DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the application and claims filed 02/15/2024. Claims 1-5 are pending and have been examined. Claims 1-5 are rejected.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statement filed 2/15/2024, which complies with 37 CFR 1.97. As such, the information disclosure statement has been placed in the application file and the information referred to therein has been considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The present application claims foreign priority based on Japanese Patent Application No. JP2023-035702, filed on 03/08/2023.
Specification
The title of the invention is objected to as not being descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. In particular, the title of the invention is “METHOD AND INFORMATION PROCESSING APPARATUS”; however, the title is not clearly indicative of the claimed invention. The examiner suggests that one way to address this objection would be to amend the title to read along the lines of “METHOD AND INFORMATION PROCESSING APPARATUS FOR PARAMETER-BASED EVALUATION OF MACHINE LEARNING MODELS TO REDUCE AMOUNT OF COMMUNICATION DATA FLOW BETWEEN APARATUSES”.
A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See, MPEP § 606.01. However, the title of the invention should be limited to 500 characters. The examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Claim Objections
Claims 1-3 are objected to because of the following informalities:
There is a missing “and” between the penultimate and last step of claim 1 (i.e., “… apparatuses; updating” should read “… apparatuses; and updating …”).
In lines 2-3 of claim 3, the recitation of “the plurality of the second information processing apparatus” is grammatically incorrect and should read “the plurality of the second information processing apparatuses” (see, “the plurality of the second information processing apparatuses” recited in claim 1).
Claim 2, which depend directly or indirectly from claim 1, is objected to based on their respective dependencies from claim 1.
Appropriate correction is required.
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 3 and 5 are rejected under 35 U.S.C. 112(b) 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 “the evaluation value is the best” in claim 3 line 5 is a relative term which renders the claim indefinite. The term “best” 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.
The specification merely repeats the claim language in paragraphs 58 and 61. Therefore, the specification fails to describe or define what is meant by this term. In particular, it is unclear what metrics or standards are used for ascertaining the requisite degree of quality of evaluation values for the claimed “evaluation value is the best.” As such, the specification does not provide a standard for determining the requisite degree of quality for the claimed “evaluation value is the best” in claim 5. For the purposes of determining patent eligibility and comparison with the prior art, the Examiner is interpreting “evaluation value is the best” as any “evaluation value”. Appropriate correction is required.
Claims 1-3, are rejected under 35 U.S.C. 112(b).
unclear if updating is done by the machine learning model or the updating is happening to the first and second information processing apparatuses *and* the machine learning model. In the last four lines of claim 1, the recitation of “updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model based on the aggregate result of the plurality of the evaluation values” is grammatically incorrect, appears to be missing one or more words and is unclear.
In particular, it is unclear if the recited updating of the apparatuses is done by “the machine learning model based on the aggregate result of the plurality of the evaluation values” or if the updating being performed on “the first information processing apparatus and the plurality of the second information processing apparatuses” and “the machine learning model” where the updating of the apparatuses and the model is “based on the aggregate result of the plurality of the evaluation values.”
In the last four lines of claim 1, the recitation of “updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model based on the aggregate result of the plurality of the evaluation values” is grammatically incorrect, appears to be missing one or more words and is unclear.
In particular, it is unclear if the recited updating of the apparatuses is done by “the machine learning model based on the aggregate result of the plurality of the evaluation values” or if the updating being performed on “the first information processing apparatus and the plurality of the second information processing apparatuses” and “the machine learning model” where the updating of the apparatuses and the model is “based on the aggregate result of the plurality of the evaluation values.”
Also, claims 2 and 3, which each depend directly or indirectly from claim 1, are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 1.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding independent claim 1, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is directed to a method, corresponding to a process, which is one of the statutory categories.
Step 2A Prong 1: The claim recites
updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model based on the aggregate result of the plurality of the evaluation values1.
The “updating … the machine learning model” limitation, as drafted, under its Broadest Reasonable Interpretation (BRI), covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) to determine the new updated machine learning model (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
by a first information processing apparatus
by the plurality of the second information processing apparatuses,
by the plurality of the second information processing apparatuses
by the first information processing apparatus
by the first information processing apparatus
The information processing apparatuses are recited at a high level of generality as mere instructions to implement an abstract idea on a computer and amount to the recitation of the words “apply it” (or an equivalent) or amount to no more than mere instructions to implement an abstract idea or other exception on a computer or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
The claim also recites “acquiring, by the plurality of the second information processing apparatuses, an evaluation value” and “aggregating, by the first information processing apparatus, a plurality of evaluation values received”.
