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 .
Specification
The disclosure is objected to because of the following informalities:
Regarding paragraph [0012], the paragraph ends in “and” with no punctuation and then goes to a new sentence starting paragraph [0013].
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-18 recite method claims. Claims 19-20 are machine/system/product claims. Therefore, claims 1-20 are directed to one of the four statutory categories of patentable subject matter.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites the step:
“by determining the model operators included in the artificial intelligence-based model based on the model information,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the operators to be included with consideration for the information)
“by determining the target operators which are criteria for changing the model operators, based on the target device information;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually determine the operators to be included with consideration for the information)
“comparing the model operator list and the target operator list;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III.)
“and changing the artificial intelligence-based model to a target model which is executable at the target device, based on a result of the comparison.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually change the model to a different model based on the comparison)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
“performed by a computing device,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtaining model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
“obtaining a model operator list comprising model operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“and obtaining a target operator list comprising target operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“performed by a computing device,” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtaining model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“obtaining a model operator list comprising model operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and obtaining a target operator list comprising target operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 recites the additional element
“wherein the target operator list comprises operators supportable by the target device.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“wherein the target operator list comprises operators supportable by the target device.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“wherein the obtaining the target operator list comprises determining the target operators which are criteria for changing at least one of the model operators, based on the target device information and model type information obtained from the model information.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user could manually decide their own “target operators” taking into account the obtained information)
Thus, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 does not further recite any additional elements. Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 3 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
“The method of claim 1,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
and wherein the model operator list comprises operators constituting the artificial intelligence-based model, obtained by analyzing the model information. (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. -a user can manually analyze the model information.)
Thus, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 recites the additional element
“wherein the model information comprises at least one of a model file corresponding to the artificial intelligence-based model or identification information identifying the artificial intelligence-based model, provided by a user,” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“wherein the model information comprises at least one of a model file corresponding to the artificial intelligence-based model or identification information identifying the artificial intelligence-based model, provided by a user,” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
“The method of claim 1, wherein the obtaining the target operator list comprises:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“determining whether retraining of the artificial intelligence-based model is available, based on a model type included in the model information;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually look at the previously gathered information and make a mental determination that retraining the model is available.)
“and determining the target operators to be included in the target operator list in different manners depending on whether the retraining of the artificial intelligence-based model is available.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine what target operators to include from the list)
Thus, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements. Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 5 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
“The method of claim 1, wherein the obtaining the target operator list comprises:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“determining whether retraining of the artificial intelligence-based model is available, based on model type information included in the model information;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually look at the previously gathered information and make a mental determination that retraining the model is available or not.)
“and determining the target operators to be compared with the model operators by excluding operators for which training is required from operators supportable by the target device,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine what target operators to exclude from the list)
“when it is determined that the retraining of the artificial intelligence-based model is not available.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually look at the previously gathered information and make a mental determination that retraining the model is available or not.)
Thus, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 6 is subject-matter ineligible.
Regarding claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
“The method of claim 1, wherein the comparing the model operator list and the target operator list comprises,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“determining whether a model operator included in the model operator list matches to at least one of the target operators included in the target operator list, in order from an input operator of the artificial intelligence-based model to an output operator of the artificial intelligence-based model.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually go through a list in order from input operator to output operator and decide if there is a match between the lists.)
Thus, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements. Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 7 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 7 is subject-matter ineligible.
Regarding claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
“The method of claim 7, wherein the comparing the model operator list and the target operator list” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 7.)
Thus, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 recites the additional element
“is performed for each of the model operators included in the model operator list.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“is performed for each of the model operators included in the model operator list.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Therefore, claim 8 is subject-matter ineligible.
Regarding claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
“The method of claim 1, wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison comprises” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“determining not to change a first model operator which matches to a target operator included in the target operator list, among the model operators as the result of the comparison,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can decide to do nothing)
and determining to change a second model operator which does not match to the target operators included in the target operator list, among the model operators as the result of the comparison. (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually decide that they wish to change an operator after comparing lists.)
Thus, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 9 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 9 is subject-matter ineligible.
Regarding claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites
“The method of claim 1, wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison comprises,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
“changing a second model operator which does not match to the target operators included in the target operator list, among the model operators to a replacement operator which is matchable to a target operator included in the target operator list.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. -a user can manually determine if the operators are matched)
Thus, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 recites
“The method of claim 1, wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison comprises:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 1)
“determining a model operator to be changed within the model operator list, based on the result of the comparison;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually choose a model operator to change with consideration of the result.)
“determining an operator type of the model operator to be changed, based on an operator characteristic corresponding to the model operator to be changed;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the operator to be changed with consideration of the characteristic)
“and changing the model operator to a replacement operator based on the determined operator type.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the replacement operator)
Thus, claim 11 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 does not further recite any additional elements. Therefore, claim 11 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 11 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 11 is subject-matter ineligible.
Regarding claim 12:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites
“The method of claim 11,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 11)
“wherein the operator characteristic indicates a functional characteristic or an operational characteristic of an operator, and the operator characteristic is assigned to each of the model operators,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually assign characteristics to the operators)
“and wherein the operator type comprises: a first operator type indicating an activation operation; a second operator type indicating a simple operation having an operation difficulty below a predetermined level; and a third operator type indicating a layer-related operation rather than the simple operation.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. – a user can manually assign specific characteristics to the operators)
Thus, claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 does not further recite any additional elements. Therefore, claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 12 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 12 is subject-matter ineligible.
