DETAILED ACTION
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 statement (IDS) submitted on 11/27/2023 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 11 objected to because of the following informalities:
grammatical error on “A non-transitory computer-readable date carrier” instead of “A non-transitory computer-readable data carrier”
grammatical error on “executed by a a computer” instead of “executed by a computer”
Appropriate correction is required.
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.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“providing unit, detection unit, ascertaining unit, and training unit” in Claims 6 and 10
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 1-11 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, Claims 1-5 are directed to a method, Claims 6-10 to a system claim, and Claim 11 to a non-transitory data carrier claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1:
2A Prong 1:
detecting at least one specification regarding available resources; (This step for detecting at least one specification is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; (This step for ascertaining a cost function is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
providing training data for training the artificial neural network; (The step directed to providing training data, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
and training the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
providing training data for training the artificial neural network; (This step is directed to providing training data, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
and training the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 5:
2A Prong 1:
A method for classifying image data, the method comprising:classifying the image data (This step for classifying images is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
detecting at least one specification regarding available resources, (This step for detecting at least one specification is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources, (This step for ascertaining a cost function is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
using an artificial neural network that is trained to classify image data; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
providing training data for training the artificial neural network, (The step directed to providing training data, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
and training the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
using an artificial neural network that is trained to classify image data; (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
providing training data for training the artificial neural network, (This step is directed to providing training data, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
and training the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 6: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional elements: providing unit, detection unit, ascertaining unit, and training unit (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 10:
2A Prong 1:
A system for classifying image data, wherein the system is configured to classify image data (This step for classifying images is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
(This step for detecting at least one specification is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
(This step for ascertaining a cost function is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
using an artificial neural network that is trained to classify image data, and wherein the artificial neural network has been trained using a system for training an artificial neural network including: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
providing unit, detection unit, ascertaining unit, and training unit (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
to provide training data for training the artificial neural network; (The step directed to providing training data, which is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).)
to train the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
using an artificial neural network that is trained to classify image data, and wherein the artificial neural network has been trained using a system for training an artificial neural network including: (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., classifying) - see MPEP 2106.05(f).)
providing unit, detection unit, ascertaining unit, and training unit (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
to provide training data for training the artificial neural network; (This step is directed to providing training data, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV)))))
to train the artificial neural network based on the provided training data using the ascertained cost function. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 11: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional elements: A non-transitory computer-readable date carrier on which is stored a computer program including program code for training an artificial neural network, the program code, when executed by a a computer, causing the computer to perform the following steps: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 2 and 7
2A Prong 1:
(i) a pruning method, and/or (ii) a quantization method during training of the artificial neural network. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
2A Prong 2 & 2B: There are no additional elements.
Regarding Claim 3 and 8
2A Prong 1: None
2A Prong 2 & 2B: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the at least one specification regarding available resources contains one or more of a specification regarding available memory capacities or a specification regarding an available bandwidth or a specification regarding a possible number of bit operations. (The specification of resource is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h)))
Regarding Claim 4 and 9
2A Prong 1: None
2A Prong 2 & 2B: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the training data contain sensor data. (The specification of resource is directed be a field of use limitation, which is understood to be field of use and technological environment (MPEP2106.05(h)))
Claim Rejections - 35 USC § 102
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(s) 1-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chai et al. ("US 20210241108 A1", hereinafter "Chai").
Regarding Claim 1
Chai discloses: A method for training an artificial neural network, wherein the method comprising the following steps:
providing training data for training the artificial neural network; ([Chai, 0111] discloses receiving training data for training DNN (i.e artificial neural network))
detecting at least one specification regarding available resources; ([Chai, 0057, 0064, 0072] discloses obtaining operational information (i.e specification regarding available resource) from computing system to train DNN models (i.e artificial neural network): “The runtime engine determines a current operational context and then selects target runtime settings based on the current operational context. It is possible to obtain current runtime information from the computing system, such as processor utilization and memory bandwidth.”)
ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; ([Chai, 0072-0073, 0153] discloses ascertaining a loss function to improve a neural network’s accuracy and confidence scores: “Video and images that produce low confidence scores can be used to retrain the DNN model using an active learning approach to continue refining the overall DNN model accuracy.” [0057, 0064, 0074] discloses that the loss function takes into account of operational parameters that include operational information (i.e specification regarding available resources))
and training the artificial neural network based on the provided training data using the ascertained cost function. ([Chai, 0072-0074] discloses training DNN models (i.e artificial neural network) based on loss function (i.e ascertained cost function))
Regarding Claim 2
Chai discloses: wherein the step of training the artificial neural network includes applying: (i) a pruning method, and/or (ii) a quantization method during training of the artificial neural network. ([Chai, Fig. 2, 0110] discloses an AI training system 110 including a quantization module 112 and a pruning module 113 for DNN (i.e artificial neural network). [0051-0052, 0084] further discloses using pruning and quantization during the training process.)