These are insignificant extra-solution activities that are not integrated into the claim as a whole and do not add a meaningful limitation to the above-noted mental processes specified in this claim. That is, “acquiring … an evaluation value” and “aggregating ... a plurality of evaluation values received” amount to mere data gathering (i.e., acquiring and aggregating received/transmitted data values) (See MPEP § 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “first information processing apparatus”, “the plurality of the second information processing apparatuses” and “the first information processing apparatus” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element represents mere instructions to apply an exception (i.e., the additional element recite one or more information processing apparatuses for applying the abstract ideas). Mere instructions to describe via one or more information processing apparatuses do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic information processing apparatus cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claim recites:
transmitting … to a plurality of second information processing apparatuses, first information used to acquire values of parameters of a machine learning model;
acquiring … an evaluation value of the machine learning model when a value of the parameters acquired based on the first information is applied to the machine learning model;
transmitting … the evaluation value to the first information processing apparatus;
aggregating … a plurality of evaluation values received from the plurality of the second information processing apparatuses;
transmitting … an aggregate result of the plurality of the evaluation values to the plurality of the second information processing apparatuses.
The “transmitting … to a plurality of second information”, “acquiring … an evaluation value of the machine learning model”, “transmitting … the evaluation value”, “aggregating … a plurality of evaluation values”, “transmitting … an aggregate result” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation of acquiring values, transmitting results, values, and aggregating values, or information are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Regarding claim 2, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 2 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: Claim 2 does not recite any abstract ideas but is dependent on claim 1. See the analysis of claim 1 above.
Step 2A Prong 2: The claim recites the additional element:
the first information is a string including a predetermined number of characters.
The “first information is a string” limitation describes data gathering with a character string as the data. This information is a string limitation can be characterized as insignificant extra-solution activity (i.e., data gathering). See MPEP 2106.05(g).
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “first information is a string” limitation does not include additional elements that are sufficient to amount to significantly more than the judicial exception. “information is a string including … characters” data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of stating “information is a string” are well-understood, routine, conventional activities of storing data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 3 is directed to a method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong 1: The claim recites:
set parameters acquired based on the first information in which the aggregate result of the evaluation value is the best2 among the plurality of first information to update the machine learning model.
The “set parameters acquired based on the first information” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) to determine/set parameters acquired (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional element:
the first information processing apparatus and the plurality of the second information processing apparatus
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “the first information processing apparatus and the plurality of the second information processing apparatus” limitation amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – generic information processing apparatuses performing generic computer functions) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Step 2B: As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited that impose any meaningful limits on practicing the abstract idea. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Therefore, the additional elements of this dependent claim are not sufficient to amount to significantly more than the abstract idea.
This claim is not patent eligible.
Regarding independent claim 4, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 4 is directed to an information processing apparatus corresponding to a machine one of the statutory categories.
Step 2A Prong 1: The claim recites:
updating the machine learning model based on the aggregate result of the plurality of the evaluation values.
The “updating the machine learning model” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) to determine “the updated machine learning model” (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
An information processing apparatus comprising a processor configured to:
aggregate a plurality of evaluation values received from the plurality of the information processing devices;
The “aggregate a plurality of evaluation values” limitation does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving, communicating, storing, and aggregating data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations to “aggregate a plurality of evaluation values received from … the information processing devices” are well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “information processing apparatus comprising a processor” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – generic information processing apparatus performing generic computer functions) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The “information processing apparatus comprising a processor” limitation does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element represents mere instructions to apply an exception (i.e., the additional element recites a processing apparatus for applying the abstract ideas). Mere instructions to associate a processor with an information processing apparatus do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic information processing apparatus cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claim recites:
transmit, to a plurality of information processing apparatuses, first information used to acquire values of parameters of a machine learning model;
transmitting an aggregate result of the plurality of the evaluation values to the plurality of the information processing devices; and
The “transmit, to a plurality of information processing apparatuses” and “transmitting an aggregate result of the plurality of the evaluation values” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation of “transmitting values and apparatuses” are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Regarding independent claim 5, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 5 is directed to an information processing apparatus corresponding to a machine, one of the statutory categories.
