. Thus, Zhang remains teaching this limitation.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitation(s) is/are:
Claim 8: a first output data generation unit…; ”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 1 recites An embedded control device, thus a machine, one of the four statutory categories of patentable subject matter (Step 1). However, Claim 1 further recites:
identify an output node corresponding to the input data and generates associated output data...;, which are evaluations or judgements that can be performed in the human mind;
perform additional learning processing for the learned decision tree by generating updated output data by updating the output data associated with the output node, based on the output data and the actual data such that a change in structure of the learned decision tree or a change in a branch condition does not occur, which are evaluations or judgements that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2, the claim does not include any additional elements which integrate the abstract idea into a practical application, since the additional elements consist of:
A memory and a processor, the processor configured to…, as the performance of an abstract idea on a computer is not more than instructions to "apply it" on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application;
acquire, as input data, data acquired from a target device, which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
provide the target device with the output data, which is merely an insignificant extra-solution activity of data transfer, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
acquire actual data acquired from the target device, the actual data corresponding to the input data, which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
and store, in the memory, the learned decision tree for which the additional learning processing is performed, which is merely an insignificant extra-solution activity of data storage, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards an abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) and the activity of data gathering (MPEP 2106.05(g)) cannot provide significantly more, as storing and retrieving information in memory is well understood, routine, and conventional (MPEP 2106.05(d)(II)(iv)); the activity of data transfer (MPEP 2106.05(g)) cannot provide significantly more, as receiving or transmitting data over a network is well understood, routine, and conventional (MPEP 2106.05(d)(II)(i), buySAFE, Inc. v. Google, Inc). The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible. The combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 3, dependent upon Claim 1, further recites
the updated output data is an arithmetic average value of the output data before updating and the actual data, which is a mathematical concept.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 4, dependent upon Claim 1, further recites
the updated output data is a weighted average value of the output data before updating and the actual data, with reference to the number of data pieces associated with the output node, which is a mathematical concept.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 5, dependent upon Claim 1, further recites
adding, to the output data before updating, a result of multiplying a difference between the output data before updating and the actual data by a learning rate, which is a mathematical concept.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 6, dependent upon Claim 5, further recites
the learning rate changes according to the number of times the additional learning processing is performed, which is merely a detail of an abstract idea (multiplying a difference between the output data before updating and the actual data by a learning rate).
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 7, dependent upon Claim 1, further recites no additional abstract ideas. However:
Under Step 2A Prong 2, the claim does not include any additional elements which integrate the abstract idea into a practical application, since the additional elements consist of:
the learned decision tree is one of a plurality of decision trees for ensemble learning, which merely specifies the particular field of use or particular technological environment in which the abstract idea is to be performed, which by MPEP 2106.05(h) cannot integrate the abstract idea into a practical application.
Thus, the claim is directed towards an abstract idea.
Further, under Step 2B, the additional element does not provide significantly more than the abstract idea itself, because the particular field of use or particular technological environment (MPEP 2106.05(h)) cannot provide significantly more. Thus, the claim is ineligible.
Claim 8 recites an embedded control device, thus a machine, one of the four statutory categories of patentable subject matter (Step 1). However, Claim 8 further recites:
a first output data generation unit that generates first output data, which is an evaluation or judgement that can be performed in the human mind;
generate second output data by identifying an output node corresponding to the reference input data , which is an evaluation or judgement that can be performed in the human mind;
and generated by performing machine learning based on the training input data and differential training data between the output data , which is an evaluation or judgement that can be performed in the human mind;
generate final output data..., which is an evaluation or judgement that can be performed in the human mind;
and perform additional learning processing for the learned decision tree by generating updated output data by updating the second output data associated with the output node, based on the second output data and differential data between the first output , which is an evaluation or judgement that can be performed in the human mind.
Thus, the claim recites an abstract idea under Step 2A Prong 1.Under Step 2A Prong 2, the claim recites:
An embedded control device comprising: a memory and a processor, the processor configured to, as the performance of an abstract idea on a computer is not more than instructions to 'apply it' on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application;
acquire reference input data from a target device, which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
inputting the reference input data into a model generated based on training input data and training correct data corresponding to the training input data, which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
by inputting the reference input data into a latest learned decision tree stored in the memory , which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
generated by inputting the training input data into the model and the training correct data , which is merely an insignificant extra-solution activity of data gathering, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
to be used to control the target device, based on the first output data and the second output data; , which merely specifies the particular field of use or particular technological environment in which the abstract idea is to be performed, which by MPEP 2106.05(h) cannot integrate the abstract idea into a practical application;
provide the target device with the final output data which is used to control the target device, which is merely an insignificant extra-solution activity of data transfer, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
acquire reference correct data from the target device, the correct data corresponding to the input data, which is merely an insignificant extra-solution activity of data transfer, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application;
and store, in the memory, the learned decision tree for which the additional learning processing is performed, which is merely an insignificant extra-solution activity of data transfer, which by MPEP 2106.05(g) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards and abstract idea.
