Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/13/2023 and 03/16/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 7-10 and 13 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 9, 10, 14, and 17-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, 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 “small" or "relatively small” in claims 1-5, 9, 10, 14, and 17-20 is a relative term which renders the claim indefinite. The term “small" or "relatively small” 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 "small" or "relatively small" the determinations of differences between local models and/or parameters of the models is rendered indefinite. Without any indication of what a "small" difference is relative to, the claims are rendered indefinite.
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 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:
“performing an evaluation of a difference between models in a parameter of the local model trained for each of the facilities”. 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.
“correcting the training of the local model such that the difference between the models is small based on a result of the evaluation”. 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 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 one or more 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 (processors and information processing system) (See MPEP 2106.05(f)).
“performing a training 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 using data of each facility collected at each of a plurality of facilities”. This additional elements 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.
As discussed above, the additional element(s) of “A machine learning method executed by an information processing system including one or more processors” 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 (processors and information processing system) (See MPEP 2106.05(f)).
The additional element(s) of “performing a training of a local model that predicts, for each of the facilities, a behavior of a user on an item” 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.
The additional element(s) of “by using data of each facility collected at each of a plurality of facilities” 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 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“wherein the correcting of the training of the local model includes changing the parameter such that the difference between the models is 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 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 small 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 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“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 relatively small, and by deleting a feature amount, in which the difference between the models in the parameter is relatively large, 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 selecting or deleting parameters.
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 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 small 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 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 relationship with a small difference between models.
“deleting a relationship, in which the difference in the correlation coefficient between models is relatively large, 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 11:
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 information processing system includes a plurality of information processing apparatuses, which execute the training of the local model, corresponding to each of the 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 that is 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)).
“the training is performed by using federated learning for communicating 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 learning) 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 information processing system includes a plurality of information processing apparatuses, which execute the training of the local model, corresponding to each of the plurality of facilities” and “a server that is connected to each of the plurality of information processing apparatuses via an electric communication line in a communicable manner” 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.
The additional element(s) of “the training is performed by using federated learning for communicating 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 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 12:
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 server acquires the parameter of the local model from each of the plurality of information processing apparatuses, performs an evaluation of the difference between the models in the parameter of the local model, and performs an instruction of correcting the training with respect to 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 (server) (See MPEP 2106.05(f)).
“each of the plurality of information processing apparatuses performs at least one of changing the parameter of the local model or selecting a feature amount, based on the instruction”. 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)).
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 server acquires the parameter of the local model from each of the plurality of information processing apparatuses, performs an evaluation of the difference between the models in the parameter of the local model, and performs an instruction of correcting the training with respect to each of the plurality of information processing apparatuses” and “each of the plurality of information processing apparatuses performs at least one of changing the parameter of the local model or selecting a feature amount, based on the instruction” 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.
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 15:
Due to claim language similar to that of Claim 11, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 11.
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 elements 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.
Claim Rejections - 35 USC § 103
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 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.
Claim(s) 1, 2, 11, 12, and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crandall et al (US 20210390408 A1, hereinafter Crandall), in view of Mazumder et al (WO2020191282, hereinafter Mazumder).
Regarding Claim 1:
Crandall teaches
A machine learning method executed by an information processing system including one or more processors, the machine learning method comprising: causing the information processing system to execute: (Crandall [0039]: "As shown in FIG. 4, in an embodiment, each device 402 includes a CDL node 403a, which includes a local data set 404, a local model being trained 406, a communications buffer 408. In FIG. 4, in an embodiment, each device 402 also includes a processor 410, a memory 412, and a communications device 414.")
performing a training, by using data of each facility collected at each of a plurality of facilities (Crandall [0038]: "FIG. 4 shows 3 devices 402 (which can function as CDL nodes) that use CDL to learn a model on a data set that is distributed over several computational nodes in a decentralized manner. While 3 devices are shown in FIG. 4, it should be understood that any number of devices can be used in a CDL system in accordance with embodiments of the present disclosure. Devices 402 can communicate with each other via respective communications devices 414 and communications buffers 408.")