Step 2A Prong 1: The claim recites:
updating the machine learning model based on the aggregate result of the evaluation values.
The “updating the machine learning model” limitation, as drafted, under its BRI, covers concepts performed in the human mind (including an observation, evaluation, judgement, or opinion) to determine the updated machine learning model. (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong 2: The claim recites the additional elements:
An information processing apparatus comprising a processor configured to:
receive, from a first information processing apparatus, first information used to acquire values of parameters of a machine learning model;
acquire an evaluation value of the machine learning model when a value of the parameters acquired based on the first information is applied to the machine learning model;
receiving, from the first information processing apparatus, an aggregate result of a plurality of evaluation values of the plurality of information processing apparatuses that have received the first information;
The judicial exceptions are not integrated into a practical application. In particular, the claim recites the “information processing apparatus comprising a processor” limitation which amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer or merely uses a computer as a tool to perform an abstract idea (i.e., generic computer components – generic processing apparatus performing generic computer functions) which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
The “receive, from a first information processing apparatus”, “acquire an evaluation value of the machine learning model” and “receiving, from the first information processing apparatus” limitations describe data gathering with information and values as the data. These receiving/acquiring values or information limitations can be characterized as insignificant extra-solution activity (i.e., data gathering). See MPEP 2106.05(g).
Step 2B: The “information processing apparatus comprising a processor” limitation does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element represents mere instructions to apply an exception (i.e., the additional element recites a processing apparatus for applying the abstract ideas). Mere instructions to associate a processor with an information processing apparatus do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic information processing apparatus cannot provide significantly more than the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept.
The “receive, from a first information processing apparatus”, “acquire an evaluation value of the machine learning model” and “receiving, from the first information processing apparatus” limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Receiving, communicating, storing or acquiring data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions… i. Receiving or transmitting data over a network…iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)). Therefore, recitations of receiving and acquiring information or values are well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
The claim recites:
transmitting the evaluation value to the first information processing apparatus;
The “transmitting the evaluation value” limitation does not include additional elements that are sufficient to amount to significantly more than the judicial exception. According to MPEP 2106.05(d) Subsection II, "The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation of receiving apparatuses, acquiring values and transmitting values are the well-understood, routine, conventional activities of receiving or transmitting data over a network, as discussed in MPEP § 2106.05(d).
This claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li. (U.S. Publication No. 20210182661, hereinafter “Li”).
Regarding independent claim 1, Li discloses the invention as claimed including A method comprising: transmitting, by a first information processing apparatus, to a plurality of second information processing apparatuses, first information used to acquire values of parameters of a machine learning model; (see, e.g., paragraphs 9 and 92, “first configuration data includes parameters for the first version of the neural network model” and “the sub-master may send first configuration data from the master device 842 to a first slave device 848, which may in turn use it as well as forward it on to a second slave device 850” [i.e., transmitting by a first apparatus/master device, to second apparatuses/slave devices, first information of a ML model/parameters of the NN model]);
acquiring, by the plurality of the second information processing apparatuses, an evaluation value of the machine learning model when a value of the parameters acquired based on the first information is applied to the machine learning model; (see, e.g., paragraph 23, “first configuration data for the neural network model based on a first version of the neural network model; sending, from the master device, the first configuration data to a slave device” [i.e., the plurality of the second information processing apparatuses acquire an evaluation value which is the same first information/first configuration data discussed in the claim only referred to differently as evaluation value after being processed in the machine learning model]);
transmitting, by the plurality of the second information processing apparatuses, the evaluation value to the first information processing apparatus (see, e.g., paragraph 92, “In this case, the sub-master may send first configuration data from the master device 842 to a first slave device 848, which may in turn use it as well as forward it on to a second slave device 850. This may be repeated around the ring. Each slave device may also forward second configuration data that is eventually received by the sub-master 846, which may forward the second configuration data to master 842” [i.e., transmitting, by the plurality of the second information processing apparatuses, the evaluation value to the first information processing apparatus/Each slave device may forward data so that it is received by the submaster or master device]);
aggregating, by the first information processing apparatus, a plurality of evaluation values received from the plurality of the second information processing apparatuses (see, e.g., paragraph 95, “the master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices” [i.e., aggregating, by the first information processing apparatus, a plurality of evaluation values from the second information processing apparatuses/aggregate data from plurality of slave devices configured by the master device]);
transmitting, by the first information processing apparatus, an aggregate result of the plurality of the evaluation values to the plurality of the second information processing apparatuses (see, e.g., paragraph 88, “The slave devices 722, 724, and 726 are communicatively coupled to the master device 710 via one or more communication networks 750. The master device 710 is configured to transmit first configuration data 780 for distribution to the plurality of slave devices 722, 724, and 726 via one or more communication networks 750” [i.e., first information processing apparatus transmits aggregate results to second information processing apparatus/master device is configured to transmit data to a plurality of slave devices]);
updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model based on the aggregate result of the plurality of the evaluation values3 (see, e.g., paragraph 95, “the master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices to update parameters for the first version of the neural network model” [i.e., updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model based on aggregate … values/aggregate data from second device used by master device to update neural network model – thus all three devices at some point have updated values or data]).
