DETAILED ACTION
This Office Action is in response to the amendments filed on 06/26/2026.
Claims 1-3, 5, 9, 10, 13, 14, and 16-20 are currently amended.
Claims 4, 11, 12, and 15 are currently canceled.
Claims 1-3, 5-10, 13, 14, and 16-20 are currently pending in this application and have been examined.
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 .
Response to Arguments
In response to Applicant’s arguments on page(s) 13-14 regarding rejections made under 35 U.S.C. 112(b):
Claims 1-5, 9, 10, 14, and 17-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre- AIA ), second paragraph.
On page 3 of the Office Action, the Office stated that the term "small" or "relatively small" in claims 1-5, 9, 10, 14, and 17-20 is a relative term which renders the claim indefinite.
To address this rejection, Applicant has amended the claimed invention to replace the limitations "small" and "smaller" with "within an allowable range" based on at least paragraph [0124] of the as-filed specification.
Furthermore, Applicant has amended the claimed invention to replace the limitation "relatively small" with "less than a constant value", and the limitation "relatively large" with "equal to or greater than the constant value" based on at least paragraph [0222] of the as-filed specification.
Applicant respectfully submits that the rejection of these claims is rendered moot in view of the above amendments.
Accordingly, withdrawal of the claim rejections under 35 U.S.C. 112(b) is respectfully requested.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant has amended the claims away from language that recited “small”, “smaller”, and “relatively small” to include language that recites “within an allowable range”, “less than a constant value” and "equal to or greater than the constant value", however these amendments are still indefinite since they do not include any indication about how the range or constant values are determined or what they are based on. It is the recommendation of the Examiner to include information from the specification that clearly defines the range and the constant value(s).
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 112(b) are maintained and updated below.
In reference to Applicant’s arguments on page(s) 14-16 regarding rejections made under 35 U.S.C. 101:
Claims 1-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
These specific data structure and machine learning model manipulations eliminate manual or mental operations of the human mind, as a human mind cannot mentally parse distributed multi- dimensional parameters across a network, aggregate mathematical weights using federated learning, and execute structural node feature deletions (Step 2A, Prong 1: No).
The claimed invention provides a specific technological solution to this system limitation: Claim 1 explicitly solves this by anchoring the method to a hardware architecture where a server aggregates parameters to evaluate cross-model variance, and triggers the local apparatuses to alter their local models by deleting specific feature amounts where the difference is equal to or greater than a constant value. By actively deleting features that cause domain shift based on networked evaluations, the system filters out facility-specific noise to extract universal relationships that do not depend on the facility. Under the principles of Enfish, this specific parameter-adjustment and structural modification mechanism directly improves model robustness for unknown facilities even in a case where data of a plurality of facilities cannot be shared outside the facility, without compromising data privacy.
Thus, the claimed invention constitutes a specific, patent-eligible technological improvement and meets the requirement of Step 2A, Prong 2 (Contributing to a technical improvement in the technology field) (Step 2A, Prong 2: Yes).
Additionally, the ordered combination of the claimed elements provides an inventive concept. It is not well-understood, routine, or conventional in the art to arrange a server and multiple apparatuses to execute federated learning wherein the server aggregates parameters for the express purpose of instructing local nodes to delete their specific feature amounts in which the difference is equal to or greater than a constant value. This specific hardware- software interaction amounts to significantly more than any alleged abstract idea (Step 2B: Yes).
Therefore, Claim 1 constitutes a patent-eligible practical application and is patentable under 35 U.S.C. § 101.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the actions recited in the claims cannot be practically performed in the human mind. Examiner disagrees. The actions of parsing data, aggregating weight values, and deleting data can all be reasonably performed in the human mind. The inclusion of the actions being performed by a server does not preclude them from being mental processes.
Applicant argues that the instant application provides an improvement to the technological field of federated learning. Examiner disagrees. The instant application sets out to solve the problem of domain shift but attempts to do so in a way that can be reasonably seen as removing certain pieces of data or certain node structures from a dataset so as to alleviate the model’s learning from being dominated by one domain. This does not represent a technological improvement as a technological improvement cannot arise from an abstract idea, in this case the parsing, aggregating, and removal of specific data in the dataset.