Regarding claim 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites
“The method of claim 11,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 11)
“wherein the changing the model operator based on the determined operator type comprises changing the model operator using one change algorithm of: a first change algorithm changing an operator by using similarity decision of an output value to an input value depending on the determined operator type;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine how similar the values are)
“a second change algorithm changing an operator based on whether mathematical results of operations are matched;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine that the results are matched)
“or a third change algorithm changing an operator based on similarity of mathematical results of operations.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually decide of how close the results are)
Thus, claim 13 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 does not further recite any additional elements. Therefore, claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 13 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 13 is subject-matter ineligible.
Regarding claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites
“The method of claim 11,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 11)
“wherein the changing the model operator based on the determined operator type comprises: changing the model operator based on a first change algorithm changing an operator by using similarity decision of an output value to an input value, when an operator type of the model operator to be changed is determined as a first operator type indicating an activation operation;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually decide how similar the values are)
“changing the model operator based on a second change algorithm changing an operator based on whether mathematical results of operations are matched, when an operator type of the model operator to be changed is determined as a second operator type indicating a simple operation having an operation difficulty below a predetermined level;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually look at results and determine if they match as well as determine if it is a simple operation)
“and changing the model operator based on a third change algorithm changing an operator based on similarity of mathematical results of operations, when an operator type of the model operator to be changed is determined as a third operator type indicating a layer-related operation rather than the simple operation.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine a similarity of results)
Thus, claim 14 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 does not further recite any additional elements. Therefore, claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 14 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 14 is subject-matter ineligible.
Regarding claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
“The method of claim 11, wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison further comprises,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 11)
“determining whether to change the model operator by comparing a user threshold included in a user input with similarity between the model operator and the replacement operator.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can make a decision to change the model operator based on a manual comparison)
Thus, claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 does not further recite any additional elements. Therefore, claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 15 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 15 is subject-matter ineligible.
Regarding claim 16:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites
“The method of claim 11, wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison further comprises,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process in claim 11)
“determining whether to change the model operator based on whether the replacement operator is included in the target operator list.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine if an operator is included in a list and make a decision off of that)
Thus, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 does not further recite any additional elements. Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 16 does not provide significantly more than the abstract idea itself, taken alone and in combination.
Therefore, claim 16 is subject-matter ineligible.
Regarding claim 17:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 17 recites
“The method of claim 1, further comprising:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 17 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
“providing a first result indicating a replacement operator to which the change is made in the changed target model, and a second result indicating whether the changed target model is required for retraining.” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
Claim 17 recites the additional element
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 17 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“providing a first result indicating a replacement operator to which the change is made in the changed target model, and a second result indicating whether the changed target model is required for retraining.” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 17 is subject-matter ineligible.
Regarding claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 18 recites
“The method of claim 1, further comprising:” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - refers to the mental process continued from claim 1.)
Thus, claim 18 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 18 recites the additional element
“providing a benchmark result obtained by” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g))
“executing the target model to which the artificial intelligence-based model is changed, at the target device.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 18 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“providing a benchmark result obtained by” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of presenting offers and gathering statistics, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“executing the target model to which the artificial intelligence-based model is changed, at the target device.” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Therefore, claim 18 is subject-matter ineligible.
Regarding claim 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites
“by determining the model operators included in the artificial intelligence-based model based on the model information,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the operators with consideration for the information)
“by determining the target operators which are criteria for changing the model operators, based on the target device information;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the operators with consideration for the information)
“comparing the model operator list and the target operator list;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually compare the two lists.)
“and changing the artificial intelligence-based model to a target model which is executable at the target device, based on a result of the comparison.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually change the model based on the comparison)
Thus, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 recites the additional element
“allows a computing device to perform following operations to change an artificial intelligence-based model to suit a target device based on a device awareness when executed by the computing device, and wherein the operations comprise:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtaining model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
“obtaining a model operator list comprising model operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
“and obtaining a target operator list comprising target operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“allows a computing device to perform following operations to change an artificial intelligence-based model to suit a target device based on a device awareness when executed by the computing device, and wherein the operations comprise:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtaining model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“obtaining a model operator list comprising model operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and obtaining a target operator list comprising target operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 19 is subject-matter ineligible.
Regarding claim 20:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 20 recites
“by determining the model operators included in the artificial intelligence-based model based on the model information,” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the operators with consideration for the information)
“by determining the target operators which are criteria for changing the model operators, based on the target device information;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually determine the operators with consideration for the information)
“compares the model operator list and the target operator list;” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually compare the two lists.)
“and changes the artificial intelligence-based model to a target model which is executable at the target device, based on a result of the comparison.” (mental process - This is an observation, evaluation, judgement, or opinion, i.e. a concept performed in the human mind. See MPEP 2106.04(a)(2), III. - a user can manually change the model based on the comparison)
Thus, claim 20 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 20 recites the additional element
“at least one processor; and a memory, wherein the at least one processor:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtains model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
“obtains a model operator list comprising model operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
“and obtaining a target operator list comprising target operators” (This element does not integrate the abstract idea into a practical application because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)).)