Regarding claim 2, as discussed above, Li discloses the method of claim 1.
Li further discloses the first information is a string including a predetermined number of characters (see, e.g., paragraph 46, “For example, the neural network model may be an acoustic model that receives audio data as input and outputs linguistic data … The linguistic features may comprise one or more of phonemes, graphemes, characters, letters, words and sub-word unit This linguistic data may be used by a language model, which may itself be another neural network model, to convert the input audio data into text and/or command data associated with a spoken phrase.” [e.g., the first information is a string including a predetermined number of characters/audio data may contain text or characters converted by a language model]).
Regarding claim 3, as discussed above, Li discloses the method of claim 1.
Li further discloses the first information processing apparatus and the plurality of the second information processing apparatus set parameters acquired based on the first information in which the aggregate result of the evaluation value is the best4 among the plurality of first information to update the machine learning model (see, e.g., paragraph 95, “master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices to update parameters for the first version of the neural network model … the plurality of slave devices may be aggregated by averaging or another suitable statistical function. In one case, the gradient data from different slave devices may be compared to selectively update the parameters for the first version of the neural network model” [e.g., first … apparatus and plurality of the second … apparatus set parameters acquired … aggregate result of the evaluation value is the best … to update the machine learning model/master device and slave device (which both contain aggregate data) to selectively update the neural network model/choosing the best/highest/largest value]).
Regarding independent claim 4, Li discloses the invention as claimed including An information processing apparatus comprising a processor configured to: (see, e.g., paragraph 22, “In accordance with various aspects of the present invention, the method includes loading, at the slave device, a binary executable; and executing, using at least one processor of the slave device” [i.e., information processing apparatus/slave device comprising of a processor/using at least one processor]);
transmit, to a plurality of information processing apparatuses, first information used to acquire values of parameters of a machine learning model (see, e.g., paragraphs 9 and 92, “first configuration data includes parameters for the first version of the neural network model” and “the sub-master may send first configuration data from the master device 842 to a first slave device 848, which may in turn use it as well as forward it on to a second slave device 850” [i.e., transmit, to a plurality of information processing apparatuses/sub-master (a part of the master device) sends data from master device to a first or also a second slave device, while first information used to acquire values of parameters of a machine learning model/first configuration data includes parameters of the neural network model]);
receive, from the plurality of the information processing apparatuses, an evaluation value of the machine learning model when the value of the parameters acquired based on the first information is applied to the machine learning model (see, e.g., paragraphs 23 and 25, “first configuration data for the neural network model based on a first version of the neural network model; sending, from the master device, the first configuration data to a slave device” and “In accordance with various aspects of the present invention, the receiving includes receiving second configuration data from a plurality of slave devices” [i.e., receive, by the plurality of the information processing apparatuses, an evaluation value of the machine learning model when a value of the parameters acquired based on the first information is applied to the machine learning model (interpretation: the plurality of the information processing apparatuses acquire an evaluation value from the machine learning model from feeding it first information)/data of the neural network model being acquired by the plurality of slave devices and master device]);
aggregate a plurality of evaluation values received from the plurality of the information processing devices (see, e.g., paragraph 95, “the master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices” [i.e., aggregate a plurality of evaluation values from the plurality of information processing apparatuses/aggregate data from plurality of slave devices configured by the master device]);
transmitting an aggregate result of the plurality of the evaluation values to the plurality of the information processing devices (see, e.g., paragraph 88, “The slave devices 722, 724, and 726 are communicatively coupled to the master device 710 via one or more communication networks 750. The master device 710 is configured to transmit first configuration data 780 for distribution to the plurality of slave devices 722, 724, and 726 via one or more communication networks 750” [i.e., transmitting an aggregate result of the plurality of the evaluation values to the plurality of the information processing devices / transmitting data to a plurality of slave devices]);
updating the machine learning model based on the aggregate result of the plurality of the evaluation values (see, e.g., paragraph 95, “the master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices to update parameters for the first version of the neural network model” [i.e., updating the machine learning model based on aggregate … values/aggregate data from multiple devices to update neural network model]);