Applicant argues that the specific actions taken in the instant application are novel in their intention and provide an inventive concept that amounts to significantly more than any alleged abstract idea. Examiner disagrees. The amended limitations still recite actions that can be reasonably performed in the human mind and thus are not patent eligible. The claimed novelty of the instant application lies in the idea that local nodes are being instructed to delete an amount of their features based on an indefinite “constant value”. It is the recommendation of the Examiner to include language from the specification to support a more definite “constant value” and how that value is utilized in the unique organization of components.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 101 are maintained and updated below.
In reference to Applicant’s arguments on page(s) 16-21 regarding rejections made under 35 U.S.C. 103:
Regarding independent claim 1, the Applicant respectfully submits that Crandall, in view of Mazumder, or further view of Zhou, fail to render obvious the features "aggregating, by the server, the parameter of the local model from each of the plurality of information processing apparatuses; performing, by the server, an evaluation of a difference between models in the parameter of the local model trained for each of the facilities based on the aggregated parameter" and "wherein the correcting of the training of the local model includes changing the local model by, from among a plurality of feature amounts included in the local model, selecting a feature amount in which the difference between the models in the parameter is less than a constant value, and by deleting a feature amount in which the difference between the models in the parameter is equal to or greater than the constant value, from the local model" recited in claim 1.
On pages 28-30 of the Office Action, the Office indicated that paragraphs [0031] and [0038]-[0039] of Crandall, in view of paragraph [0038] of Mazumder, disclosed the features of claim 1.
Referring to the teaching of Crandall, Crandall teaches a "Consensus Driven Learning" (CDL) system where decentralized computational nodes exchange model parameters with each other via request and response messages. When a node receives parameters from another node, it calculates an update based on the difference between the parameter values and adds a scaled version of this update to its local model parameters.
While Crandall's calculation of an update based on the difference between parameter values and the subsequent local model adjustment using a scaling term " y ", each node in Crandall simply calculates a local average with its neighbors to force all nodes to converge into a single global consensus model.
The Office further relies on Mazumder to teach predicting the behavior of a user on an item. However, Mazumder merely teaches anticipating user actions based on collected historical data to improve the usefulness of an electronic personal assistant on a smart device.
Mazumder is silent regarding parameter aggregation, or evaluating parameter differences between distinct machine learning models across different facilities.
In addition, even though Mazumder teaches a server I 12 which facilitates the collection of data from a variety of different data sources 102, 104, 106, modifying Crandall to incorporate a centralized architecture for aggregation would fundamentally destroy Crandall's core principle of operation.
Crandall is explicitly directed to a decentralized learning system designed to overcome the drawbacks of centralized servers. Specifically, Crandall states that its CDL system "does not require a central server to coordinate models like most conventional technologies" (see Crandall's paragraph [0017]). Because Crandall is designed to eliminate the need for a central server (e.g., to support robots operating in unknown environments with intermittent communication), a person of ordinary skill in the art would have no motivation to modify Crandall by introducing the centralized server of Mazumder.
Therefore, Mazumder cannot cure the deficiencies of Crandall regarding the above- mentioned features of claim 1.
Thus, Zhou does not disclose comparing a parameter of a specific feature across different models to determine a "difference between the models", nor does Zhou teach using such a difference as the basis for deleting a feature.
Regarding independent claims 14 and 16-20, for the same reasons set forth above for traversal of claim 1, the cited references fail to render obvious features in claims 14 and 16-20. Withdrawal of the rejection to claim 14 and 16-20 is respectfully requested.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant argues that the applied prior art references of Mazumder and Crandall were mischaracterized and do not teach the claims as amended. Examiner agrees. Upon further consideration of the applied references, it is clear to the Examiner that Mazumder teaches anticipating user actions based on collected historical data, and while the actions taken are similar to that of the instant application, the core concept is dissimilar. The deficiencies of Mazumder are not remedied by the inclusion of Crandall since Crandall merely teaches a decentralized learning system designed to overcome the drawbacks of centralized servers, which is antithetical to the concept of the instant application. The inclusion of Zhou does not remedy the deficiencies of Mazumder and Crandall.