Further, the additional element(s), alone or in combination, do not provide significantly more than the abstract idea itself, because the activity of data gathering (MPEP 2106.05(g)) cannot provide significantly more, as storing and retrieving information in memory is well understood, routine, and conventional (MPEP 2106.05(d)(II)(iv)); the activity of data transfer (MPEP 2106.05(g)) cannot provide significantly more, as receiving or transmitting data over a network is well understood, routine, and conventional (MPEP 2106.05(d)(II)(i), buySAFE, Inc. v. Google, Inc); the particular field of use or particular technological environment (MPEP 2106.05(h)) cannot provide significantly more; implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. The combination of these additional elements does not provide an inventive concept; thus, the claim remains ineligible.
Claim 10, dependent upon Claim 8, further recites
the updated output data is an arithmetic average value of the second output data before updating and the differential data, which is merely a detail of an abstract idea (performs additional learning processing).
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 11, dependent upon Claim 8, further recites
the updated output data is a weighted average value of the second output data before updating and the differential data, with reference to the number of data pieces associated with the output node, which is a mathematical concept.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 12, dependent upon Claim 8, further recites
adding, to the second output data before updating, a result of multiplying a difference between the second output data before updating and the differential data by a learning rate, which is a mathematical concept.
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 13, dependent upon Claim 8, further recites
the learning rate changes according to the number of times the additional learning processing is performed, which is merely a detail of a judicial exception (multiplying a difference between the output data before updating and the actual data by a learning rate).
Thus, the claim recites an abstract idea under Step 2A Prong 1.
Under Step 2A Prong 2 and 2B, the claim does not recite any new additional elements which could integrate the abstract idea into a practical application or provide significantly more than the abstract idea itself. Thus, the claim is ineligible.
Claim 14 recites A learning method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 14 recites precisely the abstract ideas and additional elements of Claim 1. Therefore, Step 2A Prong 1, Step 2A Prong 2, and Step 2B analyses remain the same. Claim 14 is rejected as subject-matter ineligible for reasons set forth in the rejections of Claim 1.
Claim 15 recites A control method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 15 recites precisely the abstract ideas and additional elements of Claim 8. Therefore, Step 2A Prong 1, Step 2A Prong 2, and Step 2B analyses remain the same. Claim 15 is rejected as subject-matter ineligible for reasons set forth in the rejections of Claim 8.
Claim 16, dependent upon Claim 14, further recites no additional abstract ideas. However:
Under Step 2A Prong 2, the claim does not include any additional elements which integrate the abstract idea into a practical application, since the additional elements consist of:
A non-transitory computer readable medium storing a control program for a device for executing the control method, as the performance of an abstract idea on a computer is not more than instructions to "apply it" on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards an abstract idea.
Further, under Step 2B, the additional element does not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. Thus, the claim is ineligible.
Claim 17, dependent upon Claim 15, further recites no additional abstract ideas. However:
Under Step 2A Prong 2, the claim does not include any additional elements which integrate the abstract idea into a practical application, since the additional elements consist of:
A non-transitory computer readable medium storing a control program for a device for executing the control method, as the performance of an abstract idea on a computer is not more than instructions to "apply it" on a computer, which by MPEP 2106.05(f) cannot integrate an abstract idea into a practical application.
Thus, the claim is directed towards an abstract idea.
Further, under Step 2B, the additional element does not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more. Thus, the claim is ineligible.
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al., "Persistent Anytime Learning of Objects from Unseen Classes" (hereinafter Zhang
Regarding Claim 1, Zhang teaches:
An embedded control device comprising: a memory and a processor, the processor configured to: (Zhang [p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires a memory and a processor.)
acquire, as input data, data acquired from a target device;
(Zhang [p.11 ¶4] iGBDT merges the original data D1 and the new batch data D2 (both ordered according to attribute k) to obtain an ordered data set D3. So the number of samples in D3 = D2 + D1
[p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires memory, which corresponds to the target device.)
identify an output node corresponding to the input data and generates associated output data to be used to control the target device, through inference processing using a latest learned decision tree stored in the memory; provide the target device with the output data;
(Zhang, Algorithm 2,
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[p.6 ¶3] In line 1, GBDT first initialises the model with the mean value of the class values of all the instances.