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 20 do not provide significantly more than the abstract idea itself, taken alone and in combination because
“at least one processor; and a memory, wherein the at least one processor:” (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f))
“obtains model information corresponding to the artificial intelligence-based model, and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“obtains a model operator list comprising model operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
“and obtaining a target operator list comprising target operators” (This element does not provide significantly more because it amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) in the form of storing and retrieving information, which is well-known, routine, and conventional (see MPEP 2106.05(d)(II)).)
Therefore, claim 20 is subject-matter 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.
(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) 1-3, 19, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady).
Regarding claim 1, Brady teaches
“A method for changing an artificial intelligence-based model to suit a target device based on a device awareness,” (Brady paragraph [0046] “In some implementations, an improved compiler may be configured to consume a machine learning framework's (e.g., TensorFlow, Caffe™, etc.) representation (e.g., 110) of a Deep Neural Network (DNN), adapt and optimize it for a selected target (e.g., 125) and produce a binary executable (e.g., 150) corresponding to the selected target hardware 125 in a way that allows for compile time target specific optimizations.” Examiner notes that the Deep Neural Network is an artificial intelligence-based model.)
“performed by a computing device,” (Brady paragraph [0031] “In some implementations, an example system 205 may be implemented as a computer device, such as a personal computing device, mobile computing device, server computing system (e.g., a rack scale, blade server, or other server computer), among other examples.” Examiner notes system 205 is the system referred to in Figure 2 [see below] which implements the compiler.)
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“comprising: obtaining model information corresponding to the artificial intelligence-based model, (Brady paragraph [0024] “For instance, a machine learning model, such as a graph definition 110 of an example neural network model (or other deep learning model) may be provided as an input for consumption by an example neural network compiler 105.”)
and target device information indicating a characteristic of the target device on which the artificial intelligence-based model is executed;” (Brady [0024] “Compilation descriptor data 115 may be provided to indicate one or more compilation sweeps to be performed based on attributes of one or both of the neural network model and/or the underlying hardware, as well as target descriptor data 120 to describe attributes of a target hardware processing device 125, which is targeted for executing the code to be generated by the compiler 105 from the graph definition 110.”)
“obtaining a model operator list comprising model operators by determining the model operators included in the artificial intelligence-based model based on the model information,” (Brady Paragraph [0047] “When a neural network model is consumed from the front-end of an example compiler (e.g., 105), an intermediate representation (IR) 140 may be generated as discussed above. In one example, the IR 140 may be constructed by the compiler by parsing the neural network model 110 to identify the respective operations and data flow used to implement the neural network.”)
“and obtaining a target operator list comprising target operators by determining the target operators which are criteria for changing the model operators, based on the target device information;” (Brady Paragraph [0077]-[0078]
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[0078] “In the above example, a target descriptor file may include a variety of information describing resources of an example target machine learning device. For instance, as shown in the example above, a target descriptor may identify a number of operations (e.g., corresponding to operations defined in the compiler's operation registry) and name the individual computation resources capable of performing the operation.” Examiner notes the list is criteria for changing the model operators as it is the list that determines if the hardware can run a specific operator.)
“comparing the model operator list and the target operator list;” (Brady Paragraph [0046] “ To produce such a binary 150, an improved compiler 105 may be provided that is implemented to optimize performance of deep learning applications. In some implementations, the compiler 105 may access the neural network model 110, together with information (e.g., target descriptor file 120) concerning the application and the target hardware 125 and generate an improved intermediate representation (IR) 140 from which the binary 150 is to be generated.”) Examiner notes the target descriptor file determined to be the target operator list (see above) is compared to a list of model operators e.g. the model provided.
“and changing the artificial intelligence-based model to a target model” (Brady Paragraph [0067] “The example operator model 1005 may reflect the operator model as transformed by one or more compilation passes (e.g., adaptation and/or optimization passes).”)
“which is executable at the target device, based on a result of the comparison.” (Brady Paragraph [0065] “Following completion of the finalization passes 1244, a final validation pass 1246 may be performed, before sending the further modified computation model 140 to compiler backend 1250, where serialization passes 1252 are performed on the computation model 140 to generate a binary 150 capable of being executed by the target hardware to implement the neural network.” Examiner notes that the final validation pass is done after the compilation passes complete and is dependent on the result of the comparison Brady Paragraph [0062] “For instance, in one example, a set of one or more adaptation compilation passes 1236 may be defined and performed before other categories of compilation passes (e.g., optimization passes 1240 and/or finalization passes 1244, etc.).”)
Regarding claim 2, Brady teaches all the limitations of claim 1.
Brady further teaches
“wherein the target operator list comprises operators supportable by the target device.” (Brady Paragraph [0066] “Attributes of the target hardware may include attributes identifying the computation resources of the target hardware including identifying which computation resources of the target are capable of performing which types of operations (e.g., as understood by the compiler (from operation registry 1212)).” Examiner notes that the “which types of operations” are the operators supportable by the target device.)
Regarding claim 3, Brady teaches all the limitations of claim 1.