A new search was conducted in an attempt to find new art to apply to the amended claims in order to provide a proper examination, and as a result no new art was found.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 103 are withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5, 9, 10, 14, and 16-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term(s) “within an allowable range”, “less than a constant value”, or “equal to or greater than the constant value” in claim 1-3, 5, 9, 10, 14, and 16-20 is a relative term which renders the claim indefinite. The term “within an allowable range” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Due to the use of indefinite terms .
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5-10, 13, 14, and 16-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a machine learning method, therefore falling into the statutory category of process. Independent Claim 14 recites, in part, an information processing system, therefore falling into the statutory category of manufacture. Independent Claim 16 recites, in part, an information processing apparatus, therefore falling into the statutory category of machine. Independent Claim 17 recites, in part, a server, therefore falling into the statutory category of machine. Independent Claims 18, 19, and 20 all recite, in part a non-transitory, computer-readable tangible recording medium which records thereon a program, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“aggregating the parameter of the local model from each of the plurality of information processing apparatuses”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses aggregating parameters.
“performing an evaluation of a difference between models in the parameter of the local model trained for each of the facilities based on the aggregated parameter”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses evaluating the difference between model parameters, based on an aggregated parameter.
“correcting the training of the local model such that the difference between the models is within an allowable range based on the instruction, wherein the correcting of the training of the local model includes changing the local model by, from among a plurality of feature amounts included in the local model, selecting a feature amount in which the difference between the models in the parameter is less than a constant value, and by deleting a feature amount in which the difference between the models in the parameter is equal to or greater than the constant value, from the local model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses correcting parameters of a model based on an evaluation by selecting an amount of features to include or remove based on a threshold value.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“A machine learning method executed by an information processing system including a plurality of information processing apparatuses corresponding to each of a plurality of facilities”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (information processing system) (See MPEP 2106.05(f)).
“a server connected to each of the plurality of information processing apparatuses via an electric communication line in a communicable manner”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (server) (See MPEP 2106.05(f)).
“performing a training, by using data of each facility collected at each of the plurality of facilities, of a local model that predicts, for each of the facilities, a behavior of a user on an item”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (federated/distributed learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“by each of the plurality of information processing apparatuses”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (information processing apparatus) (See MPEP 2106.05(f)).
“communicating, using federated learning, at least one of the parameter of the local model or an update amount of the parameter between the information processing apparatus and the server without communicating the data of each facility”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (federated/distributed learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“by the server”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (server) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “A machine learning method executed by an information processing system including a plurality of information processing apparatuses corresponding to each of a plurality of facilities”, “a server connected to each of the plurality of information processing apparatuses via an electric communication line in a communicable manner”, “by each of the plurality of information processing apparatuses”, and “by the server” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (information processing system, apparatuses, and a server) (See MPEP 2106.05(f)).
The additional element(s) of “performing a training, by using data of each facility collected at each of the plurality of facilities, of a local model that predicts, for each of the facilities, a behavior of a user on an item” and “communicating, using federated learning, at least one of the parameter of the local model or an update amount of the parameter between the information processing apparatus and the server without communicating the data of each facility” is/are directed to particular field(s) of use (federated/distributed learning) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“wherein the correcting of the training of the local model further includes changing the parameter of the selected feature amount such that the difference between the models in the parameter is within an allowable range”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses correcting parameters of a model based on an evaluation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“changing the parameter such that the difference between the models in the parameter, which is a weight of the cross feature amount, is within an allowable range in the case of correcting the training of the local model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses correcting parameters of a model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the local model includes a cross feature amount”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (federated/distributed learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“the machine learning method further comprises causing the information processing system to execute”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “the machine learning method further comprises causing the information processing system to execute” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (information processing system) (See MPEP 2106.05(f)).