performing an evaluation of a difference between models in a parameter of the local model trained for each of the facilities (Crandall [0031]: "In an embodiment, if the incoming message is a request message 218, it contains the model parameters of another node. In an embodiment, in this case, an update is calculated 220 based on the difference between the parameter values")
correcting the training of the local model such that the difference between the models is small based on a result of the evaluation (Crandall [0031]: "In an embodiment, γ is a scaling term (e.g., a number between 0 and 1) that scales how far to move an estimate in each iteration. For example, γ can be a user-configured term set based on the application. In an embodiment, this update is added 222 to the parameters in the local model 204 and is sent 224 back to the node that originally made the request as a response message via communication API 206"; (EN): the scaling factor used to move the estimate for each iteration is analogous to correcting the training model via a small difference).
Crandall does not distinctly disclose
of a local model that predicts, for each of the facilities, a behavior of a user on an item
However, Mazumder teaches
of a local model that predicts, for each of the facilities, a behavior of a user on an item (Mazumder [0038]: "In one embodiment, a user's history (or historical data) of interactions with incoming data may also be used in order to predict the actions or behavior of the user 130 in response current or incoming data. In one embodiment, shared knowledge of other user data (e.g., user behavior and actions) may be used to assist in predicting the actions and behavior of the user 130")
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall with the method for providing recommendations to a user based on learned user behavior of Mazumder in order to provide a method for local models of a federated learning system to make predictions based on user behavior. The method presented in Mazumder is beneficial for Crandall in that it allows for improved learning and anticipation of end-user behavior and would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior (Mazumder [0002]: “Improved learning and anticipation of end-user behavior would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior. Some applications attempt to aid users by anticipating user actions based on collected user data and information. While such applications may attempt to understand the behaviors of the user by classifying the collected user data and information, there are numerous limitations as to the accuracy and value of the assistance provided by the application as the collected user data and information are too simplistic, generic, broad or vague to accurately predict how the user may respond to the incoming data”).
Regarding Claim 2:
Crandall teaches
The machine learning method according to claim 1, wherein the correcting of the training of the local model includes changing the parameter such that the difference between the models is small (Crandall [0031]: "In an embodiment, γ is a scaling term (e.g., a number between 0 and 1) that scales how far to move an estimate in each iteration. For example, γ can be a user-configured term set based on the application. In an embodiment, this update is added 222 to the parameters in the local model 204 and is sent 224 back to the node that originally made the request as a response message via communication API 206").
Regarding Claim 11:
Crandall teaches
The machine learning method according to claim 1, wherein the information processing system includes a plurality of information processing apparatuses, which execute the training of the local model, corresponding to each of the plurality of facilities (Crandall [0039]: "As shown in FIG. 4, in an embodiment, each device 402 includes a CDL node 403a, which includes a local data set 404, a local model being trained 406, a communications buffer 408. In FIG. 4, in an embodiment, each device 402 also includes a processor 410, a memory 412, and a communications device 414.")
the training is performed by using federated learning for communicating 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 (Crandall [0038]: "FIG. 4 shows 3 devices 402 (which can function as CDL nodes) that use CDL to learn a model on a data set that is distributed over several computational nodes in a decentralized manner. While 3 devices are shown in FIG. 4, it should be understood that any number of devices can be used in a CDL system in accordance with embodiments of the present disclosure. Devices 402 can communicate with each other via respective communications devices 414 and communications buffers 408."; [0031]: "In an embodiment, γ is a scaling term (e.g., a number between 0 and 1) that scales how far to move an estimate in each iteration. For example, γ can be a user-configured term set based on the application. In an embodiment, this update is added 222 to the parameters in the local model 204 and is sent 224 back to the node that originally made the request as a response message via communication API 206"; (EN): the scaling factor used to move the estimate for each iteration is analogous to correcting the training model via a small difference).