[p.9 3rd ¶] In the process of building the GBDT model, the key point is to find the best split attribute which makes sure the square loss error is minimised.
[p.10 3rd ¶] After the best split points for all the attributes have been computed, the attribute that has the smallest sum least square loss error will be chosen as the best split attribute, the corresponding best split point (value) will be used to horizontally split the data into the left and right parts. Then the process for finding the best split attribute (and point) for a given node in GBDT finishes.
[p.11 section 4.3] As new batches of data are continuously arriving, the ensemble model built by GBDT which may contain thousands of decision trees should be timely updated so as to accurately model/reflect the complete data which contains the newly arrived instances) (Note: x corresponds to the input data; class value yi is related to x, thus based on the training input data; F0(x) corresponds to the generated first output data; the class values (yi) correspond to the training correct data; the given node when the GBDT finishes corresponds to the output node; the best split attribute corresponds to the output data associated with the output node.)
acquire actual data acquired from the target device, the actual data corresponding to the input data;
(Zhang [p.11 ¶4] iGBDT merges the original data D1 and the new batch data D2 (both ordered according to attribute k) to obtain an ordered data set D3. So the number of samples in D3 = D2 + D1
) (Note: the new batch data correspond to the actual data)
perform additional learning processing for the learned decision tree by generating updated output data by updating the output data associated with the output node, based on the output data and the actual data such that a change in structure of the learned decision tree or a change in a branch condition does not occur, and store, in the memory, the learned decision tree for which the additional learning processing is performed
(Zhang [p.11 section 4.3] As new batches of data are continuously arriving, the ensemble model built by GBDT which may contain thousands of decision trees should be timely updated so as to accurately model/reflect the complete data which contains the newly arrived instances… iGBDT will check and update the existing GBDT ensemble model.
(Zhang [p.14, section 2(b) (line 12)]
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) (Note: the new batch data correspond to the actual data; the timely updated ensemble model using the new batches of data corresponds to the updated output data; rebuilding the subtree, which corresponds to updating the output data, maintains the rest of the trees, thus corresponding to not changing the structure of the learned decision tree; the rebuilt iGBDT performs the additional learning and thus corresponds to the stored learned decision tree)
Regarding Claim 2, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Zhang further teaches:
The control device according to claim 1, wherein the additional learning processing unit updates the output data associated with the output node, based on the output data and the actual data, without involving a change in structure of the learned decision tree or a change in branch condition.
(Zhang [p.14, section 2(b) (line 12)]
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) (Note: rebuilding the subtree, which corresponds to updating the output data, maintains the rest of the rest of the trees, thus corresponding to not changing the structure of the learned decision tree)
Regarding Claim 3, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Zhang further teaches:
The control device according to claim 1, wherein the updated output data is an arithmetic average value of the output data before updating and the actual data.(Zhang [p.14, section 2(a); (b) (line 12)]
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[p.9 3rd ¶] In Fig. 3, let g be the splitting point (value) of an attribute, μl (resp. μr) be the mean value of the class values of all the instances on the left (resp. right) node, and l (resp. r) be number of instances on the left (resp. right) node after splitting. The definitions of μl and μr are given below.
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) (Note: the split value in depends on the mean value of the class values, thus corresponding to the arithmetic average value of the output data before updating; yi (line 3) used to update the output data correspond to the actual data)
Regarding Claim 4, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Zhang further teaches:
The control device according to claim 1, wherein the updated output data is a weighted average value of the output data before updating and the actual data, with reference to the number of data pieces associated with the output node.
(Zhang [p.14, section 2(a); (b) (line 12)]
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[p.9 3rd ¶] In Fig. 3, let g be the splitting point (value) of an attribute, μl (resp. μr) be the mean value of the class values of all the instances on the left (resp. right) node, and l (resp. r) be number of instances on the left (resp. right) node after splitting. The definitions of μl and μr are given below.