Brady further teaches
“wherein the obtaining the target operator list comprises determining the target operators which are criteria for changing at least one of the model operators, based on the target device information and model type information obtained from the model information.” (Brady Paragraph [0024] “Compilation descriptor data 115 may be provided to indicate one or more compilation sweeps to be performed based on attributes of one or both of the neural network model and/or the underlying hardware, as well as target descriptor data 120 to describe attributes of a target hardware processing device 125, which is targeted for executing the code to be generated by the compiler 105 from the graph definition 110.” Brady Paragraph [0055] “However, it should be appreciated that the scope of a single compilation pass is not restricted, but is usually oriented on solving an isolated task, such as assigning static populated tensor to constant-like memory or replacing sub-graph of operations with more efficient equivalents, among other examples. In some implementations, this compilation process transforms a generic, target agnostic entry form of the neural network graph model into representation appropriate for the target hardware. As part of that process, the intermediate representation is used to assign computation resources to operations (simultaneously with replacement of generic operations with target defined equivalents) and memory resource to tensors.” Brady Paragraph [0063] “Optimization passes 1240 may include compilation passes to determine the optimal computation resources of the target hardware (e.g., using an operator model of the intermediate representation) to perform each of the set of operations determined for the neural network (e.g., the pared set of operations resulting from adaptation passes 1236).” Examiner notes the hardware attributes and the model information are the information that Brady uses to change model operators through the compilation sweeps.)
Regarding Claim 19, Brady teaches “a non-transitory computer readable medium storing a computer program, wherein the computer program allows a computing device to perform following operations to change an artificial intelligence-based model to suit a target device based on a device awareness when executed by the computing device,” (Brady Paragraph [0028] “In some embodiments, an example compiler (e.g., 105), such as an example neural network compiler such as discussed herein, as well as other components, may be implemented in software stored in memory 215, and operate on the processor 210. The memory 215 can be a non-transitory computer readable medium, flash memory, a magnetic disk drive, an optical drive, a programmable read-only memory (PROM), a read-only memory (ROM), or any other memory or combination of memories.”) where the operations are the method of claim 1 (see rejection of claim 1) and is therefore rejected under the same analysis.
Regarding claim 20, Brady teaches “at least one processor,” (Brady Paragraph [0089] “FIG. 17 is an example illustration of a processor according to an embodiment. Processor 1700 is an example of a type of hardware device that can be used in connection with the implementations above.”) “and a memory,” (Brady Paragraph [0090], “FIG. 17 also illustrates a memory 1702 coupled to processor 1700 in accordance with an embodiment.”) where the implementations are for the method of claim 1 (see rejection of claim 1) and is therefore rejected under the same analysis.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Liu et al. (US 2022/0092439 A1) (hereafter referred to as Liu).
Regarding claim 4, Brady teaches all the limitations of claim 1.
Brady further teaches
“and wherein the model operator list comprises operators constituting the artificial intelligence-based model, obtained by analyzing the model information.” (Brady Paragraph [0047] “When a neural network model is consumed from the front-end of an example compiler (e.g., 105), an intermediate representation (IR) 140 may be generated as discussed above. In one example, the IR 140 may be constructed by the compiler by parsing the neural network model 110 to identify the respective operations and data flow used to implement the neural network.”)
Brady does not distinctly disclose:
“wherein the model information comprises at least one of a model file corresponding to the artificial intelligence-based model or identification information identifying the artificial intelligence-based model, provided by a user,”
However, Liu teaches:
“wherein the model information comprises at least one of a model file corresponding to the artificial intelligence-based model or identification information identifying the artificial intelligence-based model, provided by a user,” (Liu Paragraph [0074] “With such a decoupled architecture, the user only needs to provide the model file for which framework this model is pre-trained, then the architecture can deploy it onto any platform on any device.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the model file provided by a user of Liu in order to create a more dynamic system (Liu, Paragraph [0005], lines 1-9).
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Xu et al. (https://dl.acm.org/doi/fullHtml/10.1145/3308558.3313591) (hereafter referred to as Xu) further in view of Liu et al. (CN114298272A) (Hereafter referred to as Liu).
Regarding claim 5, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“determining whether retraining of the artificial intelligence-based model is available, based on a model type included in the model information;”
“and determining the target operators to be included in the target operator list in different manners depending on whether the retraining of the artificial intelligence-based model is available.”
However, Xu teaches:
“determining whether retraining of the artificial intelligence-based model is available, based on a model type included in the model information;” (Xu, Page 4, Table 2 [see below]
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Examiner notes the table determines whether a model type supports retraining a pre-trained model including information such as the model type information and format type extension.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination of training support of Xu in order to create a faster and more lightweight model (Xu, Page 4, last 3 lines).
Brady as modified does not distinctly disclose:
“determining the target operators to be included in the target operator list in different manners depending on whether the retraining of the artificial intelligence-based model is available.”
However, Liu teaches:
“determining the target operators to be included in the target operator list in different manners depending on whether the retraining of the artificial intelligence-based model is available.” (Liu Page 6, Paragraph [0008], “In some embodiments, determining the target operator candidate set refers to constructing or updating the target operator candidate set using operators used by a neural network model that meet preset functional requirements.” Examiner notes that retraining of the artificial intelligence-based model is a functional requirement.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified by Xu with the modification of the target operator list of Liu in order to ensure that preset functional requirements are met (Liu Paragraph [0008], lines 1-6).