The additional element(s) of “wherein the local model includes a cross feature amount” is/are directed to particular field(s) of use (cross features) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“from among the plurality of feature amounts including the cross feature amount, selecting a cross feature amount, in which the difference between the models in the parameter that is a weight of the cross feature amount is relatively small”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a number of features.
“deleting a cross feature amount, in which the difference between the models in the parameter is relatively large, in the case of correcting the training of the local model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses deleting a number of parameters.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the local model includes a cross feature amount”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (federated/distributed learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“the machine learning method further comprises causing the information processing system to execute”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the local model includes a cross feature amount” is/are directed to particular field(s) of use (cross features) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “the machine learning method further comprises causing the information processing system to execute” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (information processing system) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the weight of the cross feature amount is represented in a relation between embedding representations of each of the feature amounts”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (cross features) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the weight of the cross feature amount is represented in a relation between embedding representations of each of the feature amounts” is/are directed to particular field(s) of use (cross features) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the relation between the embedding representations of each of the feature amounts is an inner product of vectors indicating each of the feature amounts”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (cross features) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the relation between the embedding representations of each of the feature amounts is an inner product of vectors indicating each of the feature amounts.” is/are directed to particular field(s) of use (cross features) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the local model is a model that performs a neighborhood-based collaborative filtering, which is based on at least one of relationships between users or between items, and the parameter of the local model includes a correlation coefficient that indicates at least one of the relationships between the users or between the items”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (filtering) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the local model is a model that performs a neighborhood-based collaborative filtering, which is based on at least one of relationships between users or between items, and the parameter of the local model includes a correlation coefficient that indicates at least one of the relationships between the users or between the items” is/are directed to particular field(s) of use (cross features) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 1 analysis:
Claim 9 recites in part:
“changing the correlation coefficient such that a difference in the correlation coefficient between the models is made to be within an allowable range in the case of correcting the training of the local model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses changing the value of a coefficient.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the machine learning method further comprises causing the information processing system to execute”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “the machine learning method further comprises causing the information processing system to execute” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (information processing system) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 1 analysis:
Claim 10 recites in part:
“from among a plurality of the relationships included in the local model, selecting a relationship, in which a difference in the correlation coefficient between the models is less than a constant value”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a relationship with a small difference between models.
“deleting a relationship, in which the difference in the correlation coefficient between models is equal to or greater than the constant value, from the local model, in the case of correcting the training of the local model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses deleting a relationship between models where the difference is large.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“causing the information processing system to execute”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (information processing system) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “causing the information processing system to execute” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (information processing system) (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 13:
Due to claim language similar to that of Claim 8, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 14:
Due to claim language similar to that of Claim 1, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 16:
Due to claim language similar to that of Claims 1 and 14, Claim 16 is rejected for the same reasons as presented above in the rejection of Claims 1 and 14, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“one or more first processors”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (server) (See MPEP 2106.05(f)).
“one or more first storage devices”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (information processing apparatus) (See MPEP 2106.05(f)).
“transmit a parameter of the first local model, on which the training is performed, to a server”. This additional element is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination
do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “one or more first processors” and “one or more first storage devices” is/are directed to particular field(s) of use (server and information processing apparatus) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
The additional element(s) of “transmit a parameter of the first local model, on which the training is performed, to a server” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 17:
Due to claim language similar to that of Claims 1, 14, and 16, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 1, 14, and 16.
Regarding Claim 18:
Due to claim language similar to that of Claims 1, 14, 16, and 17, Claim 18 is rejected for the same reasons as presented above in the rejection of Claims 1, 14, 16, and 17.
Regarding Claim 19:
Due to claim language similar to that of Claims 1, 14, 16, 17, and 18, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 1, 14, 16, 17, and 18.
Regarding Claim 20:
Due to claim language similar to that of Claims 1, 14, 16, 17, 18, and 19, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 1, 14, 16, 17, 18, and 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/COREY SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