Crandall does not distinctly disclose
a server that is connected to each of the plurality of information processing apparatuses via an electric communication line in a communicable manner
However, Mazumder teaches
a server that is connected to each of the plurality of information processing apparatuses via an electric communication line in a communicable manner (Mazumder [0034]: "As illustrated, the server 112 is located within network 110, such as a cloud-based network, and may facilitate the collection of data from a variety of different data sources 102, 104, 106, which data may be delivered to one or more users 130. Server 112 can be, for example, a server computer, computing device, storage service (e.g., a ‘cloud’ service), etc.")
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall with the method for providing recommendations to a user based on learned user behavior of Mazumder in order to provide a method for local models of a federated learning system to make predictions based on user behavior. The method presented in Mazumder is beneficial for Crandall in that it allows for improved learning and anticipation of end-user behavior and would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior (Mazumder [0002]: “Improved learning and anticipation of end-user behavior would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior. Some applications attempt to aid users by anticipating user actions based on collected user data and information. While such applications may attempt to understand the behaviors of the user by classifying the collected user data and information, there are numerous limitations as to the accuracy and value of the assistance provided by the application as the collected user data and information are too simplistic, generic, broad or vague to accurately predict how the user may respond to the incoming data”).
Regarding Claim 12:
Crandall teaches
The machine learning method according to claim 11, wherein the server acquires the parameter of the local model from each of the plurality of information processing apparatuses, (Crandall [0031]: "In an embodiment, if the incoming message is a request message 218, it contains the model parameters of another node. In an embodiment, in this case, an update is calculated 220 based on the difference between the parameter values")
performs an evaluation of the difference between the models in the parameter of the local model, (Crandall [0031]: "In an embodiment, if the incoming message is a request message 218, it contains the model parameters of another node. In an embodiment, in this case, an update is calculated 220 based on the difference between the parameter values") performs an instruction of correcting the training with respect to each of the plurality of information processing apparatuses, (Crandall [0031]: "In an embodiment, γ is a scaling term (e.g., a number between 0 and 1) that scales how far to move an estimate in each iteration. For example, γ can be a user-configured term set based on the application. In an embodiment, this update is added 222 to the parameters in the local model 204 and is sent 224 back to the node that originally made the request as a response message via communication API 206")
each of the plurality of information processing apparatuses performs at least one of changing the parameter of the local model or selecting a feature amount, based on the instruction (Crandall [0031]: "In an embodiment, γ is a scaling term (e.g., a number between 0 and 1) that scales how far to move an estimate in each iteration. For example, γ can be a user-configured term set based on the application. In an embodiment, this update is added 222 to the parameters in the local model 204 and is sent 224 back to the node that originally made the request as a response message via communication API 206").
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 15:
Due to claim language similar to that of Claim 11, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 11.
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.
Crandall teaches
An information processing apparatus comprising: one or more first processors; and one or more first storage devices (Crandall [0039]: "As shown in FIG. 4, in an embodiment, each device 402 includes a CDL node 403a, which includes a local data set 404, a local model being trained 406, a communications buffer 408. In FIG. 4, in an embodiment, each device 402 also includes a processor 410, a memory 412, and a communications device 414.")