Regarding claim 6, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“determining whether retraining of the artificial intelligence-based model is available, based on model type information included in the model information;”
“and determining the target operators to be compared with the model operators by excluding operators for which training is required from operators supportable by the target device, when it is determined that the retraining of the artificial intelligence-based model is not available.”
However, Xu teaches:
“determining whether retraining of the artificial intelligence-based model is available, based on model type information included in the model information;” (Xu, Page 4, Table 2 [see below]
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Examiner notes the table determines whether a model type supports training including information such as the model type information and format type extension.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination of training support of Xu in order to create a faster and more lightweight model (Xu, Page 4, last 3 lines).
Brady as modified does not distinctly disclose:
“and determining the target operators to be compared with the model operators by excluding operators for which training is required from operators supportable by the target device, when it is determined that the retraining of the artificial intelligence-based model is not available.”
However, Liu teaches:
“and determining the target operators to be compared with the model operators by excluding operators for which training is required from operators supportable by the target device, when it is determined that the retraining of the artificial intelligence-based model is not available.” (Liu Page 6, Paragraph [0008], “In some embodiments, determining the target operator candidate set refers to constructing or updating the target operator candidate set using operators used by a neural network model that meet preset functional requirements.” Examiner notes that retraining of the artificial intelligence-based model is a functional requirement.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified by Xu with the modification of the target operator list of Liu in order to ensure that preset functional requirements are met (Liu Paragraph [0008], lines 1-6).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Lin et al. (CN 116362316A) (hereafter referred to as Lin) further in view of Ma et al. (CN 113705798 A) (hereafter referred to as Ma).
Regarding claim 7, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“determining whether a model operator included in the model operator list matches to at least one of the target operators included in the target operator list,”
“in order from an input operator of the artificial intelligence-based model to an output operator of the artificial intelligence-based model.”
However, Lin teaches:
“determining whether a model operator included in the model operator list matches to at least one of the target operators included in the target operator list,” (Lin Page 3, Paragraphs 11-12 “reconstructing the operator information of the source model according to the difference information of the operator information of the target model and the operator information of the source model to obtain reconstructed operator information; matching the reconstructed operator information in an operator library to obtain a matching result;”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the matching of operators of Lin in order to improve model conversion quality (Lin Page 3, Paragraph 7).
Brady as modified does not distinctly disclose:
“in order from an input operator of the artificial intelligence-based model to an output operator of the artificial intelligence-based model.”
However, Ma teaches:
“in order from an input operator of the artificial intelligence-based model to an output operator of the artificial intelligence-based model.” (Ma Page 6, Paragraph 3, “searching each path extending from the input operator to the output operator of the calculation graph to be optimized,”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified by Lin with the ordering of the operators of Ma in order to ensure the model information satisfies requirements (Ma Page 16, Paragraph 2).
Regarding claim 8, Brady as modified by Lin and Ma teach all the limitations of claim 7.
Brady as modified further teaches:
“wherein the comparing the model operator list and the target operator list is performed for each of the model operators included in the model operator list.” (Ma Page 6, Paragraph 3, “searching each path extending from the input operator to the output operator of the calculation graph to be optimized,”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified by Lin with the ordering of the operators of Ma in order to ensure the model information satisfies requirements (Ma Page 16, Paragraph 2).
Claim(s) 9-11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Pu et al. (CN 113469360 A) (hereafter referred to as Pu).
Regarding claim 9, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“determining not to change a first model operator which matches to a target operator included in the target operator list, among the model operators as the result of the comparison,”
“and determining to change a second model operator which does not match to the target operators included in the target operator list, among the model operators as the result of the comparison.”
However, Pu teaches:
“determining not to change a first model operator which matches to a target operator included in the target operator list, among the model operators as the result of the comparison,” (Pu, Page 10, Paragraph 5, “Fig. 2 is a flowchart of a method for determining an updated neural network model according to an embodiment of the present disclosure. Referring to fig. 2, a device may determine a computation graph of a neural network model by using the neural network model as an input, where the computation graph includes a plurality of computation nodes, sequentially traverse each computation node according to an order of the plurality of computation nodes, determine, according to a supported operator list, whether an operator that is not supported by target hardware exists among operators included in currently traversed computation nodes, determine, if an operator that is not supported does not exist, whether traversal is completed, if traversal is completed, output an updated neural network model, and if traversal is not completed, traverse a next computation node.” Examiner notes the changing of operators only occurs in the event of an unsupported operator, otherwise the embodiment determines whether the updating of the model is complete.)
“and determining to change a second model operator which does not match to the target operators included in the target operator list, among the model operators as the result of the comparison.” (Pu, Page 10, Paragraph 5, “determine, according to a supported operator list, whether an operator that is not supported by target hardware exists among operators included in currently traversed computation nodes, … And if the unsupported operator exists, judging whether the unsupported operator is a replaceable operator, namely whether the unsupported operator is the second type operator, according to the replacement operator list, if the unsupported operator is the second type operator, replacing the unsupported operator with the corresponding supported operator in the replacement operator list to obtain an updated neural network model, and then executing the step of judging whether the traversal is finished.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination to change an operator with respect to if it is supported by the hardware of Pu in order to ensure operators can be automatically adapted to equipment so models can complete reasoning tasks (Pu, Page 23, Paragraph 6).
Regarding claim 10, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“changing a second model operator which does not match to the target operators included in the target operator list, among the model operators to a replacement operator which is matchable to a target operator included in the target operator list.”