Regarding Claim 3
Chai discloses: wherein the at least one specification regarding available resources contains one or more of a specification regarding available memory capacities or a specification regarding an available bandwidth or a specification regarding a possible number of bit operations. ([Chai, 0057] discloses operational information (i.e specification regarding available resource) collected by runtime engine to contain memory and bandwidth: “It is possible to obtain current runtime information from the computing system, such as processor utilization and memory bandwidth… In another embodiment, our technique generates an operational plan based on available resource (e.g. power, compute resource, memory) to processing the DNN model.”)
Regarding Claim 4
Chai discloses: wherein the training data contain sensor data. ([Chai, 0058, Fig. 19, 0158] discloses DNN is trained to detect objects in an image and provide contextual information on scenes and events, which is sensor data since they are a physical measurement of an environment. [Fig. 19, 0141-0142] provides more examples with face/hair and retail)
Regarding Claim 5
Chai teaches A method for classifying image data, the method comprising:classifying the image data using an artificial neural network that is trained to classify image data; wherein the artificial neural network has been trained by: ([Chai, 0095, 0110] discloses neural network is trained to classify objects in an image)
providing training data for training the artificial neural network, ([Chai, 0111] discloses receiving training data for training DNN (i.e artificial neural network))
detecting at least one specification regarding available resources, ([Chai, 0057, 0064, 0072] discloses obtaining operational information (i.e specification regarding available resource) from computing system to train DNN models (i.e artificial neural network): “The runtime engine determines a current operational context and then selects target runtime settings based on the current operational context. It is possible to obtain current runtime information from the computing system, such as processor utilization and memory bandwidth.”)
ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources, ([Chai, 0072-0073, 0153] discloses ascertaining a loss function to improve a neural network’s accuracy and confidence scores: “Video and images that produce low confidence scores can be used to retrain the DNN model using an active learning approach to continue refining the overall DNN model accuracy.” [0057, 0064, 0074] discloses that the loss function takes into account of operational parameters that include operational information (i.e specification regarding available resources))
and training the artificial neural network based on the provided training data using the ascertained cost function. ([Chai, 0072-0074] discloses training DNN models (i.e artificial neural network) based on loss function (i.e ascertained cost function))
Regarding Claim 6
Chai discloses: A system for training an artificial neural network, comprising:
a providing unit configured to provide training data for training the artificial neural network; ([Chai, Fig. 1, 0111] discloses training data 102 (i.e providing unit) to provide a prepared data set (i.e training data) to train a neural network (i.e artificial neural network))
a detection unit configured to detect at least one specification regarding available resources; ([Chai, Fig. 1, 0111] discloses hardware profile 124 (i.e detection unit) to detect computational and memory (i.e specification regarding available resources). [0057, 0064, 0072, 0119] provide additional further discloses of how the hardware profile 124 is generated as seen in Fig. 1)
an ascertaining unit configured to ascertain a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; ([Chai, Fig. 1, 0072-0073, 0153] discloses deployment manager (i.e ascertaining unit) configured to ascertain a loss function (i.e cost function) by generating operational performance parameters in the loss function. [0057, 0064, 0074] discloses that the loss function takes into account of operational parameters that include operational information (i.e specification regarding available resources))
and a training unit configured to train the artificial neural network based on the provided training data using the ascertained cost function. ([Chai, Fig. 1, Fig. 2, 0110-0111] discloses AI training 110 (i.e training unit) to train DNN (i.e artificial neural network) based on provide a prepared data set (i.e training data) to train a neural network (i.e artificial neural network). [0072] discloses that a loss function (i.e ascertained cost function) is used in training: “operational performance parameters that can be used to optimize and train the DNN models by optimizing a loss function”)
Regarding Claim 7
Chai discloses: wherein the training unit is configured to apply a pruning method and/or a quantization method during training of the artificial neural network. ([Chai, Fig. 2, 0110] discloses an AI training system 110 (i.e training unit) including a quantization module 112 and a pruning module 113 for DNN (i.e artificial neural network). [0051-0052, 0084] further discloses using pruning and quantization during the training process.)
Regarding Claim 8
(Claim 8 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Regarding Claim 9
(Claim 9 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 10
Claim 10 has similar limitations to Claim 6, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 10 includes additional limitations below that rejected under Chai.
Chai teaches: A system for classifying image data, wherein the system is configured to classify image data using an artificial neural network that is trained to classify image data, and wherein the artificial neural network has been trained using a system for training an artificial neural network including: ([Chai, Fig. 1, 0110] discloses the machine learning system. [0095] discloses DNN (i.e artificial neural network) is trained to classify objects)
Regarding Claim 11
Claim 11 has similar limitations to Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 11 includes additional limitations below that rejected under Chai.
Chai teaches: A non-transitory computer-readable date carrier on which is stored a computer program including program code for training an artificial neural network, the program code, when executed by a a computer, causing the computer to perform the following steps: ([Chai, 0045-0046, Fig. 1, 0110] discloses memory and program code for training a neural network)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amanda D. Nguyen whose telephone number is (571)270-1854. The examiner can normally be reached M-F, 7:30am to 5:00 pm ET First Fridays off, 2nd Friday 7:00 am - 4:00 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMANDA D NGUYEN/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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