Regarding independent claim 5, Li discloses the invention as claimed including An information processing apparatus comprising a processor configured to: (see, e.g., paragraph 22, “In accordance with various aspects of the present invention, the method includes loading, at the slave device, a binary executable; and executing, using at least one processor of the slave device” [i.e., information processing apparatus/slave device comprising of a processor/using at least one processor]);
receive, from a first information processing apparatus, first information used to acquire values of parameters of a machine learning model (see, e.g., paragraph 23, “at a master device, first configuration data for the neural network model based on a first version of the neural network model; sending, from the master device, the first configuration data to a slave device” [i.e., receive, from a first information processing apparatus, first information used to acquire values of parameters of a machine learning model /slave device receives from the master device data sent to the slave device, first configuration data associated with the neural network model]);
acquire an evaluation value of the machine learning model when a value of the parameters acquired based on the first information is applied to the machine learning model (see, e.g., paragraph 23, “first configuration data for the neural network model based on a first version of the neural network model; sending, from the master device, the first configuration data to a slave device” [i.e., the slave device or any such device acquires an evaluation value which is the same first information/first configuration data discussed in the claim only referred to differently as evaluation value after being processed in the machine learning model]);
transmitting the evaluation value to the first information processing apparatus (see, e.g., paragraph 92, “In this case, the sub-master may send first configuration data from the master device 842 to a first slave device 848, which may in turn use it as well as forward it on to a second slave device 850. This may be repeated around the ring. Each slave device may also forward second configuration data that is eventually received by the sub-master 846, which may forward the second configuration data to master 842” [i.e., transmitting the evaluation value to the first information processing apparatus/data forwarded so that it is received by the submaster or master device]);
receiving, from the first information processing apparatus, an aggregate result of a plurality of evaluation values of the plurality of information processing apparatuses that have received the first information (see, e.g., paragraph 11, “the master device may be configured to use aggregate data derived from the second configuration data output by the plurality of slave devices to update parameters for the first version of the neural network model” [i.e., receiving, from the first information processing apparatus, an aggregate result of a plurality of evaluation values of the plurality of information processing apparatuses that have received the first information/ master device uses aggregated data derived from other devices to update parameters]);
updating the machine learning model based on the aggregate result of the evaluation values (see, e.g., paragraph 95, “the master device may be configured to use aggregate data derived from the second configuration data output … to update parameters for the first version of the neural network model” [i.e., updating the machine learning model based on aggregate … values/aggregate data to update neural network model]).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
The references listed on form PTO-892 are all generally related to techniques, methods and systems for providing selective access to model output data that are disclosed and at the same time protect the confidentiality of certain data.
For example, Wellmann; Benjamin et al. (U.S. Publication No. 20220335518) discloses “computerized methods and systems for analyzing documents and, more particularly, to computerized systems and method for using computerized modeling to analyze extracted document data and predict institutional risks”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PENG whose telephone number is (571)270-0897. The examiner can normally be reached Monday - Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/STEVEN PENG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 As indicated above in the section 112(b) rejection of this claim, “updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model” has been interpreted as “updating, the first information processing apparatus, the plurality of the second information processing apparatuses, and the machine learning model”
2 As indicated above in the section 112(b) rejection of this claim, “evaluation value is the best” has been interpreted as any “evaluation value”.
3 As indicated above in the section 112(b) rejection of this claim, “updating, the first information processing apparatus and the plurality of the second information processing apparatuses, the machine learning model” has been interpreted as “updating, the first information processing apparatus, the plurality of the second information processing apparatuses, and the machine learning model”
4 As indicated above in the section 112(b) rejection of this claim, “evaluation value is the best” has been interpreted as any “evaluation value”.
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