However, Pu teaches:
“changing a second model operator which does not match to the target operators included in the target operator list, among the model operators to a replacement operator which is matchable to a target operator included in the target operator list.” (Pu, Page 10, Paragraph 5, “determine, according to a supported operator list, whether an operator that is not supported by target hardware exists among operators included in currently traversed computation nodes, … And if the unsupported operator exists, judging whether the unsupported operator is a replaceable operator, namely whether the unsupported operator is the second type operator, according to the replacement operator list, if the unsupported operator is the second type operator, replacing the unsupported operator with the corresponding supported operator in the replacement operator list to obtain an updated neural network model, and then executing the step of judging whether the traversal is finished.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination to change an operator with respect to if it is supported by the hardware of Pu in order to ensure operators can be automatically adapted to equipment so models can complete reasoning tasks (Pu, Page 23, Paragraph 6).
Regarding claim 11, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“determining a model operator to be changed within the model operator list, based on the result of the comparison;”
“determining an operator type of the model operator to be changed, based on an operator characteristic corresponding to the model operator to be changed;”
“and changing the model operator to a replacement operator based on the determined operator type.”
However, Pu teaches:
“determining a model operator to be changed within the model operator list, based on the result of the comparison;” (Pu, Page 10, Paragraph 5, “sequentially traverse each computation node according to an order of the plurality of computation nodes, determine, according to a supported operator list, whether an operator that is not supported by target hardware exists among operators included in currently traversed computation nodes, determine, if an operator that is not supported does not exist, whether traversal is completed, if traversal is completed, output an updated neural network model, and if traversal is not completed, traverse a next computation node. And if the unsupported operator exists, judging whether the unsupported operator is a replaceable operator, namely whether the unsupported operator is the second type operator, according to the replacement operator list, if the unsupported operator is the second type operator, replacing the unsupported operator with the corresponding supported operator in the replacement operator list to obtain an updated neural network model, and then executing the step of judging whether the traversal is finished.” Examiner notes that Pu goes through each node in the graph in order comparing it to a supported list. In the event that a match is not on the list, Pu determines to complete the steps for changing the unsupported operation.)
“determining an operator type of the model operator to be changed, based on an operator characteristic corresponding to the model operator to be changed;” (Pu, Page 10, Paragraph 5, “And if the unsupported operator exists, judging whether the unsupported operator is a replaceable operator, namely whether the unsupported operator is the second type operator, according to the replacement operator list,” Examiner notes the operator characteristic that determines the type is whether or not the operation is supported.)
“and changing the model operator to a replacement operator based on the determined operator type.” (Pu, Page 10, Paragraph 5, “if the unsupported operator is the second type operator, replacing the unsupported operator with the corresponding supported operator in the replacement operator list to obtain an updated neural network model,”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination to change an operator with respect to if it is supported by the hardware of Pu in order to ensure operators can be automatically adapted to equipment so models can complete reasoning tasks (Pu, Page 23, Paragraph 6).
Regarding claim 16, Brady as modified in claim 11 teaches all the limitations of claim 11.
Brady as modified further teaches:
“wherein the changing the artificial intelligence-based model to the target model which is executable at the target device, based on the result of the comparison further comprises, determining whether to change the model operator based on whether the replacement operator is included in the target operator list.” (Pu Page 9, Paragraph 2 “Illustratively, the list of support operators includes a conv operator, a pooling operator, a Relu operator, a sigmoid operator, a convfeaturescape operator, etc., and the list of replacement operators includes a convfeaturescape operator and a corresponding featurescape operator, indicating that the featurescape operator is a replaceable operator of the convfeaturescape operator. Assuming that a computation node of a computation graph of a neural network model comprises a featurescape operator, a conv operator and a SoftMax operator, when traversing to the computation node, the conv operator can be determined as a supported operator by comparing a support operator list, the featurescape operator and the softmax operator are unsupported operators, the device can determine both the featurescape operator and the softmax operator as target operators, and then determine that the featurescape operator is contained in a replacement operator list by comparing the replacement operator list, a corresponding replaceable operator exists, and the softmax operator is not contained in the replacement operator list, and no corresponding replaceable operator exists, so that the softmax operator can be determined as a first type of operator.” Pu, Page 10, Paragraph 5, “if the unsupported operator is the second type operator, replacing the unsupported operator with the corresponding supported operator in the replacement operator list to obtain an updated neural network model,”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the determination to change an operator with respect to if it is supported by the hardware of Pu in order to ensure operators can be automatically adapted to equipment so models can complete reasoning tasks (Pu, Page 23, Paragraph 6).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) in view of Pu et al. (CN 113469360 A) (hereafter referred to as Pu) as applied in claim 11, further in view of Onnx Operator Schemas (https://web.archive.org/web/20220312170328/https://github.com/onnx/onnx/blob/main/docs/Operators.md) (hereafter referred to as Onnx).
Regarding claim 12, Brady as modified in claim 11 teaches all the limitations of claim 11.
Brady as modified further teaches:
“wherein the operator characteristic indicates a functional characteristic or an operational characteristic of an operator,” (Brady, Fig. 13 [see below])
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(Examiner notes the operator type is listed as the title and represents the function of the operator)
“and the operator characteristic is assigned to each of the model operators, and wherein the operator type comprises:” (Brady, Fig. 13 [see below])
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(Examiner notes each operator is labeled with a name characteristic.)