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) (Note: the split value in depends on the mean value of the class values, thus corresponding to the arithmetic average value of the output data before updating; yi (line 3) used to update the output data correspond to the actual data; the split values are modified by pr and ym parameters, which correspond weighting the average values)
Regarding Claim 5, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Zhang further teaches:
The control device according to claim 1, wherein the updated output data is a value obtained by adding, to the output data before updating, a result of multiplying a difference between the output data before updating and the actual data by a learning rate. (Zhang [p.14, section 2(a); (b) (line 12)]
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) (Note: rebuilding the decision tree corresponds to performing the straightforward GBDT algorithm (Algorithm 2); γm corresponds to a learning rate; the new value h(x;am) corresponds to the inference of the weak learner using the updated am and thus difference between the output data before updating and the actual data)
Regarding Claim 6, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 5. Zhang further teaches:
The control device according to claim 5, wherein the learning rate changes according to the number of times the additional learning processing is performed. (Zhang
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[p.6 ¶3] Since this a case of tree models, a different multiplier γm is computed for every leaf.) (Note: γm is computed for every leaf, thus changing according to the number of times the additional learning processing is performed)
Regarding Claim 7, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 1. Zhang further teaches:
The control device according to claim1, wherein the learned decision tree is one of a plurality of decision trees for ensemble learning. (Zhang [p.11 section 4.3] As new batches of data are continuously arriving, the ensemble model built by GBDT which may contain thousands of decision trees should be timely updated so as to accurately model/reflect the complete data which contains the newly arrived instances… iGBDT will check and update the existing GBDT ensemble model.)
Regarding Claim 8, Zhang teaches:
An embedded control device comprising: a memory and a processor, the processor configured to: (Zhang [p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires a memory and a processor.)
acquire reference input data from a target device;;
(Zhang [p.11 ¶4] iGBDT merges the original data D1 and the new batch data D2 (both ordered according to attribute k) to obtain an ordered data set D3. So the number of samples in D3 = D2 + D1
[p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires memory, which corresponds to the target device.)
a first output data generation unit that generates first output data by inputting the reference input data into a model generated based on training input data and training correct data corresponding to the training input data;
(Zhang, Algorithm 2,
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[p.6 ¶3] In line 1, GBDT first initialises the model with the mean value of the class values of all the instances.
[p.9 3rd ¶] In the process of building the GBDT model, the key point is to find the best split attribute which makes sure the square loss error is minimised.) (Note: x corresponds to the training input data; class value yi is related to x, thus based on the training input data; F0(x) corresponds to the generated first output data; the class values (yi) correspond to the training correct data; each split attribute output corresponds to each output hereon)
generate second output data by identifying an output node corresponding to the reference input data by inputting the reference input data into a latest learned decision tree stored in the memory and generated by performing machine learning based on the training input data and differential training data between the output data generated by inputting the training input data into the model and the training correct data;
(Zhang, Algorithm 2,
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[p.6 ¶3] In line 1, GBDT first initialises the model with the mean value of the class values of all the instances. Then M = pt models (trees) of depth ≤ pd are sequentially learned in M steps using the for loop. In line 3, under the least square loss function, GBDT computes the residual of the existing m − 1 models, which have already been learned so far. With the above residual as the class values of the corresponding instances in the training data, it trains the mth decision tree model using CART. Since this a case of tree models, a different multiplier γm is computed for every leaf. Then, by incorporating the new decision tree model, it updates the ensemble model Fm−1(x) to form the new ensemble model Fm(x), through linear superposition (line 5).) (Note: each iteration of the previous and updated Fm(x) corresponds to the learned decision tree; each updated split attribute of the updated learned decision tree corresponds to the second output data; each residual (line 3) corresponds to each differential training data between each output data and the training correct data)
generate final output data to be used to control the target device, based on the first output data and the second output data; provide the target device with the final output data which is used to control the target device; acquire reference correct data from the target device, the correct data corresponding to the input data;
(Zhang [p.11 ¶4] iGBDT merges the original data D1 and the new batch data D2 (both ordered according to attribute k) to obtain an ordered data set D3. So the number of samples in D3 = D2 + D1
[p.11 section 4.3] As new batches of data are continuously arriving, the ensemble model built by GBDT which may contain thousands of decision trees should be timely updated so as to accurately model/reflect the complete data which contains the newly arrived instances
[p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the new batch data correspond to the reference correct data; the timely updated ensemble model using the new batches of data corresponds to the final output data; iGBDT receiving the new batches of data corresponds to the target device being provided the final output data)