Crandall does not distinctly disclose
transmit a parameter of the first local model, on which the training is performed, to a server;
However, Mazumder teaches
transmit a parameter of the first local model, on which the training is performed, to a server (Mazumder [0034]: "As illustrated, the server 112 is located within network 110, such as a cloud-based network, and may facilitate the collection of data from a variety of different data sources 102, 104, 106, which data may be delivered to one or more users 130. Server 112 can be, for example, a server computer, computing device, storage service (e.g., a ‘cloud’ service), etc.");
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall with the method for providing recommendations to a user based on learned user behavior of Mazumder in order to provide a method for local models of a federated learning system to make predictions based on user behavior. The method presented in Mazumder is beneficial for Crandall in that it allows for improved learning and anticipation of end-user behavior and would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior (Mazumder [0002]: “Improved learning and anticipation of end-user behavior would improve the usefulness of smart devices in fulfilling the role of intelligent companions or electronic personal assistants on the smart devices that recommend, guide, and direct end user behavior. Some applications attempt to aid users by anticipating user actions based on collected user data and information. While such applications may attempt to understand the behaviors of the user by classifying the collected user data and information, there are numerous limitations as to the accuracy and value of the assistance provided by the application as the collected user data and information are too simplistic, generic, broad or vague to accurately predict how the user may respond to the incoming data”).
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.
Claim Rejections - 35 USC § 103
Claim(s) 3-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crandall and Mazumder as applied to claims 1, 14, and 16-10 above, and further in view of Zhou et al (US 20210234687 A1, hereinafter Zhou).
Regarding Claim 3:
Crandall + Mazumder does not distinctly disclose
The machine learning method according to claim 2, wherein the local model includes a cross feature amount,
and the machine learning method further comprises causing the information processing system to execute changing the parameter such that the difference between the models in the parameter, which is a weight of the cross feature amount, is small in the case of correcting the training of the local model.
However, Zhou teaches
The machine learning method according to claim 2, wherein the local model includes a cross feature amount (Zhou [0023]: "According to an aspect of the present disclosure, the multi-model training method based on federated feature extraction effectively fuses feature data of a plurality of collaborators, and effectively screens cross features, based on federated learning."; (EN): the inclusion of screening cross features implies the use of cross features),
and the machine learning method further comprises causing the information processing system to execute changing the parameter such that the difference between the models in the parameter, which is a weight of the cross feature amount, is small in the case of correcting the training of the local model (Zhou [0037]: "the screening the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborator to form the second data set with the screened plurality of one-hot encoded feature columns and the first data set comprises: filtering out one-hot encoded feature columns with weights less than a first threshold from the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborators to obtain first remaining one-hot encoded feature columns").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall + Mazumder with the multi-model training method based on federated feature extraction of Zhou in order to provide a method for local models of a federated learning system to select the most relevant features. The method presented in Zhou is beneficial for Crandall + Mazumder in that it allows for improved robustness and generalization for large amounts of data (Zhou [0003]: “A large number of experimental results have proved that machine learning models have good robustness and generalization. When a recommendation engine is used for advertising services, in order to increase the diversity of training data, it is desired that data from a plurality of companies can be fused to train the recommendation engine.”).
Regarding Claim 4:
Crandall + Mazumder does not distinctly disclose
The machine learning method according to claim 1, 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 relatively small, and by deleting a feature amount, in which the difference between the models in the parameter is relatively large, from the local model.
However, Zhou teaches
The machine learning method according to claim 1, 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 relatively small, and by deleting a feature amount, in which the difference between the models in the parameter is relatively large, from the local model (Zhou [0037]: "the screening the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborator to form the second data set with the screened plurality of one-hot encoded feature columns and the first data set comprises: filtering out one-hot encoded feature columns with weights less than a first threshold from the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborators to obtain first remaining one-hot encoded feature columns"; (EN): filtering out features with weights larger than a threshold value is analogous to deleting features where the difference between parameters is large).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall + Mazumder with the multi-model training method based on federated feature extraction of Zhou in order to provide a method for local models of a federated learning system to select the most relevant features. The method presented in Zhou is beneficial for Crandall + Mazumder in that it allows for improved robustness and generalization for large amounts of data (Zhou [0003]: “A large number of experimental results have proved that machine learning models have good robustness and generalization. When a recommendation engine is used for advertising services, in order to increase the diversity of training data, it is desired that data from a plurality of companies can be fused to train the recommendation engine.”).