Brady as modified does not distinctly disclose:
“a first operator type indicating an activation operation;”
“a second operator type indicating a simple operation having an operation difficulty below a predetermined level;”
“and a third operator type indicating a layer-related operation rather than the simple operation.”
However, Onnx teaches:
“a first operator type indicating an activation operation;” (Onnx last line of 148 – 3rd line of page 149,
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“a second operator type indicating a simple operation having an operation difficulty below a predetermined level;” (Onnx page 11,
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Examiner notes that the Add operator is a fundamental simple operator.)
“and a third operator type indicating a layer-related operation rather than the simple operation.” (Onnx Page 8, “The linear dequantization operator. It consumes a quantized tensor, a scale, and a zeropoint to compute the full precision tensor. The dequantization formula is y = (x -x_zero_point) * x_scale. 'x_scale' and 'x_zero_point' must have same shape, and can beeither a scalar for per-tensor / per layer quantization, or a 1-D tensor for per-axis quantization. 'x_zero_point' and 'x' must have same type. 'x' and 'y' must have same shape. In the case of dequantizing int32, there's no zero point (zero point is supposed to be 0).” Examiner notes that per layer quantization is layer-related.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified with the list of operators of Onnx in order to ensure proper version support of model operators in use. (Onnx, Page 11, Paragraph 6).
Claim(s) 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) in view of Pu et al. (CN 113469360 A) (hereafter referred to as Pu) as applied in claim 11, further in view of further in view of Gao et al. (CN114997384A) (hereafter referred to as Gao).
Regarding claim 13, Brady as modified teaches all the limitations of claim 11.
Brady as modified does not distinctly disclose:
changing the model operator using one change algorithm of: a first change algorithm changing an operator by using similarity decision of an output value to an input value depending on the determined operator type;
a second change algorithm changing an operator based on whether mathematical results of operations are matched;
or a third change algorithm changing an operator based on similarity of mathematical results of operations.
However, Gao teaches:
changing the model operator using one change algorithm of: a first change algorithm changing an operator by using similarity decision of an output value to an input value depending on the determined operator type;
a second change algorithm changing an operator based on whether mathematical results of operations are matched;
or a third change algorithm changing an operator based on similarity of mathematical results of operations. (Gao Page 4, Paragraphs 8-11, “Further, the adjusting the conversion parameter based on the data sample according to the performance of an operator in the converted neural network model includes: establishing a directed acyclic graph according to the dependency relationship of each operator in the neural network model; taking the data sample as input data of the neural network model, and sequentially executing operators of the neural network model according to the topological sequence of the directed acyclic graph; if the operator is the operator to be accelerated, comparing the execution results of the original operator to be accelerated and the converted operator to be accelerated, if the similarity of the execution results exceeds a preset threshold, adjusting the conversion parameter, judging whether the similarity of the execution results of the original operator to be accelerated and the converted operator to be accelerated, which adjusts the conversion parameter for a preset number of times, exceeds a preset threshold, and if so, reducing the converted operator to be accelerated into the original operator to be accelerated.” Examiner notes the similarity output is compared with the threshold input in order to determine how to change the operator.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified with the similarity decision of Gao in order to guarantee algorithm performance optimization effect and algorithm safety. (Gao, Page 2, Paragraph 10).
Regarding claim 15, Brady as modified teaches all the limitations of claim 11.
Brady as modified does not distinctly disclose:
“determining whether to change the model operator by comparing a user threshold included in a user input with similarity between the model operator and the replacement operator.”
However, Gao teaches:
“determining whether to change the model operator by comparing a user threshold included in a user input with similarity between the model operator and the replacement operator.” (Gao Page 8, Paragraph 2, “In this embodiment, the similarity is calculated by using a similarity evaluation index, and specifically, referring to fig. 4, for the case of the transformed operator, the transformed result and the original result are evaluated by using the similarity evaluation index (e.g., mean square error MSE). If the obtained similarity evaluation index exceeds the threshold set by the user, updating the conversion parameters by using a heuristic algorithm (such as grid search) or a back propagation algorithm, and then re-executing the converted operator to evaluate the similarity. And when the updating period exceeds a certain threshold value or the evaluation index exceeds a certain threshold value, restoring the converted operator, marking the operator as being executed by using a neural network training inference system defined by a user, and dividing the model again.”)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady as modified with the user-set threshold score of Gao in order to guarantee algorithm performance optimization effect and algorithm safety. (Gao, Page 2, Paragraph 10).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) in view of Pu et al. (CN 113469360 A) (hereafter referred to as Pu), further in view of Onnx Operator Schemas (https://web.archive.org/web/20220312170328/https://github.com/onnx/onnx/blob/main/docs/Operators.md) (hereafter referred to as Onnx). as applied in claim 12, further in view of further in view of Gao et al. (CN114997384A) (hereafter referred to as Gao)
Brady as modified teaches the determination of an operator type including indications of an activation operation, a simple operation, and a layer-related operation.