and perform additional learning processing for the learned decision tree by generating updated output data by updating the second output data associated with the output node, based on the second output data and differential data between the first output data and the reference correct data such that a change in structure of the learned decision tree or a change in a branch condition does not occur, and store, in the memory, the learned decision tree for which the additional learning processing is performed. (Zhang [p.14, section 2(b)]
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) (Note: the (generated) rebuilt sub-tree updates the ensemble model and thus corresponds to the final output data; rebuilding corresponds to running the GBDT algorithm, which uses the initialized model F0(x) (first output data) and the new batch data (reference correct data); the new residual the original best split attribute corresponds to the second output data; rebuilding corresponds to updating the second output data; rebuilding the subtree, which corresponds to updating the second output, maintains the rest of the rest of the trees, thus corresponding to not changing the structure of the learned decision tree; the rebuilt iGBDT performs the additional learning and thus corresponds to the stored learned decision tree)
Regarding Claim 9, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 8. Zhang further teaches:
The control device according to claim 8, wherein the additional learning processing unit updates the second output data associated with the output node, based on the second output data and the differential data, without involving a change in structure of the learned decision tree or a change in branch condition. (Zhang [p.14, section 2(b) (line 12)]
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) (Note: rebuilding the subtree, which corresponds to updating the second output, maintains the rest of the rest of the trees, thus corresponding to not changing the structure of the learned decision tree)
Regarding Claim 10, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 8. Zhang further teaches:
The control device according to claim 8, wherein the updated output data is an arithmetic average value of the second output data before updating and the differential data. (Zhang [p.14, section 2(a); (b) (line 12)]
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[p.9 3rd ¶] In Fig. 3, let g be the splitting point (value) of an attribute, μl (resp. μr) be the mean value of the class values of all the instances on the left (resp. right) node, and l (resp. r) be number of instances on the left (resp. right) node after splitting. The definitions of μl and μr are given below.
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) (Note: the split value in depends on the mean value of the class values, thus corresponding to the arithmetic average value of the second output data before updating; the residuals (line 3) used to updating the second output data correspond to the differential data)
Regarding Claim 11, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 8. Zhang further teaches:
The control device according to claim 8, wherein the updated output data is a weighted average value of the second output data before updating and the differential data, with reference to the number of data pieces associated with the output node. (Zhang [p.14, section 2(a); (b) (line 12)]
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[p.9 3rd ¶] In Fig. 3, let g be the splitting point (value) of an attribute, μl (resp. μr) be the mean value of the class values of all the instances on the left (resp. right) node, and l (resp. r) be number of instances on the left (resp. right) node after splitting. The definitions of μl and μr are given below.
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) (Note: the split value in depends on the mean value of the class values, thus corresponding to the arithmetic average value of the second output data; the residuals (line 3) used to updating the second output data correspond to the differential data; the node’s original best split depends on the class values, thus associated with the output node; the split values are modified by pr and ym parameters, which correspond weighting the average values.)
Regarding Claim 12, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 8. Zhang further teaches:
The control device according to claim 8, wherein the updated output data is a value obtained by adding, to the second output data before updating, a result of multiplying a difference between the second output data before updating and the differential data by a learning rate. (Zhang [p.14, section 2(a); (b) (line 12)]
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) (Note: rebuilding the decision tree corresponds to performing the straightforward GBDT algorithm (Algorithm 2); γm corresponds to a learning rate; the new value h(x;am) corresponds to the differential data )
Regarding Claim 13, Zhang respectively teaches and incorporates the claimed limitations and rejections of Claim 12. Zhang further teaches:
The control device according to claim 12, wherein the learning rate changes according to the number of times the additional learning processing is performed. (Zhang
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[p.6 ¶3] Since this a case of tree models, a different multiplier γm is computed for every leaf.) (Note: γm is computed for every leaf, thus changing according to the number of times the additional learning processing is performed)
Independent Claim 14 recites a method to perform precisely the methods of Claim 1. Thus, Claim 14 is rejected for reasons set forth in Claim 1.
Independent Claim 15 recites A control method to perform precisely the methods of Claim 8. Thus, Claim 15 is rejected for reasons set forth in Claim 1.
Claim 16, dependent on claim 14, recites A non-transitory computer readable medium storing a control program for a device for executing (Zhang [p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires non-transitory computer-readable medium.) precisely the methods of Claim 1. Thus, Claim 16 is rejected for reasons set forth in Claim 1.
Claim 17, dependent on claim 15, recites A non-transitory computer readable medium storing a control program for a device for executing (Zhang [p.15 2nd ¶] iGBDT and GBDT are both implemented using the Java programming language.) (Note: the original data corresponds to the reference input data; using the Java programming language requires a non-transitory computer-readable medium.) precisely the methods of Claim 8. Thus, Claim 17 is rejected for reasons set forth in Claim 8.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.H./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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