Regarding Claim 5:
Crandall + Mazumder does not distinctly disclose
The machine learning method according to claim 4, wherein the local model includes a cross feature amount,
and the machine learning method further comprises causing the information processing system to execute: 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; and 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.
However, Zhou teaches
The machine learning method according to claim 4, wherein the local model includes a cross feature amount, (Zhou [0023]: "According to an aspect of the present disclosure, the multi-model training method based on federated feature extraction effectively fuses feature data of a plurality of collaborators, and effectively screens cross features, based on federated learning."; (EN): the inclusion of screening cross features implies the use of cross features)
and the machine learning method further comprises causing the information processing system to execute: 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; and 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 (Zhou [0037]: "the screening the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborator to form the second data set with the screened plurality of one-hot encoded feature columns and the first data set comprises: filtering out one-hot encoded feature columns with weights less than a first threshold from the plurality of one-hot encoded feature columns obtained by the one or more second tree models corresponding to the one or more second collaborators to obtain first remaining one-hot encoded feature columns"; (EN): filtering out features with weights larger than a threshold value is analogous to deleting features where the difference between parameters is large).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall + Mazumder with the multi-model training method based on federated feature extraction of Zhou in order to provide a method for local models of a federated learning system to select the most relevant features. The method presented in Zhou is beneficial for Crandall + Mazumder in that it allows for improved robustness and generalization for large amounts of data (Zhou [0003]: “A large number of experimental results have proved that machine learning models have good robustness and generalization. When a recommendation engine is used for advertising services, in order to increase the diversity of training data, it is desired that data from a plurality of companies can be fused to train the recommendation engine.”).
Regarding Claim 6:
Crandall + Mazumder does not distinctly disclose
The machine learning method according to claim 3, wherein the weight of the cross feature amount is represented in a relation between embedding representations of each of the feature amounts.
However, Zhou teaches
The machine learning method according to claim 3, wherein the weight of the cross feature amount is represented in a relation between embedding representations of each of the feature amounts (Zhou [0025]: "Therefore, importance of each feature can be evaluated by a proportion of samples for which the feature contributes to the final prediction. In an example of an eXtreme Gradient Boosting (XGBoost) tree model, a corresponding score, that is, a weight, of each feature is obtained by using a feature importance score, i.e. feature importances.").
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the consensus driven learning of decentralized computational nodes of Crandall + Mazumder with the multi-model training method based on federated feature extraction of Zhou in order to provide a method for local models of a federated learning system to select the most relevant features. The method presented in Zhou is beneficial for Crandall + Mazumder in that it allows for improved robustness and generalization for large amounts of data (Zhou [0003]: “A large number of experimental results have proved that machine learning models have good robustness and generalization. When a recommendation engine is used for advertising services, in order to increase the diversity of training data, it is desired that data from a plurality of companies can be fused to train the recommendation engine.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12423578 B1 – A resource set which includes multiple servers with a respective plurality of training computing devices is identified for training a machine learning model
US 11461690 B2 – A distributed, online machine learning system
US 20210004682 A1 – using sequence models to account for historical differences of user behavior with a computing system, as a way to better predict future behaviors with the computing system
US 20180322402 A1 – a novel architecture in which competing suggestions, possibly generated by competing systems, are selected by a Cognitive Unit
Jiang, X., Zhao, S., Jacobson, G., Jana, R., Hsu, W.-L., Talasila, M., … Borcea, C. (2021). FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2106.08946 – a system for fine-grained location prediction (FGLP) of mobile users, based on GPS traces collected on the phones.
S. Abdulrahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi and M. Guizani, "A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond," in IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5476-5497, 1 April1, 2021, doi: 10.1109/JIOT.2020.3030072. – we elaborate comprehensive taxonomies covering various challenging aspects, contributions, and trends in the literature, including core system models and designs, application areas, privacy and security, and resource management
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
Prosecution Insights