Brady as modified does not distinctly disclose:
“changing the model operator based on a first change algorithm changing an operator by using similarity decision of an output value to an input value, when an operator type of the model operator to be changed is determined as …
changing the model operator based on a second change algorithm changing an operator based on whether mathematical results of operations are matched, when an operator type of the model operator to be changed is determined as …
and changing the model operator based on a third change algorithm changing an operator based on similarity of mathematical results of operations, when an operator type of the model operator to be changed is determined as a ...”
However, Gao teaches:
“changing the model operator based on a first change algorithm changing an operator by using similarity decision of an output value to an input value, when an operator type of the model operator to be changed is determined as …
changing the model operator based on a second change algorithm changing an operator based on whether mathematical results of operations are matched, when an operator type of the model operator to be changed is determined as …
and changing the model operator based on a third change algorithm changing an operator based on similarity of mathematical results of operations, when an operator type of the model operator to be changed is determined as a ...” (Gao Page 4, Paragraphs 8-11, “Further, the adjusting the conversion parameter based on the data sample according to the performance of an operator in the converted neural network model includes: establishing a directed acyclic graph according to the dependency relationship of each operator in the neural network model; taking the data sample as input data of the neural network model, and sequentially executing operators of the neural network model according to the topological sequence of the directed acyclic graph; if the operator is the operator to be accelerated, comparing the execution results of the original operator to be accelerated and the converted operator to be accelerated, if the similarity of the execution results exceeds a preset threshold, adjusting the conversion parameter, judging whether the similarity of the execution results of the original operator to be accelerated and the converted operator to be accelerated, which adjusts the conversion parameter for a preset number of times, exceeds a preset threshold, and if so, reducing the converted operator to be accelerated into the original operator to be accelerated.” Examiner notes the similarity output is compared with the threshold input in order to determine how to change the operator.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device with consideration of operator types of Brady as modified with the similarity decision of Gao in order to guarantee algorithm performance optimization effect and algorithm safety. (Gao, Page 2, Paragraph 10).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Bigaj et al. (US 2019/0251474 A1) (hereafter referred to as Bigaj).
Regarding claim 17, Brady teaches all the limitations of claim 1.
Brady further teaches:
“providing a first result indicating a replacement operator to which the change is made in the changed target model,” (Brady Paragraph [0055], “An example compiler utilizes the sub-models of the intermediate representation to perform a collection of compilation passes to generate an executable tuned to particular target hardware. Depending on the compilation pass, a particular one of the intermediate representation sub-models may be selected and used to perform the compilation pass. ... In some implementations, this compilation process transforms a generic, target agnostic entry form of the neural network graph model into representation appropriate for the target hardware. As part of that process, the intermediate representation is used to assign computation resources to operations (simultaneously with replacement of generic operations with target defined equivalents) and memory resource to tensors.” Examiner notes the representation appropriate for the target hardware indicates any replacement operators.)
Brady does not distinctly disclose:
“and a second result indicating whether the changed target model is required for retraining.”
However, Bigaj teaches:
“and a second result indicating whether the changed target model is required for retraining.”(Bigaj figure 1 [see below]
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Examiner notes the determination that the quality value is below the threshold value is the indicator that the model is required for retraining.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the training indicator of Bigaj in order to ensure that the model is still performing well (Bigaj Paragraph [0005]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brady et al. (US 2019/0392296 A1) (hereafter referred to as Brady) as applied to claim 1, in view of Li et al. (CN113033762A) (hereafter referred to as Li).
Regarding claim 18, Brady teaches all the limitations of claim 1.
Brady does not distinctly disclose:
“providing a benchmark result obtained by executing the target model to which the artificial intelligence-based model is changed, at the target device.”
However, Li teaches:
“providing a benchmark result obtained by executing the target model to which the artificial intelligence-based model is changed, at the target device.” (Li Page 4, Paragraph 7, “In another embodiment, the benchmark result of the present disclosure may be a result obtained by performing an initial test on the test hardware platform using test data for a plurality of operators, and the test result may be a result obtained by performing an operation on the entire neural network after representing a non-target operator in a benchmark data type for participating in the operation and representing a target operator in a test data type for participating in the operation on the test hardware platform.” Examiner notes the test hardware platform can refer to the target device.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the method for compiling an artificial intelligence-based model for a target device of Brady with the benchmarking of Li in order to obtain an effective measure of model performance. (Li, Page 4, Paragraph 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. METHOD FOR PROVIDING INFORMATION ABOUT NEURAL NETWORK MODEL AND ELECTRONIC APPARATUS FOR PERFORMING THE SAME (KR102500341B1) also discloses a method of optimizing an AI based model for a target device. DEVICE AND METHOD FOR PROVIDING BENCHMARK RESULT OF ARTIFICIAL INTELLIGENCE BASED MODEL (US-20240289585-A1) also discloses a method determining a benchmark result on an AI based model. ARTIFICIAL INTELLIGENCE MODEL TRANSFORMATION METHOD, ARTIFICIAL INTELLIGENCE MODEL TRANSFORMATION APPARATUS, ARTIFICIAL INTELLIGENCE MODEL DRIVING METHOD AND ARTIFICIAL INTELLIGENCE MODEL DRIVING APPARATUS (Korean unexamined publication number 1020230014264) also discloses methods for converting a model to other hardware devices.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Peter T Annis whose telephone number is (571)270-1059. The examiner can normally be reached M-F, 7:30am to 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER THOMAS ANNIS/Examiner, Art Unit 2123 /ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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